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

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

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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Modern ABM and Demand Generation in the Age of Buyer Fatigue

Modern ABM and Demand Generation in the Age of Buyer Fatigue 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. Strategic ABM Checklist: Define your Ideal Customer Profile (ICP) based on firmographic and technographic criteria Segment and tier accounts (e.g., Tier 1: High-Value, Tier 2: Strategic Growth, Tier 3: Scalable Outreach) Involve sales in validating and refining account lists Build cross-functional account plans with sales and SDRs Develop persona-specific messaging and value narratives Align marketing and sales cadences for multi-threaded outreach Choose channels and tactics based on account tier (field, digital, gifting, events) Establish shared KPIs and cohort-based measurement plans Start small (e.g., pilot 25 Tier 1 accounts), iterate based on learnings Scale with technology only after 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.” Infographic: 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:   Infographic: 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

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

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

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

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,

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Decoding the Buying Group Model: Strategies for Success

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

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Building & Scaling RevOps in an Enterprise

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

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

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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