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Data Before AI: Building a Clean Foundation for Smarter RevOps

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

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

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: Metric Improvement Deal Size 2.6x increase (in certain segments) Conversion to Pipeline Significant lift over MQL-only opps Closed-Won Rates Higher for opportunities with buying groups Pipeline Quality Larger, multi-threaded deals Coverage 12% of Q2 opps had buying groups; target = 15%   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

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

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

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

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

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

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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15 Sales Optimization Tools to Fast Track Your Deals in 2025

Top 5 Trends That Will Impact Sales Operations in 2025 RevOps 10 min Imagine having a machine. A machine with all the parts in working condition, it’s even run daily but produces suboptimal outputs.  The issue is – ‘Oiling.’ Without proper and regular oiling, the various parts of the machine can’t function properly. To make the machine work cohesively, oiling is required.  Sales optimization tools can help with the oiling. It is done so that the sales function produces optimal results.  What is sales process optimization? Sales process optimization refers to refining and improving the steps in selling a product or service to a customer. The process typically includes steps such as lead generation, lead qualification, needs assessment, proposal creation, negotiation, and closing the sale. Sales process optimization aims to increase efficiency and effectiveness, resulting in more successful sales and increased revenue. How do Sales Optimization Tools Work? Sales process optimization is essential in B2B sales, where the sales cycle is often longer and more complex than in B2C sales. In B2B sales, the process typically involves multiple decision-makers and influencers, and the sales rep needs to demonstrate how the product or service will address the business’s specific needs. Here are some key ways in which sales process optimization works in B2B sales: 1. Identifying and targeting the right prospects Sales process optimization in B2B sales involves identifying and targeting the most promising prospects with customized messaging and outreach. This involves using data analytics to identify businesses that are a good fit for the product or service and creating targeted marketing campaigns to reach them. 2. Understanding the buying process B2B sales often involve multiple decision-makers and influencers, and it’s important to understand the buying process for each prospect. Sales reps need to identify the key decision-makers, understand their needs and priorities, and tailor their messaging accordingly. 3. Creating a value proposition In B2B sales, it’s important to demonstrate how the product or service will provide value to the business. Sales reps need to articulate a compelling value proposition that addresses the prospect’s specific needs and pain points. 4. Managing the sales process B2B sales often involve a longer sales cycle than B2C sales, and it’s essential to manage the process effectively. This may include using CRM software to track leads and opportunities, setting up regular touchpoints with the prospect, and managing the negotiation and closing process. 5. Analyzing and optimizing the sales process Finally, sales process optimization in B2B sales involves analyzing sales data and feedback to identify areas for improvement. Sales reps need to be able to adjust their approach based on what works and what doesn’t and continually refine their sales process to improve outcomes. A Closer Look At a Sales Optimization Process Now that we have understood the role of sales optimization in B2B segment, let’s understand the process in a step-by-step format:   1. Define your goals The first step in sales optimization is to define your goals. This may involve setting specific targets for revenue, number of sales, or other metrics that are important to your business. 2. Analyze your current sales process The next step is to analyze your current sales process to identify areas for improvement. This may involve reviewing your sales data, talking to your sales team, and gathering feedback from customers. 3. Develop a plan Based on your analysis, develop a plan for optimizing your sales process. This may involve making changes to your sales strategy, implementing new tools or technologies, or providing additional training to your sales team. 4. Implement your plan Once you have a plan in place, it’s time to implement it. This may involve rolling out new processes or tools, providing training to your sales team, or adjusting your sales strategy. 5. Monitor your results As you implement your plan, it’s important to monitor your results. Track your progress against your goals, analyze your sales data, and gather feedback from your team and customers to identify areas where you can continue to improve. 6. Continuously improve Sales optimization is an ongoing process, so it’s important to continue refining and improving your sales process over time. This may involve making small adjustments based on feedback and data analysis or making larger changes if your goals or market conditions shift. Let’s have a look at some sales optimization examples to understand the use cases of the above processes in real-life.  Use Cases of Sales Optimization Tools Sales optimization has a wide range of use cases in B2B sales, including automating lead generation, personalizing the sales experience, improving group intelligence, and implementing a sales enablement strategy. By leveraging sales optimization tools and techniques, businesses can improve their sales outcomes, increase efficiency, and drive revenue growth. Here are some examples of sales optimization that businesses implement to improve their sales outcomes: 1. Automate lead generation Businesses can use automated lead generation tools to identify potential customers based on specific criteria instead of relying solely on manual prospecting. This can help streamline the sales process and reduce the time and effort required to find new leads. 2. Personalize the sales experience Personalization can help sales teams better understand the specific needs and preferences of the stakeholders in a buying committee. This allows them to tailor their messaging and approach to each stakeholder, increasing the likelihood of resonating with each individual and ultimately closing the deal. By having insights into all the stakeholders, sales teams can also multithread better, meaning they can engage with multiple stakeholders simultaneously and build a rapport with each of them.  3. Open more doors with more active contacts Encourage multithreaded conversations by equipping your reps with additional contacts. Provide your representatives with more contacts automatically discovered from all sales tools.  4. Improved group intelligence Instantly grant your reps the knowledge of a buying committee map, so they know whom to engage, how, and when to best influence deal progress. 5. Real-time activity intelligence Automatically capture both structured and unstructured data and update it in real-time against active opportunities

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