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,

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.

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