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How MoEngage used Nektar to Increase Opportunity Multithreading by 131%

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How MoEngage used Nektar to Increase Opportunity Multithreading by 131% INDUSTRY Enterprise Software HEADQUARTERS San Francisco, California WEBSITE https://www.moengage.com/ Book a Demo See Nektar in Action 131% increase in multithreading score 318+ relevant leads identified & added (QoQ) $932k+ added to qualified pipeline QoQ As sales organizations scale, one challenge becomes harder to ignore: maintaining complete and accurate CRM data. MoEngage was growing quickly, and measuring sales performance had become critical to improving rep productivity and accelerating revenue. But the data needed to understand pipeline health, buyer engagement, and sales execution was not fully available in Salesforce. Critical customer interactions were scattered across sales reps’ emails and calendars. Many contacts, activities, and engagement signals never made it into Salesforce. As a result, sales leaders did not have a complete view of which stakeholders reps were engaging, whether opportunities were properly multithreaded, or what was driving successful deals. That challenge made one thing clear: if MoEngage wanted better pipeline visibility and stronger sales coaching, it first needed a more reliable data foundation. The Challenge: Incomplete CRM data was limiting pipeline visibility MoEngage lacked the Salesforce data needed to accurately measure pipeline performance and understand whether sales reps were engaging the right stakeholders throughout the buying journey. A significant portion of customer interactions remained siloed in sales reps’ emails and calendars, leaving Salesforce incomplete and difficult to trust. As the sales team expanded, inconsistent CRM updates and process adherence made it increasingly challenging to: Measure the strength and depth of buyer relationships Identify whether opportunities were properly multithreaded Correlate seller activities with deal outcomes Coach reps based on objective engagement data instead of verbal updates Before switching to Nektar, MoEngage used a plugin-based solution that missed capturing ~75% of their data into Salesforce. It also did not attach contacts or activities to the opportunity level, only to the account. That made it difficult for the team to understand which opportunities reps were spending time on, whether deals were single-threaded, and the quality and frequency of interactions happening across active opportunities. MoEngage’s approach relied heavily on correlating data from spreadsheets, incomplete Salesforce records, and verbal updates from reps. For a fast-scaling sales organization, that was not sustainable. Why opportunity-level engagement data mattered For MoEngage, the problem was not just missing data. It was missing context. AEs were engaging with multiple prospects and stakeholders, but only a portion of those contacts were being added to Salesforce. This meant key buyers, decision-makers, and influencers were often missing from opportunity records. As Karthik R, Sales Ops Manager at MoEngage, explained: Karthik RajaramSales Ops Manager AEs engage with a lot of prospects, but sometimes they end up adding just one contact to an opportunity or a lead. They do not have the time or bandwidth to add every contact they’re speaking to. These were key contacts we were missing out on and Nektar helped us to automate this data capture, which enabled us to target our prospects with campaigns and tailored messaging. MoEngage partnered with Nektar to solve three immediate priorities: Add missing contacts into Salesforce Identify execution gaps across active opportunities​ Understand key indicators of successful sales execution The Solution: Turning fragmented GTM activity into structured Salesforce data Nektar helped MoEngage capture and structure GTM activity that was previously hidden across emails and calendars. With more than 50 reps, MoEngage had a significant number of contacts hidden in sales reps’ inboxes. Nektar mined these inboxes to identify missing buyers, decision-makers, and key influencers, and brought that data into Salesforce at the opportunity level. By leveraging Nektar’s data, including 3.6K Opportunity Contact Roles, MoEngage was able to test its hypotheses around buyer engagement, persona coverage, and multithreading. The analysis revealed that: High-performing reps had 3.8x higher multithreading scores compared to others High-performing reps engaged decision-makers in the early stages of the sales cycle Nektar also surfaced 318 leads quarter-over-quarter that reps were already in contact with. These leads were more likely to engage with marketing and sales efforts than cold leads from a third-party database, resulting in an additional qualified pipeline of $932K quarter-over-quarter. Nektar helped MoEngage move from scattered activity to actionable sales insights Automated data capture gave MoEngage a stronger foundation for tracking pipeline health and identifying execution gaps. Instead of relying on spreadsheets and verbal updates, MoEngage used Nektar data and insights to get a comprehensive view of account and opportunity-level engagement, including where reps were spending their time, which people were involved, and why certain deals were stalling. There’s a lot of opportunities, but we only have certain amount of time in the day… Nektar helped in breaking it down to 2 or 3 areas we can coach a rep on, and which deals matter… Nektar flags for me what I should spend my time on. Manohar NandigamSenior Director, Sales Enablement This helped managers focus on the deals and coaching areas that mattered most, instead of spending time chasing updates or manually piecing together activity data. Building sales performance leading indicators With buyer-seller activity tracked through Nektar, MoEngage managers were able to shift from verbal deal updates to objective coaching conversations. Nektar helped establish leading indicator benchmarks such as Multithreading Score and Hustle Score, based on top-performer analysis. These indicators gave managers a quantitative way to understand sales execution, follow-up cadence, stakeholder engagement, and process adherence. The best part of Nektar leaderboards is that it gives quantitative data on how the reps are pushing across deals, what’s their follow-up cadence, sales process adherence. Hastu KshitijSenior Vice President This gave MoEngage a clearer view of how reps were executing across opportunities and where managers could intervene earlier to improve outcomes. The Impact By partnering with Nektar, MoEngage was able to turn fragmented GTM activity into structured Salesforce data that sales leaders could trust. The impact included: 131% increase in opportunity multithreading 3.6K+ missing OCRs added to Salesforce 46K+ sales activities captured in Salesforce $932K in additional qualified pipeline generated QoQ Additionally, Nektar improved visibility into execution gaps and risks across in-flight opportunities, along

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How Nektar Powered Mimecast’s Agentic GTM AI

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How Nektar Powered Mimecast’s Agentic GTM AI with Complete Customer Signals INDUSTRY Cybersecurity HEADQUARTERS London, England WEBSITE https://www.mimecast.com/ Hear directly from Tim about the challenges, decisions, and lessons behind Mimecast’s AI and GTM transformation. Watch Webinar $10M in total expansion revenue $150M+ in additional pipeline identified 2K+ new activities logged through historical data backfill As enterprise AI evolves, one thing is becoming clear: models are getting easier to access, and agent frameworks are becoming easier to build. The real advantage comes from something harder to replicate — proprietary data. Mimecast recognized this early in its AI journey. The company was investing in internal generative AI capabilities to surface better customers and prospect insights across the go-to-market lifecycle. The vision was ambitious: build AI applications that could support teams across acquisition, expansion, retention, renewal, and prospecting. But like many enterprises, Mimecast ran into a familiar challenge. The data needed to power those applications was spread across disconnected systems, inconsistent workflows, and siloed teams. Valuable engagement signals existed, but they were difficult to access, difficult to standardize, and hard to use at the level of granularity required for meaningful AI outcomes. That challenge made one thing clear: if Mimecast wanted AI to create real business value, it first needed a stronger data foundation. The Challenge: AI is only as strong as the data behind it Mimecast had already built its own internal generative AI engine to identify customer and prospect insights. But success depended on capturing and organizing the right GTM data inside its own data model. That was easier said than done. Critical information about customer engagement, buying committee members, and deal influence was spread across multiple processes and systems. Some GTM tools did not provide access to the data Mimecast needed. In other cases, the data was available, but not in a form detailed enough to support the outcomes the team was after. As Tim Seamans, VP of AI Acceleration at Mimecast, explained: Tim SeamansVP, Al Acceleration & Transformation We build our own generative AI engine internally to identify customer and prospect insights. What’s paramount for success is capturing available data and aligning it in operational systems. He added: “It’s really difficult to access data across disparate processes and systems so that we can get the right data, in the right place, at the right time.” Mimecast’s challenge was not a lack of AI ambition. It was the difficulty of bringing together the underlying data required to make AI applications accurate, useful, and scalable. Why proprietary data became central to Mimecast’s AI strategy Mimecast’s approach was rooted in a clear belief: while models and agents continue to improve, proprietary data is what ultimately creates a durable advantage. That thinking shaped the company’s AI roadmap. Mimecast began building 8–10 specialized AI applications and agents across the customer lifecycle, including applications for: Acquisition Expansion Retention Renewal management Prospecting These applications were designed to help teams act on customer and prospect signals more intelligently. But for them to work well, Mimecast needed better access to engagement data and customer context across the business. The priority was not simply generating more output. It was making sure AI systems had the right inputs to produce accurate, trustworthy, and business-relevant outcomes. The Solution: Unlocking the GTM data layer with Nektar Nektar helped Mimecast access the data and metadata it needed to strengthen the foundation behind its AI strategy. By capturing GTM engagement data that had previously been fragmented or unavailable, Nektar helped Mimecast unify important customer signals and make them available downstream. Just as importantly, that data could be delivered into the systems where Mimecast needed it most — including its CRM and data lake. That meant Mimecast could use Nektar not as another destination system, but as a data layer that supported its existing architecture and internal AI applications. Nektar helped us get the data (and metadata) we needed that was previously locked up or not available. And they can pipe it to our CRM or our data lake. Tim SeamansVP, Al Acceleration & Transformation This was a meaningful shift. Instead of relying on incomplete signals or inaccessible information, Mimecast could work with a richer and more structured view of customer engagement. Nektar helped Mimecast turn fragmented GTM data into an AI-ready signal layer Mimecast’s AI strategy depended on one thing: having complete, usable customer and prospect data inside its own systems. Nektar helped make that possible by unlocking and structuring engagement data that had previously been siloed, incomplete, or inaccessible. With Nektar, Mimecast was able to add meaningful scale and depth to the data powering its internal AI applications, including: 24K+ net-new contacts added 200K+ historical and ongoing emails captured and enriched 1K+ hours of manual rep work saved annually That data foundation gave Mimecast a much richer signal layer for customer insights, prospect intelligence, feature engineering, and AI-driven workflows. Instead of working from partial records and missing context, the team could feed its AI applications with complete interaction history across the customer lifecycle. Building for accuracy, not just automation For Mimecast, the goal was never to deploy AI for its own sake. The goal was to make AI outputs reliable enough to drive action. That required more than models. It required structured data, stronger context, and the ability to capture the signals that shape real customer outcomes. With Nektar helping fill those gaps, Mimecast was able to improve the quality of inputs behind its AI applications. That, in turn, supported more accurate insights across critical GTM workflows and spending more time acting on actionable signals and less time finding and structuring the data. This was especially important for a company building specialized applications across the customer lifecycle. Better data meant better context, better context meant better output, and better output made it easier to tie AI efforts to tangible business value. The business results came quickly. In the first 80 days after launching just one of Mimecast’s Proprietary AI tools, “Expansion AI”, the company achieved: The impact: $2M in expansion revenue and

Nektar.ai vs People.ai: A Buyer's Guide
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Nektar.ai vs People.ai: A Buyer’s Guide

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2026 Guide for Enterprise GTM Teams Seeking Backstory (People.ai) Alternatives Buyer’s Guide 11 min Jan 27, 2026 Updated: July 28, 2026 A quick note before you read further: People.ai rebranded to Backstory in April 2026, repositioning as a “Revenue Answers Platform” with a conversational AI layer on top of its existing activity-capture technology. Same underlying company and core capture technology, new name and pitch. This guide refers to it as Backstory throughout, noting the People.ai history where it’s directly relevant (its Gartner recognition, for instance, was announced under the People.ai name). Introduction: Two Different Approaches to the Same Problem Both Backstory and Nektar operate in the revenue data capture category, helping enterprises automatically capture GTM activity and enrich their CRM using AI. However, they solve fundamentally different problems for different buyers. Backstory (formerly People.ai) is an established revenue intelligence platform with strong analytics capabilities, recognition as a Visionary in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration (awarded under the People.ai name, prior to the April 2026 rebrand), and a mature suite of tools including ClosePlan, account planning, and leadership dashboards, now layered with a conversational query interface as part of its Backstory repositioning. Nektar is an advanced data-first GTM telemetry solution focused on delivering clean, accurate, AI-ready CRM data directly into standard Salesforce objects, designed specifically for enterprises that want to power their existing BI stacks rather than adopt another analytics platform. This guide is intended for GTM leaders, RevOps leaders, Sales Operations teams, and Data teams evaluating both solutions. It draws on direct enterprise evaluation feedback, product analysis, and independent research to help you determine which solution fits your specific needs. Get our latest insights into your inbox Who This Guide Is For This comparison is most relevant if your organization: Already operates a mature BI stack (Databricks, Snowflake, Looker, Tableau) Has dedicated RevOps or SalesOps teams building custom analytics Prioritizes CRM data accuracy over out-of-the-box dashboards Needs granular control over what data syncs to Salesforce Requires specific detail around internal and external participation or meeting attendance intelligence (not just invitee data) If your priority is comprehensive analytics UI, pre-built dashboards, and account planning tools, Backstory may be the stronger fit for your organization. But if you’re looking to solve the data problem at its core without the additional enablement effort of new training, Nektar is a better bet. This guide focuses on scenarios where data infrastructure is the primary buying criterion. The Core Difference: Analytics-First vs Data-First The fundamental difference between these platforms comes down to philosophy, and the April 2026 rebrand sharpened rather than changed this distinction: Backstory is built around the premise that revenue teams need better analytics and insights delivered through their platform, now explicitly reframed around conversational, natural-language answers rather than dashboards alone. Data capture exists to power those answers, scorecards, and AI-driven recommendations. Nektar is built around the premise that enterprises already have analytics tools they trust. What they lack is clean, accurate, complete, unified rep activity data in the CRM to feed those tools. Nektar focuses on being the best possible data layer, not an additional interface to learn. Neither approach is inherently superior; they serve different organizational needs. The question is which approach matches your GTM infrastructure strategy, and whether a conversational interface actually solves your problem or just adds a new way to ask a question the underlying data still can’t fully answer. Considering an Alternative to Backstory? See how Nektar delivers 95%+ attribution accuracy directly into your Salesforce, without the Backstory price tag. Check the Comparison Why Enterprises Evaluate Backstory Alternatives Based on conversations with enterprise buyers evaluating both platforms, several consistent themes emerge: Existing Analytics Investment Many large enterprises have already invested significantly in Databricks, Snowflake, Looker, or Tableau. Their internal ops teams build custom dashboards tailored to their specific sales motions. For these organizations, adopting another analytics platform, conversational interface or not, creates redundancy rather than value. They want the underlying data, not another UI. Salesforce Integration Model Backstory (like People.ai before it) uses a managed package approach that creates custom objects in Salesforce. While this provides rich functionality within Backstory’s own ecosystem, some enterprises report challenges including: Additional automation required to map data into standard Salesforce fields Complexity when using captured data in existing workflows or forecasting Duplicate participant records requiring cleanup Nektar writes directly to standard Salesforce objects (Events, Tasks, Contacts), which can simplify integration with existing processes but may offer less specialized functionality. Meeting Attendance Requirements A significant differentiator for some buyers is meeting attendance intelligence. Backstory’s meeting data typically relies on calendar invites and recorded calls via conversation-intelligence platform integrations. Nektar captures both invitees and actual attendees, along with meeting status (completed, cancelled, no-show, under 10 minutes), without requiring recording. For organizations focused on coaching, churn analysis, or executive involvement tracking, this distinction can be decisive, and it isn’t something a conversational query layer on top of the same underlying capture gap actually solves. Data Volume Control Some enterprises express concern about data volume and Salesforce storage costs. Nektar offers granular sync controls that let administrators define which activities to capture, which contacts to create, and what thresholds to apply. Backstory’s capture approach may generate higher data volumes, which can be beneficial for analytics but challenging for storage-conscious organizations. Key Differentiators: A Deeper Look Opportunity Matching Accurately attributing activities to the correct opportunity is critical for pipeline analytics and forecasting. The two platforms take different approaches: Backstory uses configurable, rule-based matching logic that can be customized per deployment. This approach offers predictability but may require ongoing maintenance as your sales process evolves. Nektar employs graph-based machine learning that analyzes email content, domain patterns, calendar metadata, and historical matching to attribute activities. Nektar reports accuracy rates above 90% in multi-opportunity environments, with the model improving over time through self-learning. Meeting Intelligence This is one of the most significant differentiators between the platforms: Backstory captures meeting data primarily through calendar integration and conversation-intelligence partners (Gong, Zoom IQ, Webex). Insights from recorded calls

activity tracking
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How Activity Tracking Can Help You Get Better Visibility Into Deals

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How Activity Tracking Can Help You Get Better Visibility Into Deals RevOps 10 min There hasn’t been a time that demanded sustainable revenue growth more than now. Economic headwinds of the last few months have forced businesses to rethink their revenue growth strategies and focus on efficiency. This means getting away with anything that does not make a positive dent in revenue or causes revenue to leak across the sales funnel. But cutting deep costs is not the only way to increase profits. It’s about doubling down on what’s working. And investing time and resources in strategies that help the whole company march towards the same objective – increased revenue.  And there is one sure shot way of achieving this. By knowing exactly what’s happening with your deals. And how can you do that? Activity tracking. Let’s dive deep. What is Activity Tracking? Activity tracking in sales refers to monitoring and recording the various actions and behaviors undertaken by sales professionals as they engage in their sales activities. It involves tracking and measuring the specific activities performed during the sales process, such as the number of calls made, emails sent, meetings scheduled, demos conducted, and deals closed. The purpose of activity tracking in sales is to gain insights into the sales process, assess individual and team performance, and make data-driven decisions to improve sales effectiveness.  What is an Activity Tracking Software? An activity tracking software is designed to monitor and record the various activities performed by sales representatives or teams. These activities typically include interactions with leads and prospects, customer communication, follow-ups, and other sales-related tasks. The primary purpose of activity tracking software is to help sales managers and team leaders assess and improve the productivity and effectiveness of their sales teams. Why Do We Need Activity Tracking? Accurately and comprehensively capturing activity data poses a significant challenge. Despite 67% of businesses utilizing 4 to 10 digital tools, they need to track the activity data generated by these tools completely and precisely. Additionally, 79% of opportunity-related data sales representatives collect never enters the CRM. Moreover, the data recorded in systems like CRM could be more reliable, plagued by issues like outdated, missing, or incomplete entries. This lack of data accuracy is a concern for as many as 70% of revenue leaders, leading to substantial financial losses averaging around $15 million per year for organizations. The presence of accurate and complete activity data in systems like CRM creates misalignment among teams in terms of their technological tools and objectives. When sales teams grapple with questions about updated prospect contact information or the correctness of email IDs in the CRM, their efficiency could improve, positively impacting both businesses and customers. Due to lacking confidence in the data, sales, and marketing teams work in the dark, unable to leverage the full potential of significant investments like CRMs. This situation results in poor returns on investment for such resources. https://www.youtube.com/watch?v=GO6zZpHUoIg&t=1s How Does Poor Activity Data Affect Revenue? Poor data and a lack of activity data in the CRM can harm gaining accurate insights and lead to revenue leakage throughout the customer journey. Here are some key points to consider: 1. Inaccurate or incomplete data When data quality is compromised, it becomes challenging to extract meaningful insights. Only complete or updated information can lead to correct assumptions and flawed decision-making. 2. Missed opportunities Important customer interactions and touchpoints may go undocumented without comprehensive activity data. This lack of visibility can result in missed opportunities to engage prospects, address their needs, and nurture relationships, leading to potential revenue leakage. 3. Ineffective sales strategies The absence of activity data hinders the ability to analyze and optimize sales strategies. Without a clear understanding of which activities drive results, aligning sales efforts with customer preferences and needs becomes difficult, resulting in suboptimal outcomes. 4. Inefficient resource allocation With activity data, it’s easier to assess the productivity and effectiveness of sales teams. This can lead to misallocation of resources, including time, effort, and budget, resulting in revenue leakage and diminished returns on investment. Clean data is essential for Activity Tracking Software as it ensures accurate and error-free information, leading to reliable insights into sales team activities and facilitating better decision-making and performance analysis. With clean data, the software can provide a comprehensive view of sales interactions, prospect engagement, and customer behavior, enabling businesses to identify opportunities, optimize processes, and enhance overall sales efficiency.  Moreover, clean data minimizes the risk of misinterpretation or skewed reporting, fostering greater trust in the software’s output and empowering sales managers and teams to take data-driven actions to achieve their goals. Benefits of Activity Tracking Software Here’s a look at the various advantages of an activity tracking software: 1. Clear visibility into deals Increased visibility into deals serves as a prerequisite for enhancing productivity. When you have comprehensive activity data, you better understand each deal’s status, identify areas that require improvement, and prioritize values that need immediate attention.  Consider the importance of deal reviews in a successful sales process. By utilizing insights derived from unified activity data, deal reviews can evolve from impromptu events to impactful sessions, where sales managers gain clear visibility into the intricacies of every deal.  As activities related to each deal are automatically captured and updated, managers no longer need to remind sales representatives to input data into the CRM constantly. Instead, both reps and managers can access a comprehensive view of contacts and deal specifics within the pipeline, allowing them to focus on urgent matters. 2. Identification of winning rep behaviours Activity data enables you to correlate the productivity of your sales representatives with their performance. For instance, you can obtain crucial insights to answer important questions such as:  Activity data helps map sales reps’ productivity to their performance It provides answers to critical questions such as time allocation, engagement with high-value customers, decision-maker involvement, adherence to best practices, sales target progress, account engagement, and lead follow-up Insights from activity data serve as leading indicators for real-time coaching and decision-making Managers gain visibility into sales reps’

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Orchestrating Siloed Data in RevOps to Drive Business Decisions

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Orchestrating Siloed Data in RevOps to Drive Business Decisions A conversation with Mahesh Kumar, VP of RevOps at AppviewX. “The goal of RevOps is to remove the art from revenue generation—and bring in science.”— Mahesh Kumar, VP of Revenue Operations, AppViewX Go-to-market teams are facing a monumental challenge—data fragmentation. With siloed systems, disconnected tools, and inconsistent definitions, organizations struggle to form a cohesive view of their revenue engine. The result? Poor decisions, misaligned teams, and missed growth targets. In this in-depth conversation on The Revenue Lounge, Mahesh Kumar, VP of Revenue Operations at AppViewX, breaks down his playbook for navigating the messy world of siloed data. With more than 12 years of experience across sales, marketing, and operations, Mahesh offers real-world examples and strategies to help RevOps teams become not just operationally efficient—but strategically indispensable. Facebook Twitter Youtube The Problem: Data Silos and Misalignment Across Functions Mahesh began his career on the revenue side—as a pre-sales engineer, then moved to sales, built BDR/SDR teams, and later ran marketing. This 360-degree exposure gave him a unique lens into one of the most persistent challenges in GTM functions: siloed data. “Every department had its own version of the truth. Even basic definitions varied. It was impossible to align or make strategic decisions.” He recounted a particularly painful period where marketing believed it was generating high-quality leads, sales felt those leads were weak, and customer success struggled to understand what was promised to customers—because no one had a unified dataset or common definitions. This wasn’t a minor inconvenience. It was a strategic blocker. The Solution: Building a Unified, Orchestrated RevOps Engine To solve the fragmentation problem, Mahesh emphasized that the answer wasn’t just in tools—but in orchestration. “We can’t consolidate everything, and we shouldn’t try to. The key is orchestrating data across tools, teams, and processes.” Rather than force-fit every team into a single platform, Mahesh advocates for connecting tools via native integrations where possible and using custom scripts or internal workflows when necessary. At AppViewX, for example, Salesforce acts as the system of record, but data flows in from various tools—marketing automation, CS platforms, product usage systems, and internal scripts that clean and enrich records in real-time. The Orchestration Mindset Traditional Approach Orchestration Mindset Attempt to consolidate tools Embrace point solutions but integrate them One-size-fits-all reporting Custom dashboards by function Data owned by each team Centralized data strategy Ad hoc fixes Long-term scalable systems https://www.youtube.com/watch?v=4PIhMfv6j4E&t=198s Step-by-Step: Mahesh’s RevOps Orchestration Playbook Mahesh’s approach to breaking down data silos follows a deliberate, step-by-step method. Here’s how he tackled the challenge at AppViewX: 1. Secure Executive Buy-In Through Use Cases The first step is not technical—it’s cultural. Mahesh identified a few high-impact use cases where disconnected data caused pain, then presented them to executives. For example, onboarding delays were traced back to poor visibility into customer expectations during the sales cycle. By involving the CS team earlier in the sales process, the transition became seamless, resulting in faster time-to-value. “Start where the pain is loudest. When executives see the impact, they’ll back your strategy.” 2. Establish a Single System of Record One of the earliest wins came from establishing common data definitions across departments. Terms like “lead,” “MQL,” and “sales-qualified” had different meanings in different departments. “Without standard definitions and a shared system of record, you’re not speaking the same language—even if you’re in the same building.” Template: RevOps Data Dictionary Term Definition Source of Truth Owner MQL Lead with score > 70 and engaged in last 30 days HubSpot Marketing Ops Opp Stage 3 Proposal shared and scheduled for review Salesforce Sales Ops Time to First Value Days from deal close to initial onboarding value Gainsight CS Ops   3. Focus on Categorizing and Structuring the Data Once teams are aligned, the next challenge is data structuring. Mahesh’s team categorized data into four key buckets: Human-generated data (manual entry in CRM) System-to-human data (notifications, tasks, UI flows) System-to-system data (API transfers, integrations) External data (from customer intent tools, product signals) Each dataset was cleaned, normalized, and mapped to the CRM structure, making analysis and automation easier. “Every new field or process change is evaluated for its downstream data impact. It’s a data-first culture.” 4. Automate Integrations with Native Tools + Internal Scripts While AppViewX doesn’t use a classic ETL tool, Mahesh’s team built internal automation workflows using scripts to orchestrate data across systems. Whenever possible, they rely on native integrations—for example, syncing Salesforce with HubSpot, Gainsight, or internal product tools. But for more complex requirements, they’ve written scripts that move data based on business rules.   This flexibility ensures scalability without overengineering. From Tactical to Strategic: The Future of RevOps With orchestrated data in place, Mahesh believes RevOps can move beyond its reputation as a support function and become a strategic growth engine. “When you’re sitting on high-quality, unified data, you can test hypotheses, optimize processes, and influence revenue strategy directly.” Tactical vs. Strategic RevOps Tactical RevOps Strategic RevOps Report on pipeline and leads Advise GTM strategy using insights Fix sync issues in Salesforce Optimize funnel stages to reduce CAC Build dashboards on request Drive quarterly planning with data Reactive to requests Proactive in identifying GTM risks The Cultural Shift: Building a Data-First Organization One of Mahesh’s biggest insights wasn’t about tools or processes—it was about culture. Many teams look for a quick fix: “We have a problem—what tool can we buy to solve it?” But Mahesh believes success starts with a mindset shift. “Every change—whether it’s a new field, a process tweak, or a tech purchase—needs to be evaluated for its impact on data.” This long-term thinking is essential, especially in high-growth environments where new tools and processes are being adopted rapidly. Scaling for Tomorrow: How to Future-Proof Your RevOps Stack A recurring challenge in RevOps is building for now vs. building for scale. Many teams implement quick fixes that don’t scale—only to rip and replace them six months later. Mahesh recommends designing every system with scalability in mind. “Whatever you implement—ask

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Scaling Revenue Operations in High-Growth SaaS: A Strategic Playbook

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Scaling Revenue Operations in High-Growth SaaS: A Strategic Playbook A conversation with Josh Pudnos, VP, Global Head of RevOps at Exiger. In the high-stakes world of SaaS, growth is no longer just a function of adding more sellers or increasing outreach volume—it’s about scaling smart, aligning teams, and building a RevOps foundation that enables profitable and predictable revenue. Josh Pudnos, former VP and Global Head of Revenue Operations at Exiger, knows this challenge intimately. Tasked with transforming Exiger from an advisory firm into a SaaS powerhouse, Josh architected a RevOps strategy from the ground up—rebuilding tech, redefining data, restructuring teams, and guiding the company through the messy middle of SaaS evolution. In this detailed blog, we explore Josh’s RevOps transformation playbook—anchored in data integrity, stakeholder psychology, and operational precision. Whether you’re a startup building RevOps from scratch or an enterprise scaling your GTM engine, this story is packed with practical strategies you can adapt to your own environment. Facebook Twitter Youtube From Advisory Firm to SaaS: The Mandate for Change When Josh joined Exiger, the company was in the middle of a strategic pivot. It had already seen success as an advisory services firm, but growing regulatory demand, supply chain risks, and the need for scalable solutions pointed toward a SaaS future. “We saw the signals—more regulation, more risk, more complexity. To meet that need, we had to mature and become a SaaS-first business.” This shift wasn’t just a marketing change. It required reimagining the entire go-to-market (GTM) motion, from how they sold and served customers to how they structured teams and measured success. Phase 1: Rebuilding the Foundation (and the Data) Josh’s first challenge? Data chaos. “Everything within Salesforce when I joined couldn’t be trusted. There was no standardization. We had to start from scratch.” The RevOps team conducted a comprehensive audit and rebuilt core processes—from lead lifecycle to opportunity stages and product taxonomy. 🔍 Data Cleanup Framework Lead > Contact > Opportunity Conversion: Unified and documented lifecycle stages Opportunity Stage Definitions: Standardized across business units Field-Level Governance: Required fields tailored by deal type (new vs. renewal vs. growth) Product Classification: Split recurring ARR vs. one-time services This clean-up wasn’t just cosmetic—it enabled a major win. During Exiger’s private equity exit, the improved data integrity played a crucial role in underwriting the deal. “We could finally speak confidently about our pipeline and customers. That was a huge turning point.” https://www.youtube.com/watch?v=YUwL4kuwg-k&t=3s Phase 2: Building the Right Tech Stack (Without Overbuilding) Armed with a healthy budget and a mandate to modernize, Josh moved quickly to implement a stack that could support outbound motions, deal structuring, and better forecasting. Tools included: Sales engagement platform CPQ implementation Marketing intent integrations CRM and funnel automation But with the benefit of hindsight, Josh realized he moved too fast. “I discounted the reps’ perspectives more than I should have. Some of those tools weren’t adopted. I won’t renew all of them.” 🧠 Key Lesson: Don’t Over-Index on Tech Instead, focus on: User-driven design: Understand how reps actually work Iterative rollout: Prove success with pilots Onboarding and enablement: Train consistently across roles Phase 3: Building a Lean, Impactful Team With only a handful of team members, Josh structured RevOps as a hybrid of technical systems ownership and strategic business partnering. 💼 RevOps Org Design at Exiger Function Focus Area Systems Ops (2 people) Salesforce, integrations, tech stack Sales Ops (2 people) Pipeline strategy, forecasting, top-of-funnel Enablement (1 person) Training, playbooks, seller onboarding Leadership (Josh) Strategy, executive alignment, roadmap ownership   Each RevOps member was aligned with GTM leaders—BDR, AE, AM, CS—to act as a strategic partner, not a ticket taker. “They need a business partner in RevOps. Someone who helps them solve real problems—not just run reports.” Phase 4: Evolving from MQLs to Buying Groups Josh acknowledges a major industry trend: the shift from individual lead tracking (MQLs) to understanding and activating entire buying groups. “There’s no such thing as a single buyer anymore. The committee is often 10–20 people—and each one needs to be engaged differently.” This required evolving both marketing and sales strategies. Exiger began layering intent data with what Josh calls a “surround-sound” approach. 📊 Buying Group GTM Framework Tactic Execution Layer Intent data Use 3rd party and web analytics to identify signals Surround-sound engagement Target decision-makers with tailored content Cross-functional plans Sync sales & marketing on buying group plays Deal acceleration Use buying signals mid-funnel to re-engage deals   Josh noted that even if Exiger isn’t at the fully orchestrated “trigger-based play” stage, they’ve already seen lift in stalled deals simply by getting the right content in front of the right people. Phase 5: Managing Ad Hoc Chaos While Staying Strategic Every RevOps leader has felt this tension: stakeholders want dashboards and ad hoc reports—while leadership wants strategic programs and scalable systems. “You have to empower your team to say no—or at least say ‘not right now.’ Tie everything back to your quarterly initiatives.” Josh and his team communicate their goals through quarterly newsletters, stakeholder syncs, and dashboards that guide self-service. Why Josh Reports into Finance, Not Sales Exiger chose to place RevOps under the CFO instead of the CRO. For Josh, this choice provided the objectivity and strategic alignment he needed. “You don’t want RevOps to become a propaganda arm of sales. With finance, we’re aligned to profitability and operational rigor.” It also helped the team focus not just on revenue goals but on sustainable growth and operational efficiency. The Most Underrated Skill in RevOps? Psychology. “So much about RevOps is understanding how people interpret data, process, and systems. It’s psychological.” Josh recalls debates not about tech or tactics—but about philosophical decisions like how to classify a deal type or when to progress a stage. Understanding stakeholder mental models, motivations, and friction points is what unlocks true cross-functional alignment. Josh’s Retrospective: What He’d Do Differently Move Slower at the StartBuild consensus before buying tech. Map out the rep workflow first. Involve Frontline Teams EarlyEven if they’re unfamiliar with SaaS tools, their

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From MQLs to Buying Groups: How Palo Alto Networks Drove 15x Pipeline Impact

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From MQLs to Buying Groups: How Palo Alto Transformed its Funnel & Drove 15x Pipeline Impact A conversation with Lauren Daley, Director of Marketing Operations at Palo Alto Networks. “We all knew MQLs weren’t working. But we were still being measured by them. Something had to change.”— Lauren Daley, Director of Marketing Operations, Palo Alto Networks In an era where enterprise B2B buying is driven by committees, not individuals, most marketers still operate in a lead-centric, MQL-obsessed model. But at Palo Alto Networks — one of the world’s largest cybersecurity companies — a transformative shift has been quietly reshaping how demand generation connects to pipeline. Lauren Daley, Director of Marketing Operations, alongside Jeremy Schwartz, spearheaded one of the most impactful GTM transitions in recent memory: abandoning individual MQLs in favor of a buying group-driven strategy. This shift didn’t just improve pipeline metrics — it won Palo Alto Networks Forrester’s 2025 Demand and ABM Program of the Year and led to double- and triple-digit improvements in pipeline performance. Let’s walk through the detailed steps of this transformation, the cultural and technical pivots it required, and how you can apply Palo Alto’s approach to your organization. Facebook Twitter Youtube Why MQLs Failed to Deliver — And Why Buying Groups Matter For years, marketing has been measured by how many MQLs it can generate. But most B2B enterprise purchases aren’t made by individuals — they’re made by buying committees. At Palo Alto Networks, this was especially evident: they were selling multi-product, high-stakes cybersecurity solutions to government, healthcare, and large enterprises — all of which involve multiple stakeholders in the buying process. “We weren’t doing a good job of connecting all those signals, those buying group members, and packaging it in a way sellers could take action on. That was the disconnect.”— Lauren Daley Marketing teams were doing the hard work of engaging the right personas, but those efforts weren’t translating into revenue. Why? Because individual leads weren’t enough. A shift to buying groups was long overdue. The Journey Begins: From Pilot to Playbook The transformation started not with tech, but with people. Lauren and her team began small — launching a pilot focused on Business Development Representatives (BDRs) and enabling them to associate more stakeholders with each opportunity. “We didn’t boil the ocean. We started with the friendlies — people who immediately bought into the vision.”— Lauren Daley The early results were compelling enough to draw interest from other teams across the company, and that’s when momentum truly started to build. https://www.youtube.com/watch?v=xUAJSu7ebeA Buying Group Impact at Palo Alto Networks The results were staggering when buying groups were present in an opportunity: “I call it compound lift. More deals in forecast. Bigger deals. Higher win rates. That’s a lot of incremental bookings.”— Lauren Daley With buying groups: Opportunities moved into forecast at 15x the rate compared to solo leads. Deal sizes increased by 2.4x. Win rates improved by 1.4x — a 40% increase. This wasn’t just a better marketing model — it was a business growth engine. Changing Mindsets: Enabling the Shift in Marketing Thinking One of the most difficult aspects of this transition wasn’t technology — it was mindset. Marketing teams had been conditioned to focus on MQLs for years, and those targets still drove behavior. “If you put a top-line MQL target in front of a marketer, that’s what they’ll chase — whether it converts or not.”— Lauren Daley To combat this, Lauren and Jeremy went on a company-wide roadshow. They didn’t just explain the new approach — they showed teams how to take action. Campaign and field marketing teams were coached on identifying gaps in buying group coverage and targeting missing personas instead of over-focusing on one highly engaged individual. “Three lightly engaged personas in the right roles are more valuable than one highly engaged individual.”— Lauren Daley Creating the Buying Group Score: A Gartner-Inspired Framework To make the shift operational and actionable, the team developed a Buying Group Score — a clear and simple framework inspired by the Gartner Magic Quadrant. This model categorized buying group engagement into four quadrants based on: Intent Engagement Completeness (presence of key personas) Propensity (likelihood to buy) Buying Group Score Matrix Quadrant Intent Engagement Completeness Propensity Action A High High High High Prioritize immediately B High Low High Medium Campaigns: drive engagement C High High Low Medium Paid: identify missing personas D Low Low Low Low Brand nurture   “We wanted to help marketers prioritize accounts with high potential and make decisions based on data, not guesses.”— Lauren Daley This framework is now being integrated into Salesforce using a custom Buying Group Object, designed to house members of a buying group before an opportunity is even created. Using the Existing Tech Stack to Drive Change Contrary to what many assume, this transformation didn’t require a major investment in new tools. “This transformation is free. We didn’t ask for extra budget.”— Lauren Daley Key adjustments included: Turning on Lead-to-Opportunity matching in LeanData Using Demandbase to monitor engagement and intent signals Building a custom object in Salesforce to house buying group data Automating engagement scoring over time “The tech wasn’t the bottleneck — mindset and enablement were.”— Lauren Daley Evolving the Metrics: From MQLs to Coverage & Contribution The move to buying groups demanded a rethink of what marketing success looks like. Metrics that Became Obsolete: Raw MQL volume Individual engagement scores Metrics That Matter Now: Buying Group Coverage: % of opportunities with complete persona representation Campaign → Opportunity Contribution: Are campaigns driving opportunity creation or expansion? Engagement by Role: Are we nurturing decision-makers, influencers, and champions? Pipeline Conversion & Win Rate by Buying Group Status Overcoming Resistance and Driving Adoption “People immediately said: this makes sense. But changing how they work day-to-day? That takes effort.”— Lauren Daley To make adoption easier: Lauren’s team developed dashboards to visualize persona gaps Created activation playbooks tailored by channel and segment Invested in continuous enablement and real-time coaching Demonstrated the “before and after” revenue impact to stakeholders Related

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Building a Data-Driven Customer Success Strategy

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Building a Data-Driven Customer Success Strategy A conversation with Sam Slevin, Global SVP of Customer Success at Alphasense. In today’s enterprise SaaS landscape, retaining customers isn’t just about renewals—it’s about delivering continuous value from the first interaction to every milestone that follows. Sam Slevin, Global SVP of Customer Success at AlphaSense, breaks down how a truly data-driven customer success strategy is built—one that aligns people, processes, and platforms across the full customer lifecycle. In this blog, we dive deep into Sam’s frameworks for onboarding, team structuring, digital touchpoint execution, and renewal forecasting—plus how AlphaSense leverages data to power intelligent decision-making at scale. Facebook Twitter Youtube 🔑 Why Retention Starts at the First Touchpoint “There are so many things that happen over the course of a lifecycle that are not in your control. But the first interaction? That’s fully in your control.” – Sam Slevin Many organizations treat customer success as a reactive, post-sale process. Sam’s approach flips that thinking: renewal begins the moment a buyer engages with your sales team. That means every insight gathered by AEs and pre-sales needs to be transferred with context—not lost in CRM notes or email chains. 📌 Key Hand-off Elements from AE to AM: Proposal with stated customer goals Trial qualification criteria Buying committee context Success metrics in the customer’s own words These inputs directly shape the AM’s kickoff meeting, aligning expectations and building trust early on. Structuring CS Teams by “Jobs to Be Done” At AlphaSense, customer success isn’t confined to a narrow definition. The team is built to address specific jobs across the customer journey, from onboarding to support to expansion. Team Structure: Account Managers (AMs): Own renewal and collaborate with AEs on expansion. Bonus-tied to growth, not commission-heavy. Product Specialists: Technical experts aligned to product usage and value delivery. Support Ops: 24/5 global team handling tickets, internal account setup, and external queries. Pre-Sales Consultants: Integrated into CS to improve handoffs and accelerate early-stage value delivery. This model ensures that every customer gets the right expertise at the right moment—especially during trials and onboarding. https://www.youtube.com/watch?v=xdaeKaWXzzY&t=59s The Onboarding Experience: It’s Not a Meeting—It’s a Strategy Onboarding is often treated as a quick call or checklist. But for AlphaSense, it’s a strategic, data-backed process. “Onboarding isn’t a one-time call. It’s an experience—one that should be tied to usage data and milestones that indicate customer health and stickiness.” – Sam Slevin ✅ Onboarding Success Checklist: Confirm North Star Goals (from sales process) Map stakeholders to outcomes Track milestone adoption (e.g. feature usage, content access) Complete product configuration with Product Specialist Validate value realization in the customer’s language Inputs Drive Outcomes: Sam’s Performance Framework Rather than chase lagging indicators, AlphaSense tracks daily and weekly inputs that lead to renewal success. 🧮 Core Inputs: Number of high-value customer calls % of users touched per month/quarter SBRs conducted and aligned to success metrics Expansion referrals and sourced pipeline 📊 Core Outputs: Gross Renewal Rate Net Revenue Retention Forecast Accuracy “We believe if you do the right inputs consistently—like high-value calls and user engagement—the outcomes will follow.” – Sam Slevin 📌 QBR Operating Cadence: Monthly: Managers analyze rep-level data and submit summaries Quarterly: AMs present full retrospectives and forward-looking plans Discretionary compensation tied to key activity benchmarks Aligning AMs and AEs for Expansion Expansion isn’t a handoff—it’s a co-owned strategy. AMs focus on value delivery and pipeline sourcing, while AEs “hunt” into new divisions. “If a rep calls a user and they say, ‘I love AlphaSense, I was going to recommend it to you’—that’s when you know you’re doing it right.” – Sam Slevin 📌 Pro Tip: Ask happy users for intros to adjacent teams. Draft the email for them. Keep it low-lift. Digital CS: Air Cover at Scale Contrary to the belief that digital CS is only for SMBs, Sam views it as air cover for both large and small accounts. With thoughtful segmentation and trigger-based workflows, AlphaSense ensures digital motions augment—not replace—human touch. 🔍 Readiness Checklist for Digital CS: Contact hygiene validated Usage triggers mapped Strategic accounts white-labeled Segments reviewed quarterly by RevOps & CS Clean and trusted source systems (e.g. Catalyst, Salesforce) Data Hygiene: The Cornerstone of Digital Strategy “Before we move anyone to digital touch, there’s a manual scrub. We don’t just click a button and walk away.” – Sam Slevin Poor contact data leads to impersonal messages and a broken customer experience. AlphaSense blends automation with manual segmentation—then revisits it every quarter to ensure consistency. Future of CS: AI, Personalization & Smarter Data Despite being a native AI company, AlphaSense is cautious about AI overhype in CS. “AI is the right answer, but only if you have clean data and the infrastructure to support it.” – Sam Slevin Potential Use Cases: AI agents trained on call transcripts and sales collateral Enablement bots that reduce ramp time Smart segmentation based on usage and buying signals But the current state of enterprise data hygiene means these use cases are still aspirational for most CS teams. Cross-Functional Alignment & Change Management Sam attributes a huge part of AlphaSense’s success to trust and alignment across functions—from sales to marketing to product and RevOps. 🧩 Internal Collaboration Practices: Weekly one-on-ones with key functional leaders Structured kickoff plans for cross-team projects Aggregated asks to reduce “Slack fire drills” Empathy-driven partnerships: “I don’t want your job, and I know how hard yours is.” Final Advice from Sam: Start with the End in Mind “Assume your customer will cancel in 365 days. What are you doing today to change that outcome?” – Sam Slevin Sam’s frameworks are designed to help CS leaders drive accountability, adoption, and long-term value creation. His advice is clear: align on the North Star early, track your progress rigorously, and build systems that make renewal the natural outcome of ongoing success. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! 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Evolution of ABM in Modern Demand Generation

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Evolution of ABM in Modern Demand Generation A conversation with Rick Collins, VP of Demand Generation at Connectwise. “We’ve hit what I call the Great Ignore. Everyone’s overwhelmed with messages across channels… even the good ones are getting ignored.”  In the past 12–18 months, pipeline generation has become increasingly challenging. Rising revenue goals and shrinking budgets have pushed marketing teams into a corner. Traditional tactics—especially high-volume lead generation—are no longer effective. In this environment, Account-Based Marketing (ABM) has emerged as a vital strategy. But to truly succeed, teams must rethink how they define ABM, how they align with sales, and how they scale through integrated processes. This blog, based on a Revenue Lounge podcast episode with Rick Collins, VP of Demand Generation at ConnectWise, is your deep dive into: The evolution of ABM in modern demand gen Aligning go-to-market teams Building operational systems for scale Tools, data, and attribution best practices Key lessons Rick learned the hard way Facebook Twitter Youtube From IT to Demand Gen: Rick’s Unconventional Path Rick’s journey began in IT—working in QA, implementation, and CRM systems. Over time, he gravitated toward marketing operations, and eventually demand generation. “I bring a different lens to demand gen. I’ve built the ops side first, which gave me an appreciation of data, systems, and how to scale programs with precision.” He was the first marketing ops hire at ConnectWise, scaled the team through multiple acquisitions, and later took over demand gen during one of the toughest periods for pipeline creation in SaaS. The Death of Traditional Lead Generation Rick calls out three seismic changes that made legacy demand gen ineffective: The Rise of the Empowered Buyer: Buyers now reach 80–90% of the way through the journey before contacting a vendor. Digital Fatigue: Automation misuse has saturated inboxes and weakened outreach quality. Market Competition: More players, more noise, and higher ad costs. “We used to be a lead-gen machine. Now it’s all about understanding signals, providing value, and making every touchpoint count.” https://www.youtube.com/watch?v=bdDbWb-MWwI Strategies That Actually Work Rick’s team has focused on three core strategies to cut through the clutter: 1. Provide Thought Leadership Without Selling Publish content that helps the audience do their job better. Avoid product mentions in early stages. “The more we can help you without asking for anything, the more trust we build.” 2. Respond Fast When Intent is Declared If someone shows intent, ensure a quick, seamless follow-up. Architect systems for real-time handoff to sales. 3. Revive Direct Mail Physical mail cuts through the noise and makes an impact. Combine gifting with value-driven messaging. “You send me a direct mail piece—I’m going to see it. It stands out.” ABM is Not a Tool. It’s a Strategic Motion “Start with the strategy. Don’t buy the tool until you know what you want to achieve.” Too many organizations make the mistake of buying an ABM platform before defining their motion. Rick recommends starting small and proving success manually. MQLs vs Buying Groups: A Nuanced Approach Rick doesn’t claim MQLs are dead—but they are misunderstood. The definition varies drastically across companies. What’s more effective? Tracking buying group signals. “We’re operating under the buying group model in our upmarket motion. One person may raise their hand, but we’re watching the whole committee.” MQL vs Buying Group Comparison: Criteria MQL Buying Group Focus Individual Committee/Swarm Common in SMB Enterprise, Mid-market Trigger Email open, form fill Intent + multiple touchpoints Limitation Ignores influence Holistic engagement Ideal motion Automated lead nurture High-touch ABM Solving Attribution & Measurement Challenges “We use cohort reporting to measure ABM. Attribution is helpful, but it’s directional.” Attribution is complex—especially when sales teams don’t tag every contact or touchpoint in CRM. Rick’s solution is cohort-based reporting: Cohort Reporting Process: Choose a set of 500 target accounts Launch a defined campaign or series of campaigns Measure: Pipeline creation Opportunity conversion Win rates Double-click into successful accounts and identify what worked Aligning with Sales: The Non-Negotiable Element “If sales isn’t bought in, it’s just marketing playing by themselves. It doesn’t work.” Rick emphasizes that sales buy-in is crucial. Here’s his playbook for driving that alignment: Sales Alignment Checklist: Joint Sales-Marketing ABM Execution Plan Phase Action Owner Account Selection Agree on Tier 1 accounts Sales + Marketing Persona Mapping Identify roles & pains Marketing Messaging Customize value stories Marketing + Enablement Outreach Sequence delivery SDRs + Reps Follow-Up Meetings & nurture Sales Reporting Track cohort progress Ops Tech Stack and Data Activation: A Pragmatic View “Tools won’t fix your strategy. They help scale what’s already working.” Rick breaks the ABM tech landscape into three layers: Signal Aggregation – intent data, website visits, email behavior. Activation – digital ads, gifting, outreach. Measurement – pipeline contribution, cohort lift, influence. His recommendation: push data into Salesforce and trigger workflows from there. Otherwise, data sits idle. “We built a prospecting dashboard showing intent scores, untouched accounts, and pipeline priority. Next step: automate the whole motion.” Balancing Short-Term Metrics vs Long-Term Relationship Building “If someone has the answer to balancing short- and long-term pipeline generation, I’m all ears.” Rick’s team avoids meeting-based comp for SDRs. Instead, they’re measured on accepted pipeline and closed-won influence. But this is still a work-in-progress. SDR Measurement: Old vs New Model Meeting-Based Pipeline-Influence-Based Pros Easy to track Aligned with revenue Cons Short-term focus Complex to implement Outcome Flimsy meetings Better qualified pipeline The Power of Insightful Personalization A campaign that stood out to Rick? A vendor targeting ConnectWise built a hyper-personalized series referencing his CMO’s Boston roots and even tied it to Tom Brady. “It wasn’t just clever—it was relevant. And it solved a real pain. That’s what made it stick.” Lesson: Don’t just personalize. Make it insightful and timely. Final Lessons Learned “We made two big mistakes early on: Lack of executive alignment and poor account selection.” What Rick Would Do Differently: Spend more time aligning with sales leadership. Don’t rely only on systems to pick accounts—get sales input early. “Sales will throw out your list if even one account

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Building a Unified Revenue Engine: How Druva Aligns GTM and RevOps for Growth

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Building a Unified Revenue Engine: How Druva Aligns GTM and RevOps for Growth A conversation with John Hultman, Chief Revenue Officer at Druva. The path to revenue growth isn’t paved solely by sales excellence—it’s constructed through the strategic orchestration of all go-to-market (GTM) functions: sales, marketing, and customer success. John Hultman, CRO of Druva, shares his playbook for building a cohesive GTM engine by unifying data, engagement metrics, and operations under a single strategic vision. From tackling disjointed KPIs to uncovering hidden churn signals and designing intent-driven expansion plays, John offers a masterclass in what it means to lead with RevOps in the modern age. Facebook Twitter Youtube Why Alignment Across Revenue Teams is Non-Negotiable “Everybody looks at metrics vertically—‘I’m green’—but you’re still not hitting the goal. Flip it horizontally. Work backward from the outcome.”— John Hultman, CRO at Druva One of the biggest traps GTM organizations fall into is siloed success. Each team—marketing, SDRs, AEs, CS—operates in its own KPI bubble. While each may hit their own numbers, the company still misses revenue targets. John calls for a complete reorientation: from vertical success to horizontal alignment. Vertical vs. Horizontal KPI FocusBelow is an infographic that illustrates how traditional KPI silos compare to outcome-focused, horizontal alignment across GTM teams: Redefining Metrics: What Actually Moves the Needle Instead of tracking surface-level KPIs like MQLs or number of meetings, John aligns his teams around what truly impacts revenue: Metric Why It Matters Marketing-Generated Bookings Ties campaigns directly to revenue outcomes Lead Follow-Up Time Reveals AE responsiveness and SDR effectiveness Opportunity Stage Duration Detects pipeline friction points Expansion Rates Measures long-term account growth Churn Risk Scores Early indicators of customer dissatisfaction   By standardizing these metrics across departments, teams can see where things break down and act fast. “It’s not about the quantity of pipeline. It’s the quality and the conversion that matter.” https://www.youtube.com/watch?v=xQlouc3eOpw Breaking Through the Noise: New Realities in Prospecting Prospecting is harder than ever. According to industry data shared at B2BMX, first meetings are down 30–50% year-over-year. Buyers are overwhelmed by outreach—emails, cold calls, DMs—and are increasingly unresponsive. John’s solution? Shift the lens from quantity to cost-effectiveness: Analyze Customer Acquisition Cost (CAC) across different motions (MQLs vs OEM vs MSP vs Channel). Explore non-traditional channels that drive better ROI. Focus on signal-based marketing instead of shotgun-style outreach. “A traditional MQL model is expensive. Every touch—tools, SDR, data cleansing—adds up.” The Power of Buying Groups and Intent Signals B2B buying is no longer a one-person show. Recent research from 6sense shows the average buying group includes 11 stakeholders. But traditional CRMs typically capture only one. “Buying signals help us understand if something’s heating up—or if churn is around the corner.” Druva uses platforms like ZoomInfo and 6sense to: Detect intent across personas Identify expansion opportunities Predict churn within current accounts These platforms provide visibility not just into net-new accounts, but also within existing customers—surfacing signs of disengagement or interest in new products. Scaling Expansion with Dedicated Teams Druva’s go-to-market strategy separates new logo acquisition from expansion: Team Focus Area Hunters Land new accounts and manage first renewal Farmers (Expansion AEs) Drive adoption across additional workloads   Expansion AEs work closely with CSMs, partners, SEs, and TAMs to ensure full account penetration post-sale. “I was unsure about the split at first—but now I’m a believer. The expansion team builds deep relationships that unlock full value.” Retention is a Science: Detecting Risk Before It’s Too Late John outlines a multi-layered approach to protect recurring revenue: Risk Signals Druva Tracks: Decline in product usage Surge in support tickets Large-scale data exports (potential migration) Absence from events and webinars Lower NPS or QBR engagement Cadence by Segment: Customer Tier Engagement Model Enterprise Quarterly QBRs, 6-month renewal prep Mid-Market Biannual reviews SMB/Long-Tail 120-day renewal triggers via AE or renewals rep   “We built AI propensity models to flag expansion and churn risks. These are crucial for staying ahead.” Data Without Insight Is Just Noise “Salesforce is our source of truth—but it’s not about the data. It’s about how you simplify and standardize it.” Druva pulls data from multiple sources—Salesforce, Sigma, Clari, Atrium—and aggregates it into simplified dashboards. Standardization ensures teams debate strategy, not whose numbers are right. John’s RevOps team is tasked not just with collecting data—but surfacing actionable insights. Pipeline Visibility: A Continuous Feedback Loop John’s pipeline framework includes three lenses: Lens Use Case In-Quarter Pipeline Immediate revenue forecasting Next-Quarter Pipeline Forward visibility to avoid chase mode Source Breakdown Channel health by OEM, Direct, Partner   The RevOps team cuts data by geo, function, and team to uncover root causes of pipeline issues—before they impact revenue. Strategic Account Planning and Re-engagement Expansion depends on reaching beyond the initial champions. Druva ensures sellers don’t just rely on admins or tool users—they map out all key stakeholders and re-engage them as new opportunities emerge. “They may have disengaged—but they’ll re-engage when the right workload comes up. That’s where good account planning pays off.” Managing Change Across GTM Functions Unifying teams under one strategy isn’t just a data challenge—it’s a people challenge. “You can’t just communicate once. You need continuous communication with context—why we’re doing this, why now, and how it helps them.” John emphasizes: Setting a shared North Star Explaining the “why” behind every change Making everyone feel part of the journey The New Buyer Journey: Less Time with Sales, More Time in Research “Buyers spend 9 out of 12 months doing research—without ever talking to your sales team.” This shift forces GTM teams to: Use intent data to intercept buyers early Provide helpful content during research Equip sellers with consultative tools—not just decks   Golf outings and 5-hour lunches are over. Buyers want speed, value, and insight. Final Thoughts: Strategic Growth in a Changing World “You get pulled into the day-to-day. You have to fight for time to think strategically.” For John, success as a CRO means balancing operational excellence with long-term vision—aligning every function under one strategy, and enabling teams with the right data,

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From MQLs to Buying Groups: How Socure is Building the Future of Revenue Marketing

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From MQLs to Buying Groups: How Socure is Building the Future of Revenue Marketing A conversation with Heather Adams, Head of Revenue Marketing at Socure. In today’s B2B landscape, the way companies buy has changed dramatically. But many revenue teams are still stuck using outdated tactics. The classic MQL (Marketing Qualified Lead) model is no longer fit for purpose. It focuses on individuals, when buying decisions now happen in groups. It relies on form fills, while buyers prefer stealthy research. It counts leads, when what matters is engagement across an entire account. “A single-threaded, one-person conversion is not what you should base your future revenue success on.” — Heather Adams In this blog, we unpack Heather Adams’ playbook for replacing MQLs with a buyer group-first strategy at Socure. It’s a journey that includes tight sales-marketing alignment, AI-powered personalization, and a deep commitment to clean, actionable data. Facebook Twitter Youtube Why MQLs No Longer Work MQLs were once a breakthrough. They gave marketing a way to track conversions, measure impact, and hand off leads to sales. But in the modern enterprise deal cycle, they often miss the mark. Key Limitations of MQLs: Too Narrow: Often capture one person’s interest, not the whole buying committee. Reliant on Form Fills: Many buyers now avoid forms entirely. Misleading Signals: Early research from junior roles gets mistaken for high-intent activity. “We knew we had 10–15 people involved in a six or seven-figure decision. We needed to engage the whole group—not just whoever downloaded the whitepaper.” Socure realized that chasing MQLs was like trying to understand a forest by examining one leaf. It doesn’t work when the real value lies in the entire ecosystem. Introducing a Buyer Group-First Strategy Instead of measuring success by individual actions, Heather’s team shifted to tracking account-level engagement and buyer group coverage. That meant aligning across functions and changing the KPIs they reported on. The Cadence That Changed Everything At the heart of the shift is a weekly sync between: Campaign leader Market Development Rep (MDR) Account Executive (AE) Each team member brings insights to the table, driven by: First-party engagement data Third-party intent signals Buyer group activity “When we meet, we ask: What are the tasks for the AE, the MDR, and marketing? What was successful last week? What do we try next?” This regular collaboration removed silos and drove accountability. Old vs. New Metrics Traditional Metrics Modern Metrics MQL volume Account engagement Form fills Buyer group coverage Single touch attribution Pipeline influence by persona https://www.youtube.com/watch?v=8Eu1xXIcY3c Redefining Success Metrics Heather’s team moved away from individual attribution and started tracking: Account-level engagement scores Persona coverage within buying groups Pipeline impact across functions “We built dashboards to show where our buyer group coverage is strong and where it’s lacking. It helps us spot gaps and optimize outreach.” They also eliminated credit-seeking by creating a combined GTM pipeline metric presented to executive leadership and the board. Getting Sales on Board Changing KPIs is one thing. Changing minds is another. Heather emphasized the importance of trust and early wins. “We had a few AEs who leaned in early. When they saw results, others followed. Success breeds success.” Rather than waiting for sales to add contacts to Salesforce, marketing and MDRs built a draft buyer group for each target account. Sales only needed to review and refine—a low-lift ask that accelerated adoption. The Role of Technology and Data Heather’s stack includes: 6sense for buyer intent and keyword tracking Drift for ABM-focused chatbot experiences Champion tracking tech to re-engage known contacts in new roles Custom GPTs to scale personalization across verticals and personas But tech alone wasn’t enough. Data quality had to improve. “Our data was everywhere—Slack, Salesforce, Clari, GDrive. We had to build pipes, clean the data, and use AI to make sense of it.” Infographic: The Buyer Group Engine A visual of inputs (intent signals, past champions, firmographics) flowing into tools (6sense, Drift, GPTs), leading to outputs (personalized engagement, buyer group completeness, pipeline growth). Early Results and Wins With the new model, Socure saw: 2.5x YoY lift in sourced deal quality 80% of pipeline from named accounts Increased deal size and strategic fit They also moved to 100% AI-assisted personalization at scale, saving time and boosting message relevance. “We’re using our AI agents to identify lookalike accounts, research stakeholders, and draft persona-specific messaging. It’s a huge unlock.” AI: The Personalization Force Multiplier Heather’s team is using AI for: Prompt optimization Buyer group discovery Personalization at scale Intent-to-outreach orchestration “The only limitation is how well you prompt. Sometimes we use AI to help us write better prompts.” They’re currently building agentic workflows that connect flows from Slack to Salesforce to outreach platforms, enabling near-autonomous buyer group engagement. Advice for Revenue Leaders For those looking to champion a similar shift, Heather’s advice is simple: Start with trust: “Build real relationships with your sales team.” Show data: “Sellers know MQLs don’t work. Bring the evidence.” Make it easy: “Bring the first version of the buyer group to the table.” Think in systems: “Map engagement across teams, not in silos.” Conclusion: The Future of Revenue Marketing The era of MQLs is ending. In its place, a more holistic, buyer-aligned, AI-powered strategy is taking hold. At Socure, Heather Adams and her team are showing what’s possible when marketing evolves from lead generation to buyer group orchestration. This isn’t a cosmetic change. It’s a fundamental reinvention of how pipeline is created, measured, and accelerated. TL;DR: Heather’s Buyer Group Framework Weekly syncs across GTM roles Account and persona-level metrics Tech-powered orchestration with 6sense, Drift, and AI Clean, centralized data across sources Cross-functional trust and transparency “If we don’t figure this out quickly, we’re going to get left behind.” Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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Bridging the Gap Between Data and Action: A Strategic Guide for GTM and RevOps Leaders

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Bridging the Gap Between Data and Action: A Strategic Guide for GTM and RevOps Leaders A conversation with Sarah Flaccavento, SVP Strategic Initiatives at Alphasense. “Data is only as good as the insights it drives.” – Sarah Flaccavento In an age where data flows through every department, dashboard, and decision, organizations still struggle to turn that abundance into action. While most teams claim to be data-driven, the truth is that data often ends up siloed, unused, or misunderstood. Sarah Flaccavento, SVP of Strategic Initiatives at AlphaSense, believes that the ability to translate data into actionable insight is what separates good companies from great ones. In this detailed guide, drawn from her episode on The Revenue Lounge, we unpack the frameworks, prioritization techniques, and change management strategies she uses to create force-multiplying change in complex organizations. Facebook Twitter Youtube Section 1: From Gut Instinct to Insight-Driven Execution “Insight is the answer to a question—and it’s actionable.” – Sarah Flaccavento Data by itself is just noise. The real magic happens when teams identify patterns, contextualize them, and act on them. Sarah defines an insight as something that not only tells you what is happening but also guides what to do next. Data Insight Raw numbers, metrics, activity logs Meaningful answers to questions Requires interpretation Tells you what to do next Often siloed and overwhelming Cross-functional and directional Measures what happened Predicts or influences what will happen Key takeaway: Without connecting data to context and action, teams risk analysis paralysis. Real transformation starts when leaders ask better questions and turn patterns into priorities. Section 2: Prioritization Framework – WSJF (Weighted Shortest Job First) One of the core methodologies Sarah uses is WSJF—a prioritization framework from Agile’s Scaled Agile Framework (SAFe). It helps identify high-impact projects based not only on ROI but also on urgency and effort. “The most important piece WSJF adds is time criticality. ROI alone isn’t enough.” – Sarah Flaccavento How WSJF Works: WSJF = (Size of Prize / Difficulty) x Time Criticality Component Explanation Size of Prize What’s the potential upside (revenue, customers, impact)? Difficulty How complex is the execution? Time Criticality If you wait, does the opportunity disappear? Will competitors get there first? Example: Instead of targeting trillion-dollar law firm opportunities (high ROI, low urgency), Sarah’s team focused on launching generative AI search. Why? Because the need was immediate, the pain was clear, and nobody else was solving it yet. Sarah asks her team to independently score initiatives using the Fibonacci sequence for each parameter. This fosters debate and forces thoughtful decision-making. https://www.youtube.com/watch?v=IRyreib4-TU&t=3278s Section 3: Strategic Planning in 3 Tiers “You should be planning for three horizons at any given time.” – Sarah Flaccavento Sarah outlines a three-level planning model that balances execution with vision: Infographic: Strategic Planning Tiers Horizon Focus Examples Quarterly Fully fixed execution plans Launch AI search, Expand into HK Biannual (6M) Defined problems, flexible on how Solve pricing friction, Partner launches 1-3-5 Year Big bets and long-term missions Become the insights platform of record She recommends: Locking in execution for 1 quarter Having flexible priorities for 6 months Planning vision 1, 3, and 5 years out Reviewing monthly, publishing quarterly To track this, Sarah uses an Excel-based WSJF matrix and hides past columns until it’s time to review. This avoids emotional decisions and encourages accountability through data. Section 4: Creating a Culture of Data Ownership “You should never walk into a meeting with a question. You walk in with a recommendation—based on data.” – Sarah Flaccavento Sarah has built a culture at AlphaSense where data ownership is democratized, not centralized. Everyone—from reps to execs—is expected to: Bring hypotheses, not open questions Make recommendations, not just escalate problems Own inputs to company-wide decision-making The result? Data becomes everyone’s responsibility. People come prepared, speak with clarity, and decisions move faster. [Data Entry] → [Insight Generation] → [Recommendation] → [Execution] → [Feedback Loop] Sarah enforces this through: Visible use of rep-generated data in strategy meetings Celebrating usage of Salesforce notes and Gong insights Running pre-meetings with dissenters to ensure open discussion and buy-in Section 5: Salesforce: A Directional Input, Not the Whole Truth “Salesforce is a powerful, directionally accurate input to decision-making.” – Sarah Flaccavento Sarah acknowledges Salesforce as a valuable, but not infallible, data source. It excels at tracking pipeline stages and opportunity hygiene. But when it comes to customer segmentation or behavior, it often lacks nuance. Instead, her team triangulates insights from: Salesforce reports Gong transcripts Product usage data QBR feedback Pro Tip: Make the rep’s input meaningful by closing the loop. Highlight the impact of win/loss notes in company-wide decisions. Section 6: Case Study – Rethinking Pricing & Packaging AlphaSense’s pricing model originally reflected the cost of aggregating premium data. However, the market wanted flexibility—not rigid per-seat pricing. “Fear drives a lot of detraction. But data addresses that fear.” – Sarah Flaccavento Sarah’s team: Started with one FS customer segment Validated demand with usage and growth data Adjusted pricing to align with value delivered Result: AlphaSense closed the largest FS and corporate deals in company history. Each segment got a tailored model based on data-backed buying behavior. Section 7: Failing Fast in GTM “Failing fast is about making problems smaller and smaller.” – Sarah Flaccavento Instead of big bets that take quarters to prove, Sarah advocates: Breaking big hypotheses into tiny experiments Testing assumptions early (e.g. Do they have this problem? Will they pay to solve it?) Learning if it’s a true failure or just “not now” [Big Idea] → [Micro-Test] → [Data Validation] → [Fail / Scale / Postpone] This mindset saves time, protects resources, and keeps momentum high. Section 8: Data as a Cultural Operating System “If data isn’t in your company DNA, it will get in your way.” – Sarah Flaccavento Sarah closes with this imperative: data must be part of the cultural fabric. Not just a RevOps job. Not just a dashboard. But something that: Informs every strategic bet Validates every resource allocation Shapes every customer interaction Whether it’s pricing,

buying group model
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Decoding the Buying Group Model: Strategies for Success

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Decoding the Buying Group Model: Strategies for Success A conversation with Evan Liang, Founder & CEO at Leandata. In the traditional B2B playbook, the Marketing Qualified Lead (MQL) has long been the dominant metric for gauging marketing performance. It’s simple: someone fills out a form, downloads an eBook, or registers for a webinar, and voilà—they’re an MQL. That lead is then tossed over the fence to sales, where all too often it languishes, ignored or unqualified. But the B2B buying journey has fundamentally changed—and with it, the metrics and models we use must also evolve. Enter buying groups. A concept once understood only by experienced sellers, buying groups are now becoming central to how high-performing revenue teams plan, engage, and convert demand in today’s complex enterprise environments. In this episode of The Revenue Lounge, Randy Likas sits down with Evan Liang, Founder and CEO of LeanData, to unpack what buying groups actually are, why they’re gaining momentum, and most importantly—how to operationalize them successfully within your sales and marketing workflows. Facebook Twitter Youtube The Origins of LeanData and the Evolution of Go-To-Market Strategy Before founding LeanData, Evan Liang had lived the problem firsthand. Working at a previous company, he struggled to integrate marketing automation with Salesforce in a way that made the sales and marketing teams more efficient. The process was chaotic, data was fragmented, and lead routing felt like a game of chance. This personal frustration became the foundation for LeanData, which began as a lead-routing platform but quickly evolved into something much bigger: a revenue orchestration platform designed to help GTM teams align around data, process, and outcomes. “Our original mission was to make sales and marketing more efficient through data and processes. That mission hasn’t changed—only expanded.” – Evan Liang LeanData now supports over 1,000 customers, helping them orchestrate complex GTM motions beyond lead routing, including ABM and now—buying groups. Why Buying Groups? Why Now? While the concept of buying groups isn’t new to sales teams—who’ve always had to engage multiple stakeholders to close a deal—this concept is now becoming institutionalized. It’s gaining traction at the organizational level, especially in enterprise environments where buying cycles are long and decisions are rarely made by a single person. Several macro trends have converged to push buying groups into the spotlight: The Buyer Journey Has Gone DigitalBuyers today self-educate long before talking to a sales rep. Much of the research and decision-making happens across digital channels and is distributed among a group of stakeholders. Deals Are Taking Longer and Involve More PeopleResearch from Gartner and Forrester shows that the average B2B deal now involves 6 to 10 stakeholders. That makes tracking individual MQLs increasingly irrelevant. Technology Has Finally Caught UpThe concept of buying groups has existed in CRM structures for decades. The “opportunity-contact-role” relationship has always been there—but underutilized due to lack of data and automation. Today, with tools like LeanData and Nektar, organizations can automate and scale this buying group motion. “In some respects, buying groups are not a new change—they’re just the next evolution. The technology and processes are finally catching up to how enterprise sales have always worked.” – Evan Liang   https://www.youtube.com/watch?v=rNo5hizuxRA&t=639s The MQL Problem: Leads in Isolation The shortcomings of the MQL model are becoming more apparent. Marketing teams are sending individual leads to sales—often with little context, incomplete engagement history, and no visibility into whether that lead is part of a larger buying motion. This results in: Lead duplication (same person, multiple forms) Low conversion rates Frustrated sales reps who disregard “low-quality” leads In contrast, a buying group-centric approach clusters engagement data across multiple personas, providing a fuller picture of interest and intent. “An MQL is a buying group of one. That’s fine for transactional deals. But in enterprise sales, it’s just not enough.” – Evan Liang Why Adoption Is Lagging (and How to Overcome It) Evan recommends a “crawl, walk, run” approach: “Start small. Pilot in a region or with one team. Show success and build momentum.” 🎯 Pilot Criteria Matrix Despite growing interest and case studies showing tangible impact—higher win rates, faster conversions—many organizations are still hesitant to embrace buying groups. Why? The answer: Change is hard. Adopting a buying group model requires shifts in: Data models GTM processes Cross-functional alignment Sales and marketing roles “Everyone wants change… until it requires them to change something.” – Evan Liang Evan notes that the early adopters of buying groups today are mostly large enterprises—unlike ABM, which was championed by early-stage startups. These enterprises have more to gain because they’re more likely to struggle with disconnected buying signals across large organizations. How to Get Started with Buying Groups Rather than boiling the ocean, Evan recommends a phased approach to adoption. Start Small: Pilot Projects Choose a specific region, product line, or sales team. Focus on enterprise segments with long sales cycles and multiple personas. Measure and report early wins to build momentum. “Start with a pilot. Show the revenue impact. Then scale.” – Evan Liang Executive Alignment Is Critical Buying groups are not a departmental initiative. They require support from executive leadership across sales, marketing, and operations. Without that alignment, even the best technology won’t stick. “Don’t go rogue. Get executive buy-in early. It’s essential for success.” – Evan Liang Redefining Roles: What Changes in Your GTM Org Implementing buying groups doesn’t just affect systems—it affects how people work. Here’s how: BDRs and SDRs shift from lead qualification to identifying and engaging buying personas. Marketing teams move from lead-gen to persona enablement, filling gaps in mid-funnel engagement. Sales benefits from more contextual data on who’s involved and who’s missing. Evan also emphasizes that buying group strategies are not one-size-fits-all. Every company is a snowflake. Some teams may prefer using zero-dollar opportunities as placeholders, others may use custom objects. The key is to design a process that fits your business—and then align your tech stack accordingly. The Role of Technology: You Might Be Closer Than You Think Evan reassures that most companies already have the

scaling revops
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Building & Scaling RevOps in an Enterprise

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Building and Scaling RevOps in the Enterprise A conversation with Shantanu Mishra, SVP, Revenue Strategy & Operations at Pluralsight. As organizations scale and mature, the complexity of managing revenue processes across the customer lifecycle intensifies. Revenue Operations (RevOps) is emerging as the linchpin function to harmonize go-to-market strategy, unify cross-functional teams, and enable sustainable growth.  In a recent episode of The Revenue Lounge podcast, Shantanu Mishra, Senior Vice President of Revenue Strategy and Operations at Pluralsight, shared a deeply insightful and structured approach to building a high-performing RevOps organization at the enterprise level. With more than two decades of experience in leading sales operations, customer success, and strategic transformation, Shantanu provides a masterclass in RevOps design, execution, and evolution. Facebook Twitter Youtube Rethinking RevOps: The Bow-Tie Framework Traditional funnels end at the point of sale. But in SaaS businesses, revenue generation doesn’t stop once a customer signs the contract. Shantanu introduces the “bow-tie” framework—a more comprehensive visualization of the revenue journey. On the left side of the bow tie is the traditional funnel: lead generation, qualification, and closing. On the right is where the real value emerges: customer onboarding, product adoption, value realization, renewal, and expansion. “In a SaaS environment, you don’t stop at win. The second stage of the journey starts with onboarding, time-to-value, and finally renewal and expansion. That entire bow-tie has to be managed. That is what revenue operations is.” The bow-tie model reflects a strategic shift from one-time sales enablement to lifecycle value management. It forces RevOps leaders to look beyond pipeline metrics and build systems that sustain long-term customer value. Laying the Foundation: Designing for Scale Early Shantanu emphasizes that regardless of where your company is in its revenue maturity journey—whether you’re at $10M or $100M ARR—you must design for scale. Building RevOps without a long-term vision is like constructing a house without a blueprint. You need to plan for the 20-story skyscraper, even if you’re currently just laying the first floor. “Like building a house—you need the blueprint upfront. You have to know how big the foundation has to be, whether you’re building one story or twenty.” This means implementing systems for forecasting, compensation, territory design, and pipeline management that can evolve with the business. As the organization matures, RevOps must move from tactical firefighting to building scalable, repeatable systems with proactive strategy baked in.   https://www.youtube.com/watch?v=hzeZnRWTD8c&t=10s Defining the Metrics That Matter Effective RevOps is data-driven. But metrics can become noise if not structured properly. Shantanu outlines a comprehensive metric framework spanning the entire bow-tie lifecycle: Forecasting Accuracy: Strive for a forecast that is within +/- 2% accuracy by week 4 of the quarter. Pipeline Health: Track coverage ratios, opportunity hygiene, commit vs. forecast percentages. Velocity & Conversion: Measure deal velocity, stage-by-stage conversion rates, AOV, and win rates. Unit Economics: Key indicators like CAC:LTV, quota-to-OTE ratios, and bookings per rep. Customer Success Metrics: Monthly active users, license utilization, early renewal engagement. “If pipeline is clean, forecast is clean. But to scale, you need to ask—are we investing $1 and getting much more than $1 back?” This systematic approach ensures GTM teams are aligned on how success is measured across the lifecycle, and avoids the trap of siloed performance indicators. The Org Design Playbook: Horizontal vs Vertical Thinking RevOps leaders often struggle with structuring their teams. Shantanu proposes an elegant framework: differentiate between horizontal functions that span all GTM units and vertical functions tailored to specific departments. Horizontal Functions: Strategy and investment planning Data and analytics Enablement Compensation and deal desk Metrics and reporting Vertical Functions: Sales and success territory design Forecasting cadence Department-specific plays (e.g., sales sprints, CS engagements) “You don’t want sales to have one funnel and marketing to have another. You need a comprehensive view of the bow-tie.” This design allows centralized control over strategy and insights, while empowering functional leaders to adapt operations to their specific needs.   Finding the Right Talent: Beyond Ops Experts The complexity of RevOps demands a multidisciplinary team. Shantanu identifies three archetypes every RevOps team needs: Athletes: Generalists who can adapt and execute across roles. Builders: Detail-oriented executors who create infrastructure and processes. Strategists: Big-picture thinkers who drive alignment and long-term planning. He emphasizes EQ (emotional intelligence), adaptability, and complementary skill sets over pure technical expertise. “EQ is non-negotiable. The corporate world is faster now—you need stability, not just intelligence.” RevOps teams also benefit from hires with backgrounds in finance, consulting, IT/business analysis, and enablement. Data Infrastructure: From Chaos to Clarity Data can either be an asset or a liability. According to Shantanu, RevOps leaders must partner with data engineering teams early to establish clean, centralized, and accessible datasets. “Invest in data engineering early. Don’t let RevOps carry the burden of cleaning, merging, and reporting on messy datasets alone.” He suggests: Centralizing all GTM data sources (billing, product, usage, marketing automation, CRM, HR, enrichment) Building a cloud-based warehouse with proper schema design Defining KPIs before implementing tools or dashboards This strategy ensures that as tools evolve, the data structure remains robust and analytics-ready. Operating Rhythms That Drive Accountability An effective operating model is more than who reports to whom—it’s about cadence, communication, and culture. Shantanu recommends: Weekly: Forecasts, pipeline updates, hygiene checks Monthly: Reports (not meetings) summarizing key metrics Quarterly: Deep dives into KPIs, unit economics, and strategic planning He also emphasizes that metrics should be meaningful and contextualized, not just reported. RevOps should take ownership of making reporting useful for decision-making. Win-Loss Analysis: Real-Time Insights from the Field Too often, companies wait until end-of-quarter to analyze wins and losses. Shantanu recommends capturing this data continuously and cross-referencing it across sources: deal desk, CRM, sales team debriefs, and direct customer feedback. “Win-loss data should be captured daily. Don’t wait till the quarter ends to learn why you’re losing.” Understanding what’s working (or not) in pricing, positioning, or sales process enables faster course corrections and better enablement. The Future of RevOps: Powered by AI Shantanu sees artificial intelligence as a transformative force across the revenue engine.

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Selling to People, Not Personas: Redefining B2B Sales with Buyer-First Intelligence

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Selling to People, Not Personas: Redefining B2B Sales with Buyer-First Intelligence A conversation with Amarpreet Kalkat, Founder & CEO at HumanticAI. In the era of hyper-informed buyers and complex purchasing journeys, traditional sales strategies are crumbling. Outdated, persona-based approaches no longer resonate in a world where buyers are more skeptical, independent, and resistant to generic outreach. In a recent episode of Revenue Lounge, we sat down with Amarpreet Kalkat, Founder and CEO of Humantic AI, to unpack what it truly means to adopt a buyer-first approach. And how personality-driven sales is transforming the way sellers connect, engage, and win. This blog dives deep into Amarpreet’s insights, drawing from his 25+ years of experience in building intelligent products, and explores how his technology is helping sales teams humanize the sales process. Facebook Twitter Youtube The Buyer’s World Has Arrived and Sellers Must Adapt “You don’t sell. You help people buy.”— Amarpreet Kalkat In Amarpreet’s view, sales has always been about the buyer—but never more so than today. With dozens of vendors offering similar solutions, what separates winners from the rest isn’t product features or aggressive pitching. It’s perspective. The problem? Most sales methodologies—whether it’s MEDDIC, Challenger, or SPIN—are seller-centric. They teach sellers to “run their playbook,” not necessarily to understand their buyers as people. But data shows the stark gap: Average seller win rate: 17% Elite seller win rate: 62% That’s not just a small performance delta—it’s a chasm. And Amarpreet believes the secret to closing that gap lies in a true shift to buyer-first thinking. Stop Selling to Personas. Start Selling to People. Sales teams often anchor outreach strategies around personas—job titles, functions, firmographics. But Amarpreet challenges that framework: “A persona doesn’t buy. A person does.” With Humantic AI, sellers can move from broad persona targeting to individual buyer intelligence, understanding not just what a prospect does—but who they are. This includes: Communication preferences Personality traits (based on DISC profiling) Risk appetite Decision-making style Motivators and fears This human layer enables sales reps to craft emails, calls, and presentations tailored to how a specific buyer thinks—not just their role. https://youtu.be/iMDGlZVhaBc How Personality AI Works Behind the Scenes So how does Humantic AI gather this intelligence? It pulls public data from LinkedIn and other online sources It processes that data through proprietary DISC-based AI models It surfaces insights on personality traits, behavior patterns, and communication style These insights are delivered directly into tools sellers already use—Salesforce, LinkedIn, Salesloft, Outlook, and more The goal? Equip sellers with buyer-aware recommendations at every step of the deal. And it’s more than just better email intros. Amarpreet explains how Humantic can even suggest whether to open an email with a friendly “Hope you’re doing well” or skip that for a more concise greeting—based on the buyer’s disposition. The Impact: Real Results from Real Companies Skeptical about the impact of buyer intelligence? The numbers speak for themselves. One client saw win rates jump from 15% to 50% Another reported a 151% increase in pipeline for a test group Public company Domo cited a 15–30% lift in win rates after adopting Humantic AI And it’s not just about closing deals faster. Amarpreet emphasizes how personality data helps navigate complex buying committees. With up to 12–14 stakeholders involved in B2B decisions, understanding the emotional and decision triggers of each person is critical. “Deals are lost in rooms sellers never enter. We help you win in those rooms.” Operationalizing Buyer Intelligence in the Sales Process Humantic AI is designed to work across the entire sales journey—not just top-of-funnel outreach: Stage Tool/Feature Use Case BDR/SDR Chrome Extension / Outreach Integration Personalized email and call scripts AE Meeting Prep Tools Pre-meeting research and message customization Sales Team Buying Committee Map Stakeholder analysis and engagement planning RevOps Platform Integration Insight management within CRM and SEP tools   And unlike many AI tools that overwhelm teams, Humantic focuses on enhancing human touchpoints, not replacing them. AI Isn’t Just a Buzzword. It’s a Strategic Lever In today’s crowded AI market, Amarpreet warns against getting distracted by shiny tools: “AI should be wings for the flyers and crutches for the walkers.” For sales leaders evaluating AI, he recommends starting with problems, not features. What’s the root challenge—low CRM usage? Poor email response rates? Ineffective stakeholder engagement? The right AI tool should solve that problem with minimal friction. A Path to Sales Respect and Buyer Trust Amarpreet closes the conversation on a powerful, personal note. Despite being the lifeblood of the economy, sales still lacks social respect. “Nobody grows up saying, ‘I want to be a salesperson.’ But without sales, nothing moves.” He draws an analogy to doctors—once seen as quacks, now among the most respected professions. Amarpreet believes sales can earn that respect too—but only if sellers embrace empathy and buyer-first engagement at scale. Actionable Takeaways for Sales Teams Here’s how to start implementing a buyer-first approach right now: ✅ Audit your current outreach — Are you customizing based on personas or individuals? ✅ Understand your buyers’ decision-making styles — Tools like DISC can help. ✅ Invest in emotional intelligence — Winning trust requires more than just logic. ✅ Use AI to amplify, not automate — Layer intelligence onto your existing workflows. ✅ Map your buying committees — Know the silent killers and what drives them. ✅ Treat sales as a helping profession — Shift your team culture from persuasion to enablement. Final Thoughts Personality-driven selling isn’t a gimmick—it’s a competitive edge. In a world where buyers ghost generic pitches, deep personalization rooted in emotional intelligence is the new table stakes. With tools like Humantic AI and leaders like Amarpreet paving the way, the future of B2B sales looks a lot more human. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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From Chaos to Clarity: Building a Unified Revenue Engine at Scale

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From Chaos to Clarity: Building a Unified Revenue Engine A conversation with Alana Kadden Ballon, VP of Revenue Operations at Sprout Social. In a world overflowing with data, what go-to-market teams need most isn’t more information—it’s unified, trusted, and actionable data. But with siloed systems, misaligned incentives, and scattered signals, many revenue organizations face what’s best described as “data chaos.” In this episode of The Revenue Lounge, Alana Ballon, VP of Revenue Operations at Sprout Social, joins the show to talk about cutting through that chaos to build a unified revenue engine—one that aligns teams, connects insights, and drives growth at scale. Facebook Twitter Youtube The Journey from Sales to Strategy Alana’s story begins on the sales floor, grinding through BDR calls before becoming an AE, enablement leader, and finally a RevOps strategist. Her early experience shaped a foundational understanding of customer challenges and cross-functional collaboration—making her uniquely equipped to scale revenue engines in hyper-growth SaaS environments like Salesforce, Wiz, and now Sprout Social. What drew her to Sprout? The opportunity to work with trusted leaders, a public company context, and a product that sits at the intersection of social, AI, and media evolution—all within a team hungry for change. What Is a Unified Revenue Engine? To Alana, a unified revenue engine means more than systems talking to each other—it means people and incentives aligned to a single goal: doing what’s right for the customer and the company. Key takeaway:Alignment starts with incentive structures. When sales, marketing, and customer success are driven by shared metrics—like retention, expansion, and customer health—silos start to dissolve. But the path to unification is often blocked by: Frankenstein tech stacks from years of point solution purchases Poor data governance Disconnected workflows across functions Fixing this isn’t just about buying more tools. It’s about aligning people, processes, and platforms around actionable outcomes. https://www.youtube.com/watch?v=wJcdXXzWY3M&t=399s The Watermelon Analogy: Slicing Beyond Surface Metrics Alana introduces a powerful metaphor: the watermelon pipeline. On the outside, everything looks green. But slice it open, and you’ll find the red spots—underperformance in specific segments, sources, or geos. Tips for slicing your watermelon: Dimension What to Check By Source AE-generated vs. marketing vs. partners By Segment Enterprise vs. commercial By Geography Global vs. regional performance By Funnel Stage Are conversions where they should be?   This granular visibility helps GTM leaders diagnose problems early and apply the right levers—from geo-specific campaigns to product-market fit adjustments. From Volume to Value: A Shift in Sales Strategy Alana warns against the trap of prioritizing volume over value—especially in prospecting. “Generic outbound isn’t working. Buyers want value, not spam,” she explains. How Sprout Social is Shifting to Value: Using AI to personalize outreach with real-time brand and campaign data Equipping reps to lead with insight (e.g., “Here’s how your campaign is performing” vs. “Do you want to see a demo?”) Empowering SDRs to think like marketers and act like advisors RevOps Role:Lead the operational cadence that enables this—daily signal reviews, weekly experiment tracking, and cross-functional feedback loops. Aligning GTM Teams: One Plan, One Voice RevOps isn’t just about analytics—it’s about orchestration. At Sprout, Alana ensures that all GTM functions (sales, marketing, channel) plan together, report together, and adapt together. “If you plan separately, you can’t execute together,” she says. Key Practice:Monthly pipeline reviews aren’t blame games—they’re working sessions to adjust levers and optimize together. The Buying Group Shift: Earlier Multi-Threading Traditional MQLs are fading. Sprout, like many modern GTM orgs, is moving towards buying group-based strategies. “Sales calls it multi-threading. Marketing calls it buying groups. Either way, we’re pulling that motion earlier.” This shift requires: Strong opportunity data Early engagement of multiple stakeholders Alignment between sales, marketing, and product marketing Data Readiness Before AI Alana is clear: AI won’t save you if your data is messy. Clean, connected, and governed data is the foundation of any AI-driven GTM motion. Start with the daisy chain: Identify a business problem (not just a data issue). Trace the data gaps that cause it. Fix one thing. Show value. Scale iteratively. Whether it’s country misclassification or duplicate records, solve what’s blocking execution—not just what looks messy on paper. Enabling the RevOps Seat at the Table Alana’s advice for RevOps professionals who want to be seen as strategic partners? Choose your leadership wisely. Strategic RevOps needs alignment with CROs, CMOs, and customer leaders. Automate to accelerate. Her team’s move to auto-generate retro reports lets analysts focus on insights, not spreadsheet prep. Deliver impact iteratively. Big-bang data projects rarely work. Find tangible business problems and chip away. Looking Ahead: The Future of Sales and AI Alana predicts a future where AI will act as an “operator” for salespeople—pulling data, crafting outreach, and driving next steps autonomously. But the human connection won’t disappear. “Sellers will become more technical, more strategic. AI will augment—but not replace—the relationships at the heart of enterprise sales.” Final Thoughts If you’re a revenue leader navigating the messy middle of disconnected data and siloed teams, Alana’s message is clear: Align around the customer Slice the watermelon Start small, show value, and scale And never forget—clean data is the rocket fuel of RevOps Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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Data Before AI: Building a Clean Foundation for Smarter RevOps

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Data Before AI: Building a Clean Foundation for Smarter RevOps A conversation with Olga Traskova, VP of Revenue Operations at Birdeye. The AI boom has hit B2B go-to-market teams hard. Everyone wants to automate, optimize, and accelerate—but few are pausing to address the silent killer of ROI: bad data. In this episode of The Revenue Lounge, we sit down with Olga Traskova, VP of Revenue Operations at Birdeye, to unpack the unglamorous yet crucial reality behind AI success: data quality and readiness. With over 15 years in revenue and marketing operations, Olga shares battle-tested insights into building a strong data foundation, choosing the right AI use cases, and avoiding common traps in AI adoption. “We’re trying to drive a Rolls-Royce without a license—and no garage to park it in.”— Olga Traskova, VP Revenue Operations, Birdeye Facebook Twitter Youtube Olga’s Journey — From Marketing Ops to RevOps Leadership Olga began her career in marketing operations, eventually expanding into RevOps as she tackled more cross-functional business challenges. Her growth wasn’t just vertical but horizontal—across marketing, sales, and customer success. Now, as the VP of RevOps at Birdeye, she leads a global operations team serving the US, UK, and Australia. Her mission? Drive revenue efficiency by integrating people, processes, and tools across the customer lifecycle. Her approach is anti-silo: while team members have domain specialties, everyone is cross-trained. This enables agility and ensures seamless support for any GTM leader who engages with RevOps. Why AI Alone is Not the Answer AI is not a magic wand. If your systems are riddled with bad data, no amount of intelligence—artificial or otherwise—can generate reliable insights. Olga shares a relatable frustration: attempting to implement a call transcription tool that auto-fills Salesforce fields to save sales reps time. But when the tool failed to sync correctly with CRM, the data became misaligned, rendering the AI-generated insights useless. “You’re just placing a shiny object on top of garbage. Fix your foundational processes first.” This experience underscores a key RevOps truth: clean data is not optional. It’s a prerequisite. https://www.youtube.com/watch?v=Q-ZoyfU1o8A Three Common Mistakes in AI Adoption Olga highlights several pitfalls that GTM teams must avoid: No Data Foundation: Jumping into AI without structured, accurate data is like building a house on sand. Undefined Use Case: Many teams chase tools without clearly identifying the problem they’re solving. Tool Fatigue: Over-purchasing tools creates more chaos. Teams must prioritize longevity and ease of use. Mistake Description Solution Data Chaos CRM is inconsistent, fragmented, or siloed. Conduct a full data hygiene audit. Vague Goals Buying AI tools without clarity on business outcomes. Define KPIs and workflows before evaluation. Short-Term Thinking Choosing flashy new tools that lack long-term viability. Vet vendors for stability and integration. A Framework for Evaluating AI in RevOps Olga’s decision-making process is grounded in use-case prioritization and long-term alignment. Here’s how she approaches evaluating new AI technologies: Start with a problem: Identify the business gap (e.g., inaccurate forecasts, time-consuming manual tasks). Map to outcomes: Connect the tool to a measurable objective (e.g., improving forecast accuracy within 5%). Assess adoption impact: Will your reps need major retraining? Is it intuitive? Avoid vendor churn: Choose tools with longevity. Avoid investing in platforms likely to fold or get acquired. “Sometimes you don’t need a new tool. You just need to explore what your current stack can already do.” The Anatomy of a Data-Ready Organization Before AI can thrive, core data elements must be defined, standardized, and consistently used. Olga recommends: Clear object mapping: Standardize definitions for leads, contacts, accounts, and opportunities. Journey alignment: Define lifecycle stages and statuses across marketing, sales, and CS. Field governance: Ensure input fields (titles, stages, reasons) are consistent and readable. CRM integration: All third-party tools (e.g., Gong, Outreach) must sync cleanly into your CRM. Your data model should be simple enough to translate into a plain-language sentence. If AI can’t “read” it clearly, it can’t act effectively. Solving the Silo Problem Despite increasing tech stack integration, most data remains siloed. Tools like sales engagement platforms often retain data internally instead of pushing it into CRM. Olga explains that today, CRM remains the system of record for her team—especially to support forecasting and enforce sales methodologies like MEDDIC. She envisions a near-future where language-model-powered agents aggregate insights across tools seamlessly, eliminating the need for a singular data warehouse. But until that vision is reality, enforcing consistency in CRM remains critical. Measuring AI Success: Beyond Buzzwords One of the most critical, yet often ignored, aspects of AI adoption is ROI measurement. Olga encourages RevOps leaders to think deeply about what success looks like: Is your AI helping reduce CAC? Are sales cycles shortening? Is forecasting accuracy improving? In her experience, AI tools can identify improvement areas (e.g., objection handling, qualification gaps), but execution still depends on humans. That’s where enablement, coaching, and process management come in. “AI can’t execute. You still need to ensure things get done.” Who Owns AI in GTM? A Cross-Functional Responsibility At Birdeye, AI ownership sits across the go-to-market leadership—sales, marketing, CS, solutions engineering, and RevOps. While RevOps leads vendor evaluation and implementation, all stakeholders collaborate on identifying use cases and defining requirements. Security and compliance teams are also critical players, especially as legal concerns about data privacy, training models, and proprietary information increase. “To stay ahead, every GTM leader must go back to the ‘how’ and the ‘what’ of AI. You can’t afford to ignore it.” Olga’s AI Stack: Tools That Work Today While enterprise-wide rollouts are still evolving, Olga and her team leverage several tools to boost productivity: ChatGPT & Claude: For research, data analysis, and ideation Gamma: To turn findings into visual presentations quickly Zoom AI Companion: For call summaries and next steps LinkedIn Sales Nav AI: For prospect intelligence She emphasizes that many AI capabilities already exist within current tools. Leaders should prioritize exploring these features before investing in something new. Final Thoughts: The Human-AI Partnership Olga doesn’t believe AI is here to replace people—not yet. Instead, she sees it as

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From MQLs to Opportunity-Centric Revenue: How Reltio Transformed Its GTM Strategy

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From MQLs to Opportunity-Centric Revenue: How Reltio Transformed Its GTM Strategy A conversation with Joel Jacob, Director of Marketing Operations at Reltio. For years, marketing teams have been evaluated by MQLs (Marketing Qualified Leads). But as B2B buying behavior has evolved—where decisions are made by buying committees and involve longer, more complex journeys—the MQL metric no longer serves its original purpose. One person filling out a form doesn’t indicate true intent, and one lead doesn’t equal one deal. The Shift: From Leads to Opportunities Reltio, a leading B2B SaaS platform that unifies data for enterprise clients, realized this shift early. With a sales cycle averaging nine months and involving multiple stakeholders, their traditional lead-based funnel was no longer sustainable. Joel Jacob, Director of Marketing Operations at Reltio, shares how they transitioned from a legacy MQL-based model to a modern, opportunity-centric buying group strategy. This wasn’t just a process tweak—it was an end-to-end transformation of their go-to-market engine, completed in just 60 days. Facebook Twitter Youtube Why the MQL Model Failed Reltio Joel and his team began by diagnosing the inefficiencies of their MQL-centric process: 1% Conversion Rate: Only 1 out of every 100 MQLs was turning into closed-won revenue. Single-Threaded Opportunities: BDRs would often pursue individual leads without context, while AEs had to manually identify and involve the broader buying group. Misaligned Processes: Marketing, BDRs, and sales were working in silos, tracking separate KPIs and speaking different languages. High Customer Expectations: Their enterprise clients required a tailored, consultative approach, not generic drip campaigns and lead scoring. “We weren’t solving for how we sell. We needed to solve for how our customers buy.” What Changed: The Opportunity-Based Revenue Engine At the heart of Reltio’s new model is the concept of an opportunity container that is tracked from the very start of the buying journey. Key Components: Stage 0 Opportunities: Created proactively for cold target accounts to align all GTM efforts from the get-go. Buying Group Identification: Progress only happens when at least three relevant personas are identified within the opportunity. Unified Funnel Ownership: Marketing, BDRs, and AEs jointly own and advance each opportunity. Real-Time Intent + Historical Data: Powering personalized campaigns and outreach using platforms like 6sense and LeanData. Persona-Based Targeting: Ads and outreach are aligned with opportunity stage and key personas, not just job titles or industries. This model allows for marketing to target ads based on opportunity stage, for BDRs to tailor messaging using real-time insights, and for AEs to focus on qualified, committee-led opportunities.   https://www.youtube.com/watch?v=NPhOjO54wac&t=1s Overcoming Operational Hurdles Implementing this new strategy wasn’t without challenges: Time Constraint: The entire shift had to happen in just 60 days, before the start of the fiscal year. No New Tech: Reltio opted to re-architect their existing stack (Salesforce, Marketo, LeanData, 6sense) rather than buy new tools. Zero Downtime: The transition had to happen without interrupting live sales or BDR workflows. Team Alignment: Joel and team had to overcome deeply entrenched habits and misaligned incentives. “We stopped calling ourselves marketing or sales ops. We were just ‘operations’—unified behind a common goal.” Data Quality: The Real MVP Joel emphasized that none of this would have been possible without clean, connected data across marketing and sales systems. Years before the switch, Reltio had invested in data unification and intent platforms. That foundation paid off. Historical Data: Enabled predictive modeling via 6sense. Account-Centric View: Powered by LeanData and Salesforce to track buying group activity. No More Attribution Wars: Everyone works the same opportunities, making marketing influence clear without the blame game. “60 days gets the headlines, but that was only possible because we invested years into getting our data right.” The Role of AI in a Data-Ready World Joel’s team now uses AI to increase efficiency in key areas: BDR Enablement: Automating research and outreach so reps spend more time engaging and less time preparing. Predictive Signals: Using AI to model when an account is likely to move into an active buying cycle—based on engagement and historical patterns. Campaign Optimization: Automating content and ad delivery based on opportunity stage. But he warns: AI without good data is meaningless. “There is no AI without clean data. If you feed bad data into AI, you’ll just get bad results faster.” The Payoff: Faster Velocity, Better Pipeline Stickiness Reltio’s transformation delivered results fast: Pipeline Stickiness: Opportunities are more likely to progress and less likely to go dark. Faster Velocity: More deals now close within the same fiscal year, despite a 9-month average cycle. Better Alignment: GTM teams operate from the same playbook, improving efficiency and morale. Clear Attribution: Marketing and sales share credit instead of competing for it. Advice for Teams Looking to Make the Shift Joel’s parting advice for RevOps and marketing leaders: Let the Data Lead: Start with facts, not opinions. Use historical conversion rates to make the case for change. Collaborate Cross-Functionally: Ditch the silos. Align Ops, Sales, and Marketing under shared goals. Don’t Wait for Perfection: You don’t need a perfect tech stack. Use what you have and iterate. Train and Align Mindsets: It’s not just a systems change—it’s a mindset shift. Over-communicate and retrain internal teams on the new model. Stay Customer-Centric: Build your process around how your customers actually buy—not around your internal comfort zone. Final Thoughts Reltio’s journey proves that moving beyond MQLs is possible—and impactful. But it requires more than new tools or campaigns. It takes executive buy-in, operational discipline, and a deep commitment to aligning every team around opportunity creation and customer value. “We don’t talk about ABM anymore. We just call it the process. Because it’s how we work now.” Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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From MQLs to Buying Groups: How Palo Alto Networks Modernized Its GTM Engine

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From MQLs to Buying Groups: How Palo Alto Networks Modernized Its GTM Engine A conversation with Jeremy Schwartz, Sr. Manager, Global Lead Management & Strategy at Palo Alto Networks In a rapidly evolving B2B landscape, where multiple stakeholders now shape buying decisions, relying solely on traditional MQL-based models no longer cuts it. At Palo Alto Networks, Jeremy Schwartz, Senior Manager of Global Lead Management & Strategy, has been spearheading a transformation—shifting the company from an outdated lead-centric model to a buying group-focused motion. This move hasn’t just modernized their go-to-market strategy; it’s delivered tangible business results. In this blog, we break down Palo Alto Networks’ journey, the challenges they faced, and the playbook they followed to build a scalable, revenue-generating buying group engine. Facebook Twitter Youtube The Problem: A Funnel Full of Waste Jeremy had a front-row seat to the inefficiencies of the MQL model. From his experience as a campaign strategist and now as a lead management leader, one thing was clear: MQLs were often vanity metrics. “You drive great MQLs that either don’t convert or get thrown back. The lowest person on the totem pole is often the MQL—and sales doesn’t want to waste time on them.” – Jeremy Schwartz Campaigns generated leads, but many never matured into opportunities. Even when they did, sales would frequently reject them, seeing little value in a lone networking admin reaching out. The funnel was leaking at every stage. The Aha Moment: Forrester’s B2B Revenue Waterfall The real turning point came when Jeremy attended a Forrester conference and learned about their B2B Opportunity Waterfall model. It flipped the focus from individuals to buying groups. Inspired, Jeremy returned and pitched the idea internally. His leadership responded with: “Run a pilot and show us what you find.” https://www.youtube.com/watch?v=Dx74q_tiIGg&t=4s Phase 1: Building the Pilot Palo Alto’s pilot kicked off with a 3-month research phase. The team mapped out what people, processes, and systems would be impacted, then aligned with Forrester to tailor the buying group model to their environment. People First They recruited BDRs across multiple GTMs (product go-to-markets), geographies, and segments to get a representative pilot group. At the same time, they analyzed two years of closed-won data to identify real buying group personas. “You don’t need to hire a consultancy to identify your buying groups. Look at your closed-won data—it’s all there.” – Jeremy Schwartz Process Discovery They identified two key BDR motions: Create new opportunities with multiple stakeholders. Add new engaged personas to existing opportunities. Both processes, however, were painfully manual—10+ steps each. Phase 2: Launch and Learn They ran the pilot for a full quarter. Initial triggers still came via MQLs, but BDRs were trained to: Check intent platforms (like Demandbase) Identify other engaged personas at the same account Multi-thread their outreach This approach led to: More meetings booked Better response rates (especially when referencing colleagues) Higher acceptance by AEs (thanks to meetings involving multiple roles) “Mentioning a colleague in an outreach email is real personalization. And it worked.” – Jeremy Schwartz The kicker? Deals with multiple stakeholders started closing—faster and at higher values. They presented the early pilot results to their CMO. The response? “That’s cute.” So the team partnered with data science to extrapolate the results across all opportunities. The model predicted a 13% revenue lift—assuming full buying group coverage. That got attention. “Suddenly, our CMO said, ‘Do more of that.’” – Jeremy Schwartz Phase 3: Automation and Scale To make the process scalable, they built automation in their internal cloud app: When a lead was accepted by a BDR, the system automatically identified other engaged personas from that account. These individuals were assigned to the same BDR for follow-up. Adding someone to an existing opportunity became a one-click process that even notified the AE. They also created custom dashboards to track metrics like: Number of opportunities with buying groups Deal size and velocity Incremental pipeline created Coverage across accounts and products By the end of their fiscal year, these automations were live globally across all BDR teams. Results That Mattered Here’s what Palo Alto Networks achieved by moving to a buying group model: They also introduced new marketing metrics: Campaign-to-Opportunity: Replacing MQL-to-Opportunity Buying Group Coverage: How many personas per deal Buyer Representation Spread: Ensuring campaigns target multiple personas, not just admins “Our leadership is still MQL-obsessed, but now we’re reporting incremental pipeline and seeing influence in closed-won deals.” – Jeremy Schwartz Building the Future: A Signal-Based Scoring Model Palo Alto’s next frontier? Replacing lead scores with signal-based models using four dimensions: Fit: ICP match Intent: 1st, 2nd, and 3rd-party signals Engagement: Website visits, downloads, event participation Completeness: Buying group coverage per account “If three or more people are showing up from an account, with different titles, that’s a signal. You don’t wait for an MQL to act.” – Jeremy Schwartz Lessons Learned: What Jeremy Would Do Differently Push for executive alignment earlier Involve campaign marketers sooner after pilot results Don’t overdesign—start small, learn fast, course-correct Accept the reality of system complexity (especially in older enterprises) “Martech stacks are like Rome—layers upon layers built by different people over time. Nothing is clean.” – Jeremy Schwartz Advice for Companies Starting the Journey Start small with a controlled pilot. Use your own data to identify buying groups. Get BDRs involved first—they’re closest to pipeline creation. Automate before scaling. Show revenue impact, not just lead volume.   “Even if your leadership still chases MQLs, show them better conversion, deal size, and real revenue impact. That’s what moves the needle.” – Jeremy Schwartz Final Thoughts Palo Alto Networks didn’t just adopt a trendy new model. They operationalized a seismic shift in how revenue is created—by recognizing buying groups as the real unit of conversion in B2B. Whether you’re just starting or halfway through your own transformation, Jeremy’s journey is a masterclass in strategy, persistence, and practical execution. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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Winning Buying Groups: Using Data and ABM to Influence Complex B2B Deals ft. Sydney Sloan

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Welcome to The Revenue Lounge How to Influence Buying Groups with Data, Intent, and ABM A conversation with Sydney Sloan, Chief Market Officer at G2. B2B buying has transformed. What was once a one-on-one sales conversation is now a team sport, spanning roles, departments, and even geographies. Today’s buyers are informed, autonomous, and collaborative. They’re forming buying groups long before sales ever enters the conversation. And if your go-to-market (GTM) team isn’t aligned to this reality, you’re already playing catch-up. In this episode of The Revenue Lounge, Randy Likas sits down with Sydney Sloan, Chief Market Officer at G2, to unpack how marketing and sales teams can evolve to influence modern buying groups. She is a 4X CMO, board member and advisor with decades of experience in driving transformative growth and innovation for high-tech companies. Sydney offers a masterclass in using data, intent signals, and segmentation to win complex deals. Here’s a breakdown of the conversation—and why it matters. Facebook Twitter Youtube 🚨 Why Buying Groups Matter More Than Ever The traditional lead-based model is failing. As Sydney puts it, “MQLs are noise.” They flood sales with contacts that aren’t ready to buy—leading to frustration, wasted time, and missed opportunities. Instead, modern revenue teams must focus on identifying buying groups—clusters of stakeholders from the same account showing interest in your solution. These signals can come from downloading content, comparing vendors, visiting your pricing page, or just quietly researching on review platforms. A single lead might lie. But a buying group rarely does. “When you have executive alignment and more than three people in the buying cycle, close rates are 44% higher.”– Sydney Sloan, CMO, G2   🧠 Data Is the Foundation. But it Needs to Be Smart Sydney breaks down three types of intent data: Third-party: Activity across the open web (e.g., searches, keyword trends). Second-party: Data from trusted ecosystems like G2—category views, comparisons, reviews. First-party: Visitor behavior on your own website, CRM engagement history, and sales activity. The magic happens when you triangulate these data sources. For instance, if someone downloaded your whitepaper (first-party), compared your product with a competitor on G2 (second-party), and searched relevant terms online (third-party)—you’ve got a red-hot buying group signal. But here’s the catch: if your CRM is a mess or your systems are siloed, you’ll never connect those dots.   “There’s no excuse not to have tier 1 and 2 accounts built out with clean, up-to-date contacts across buying personas.” https://www.youtube.com/watch?v=NkYTDVKx5Eg 🔁 The New GTM Playbook: From Leads to Stakeholders Moving to a buying group strategy requires more than good data—it requires GTM alignment. Instead of chasing individual MQLs, Sydney recommends: Scoring accounts, not contacts. Tracking signals at the account level to prioritize outreach. Rethinking SDR metrics: focus on meetings with multiple personas, not just any meeting. Partnering marketing, sales, and product around a shared account strategy. Sydney shares how G2 moved to an account-based model where the sales team gets tailored engagement strategies based on segment (SMB, mid-market, enterprise). Every team member—from demand gen to product marketing—knows who their core personas are and how they relate to each other. 🧩 Operationalizing Buying Groups at Scale At Forrester’s recent event, a key theme emerged: evolving from “buying groups” to “buying networks.” This includes partners, peers, analysts, and ecosystems that influence buyer decisions. Sydney highlights a few scalable tactics to work with buying groups: Persona Workshops: G2 ran hands-on workshops using real Gong quotes to help every department internalize customer personas. Segmented Campaigns: Instead of generic ABM, G2 builds micro-segments like “Security companies using 6sense, not yet G2 customers,” and tailors messaging accordingly. Pipeline Meetings: Marketing, sales, and SDRs review the same data together bi-weekly to troubleshoot stuck opportunities and improve velocity. Deal Acceleration Programs: Everyone in stage 2 of the pipeline gets invited to bi-weekly virtual events to deepen relationships and drive conversion. ⚖️ Brand vs. Demand: It’s Not Either/Or Many companies struggle with where to invest: long-term brand or short-term pipeline. Sydney makes it clear: do both, early and often. Brand earns you a seat at the table. G2’s Buyer Behavior Report shows average vendor shortlists are down to just three. Demand capture turns that attention into pipeline. “Brand is giving something away with no ask. Demand is giving something away to capture a contact. Different plays, both essential.” 📈 Rethinking KPIs for Buying Group Success MQLs are out. So what’s in? Sydney advocates for shared KPIs across marketing and sales focused on: Pipeline creation Closed-won revenue Retention Internally, marketing can track velocity, lead-to-meeting time, and program-level cost-per-lead. But in cross-functional pipeline meetings, everyone should speak the same language: revenue. 🧹 The Data Problem: Why RevOps Must Lead One of the biggest blockers to activating buying group strategies is messy, siloed data. Marketing tools hoard information. Sales tools don’t sync well. And critical insights never make it to the opportunity record. The solution? A strong Revenue Operations team. “I’ve surrendered. Marketing Ops now sits in RevOps—and that’s a good thing. RevOps should own the data foundation.” Clean data doesn’t just support GTM alignment—it powers AI and automation. And as Sydney warns, “Bad data trains bad agents.” 🚀 Final Takeaways: Winning with Buying Groups Buying groups are real—and they convert better. Track and engage multiple stakeholders early. Use intent signals across data types. Build workflows that treat G2 comparisons and pricing page visits as bottom-of-funnel signals. Go beyond ABM. Focus on micro-segments to tell sharper, more personalized stories. Align GTM with shared KPIs. Eliminate the MQL silo and focus on revenue outcomes. Fix your data. Clean, enriched CRM data is essential for sales, marketing, and AI. Want to build a buying group motion that works? Start by getting your GTM teams aligned, your data house in order, and your content strategy laser-focused on each persona in the buying network. And if you’re still chasing MQLs, it might be time to hit pause—and rebuild for the way B2B buying actually works today. Want to hear more stories from revenue leaders? Subscribe to The

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Welcome to The Revenue Lounge Align Teams for ABM Success This way you can see for yourself all that we have to offer. Schedule Now. Description This is a heading This is a subheading to go more in detail about the heading. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.”   Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.”   Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.” Facebook Twitter Youtube Subscribe email to get news & updates Am fined rejoiced drawings so he elegance. Set lose dear upon had two its what seen held she sir how know.

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Account-Based Marketing vs. Lead Generation: Why It’s Time to Rethink Your Strategy

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Account Based Marketing vs Lead Generation: Why It’s Time to Rethink Your Strategy A conversation with Kristina Jaramillo, President at Personal ABM. In today’s B2B world, account based marketing vs lead generation isn’t just a battle of tactics—it’s a clash of mindsets. While lead generation focuses on volume and filling the top of the funnel, account-based marketing (ABM) is about precision, alignment, and long-term revenue growth. But here’s the catch: many companies think they’re doing ABM when they’re really just putting a shiny label on their old lead-gen playbooks. According to Kristina Jaramillo, President of Personal ABM, true ABM is not a campaign—it’s a strategic transformation. Facebook Twitter Youtube The Problem: ABM is Misunderstood and Misapplied “ABM isn’t just better targeting. It’s a company-wide go-to-market strategy that aligns marketing, sales, customer success, and product around shared revenue goals.” Most organizations jump into ABM by identifying a list of accounts, defining a few goals, and layering campaigns on top of existing demand gen efforts. But they fail to rethink their content, messaging, team structure, or go-to-market motions. In essence, they’re doing targeted lead generation, not ABM. Element Lead Generation Account Based Marketing Goal Generate as many leads as possible Land and expand strategic accounts Measurement MQLs, form fills, engagement rates Stage progression, win rates, NRR Ownership Primarily marketing Cross-functional: Sales, Marketing, CS, RevOps Approach One-to-many campaigns 1:1, 1:few, or 1:many with personalization Content Generic and persona-based Account-specific and insight-driven Why ABM Often Fails to Deliver Revenue Here’s what Kristina sees time and time again: Companies treat ABM as a bolt-on tactic, not a fundamental shift. Sales and marketing aren’t aligned on account selection, goals, or success metrics. The program lacks executive sponsorship and cross-functional ownership. Teams don’t tailor messaging to strategic priorities or address the status quo bias in buying committees. ABM is measured with tactical metrics like MQAs, not business outcomes. ABM can’t be delegated to a single marketing manager or retrofitted to an existing funnel. It has to be designed to solve the biggest revenue problems—whether that’s breaking into enterprise accounts, reducing churn, or expanding current customers. https://www.youtube.com/watch?v=oFc4f34PJpg A Better Approach to ABM: Start With the Revenue Gaps Kristina’s team begins every ABM engagement by identifying where the revenue leaks are: Are we losing to competitors we should beat? Are customers churning after a short term? Are we unable to move upmarket? Once the problem is clear, the strategy follows: Align sales, marketing, CS, and RevOps around shared objectives. Redefine the Ideal Customer Profile (ICP) based on high-value customers. Develop account-specific messaging tied to strategic business priorities. Focus on internal buyer enablement, not just external outreach. Track meaningful KPIs like deal velocity, ACV growth, and multi-threading success. “ABM is not about the next deal. It’s about driving the greatest revenue streams year over year.” Don’t Just Buy Tech. Build Strategy First Intent platforms like 6sense and Demandbase have become synonymous with ABM—but Kristina cautions against this mindset. “ABM tech doesn’t equal ABM strategy. Buying a platform doesn’t fix broken processes or align your teams.” Intent data only reflects current behaviors—it’s speculative, not predictive. It doesn’t tell you if the account is culturally aligned, ready for change, or worth pursuing. Tech should enable a strategy—not define it. Real-World Proof: How Messaging Changed Everything Kristina shared the story of a freight analytics company struggling to expand deal sizes. Their content was aimed at transportation managers—the platform users—not decision-makers. Their main competitor even offered a similar solution for free. By shifting the messaging to show how their platform integrated with demand forecasting, inventory management, and margin protection, they repositioned their value for C-suite leaders. That shift helped them land and expand accounts on Gartner’s Top 25 Supply Chain list. Metrics That Matter in ABM To measure ABM success, forget MQLs. Kristina recommends focusing on: Stage progression ACV growth Win rates against competitors Engagement with C-suite buyers NRR (Net Revenue Retention) “If your ABM isn’t improving deal size, win rate, and retention—you’re not doing ABM.” Final Thoughts: Time to Kill the Triangle One of Kristina’s boldest takeaways? It’s time to ditch the outdated ABM pyramid. The one-to-many → one-to-few → one-to-one model is too rigid and siloed. Instead, think of it as a dynamic funnel, where high-fit accounts earn deeper personalization based on engagement, strategic fit, and growth potential. TL;DR: Account Based Marketing vs Lead Generation ABM isn’t an evolution of lead gen—it’s a fundamentally different strategy. ABM focuses on revenue, retention, and relationship building, not just pipeline. True ABM requires executive sponsorship, team alignment, and account-specific engagement. Tech alone won’t save you—strategy must come first. Kill the pyramid. Build programs that are integrated, adaptive, and focused on the entire account journey. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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From MQL’s to Buying Groups: Reltio’s Success Story

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From MQLs to Buying Groups: How Reltio Transformed Its GTM Strategy A conversation with Eric Cross, CRO at Reltio. For years, revenue teams have leaned heavily on the MQL. It was the industry-standard metric for marketing success—and the lifeblood of pipeline generation for B2B companies. But in today’s world of complex buying decisions, anonymous research, and multi-threaded stakeholder involvement, the MQL is failing. The old playbooks simply don’t map to how enterprise buyers actually operate today. Eric Cross, Chief Revenue Officer at Reltio, saw this firsthand. And rather than trying to force-fit modern buyers into outdated systems, he and his team made a bold move: they rebuilt their entire go-to-market motion around buying groups. This wasn’t a pilot. It wasn’t a small A/B test. It was a company-wide transformation executed in just 60 days. And the results were staggering: 60% reduction in pipeline attrition 22–23% improvement in average time to close 20% increase in average deal size Best-in-class competitive win rates In this blog, we’ll walk you through exactly how Reltio made this shift—from early warning signs to full implementation, change management, technology, and metrics. If you’re a RevOps, Marketing, or Sales leader evaluating your next GTM evolution, this is the playbook. Facebook Twitter Youtube Spotting the Cracks: Why the MQL Model Wasn’t Enough Eric joined Reltio in 2020 and began evaluating the revenue engine. The data told a troubling story. “We had a legacy demand gen model: leads to MQLs, then into pipeline, and hopefully into opportunities. But once deals entered the pipeline, we were evaporating 35–40% of them in the first two stages. That was alarming.” The consequence? The pipeline looked deceptively healthy on paper, but in reality, a significant chunk was never going to close. “We were creating a false sense of security about how healthy our pipeline was. That was the catalyst for change.” Realignment Begins: “Sales Owns Marketing, and Marketing Owns Sales” The first step wasn’t tactical—it was cultural. “Most companies operate in silos. Sales blames marketing. Marketing blames sales. I had to rewire that thinking completely. We stopped talking about ‘sales’ and ‘marketing.’ We became one GTM team. Sales owns marketing. Marketing owns sales.” To build consensus, Eric organized a two-day offsite with cross-functional leaders from Sales, Marketing, Product, Customer Success, and Ops. “It wasn’t just a marketing and sales decision. This had to be a company decision. We locked the team in a room and said, ‘We’re walking out of here aligned.’” The team was instructed to prepare: A brief problem statement Recommended actions A vision for a new GTM model And they debated—openly and intensely. “You get highs and lows during a session like that. But we made a rule: we don’t have to agree, but we do have to commit. We were either all in or not doing it at all.” https://www.youtube.com/watch?v=xKosC5cYEpU&t=430s Burning the Boats: Why Reltio Didn’t Pilot the Buying Group Model One of the boldest decisions Reltio made was to roll the new model out across the company—not as a pilot. “Pilot programs signal you’re not committed. People think: ‘This is an exercise, I don’t have to change.’ I’ve never seen a pet project like that succeed. So we said: all in, or not at all.” That decision came with high stakes. “I told our CEO, ‘This will either be a game-changer—or you’ll be looking for a new CRO.’” But conviction won out. The team moved forward with full executive and board-level awareness and support. Why Buying Groups? Understanding the Strategic Shift Eric’s rationale for abandoning MQLs in favor of buying groups was rooted in today’s B2B buying behavior. “Enterprise buyers don’t raise their hand right away. They stay anonymous for 60–70% of the buying journey. By the time they engage, they’ve already formed a direction.” This made traditional lead generation—like cold calls and webinar follow-ups—ineffective. “We’re in the era of the great ignore. Buyers get 30 spammy emails a day. They can see automation a mile away. We needed to earn attention earlier, smarter.” The solution? Use intent data to identify surging accounts Personalize outreach for each persona within a buying group Focus on qualified engagement from multiple stakeholders, not just one lead “It’s no longer about how many people we reach. It’s about reaching the right people—the ones who matter to the deal.” The 60-Day GTM Overhaul: From Planning to Execution Eric broke the transformation into three phases: 1. Design and Planning Finalize buying group motion Align teams on definitions, personas, and ICP Redefine opportunity entry/exit criteria Introduce Forrester to validate and refine the strategy “We brought in Forrester to spend half a day with us. They stress-tested our approach and made some great suggestions we incorporated.” 2. Development and Testing Align tech stack: Salesforce, 6sense, Salesloft, Outreach Build ABX dashboards for AEs and BDRs Re-architect sales stages and qualification frameworks (BANT, MedPIC) “We created dashboards where reps could see all their accounts and intent signals. The lightbulb went off—they’d never had visibility like that before.” 3. Production Launch and Measurement Rolled out company-wide in 60 days Quietly tested with one regional team for early signals Measured success using pipeline quality, velocity, and conversion benchmarks Overcoming Resistance: How Reltio Won Buy-in from the Frontlines The biggest challenge? Change management among AEs. “The top objection? ‘Just get me meetings. I’ll take it from there.’ That mindset doesn’t work anymore.” To drive adoption, Eric: Ran listening pods with small AE groups Invited feedback to poke holes in the strategy Used individual performance data to show why change was needed “We showed them their personal conversion rates. Some were below 20%. Even if they were hitting quota, it was clear the system was broken.” While 80% of reps leaned in, 20% resisted. In a few cases, Eric made the hard call. “If you can’t get on board, we’ll reassign your accounts. This isn’t optional.” Redefining Metrics: What Success Looks Like in a Buying Group World Reltio stopped measuring MQLs and switched to two key indicators: 1. Pipeline Quality Entry

marketing attribution playbook
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The Marketing Efficiency & Attribution Playbook: What Today’s CMOs Are Tracking

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The Marketing Efficiency & Attribution Playbook: What Today’s CMOs Are Tracking RevOps 10 min Marketing attribution and efficiency metrics are becoming more critical than ever. CEOs want to know how to allocate budgets effectively across marketing, sales, and product. Investors seek clear insights into ROI. And marketers themselves need to track performance by channel and initiative to optimize their efforts. Yet, in B2B marketing, where deal cycles are long and touchpoints span multiple teams, tracking and proving marketing’s true impact is easier said than done. A recent Marketing Budget Benchmark Study by Ray Rike, Jon Miller, and Bill Macitis reveals key insights. sheds light on how top B2B marketers are approaching efficiency and attribution. Let’s explore key takeaways and how you can apply them to your own marketing strategy. What are CMOs Tracking? The Top 3 Metrics When asked about their top three performance metrics, CMOs consistently focused on: Pipeline Generation – Ensuring a steady flow of qualified leads for sales teams. Annual Recurring Revenue (ARR) – Measuring the long-term revenue impact of marketing efforts. Marketing Qualified Leads (MQLs) – Tracking lead volume and initial qualification. Notably absent from the top three were cost-related efficiency metrics, such as cost per opportunity or customer acquisition cost (CAC). This suggests that many marketing leaders are still primarily focused on volume rather than efficiency—raising the question of whether marketing investment is being optimized for maximum impact. Why Efficiency Metrics Matter While pipeline and ARR are crucial, failing to measure marketing’s efficiency can lead to wasteful spending and missed opportunities. The study revealed that larger companies tend to measure: Cost per Dollar of Pipeline – Connecting marketing spend to potential revenue. Marketing Cost per New Customer (New Logo Revenue) – Assessing acquisition efficiency. Cost of Expansion Revenue – Tracking marketing’s role in upsells and renewals. Interestingly, cost per expansion revenue remains under-tracked in many organizations, despite its importance in retention and growth strategies. In many cases, marketing’s contribution to expansion revenue is undervalued compared to account management teams.   Attribution Models: What’s Working and What’s Not Accurately attributing revenue to marketing efforts remains one of the biggest challenges in B2B. The benchmarking data highlighted five primary attribution models: First-Touch Attribution – Identifies the first interaction a prospect had with the brand. While useful for understanding top-of-funnel performance, it overlooks the full buyer journey. Last-Touch Attribution – Credits the final touchpoint before conversion. This model can be misleading, often over-attributing conversions to channels like paid search or SDR outreach. Multi-Touch Attribution – Allocates credit across all touchpoints in the buyer journey. While comprehensive, it often struggles to account for offline influences and brand awareness efforts. Marketing Mix Modeling (MMM) – Uses statistical analysis to measure the impact of different marketing activities. This approach requires significant data and investment, making it more common among large enterprises. A/B Testing – While not a full attribution model, controlled experiments can help validate the impact of specific marketing strategies. How Attribution Matures with Company Growth As companies scale, their approach to attribution evolves: Early-Stage Startups (<$5M revenue) – Often track deals manually, analyzing each conversion on a case-by-case basis. Pre-Scale Companies – Rely heavily on inbound metrics, focusing on organic sources like referrals and word-of-mouth. Scaling Companies – Experiment with first- and last-touch models but face growing pains in attribution accuracy. Mature Companies – Use multi-touch attribution combined with first- and last-touch insights to inform strategy and budgeting. Despite its potential, Marketing Mix Modeling remains underutilized in B2B tech, with adoption still below 10%. However, as organizations gather more data and refine their analytics capabilities, this approach may gain traction. The Future of Marketing Measurement To build a more efficient marketing function, leaders should move beyond simple volume metrics and embrace a more holistic approach: Adopt Blended Cost and Revenue Metrics – Instead of just tracking cost per pipeline, measure cost per revenue to better justify budget allocation. Use Multiple Attribution Models – No single model provides the full picture. A combination of first-touch, last-touch, and multi-touch insights offers better visibility. Prioritize Expansion Revenue Tracking – Marketing plays a key role in customer retention and upselling. Failing to measure its impact means missing a major component of revenue growth. By focusing on both pipeline growth and efficiency, marketing teams can drive stronger results and make a more compelling case for continued investment. Bhaswati Director of Content Marketing at Nektar.ai, an AI-led contact and activity capture solution for revenue teams. With 11+ years of experience, I specialize in crafting engaging content across blogs, podcasts, social media, and premium resources. I also host The Revenue Lounge podcast, sharing insights from revenue leaders. In this blog

intelligent sales automation
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Intelligent Sales Automation: How AI is Transforming Sales Processes

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Intelligent Sales Automation: How AI is Transforming Sales Processes RevOps 10 min Imagine this. You’re a sales rep juggling emails, follow-ups, and endless data entry. Your coffee is cold, your CRM is a mess, and before you know it, half your day is gone, with barely any actual selling done! Sounds familiar? You’re not alone. Sales studies reveal that professionals only sell 22% of the time. The rest goes to manual tasks. The result? Missed opportunities, slow sales cycles, and lost revenue. What if you had a super-powered assistant? It could handle the dull tasks, study customer behaviour, and forecast future sales trends. Intelligent Sales Automation does just that, using the magic of AI sales tools. By leveraging automation, businesses can streamline operations, boost efficiency, and maximize sales performance. This guide looks at the benefits of smart sales automation. We’ll share real-world examples and show how AI is changing sales strategies for success. What is Intelligent Sales Automation? Intelligent sales automation uses AI, machine learning (ML), and data analytics to automate repetitive sales activities. To optimize decision-making, these technologies analyze customer interactions, CRM systems, and market trends. Integrating AI sales tools lets businesses generate more leads, personalise interactions, and raise conversion rates—all without manual effort. How AI Enhances Sales Automation Artificial Intelligence (AI) has revolutionised the sales landscape. Here’s how AI-driven sales tools are making an impact: Customer Data Analysis: AI analyses sales conversations to identify trends and buying patterns. Predictive Sales Forecasting: Machine learning models provide accurate revenue predictions. Automated Email Sequences: AI personalizes follow-up emails based on customer behavior. Lead Scoring & Prioritization: AI ranks leads based on conversion potential. Chatbots for Instant Support: AI chatbots engage prospects and answer queries in real time. AI in sales is growing at an exponential rate, with adoption expected to surge by 139% between 2020 and 2023. Companies using AI-driven automation are finding a competitive edge. They boost efficiency and make sales cycles faster. 7 Powerful Use Cases of Intelligent Sales Automation 1. CRM Data & Contact Automation The Problem: Sales representatives spend a significant amount of time manually entering and updating customer data in CRM systems. In fact, 71% of sales reps cite manual CRM entry as a major time drain, leading to inefficiencies and lost selling opportunities. The AI Solution: AI-powered CRM automation streamlines data entry by capturing key customer details automatically. These intelligent tools extract information from emails, meeting notes, and other customer interactions to populate CRM fields accurately. This not only reduces manual errors but also ensures that sales reps have the most up-to-date customer insights at their fingertips. As a result, teams can spend more time engaging with prospects and closing deals rather than on administrative tasks. 2. AI-Driven Lead Management The Challenge: Generating leads is only the first step—effectively managing them determines conversion success. Companies that implement high levels of sales automation see a 16% increase in lead generation. However, manual lead qualification and follow-up can result in inefficiencies and lost opportunities. The AI Solution: AI-powered lead management takes the guesswork out of lead prioritization. Advanced algorithms assess lead behavior, engagement patterns, and historical data to score leads based on their likelihood to convert. Automated nurturing sequences then ensure timely and personalized follow-ups, keeping prospects engaged throughout the sales funnel. With AI handling lead segmentation and prioritization, sales teams can focus on high-value opportunities, boosting conversion rates. 3. Intelligent Sales Forecasting Why It Matters: Accurate sales forecasting is critical for business planning, resource allocation, and revenue growth. Yet, many sales teams struggle with imprecise forecasts due to reliance on outdated methods or incomplete data. The AI Solution: AI-driven forecasting analyzes historical sales data, market trends, and customer behaviors to generate highly accurate sales predictions. These insights allow sales leaders to make informed decisions regarding inventory, staffing, and revenue goals. AI also continuously refines its predictions by learning from new data, ensuring forecasts remain relevant and reliable over time. 4. AI Chatbots for Customer Support The Trend: AI-powered chatbots have experienced a 92% growth since 2019, highlighting their increasing role in customer interactions. The AI Solution: AI chatbots provide 24/7 support, instantly answering queries, assisting with product recommendations, and resolving customer concerns. These bots use natural language processing (NLP) to understand customer intent and deliver personalized responses. By handling routine inquiries, chatbots free up human sales agents to focus on complex, high-value conversations, ultimately improving customer satisfaction and efficiency. 5. Personalized Email Campaigns The Challenge: Generic email campaigns often fail to capture customer interest, leading to low engagement and poor conversion rates. The AI Solution: AI-driven email automation creates hyper-personalized content based on customer preferences, purchase history, and behavioral data. These intelligent systems craft subject lines, body text, and call-to-actions tailored to each recipient, significantly increasing open rates and conversions. By optimizing send times and content relevance, AI ensures that prospects receive the right message at the right time. 6. AI-Powered Sales Analytics The Insight: Understanding customer behavior and sales performance is key to refining strategies and boosting revenue. The AI Solution: AI sales analytics tools track sales trends, customer interactions, and conversion rates in real-time. These insights enable sales teams to identify successful tactics, pinpoint weaknesses, and adjust their strategies accordingly. AI also provides predictive analytics, helping businesses anticipate customer needs and proactively address market changes. 7. Sales Gamification for Performance Boost The Stat: A whopping 90% of employees say gamification improves their productivity, making it a valuable tool for sales motivation. The AI Solution: AI-powered gamification systems track sales performance, rewarding top performers with incentives, leaderboards, and performance-based challenges. These systems create a competitive yet engaging environment that motivates sales teams to achieve their targets. By integrating AI insights, gamification strategies can be customized to match individual and team goals, fostering a culture of continuous improvement. How Intelligent Sales Automation Benefits Businesses Let’s look at how sales automation actually benefits businesses:   1. Automates Repetitive Tasks The Impact: Businesses can automate over 30% of sales activities, significantly freeing up time for strategic selling. The

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Revenue Leader Caroline Holt on Putting Together the Best Sales Tech Stack

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Revenue Leader Caroline Holt on Putting Together the Best Sales Tech Stack RevOps Sales Techstack 10 min Extracting value from a sales tech stack continues to be a frustrating challenge for revenue leaders. Budget freezes across the board have forced revenue leaders to be more mindful of the tools they add to their tech stack. But it can be a daunting project to undertake with the market being so crowded with tools across multiple categories.How can revenue leaders select the best tools for their tech stack? How can they derive value from this steep investment? And how can they make their sales teams more productive? We sat down with Caroline Holt, VP Revenue Training & Enablement at Bonterra, to unpack some of these nuances around creating the best sales tech stack. Caroline shares some brilliant insights on how revenue leaders can create the best sales tech stack. And make the process more efficient and effective. If you’re short on time, here is a quick summary of the conversation.   If you enjoy our discussion, check out more episodes of our podcast. You can follow on iTunes, Spotify, YouTube or grab the RSS feed in your player of choice. What follows is a lightly edited transcript of the episode. The Sales Tech Landscape Has Exploded & Disrupted Sales   Abhijeet: Caroline, thanks for coming on the show. Caroline: Thank you so much for having me. Abhijeet: You’ve been in the sales tech industry for quite some time. How have you seen it change over the last few years? Caroline: Well, it has not only transformed. But it has exploded, right? Technology has disrupted sales. I think the buying process in some cases has not changed over the last 20 years, but the way we sell and the way that the buyer wants to purchase has changed. So when I think about my role as a BDR early on, I was calling, I was faxing, I was emailing. But I could get to someone typically. And I think in some cases the proliferation of things like cadence tools that allow people to drop somebody into a constant flow of information has actually hindered our ability to get to people that we want, who might actually need what we need to do. So to the overarching question of how technology has changed, I think in some ways it’s changed in a really incredible way. Because I am an efficiency geek. I like removing friction from the sales process. But I think that sometimes we actually get in our own way because of how we purchase technology. I think of the tech stack in terms of where your business is and what you need to be successful. And I think that’s actually the biggest challenge right now. The first thing that I would say is that when you think about technology, it’s a great solution if you have a really good process to start with. And people to manage the automation, ongoing configuration, updates, maintenance, and so on. CarolineHolt VP, Revenue Training and Enablement Technology is going to be great at a foundational level. So the first thing you need is a way to engage with people, whether that is your regular old telephone and email, or whether that is some sort of a dialer tool where you’re capturing that information. And then you need some place for that information to live. So you need some sort of CRM to be able to capture that information, figure out who you’ve been talking to, what that’s been like, if you’re opening opportunities, what that opportunity looks like. Then you need to figure out what’s actually happening in those calls.  And then that you can analyze a lot of that data over time in terms of what people are saying in aggregate. So our whole roadmap should be focused on it. It  provides just a really powerful level of insight. But I think for a lot of organizations, they don’t ever optimize those parts of the tech stack, and then they start adding new stuff. They either haven’t gotten it right the first time, or they think that that’s table stakes.  That foundational piece, particularly the architecture around the CRM, if that stuff isn’t right, none of the other tech is really that helpful because you wind up buying stuff and building stuff that doesn’t really help that whole flow from who are we talking to, what are we talking to them about, what’s happening with those deals to closing those deals. Caroline Holt VP, Revenue Training and Enablement So that’s a really simplistic way of thinking about sales technology. But I would say that most organizations need to start with those fundamental pieces and then start thinking about, okay, once we know, now we need to figure out who those prospects are. So what sort of technology is gonna help us identify who those folks are. So how you build that stuff over time becomes really powerful. And then what you do with that data and analytics becomes really powerful over time. But sometimes people invest really quickly in a lot of technologies, but they never really optimize them for performance. So the other part is just thinking about having what you can actually bite off in terms of tech investments in any given year to be able to do the right thing for your business.  Where is Sales Tech Heading Towards?   Abhijeet: If you don the hat of a sales leader who’s going to spend a hundred dollars this year across the technology stack, how should they go about their investment approach? Where should those a hundred dollars be allocated?  Caroline: So I would say that like everything in enablement or any of the back office operations, it’s where are your problems? So if the business should be investing based on what technology is going to actually help them be more effective, where they’re less effective than they could be today. So

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Welcome Efficiency Gains in 2025 with a Suite of Meeting Insights Built for Revenue Teams

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Welcome Efficiency Gains in 2025 with a Suite of Meeting Insights Built for Revenue Teams Product 10 min Imagine you’re preparing for a 42 km marathon. You’ve set a weekly running plan across terrains and weather conditions. You’ve brought the best equipment – wind-resistant clothing, a sleek water pouch, well-fitted goggles, perfectly cushioned shoes with the right grip, and a pace calculator. There were days when you completed 42 km, there were days you only did 5 km, and there were days you did 25 km, and so on. But, throughout your preparation, the pace calculator unfortunately missed capturing your pacing and the time taken to run the distance. Oops! Now, you have no idea what is the average time you take to complete 42 km or what is your average pace. So you’re going in blind and decide to pace yourself by winging it. Yes, this blog is not about preparing for a marathon. But this example is an analogy to sales. The runner is the revenue leader. The equipment refers to the sales team and tools. Each run refers to a meeting with a buyer. The pace calculator refers to a tool that is meant to provide key insights – what are you doing well, and what you should improve. So, with the analogy and this context, let me challenge you with some questions: How many meetings does it take your SMB and your enterprise teams to win a deal, respectively? How many meetings get completed out of all the scheduled meetings? How often are meetings happening in each of your accounts? How often are the different members of the buying group invited to these meetings? How often are these buying group members attending these meetings? What is the nature of the meeting? What is being discussed exactly? How much time is being spent or wasted in meetings by your sellers and deal support team like solution engineering, executives, etc.? Sure, conversation intelligence tools may help answer a couple of these questions. But, not all. Moreover, most conversation intelligence tools only capture data if they’re set to record that meeting. If it’s not set to record, then the data does not get captured. And before you jump to a conclusion, no, this is not a blog on conversation intelligence. Rather it’s about zooming into your buyer-seller data with a specific focus on meeting insights. Meeting Insights Missing from Your Engagement Data Over the last two quarters, Nektar introduced several useful features that surface insights into buyer-seller engagement. Some of them are specific to meeting data. These insights are 100% accurate because they stem from data picked up at the source of action – your calendar invites, be it Google or Outlook. What’s more? All the data is provided to you in your standard Salesforce objects – account, opportunity, contact, and lead. So you can leverage Salesforce’s powerful reporting capabilities to surface these meeting insights. Let’s dive into some insights that Nektar.ai unlocks through these recently launched features. 1. Meeting Status Every week revenue leaders conduct 1:1 deal reviews where the rep shares with them all the meetings that are scheduled, that took place, and that got canceled or rescheduled. Additionally, the rep also has to share who is invited to the meeting and who attended. The revenue leader then suggests adding a key stakeholder, and the dialogue continues. With Nektar, this ‘zero value information exchange’ can be eliminated. Instead, revenue leaders can access such data in their Salesforce. Nektar automatically marks the status of a meeting across the meeting lifecycle – scheduled, completed, aborted, canceled, missed – to give deep visibility into how meetings are impacting sales cycles, win rates, and revenue generation. The most important question this helps answer is: How many meetings do I need to complete to win an enterprise deal and an SMB deal, respectively? This can be further segmented at an industry or region level for further granularity. Layer Meeting Status with additional factors to unlock clear visibility into deal activities and understand what’s working and not working. 2. Meeting Type Let’s assume an enterprise deal had 55 meetings from creation to close. With Meeting Status you will easily know how many were completed. You may also choose to use native Salesforce reporting to slice this data across deal stages. The only insights you have are that 55 meetings were scheduled, 40 were completed, and each deal stage had a specific count of meetings. But, you’re still not sure what each meeting was about. Was it a demo meeting, a discovery meeting, a use case mapping meeting, a mutual success plan meeting, a proof of concept discussion meeting, or something else? And how many such meetings took place? This is where Activity Tagging becomes beneficial. Nektar automatically assigns tags to meetings based on the context of the meeting using certain keywords. This tag is then automatically added to a custom field on Salesforce within standard objects, making it completely reportable. Equipped with this data point, revenue leaders can easily spot what types of meetings are taking place and how many meetings of the same type are taking place. Most importantly, you can define these tags yourself. For you, you may define one of the tags as ‘use case mapping’, while another company may not. Or, you may have defined only 4 tags while another company may have defined 12 tags. It’s easily customizable to suit your revenue process. Going back to the example we started with, you’ll have the following insights – 55 meetings were scheduled, 40 were completed, 3 discovery meetings, 5 demo meetings, 3 use case mapping meetings, and so on. This insight helps you gather which types of meetings are critical to winning a deal. For example, if deals over $100,000 had more use case mapping meetings and deals less than $50,000 had more negotiation meetings, you can now optimize your plays to replicate this more often to improve your chances of winning deals. Activity tags are customizable. Based on your sales process,

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What is Salesforce Tech Debt? And How Can You Reduce it?

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What is Salesforce Tech Debt? And How Can You Reduce it? RevOps 10 min In a recent statement, Salesforce disclosed its intention to raise list prices across several product offerings, including Sales Cloud, Service Cloud, Marketing Cloud, Industries, and Tableau, with an average increase of 9%. Starting in August 2023, the revised list prices for certain Salesforce products will be implemented, targeting new customers and existing customers acquiring additional cloud services. Salesforce has outlined the following adjustments in pricing for their fundamental Sales and Service Cloud offerings: Professional Edition will increase from $75 to $80 Enterprise Edition will increase from $150 to $165 Unlimited Edition will increase from $300 to $330  These changes are going to affect a lot of organizations. Over 150,000 companies across industries use Salesforce. With specialized solutions for operations across sales, service, marketing, and commerce, it is no wonder that the hike in price would affect almost every industry. For companies using Salesforce, the major concern is: Salesforce Tech Debt.  What is Salesforce Technical Debt? Technical debt refers to the expense of having to do extra work later on due to opting for a quick solution in the present rather than investing the time in a more optimal approach. This concept is also commonly referred to as “Shift Left,” which emphasizes the idea that the sooner you identify and address issues, the more cost-effective it is to resolve them. Technical debt represents the additional effort required to rectify a hasty, less-informed solution chosen in the present (constructed quickly without a deep understanding of business requirements), as opposed to adopting a more time-consuming but superior approach. In a broader sense, technical debt encompasses any customizations, whether through code or declarative means, that were implemented when standard functionality wasn’t suitable or accessible. Technical debt can also contain situations where solutions were initially designed for a specific purpose, but as business needs evolved over time, small adjustments were tacked on. A more constructive perspective on technical debt is to recognize that virtually everything can be considered a form of technical debt, but it’s termed “debt” because it necessitates future efforts to address and resolve. In the past, technical debt was primarily associated with developers taking shortcuts in their code. However, in the era of low-code platforms such as Salesforce, technical debt can arise not only from coding decisions but also from the configuration choices made through user-friendly “clicks” within the platform. What Causes Tech Debt? Salesforce technical debt arises from rushed or suboptimal development practices, including quick fixes, inadequate adherence to best practices, complex customizations without proper planning, and neglect to update and adapt solutions over time. This debt accumulates when shortcuts are taken, making future maintenance and scalability more challenging and costly. Here are a few factors that can cause technical debt: 1. Modified or outdated design This occurs when the business requirements change, rendering certain functionalities unnecessary. However, it’s often deemed safer to retain these functionalities. 2. New releases This arises when the introduction of new platform features surpasses the capabilities of previous releases or custom development efforts. For instance, Salesforce Flows are taking precedence over process builders and workflow rules. 3. Intentional technical debt When a deliberate decision is made to expedite development, fully aware that it will entail higher long-term costs, but it’s considered the appropriate course of action. 4. Unintentional technical debt Accumulates when shortcuts are taken for various reasons, typically due to time constraints or concurrent workstreams. 5. Tacked-on technical debt Occurs when a particular functionality is continually extended incrementally and “bolted on” to maintain its functionality rather than undergoing a proper reconstruction. Up to this point, we’ve delved into the theoretical aspects of technical debt, discussing its causes and mechanisms. However, what does it actually manifest as in real-world scenarios? Let’s have a look: Common Forms of Salesforce Tech Debt Common forms of Salesforce technical debt include the accumulation of unused customizations, outdated roles and permission sets, complex and undocumented workflows, inadequate data modeling, and the absence of thorough testing. This technical debt arises when shortcuts are taken or best practices are overlooked during Salesforce development, making the system harder to maintain and optimize over time. Several prevalent forms of technical debt can be identified In Salesforce, including: 1. Visualforce component vs sales path Before the introduction of Sales Path, organizations required a visual means to depict the progress of an opportunity stage or process. To achieve this, they had to customize a Visualforce component. However, with the release of Sales Path by Salesforce, these visualizations became standardized, which subsequently led to an increase in the technical debt interest rate. 2. Adapting process automation The creators of 10K recognized the necessity of automating their invoice generation process. They initially developed an hourly function to create invoices, incorporating some basic rules. However, as their contract structures evolved, they found themselves adding more functions to their initially straightforward task. Managing these changes became increasingly challenging, prompting them to allocate time to rewrite the process based on the current state of their business operations. 3. Excessive customization As previously mentioned, Salesforce provides a user-friendly environment for creating custom Objects and code, even when a simpler declarative solution would suffice. For instance, opting for a workflow instead of resorting to triggers for scripting tasks. This form of technical debt often arises from an overly responsive approach, where every requested change is implemented without exploring alternative options within standard configurations. 4. Unused customizations Despite being promoted as a ‘no-code’ platform, Salesforce cannot handle every task declaratively. Changes in business requirements may render customizations that were once essential unnecessary. Unless these customizations are retired, they can introduce inherent complexity to every new change and potentially hinder end user adoption by making your Org more challenging to navigate. 5. Access controls You’ve likely encountered the concept of “the principle of least privilege.” On the flip side, we have the “principle of most privilege,” where users end up with excessive access as their roles within the organization evolve. While other forms of technical debt can impede progress, retaining unused profiles and permission

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Configure Contact Roles on Salesforce to Unlock Immediate Efficiency Gains

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Configure Contact Roles on Salesforce to Unlock Immediate Efficiency Gains Discover how defining OCRs can enhance visibility into your buying committee, improve sales execution, and boost win rates. RevOps 10 min Let’s begin by simply defining what is an opportunity contact role (OCR). An OCR is a standard object on Salesforce within the Opportunity object that links Contacts to Opportunities, specifying the Contact’s role in that Opportunity. Having great OCR hygiene means sales leadership teams gain better visibility into the buying committee for each opportunity. Better visibility helps monitor if reps have at least identified the necessary people needed to win the deal. If the necessary people are involved, then sales leaders can guide their teams on the right engagement playbook to navigate the deal toward success. While sales teams know the importance of identifying and engaging the entire buying committee, not many follow this through to execution. This becomes worse when we consider the OCR data available in a CRM. Open any CRM today, and you will notice that a majority would have an average of 3 contact roles. Of those 3, one is usually a required field made mandatory by the revenue/sales operations or CRM admin. In companies that practice MEDDIC (and its variations), the ‘Economic Buyer’ and ‘Champion’ are identified and added to the CRM, but the remaining buyer roles are either identified but not added to the CRM or not identified at all. So why should OCRs matter? The answer to this question lies in whether or not you’re working on improving sales execution, rep efficiency, win rates, and forecasting. You’d be surprised if we told you how often we hear prospects say “We have no idea who our sellers are talking to” or “We don’t know how often we’re engaging buyers in open deals”. ‘Who’ you are talking to and ‘how often’ are you talking to buyers are the fundamental units of generating revenue. The ‘process’ of generating revenue can only be improved by tracking and measuring such fundamental units. You may be doing a great job with creating contact lists from third-party data tools like Zoominfo or Lusha or by auto-creating contacts in accounts with tools like Clari or Gong, but if such contacts are not being linked to opportunities, you are losing out on critical data. Technically, in CRM terms, opportunities are won, not accounts. And so having contact data is not good enough. You must aim to have granular and comprehensive contact role data. Introducing Configurable OCRs Using AI, automation, and graph inference, Nektar automatically creates contacts in the relevant accounts present in Salesforce. Until recently, Nektar would automatically associate these contacts as OCRs within the relevant open opportunities. There was no configuration needed. However, through customer feedback and research, Nektar is excited to announce ‘Configurable OCR’. An OCR record is only useful if it is: associated because it is actually involved in the deal a buying role was identified and assigned to it With configurable OCR, you can define rules using buyer-seller engagement data that Nektar has already added to the (open) opportunity and account. For example, a rule can be: “If engagement with contacts in an account is more than 5 times in the last 10 days, and if there is an open opportunity in those accounts, then associate such contacts as opportunity contact roles.” This example considers the recency and frequency of buyer engagement. So, only those contacts that are frequently engaged by the seller will get added to opportunities as OCRs. As a result, sales leaders gain instant visibility into who is actually involved in deals. This is just a simple, straightforward example of a rule. You can define your own rules. Additionally, you can customize the rule for the different segments you may have. For example, have a rule specifically for strategic accounts, expansion accounts, new business accounts, vertical-specific accounts, or any other segmentation you may have. Next, you can configure the second component – the buying role. If you’ve used Nektar, you would know that it extracts job titles from email signatures. A default capability we’ve always offered is to map out job titles to the respective buying roles. With this one-time configuration, as and when Nektar links OCRs, it also assigns a buying role to the OCR based on the corresponding job title. Now, using genAI automation you can define rules to assign an appropriate buying role. You can consider a combination of job titles and engagement trends, job titles and seniority, job titles and engagement and segment – whichever factors address your requirements. After all, the process of generating revenue is unique to a company. The best part is that all this is done using the standard Salesforce records, so they are easily reportable on Salesforce. This can also be achieved for your historical opportunities by backfilling them. Benefits of configurable OCRs Nektar customers use this OCR data for deal inspections, win-loss analyses, playbook optimization, and enhancing their multithreading strategy. Every opportunity has only those contact roles that are involved in the deal while the remaining stakeholders such as legal remain in the account as contacts. So sales leaders are able to monitor which job titles and buying roles are being engaged. Since historical data is also plugged in, you can study buying committee engagement for won and lost deals to analyze what worked and did not work. Some of our customers identified new personas in their closed deals, and have now started prospecting this persona actively to generate new pipeline. By studying won deals, you can also track the engagement pattern and work towards improving your multithreading strategy. Lastly, playbooks can be transformed. For example, one of our customers now has made it mandatory to have a specific number of contact roles if the deal is in stage 3 of the sales process. Similarly, answers to who, how often, and when should different people of the buying committee be engaged can be detailed out. Outside of the sales team, a clean and

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What is Predictive Sales Analytics?

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What is Predictive Sales Analytics? Sales When it comes to weather forecasting, experts use historical weather patterns, current atmospheric conditions, and advanced technology to predict future weather. They anticipate whether it will rain, snow, or be sunny. The better their data points are, the better their predictions.  Similarly, Predictive Sales Analytics ​​examines past sales data, market trends, customer behaviors, and other factors to forecast future sales outcomes. This allows organizations to make informed decisions, allocate resources effectively, and tailor their sales strategies, much like how people adjust their plans based on the weather forecast to ensure a smooth day ahead. Let’s have a detailed walkthrough. What is Predictive Sales Analytics? Predictive sales analytics uses data, statistical algorithms, and machine learning techniques to forecast future sales outcomes and trends. The approach employs statistical modeling, data mining methods, and machine learning to detect patterns within diverse datasets. Predictive sales analytics studies historical sales data, customer behaviors, market trends, and other relevant factors, applying sophisticated mathematical models to forecast upcoming sales outcomes.  Predictive sales analytics has several features, including: 1. Data collection and preparation The process begins by collecting relevant data from multiple sources. This can include historical sales data, customer interactions, website activity, marketing campaigns, and external market trends. The data is then cleaned and organized to ensure accuracy and consistency. The quality of the data directly impacts the accuracy of predictions. 2. Feature selection In this step, the most important variables, also known as features, are identified. These features can include factors such as time of year, customer demographics, purchase history, marketing channel, and economic indicators. 3. Model building Predictive models are built using statistical algorithms and machine learning techniques. These models use historical data to establish relationships between the chosen features and the target variable, which is typically future sales. 4. Training and testing The built model is then trained using a portion of the historical data. This training process involves adjusting the model’s parameters to minimize errors and improve accuracy. The model’s performance is evaluated using testing data that the model has not seen before. This step helps ensure that the model can generalize its predictions to new data. 5. Prediction and analysis Once the model is trained and validated, it is ready to predict future sales. It takes new data inputs, such as current market conditions and customer behaviors, and generates forecasts based on established patterns. 6. Actionable insights The predictions generated by the model provide actionable insights for businesses. These insights can include forecasts of future sales volumes, identification of high-potential leads, recommendations for optimizing marketing campaigns, and insights into product demand. 7. Iterative refinement Predictive sales analytics is an ongoing process. As new data becomes available, the model can be retrained and refined to improve its accuracy and relevance. This ensures that the predictions remain aligned with changing market dynamics. 8. Decision-making and strategy formulation Businesses use the insights from predictive sales analytics to inform their decision-making and shape their sales and marketing strategies. For example, they can allocate resources based on anticipated sales trends, tailor marketing efforts to specific customer segments, and optimize inventory management. Now that we have understood the process of predictive sales analytics, let’s have a look at the companies that are using this tool.  Which Companies Use Predictive Sales Analytics? Predictive sales analytics is embraced by a diverse range of companies spanning various industries. These companies want to transform raw data into actionable insights, enabling them to anticipate customer behavior, allocate resources more efficiently, and make well-informed decisions. Let’s see how different companies across industries are using predictive sales analytics:  1. Retail Industry Retail companies use predictive sales analytics to forecast demand for products, optimize pricing strategies, and manage inventory levels. This helps them avoid stockouts and overstocking, ensuring they meet customer demands effectively. 2. Financial Services Banks and financial institutions employ predictive sales analytics to assess credit risk, detect fraudulent activities, and make informed lending decisions. This results in reduced financial losses and improves the quality of loan portfolios. 3. Healthcare Sector Healthcare organizations leverage predictive sales analytics to predict patient trends and allocate resources efficiently. It aids in optimizing hospital operations, patient admissions, and resource utilization, ultimately leading to better patient care. 4. Technology Companies Technology firms utilize predictive sales analytics to analyze customer behavior and preferences, enabling them to tailor their products and services. This helps increase customer satisfaction and loyalty. 5. Manufacturing Sector Manufacturing companies apply predictive sales analytics to optimize supply chain processes, anticipate equipment maintenance needs, and enhance production efficiency. This minimizes downtime and maximizes productivity. 6. Automotive Industry Automotive companies use predictive sales analytics to anticipate market demand for specific vehicle models, allowing them to adjust production and marketing strategies accordingly. 7. Telecommunications Telecommunication companies employ predictive sales analytics to analyze customer usage patterns and predict churn rates. It helps them design targeted retention strategies and personalized offerings. 8. Hospitality and Travel Hospitality businesses utilize predictive sales analytics to forecast occupancy rates, optimize room pricing, and enhance guest experiences by tailoring offerings based on customer preferences. 9. Energy Sector Energy companies leverage predictive sales analytics to predict energy consumption patterns, optimize energy distribution, and plan maintenance activities for power infrastructure. 10. Consumer Goods Consumer goods companies apply predictive sales analytics to identify consumer trends, anticipate shifts in demand for different products, and optimize marketing strategies. 11. Real Estate Real estate firms use predictive sales analytics to analyze property market trends, anticipate property values, and optimize pricing strategies for rental and sales properties. 12. Pharmaceutical Industry Pharmaceutical companies utilize predictive sales analytics to forecast demand for medications, optimize inventory levels, and align production with market needs. With so many companies using predictive sales analytics, it is important to understand what it actually does? How does it affect processes, and why is it so important?  Why Is Predictive Sales Analytics Important? Predictive analytics software can handle complex and demanding data processes behind the scenes, allowing sales professionals to concentrate on their core strengths. In simpler terms, predictive sales analytics assists sales professionals in transforming a potentially overwhelming volume of data into straightforward, comprehensible insights that serve the

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