Scaling GTM Teams with Data-Driven Insights & Inclusive Leadership A conversation with Barbara Pawar, VP, Head of US Sales at Avanade. Scaling go-to-market teams in today’s enterprise environment has never been more complex. The stakes are higher, customer expectations are sharper, and leadership has to balance both speed and sustainability. For Barbara Merola Pawar, VP Sales & GTM (US Northeast) at Avanade, the secret to building high-performing GTM organizations lies in an unusual but powerful combination: data discipline, AI enablement, and inclusive leadership. Barbara, who has spent two decades in leadership roles across Fortune 100 enterprises and high-growth SaaS startups, has seen the evolution of sales from the inside out. In her conversation on The Revenue Lounge, she reflected on how data accuracy, coaching culture, and inclusive hiring practices are shaping the GTM playbooks of tomorrow. Her perspective is both practical and deeply human — a reminder that while technology accelerates growth, it’s people who sustain it. Facebook Twitter Youtube Data as the Backbone of GTM Every seller knows the struggle of updating CRM systems. Logging stakeholders, capturing notes, tagging loss reasons — it often feels like an administrative tax on the real work of selling. But as Barbara puts it, data accuracy is non-negotiable. “If the data in CRM is not accurate, finance can’t plan. Marketing can’t nurture effectively. Leaders can’t decide where to invest. Data is the foundation for everything.” — Barbara Pawar She remembers the days when keeping CRM updated was an endless chore, especially without remote access. Today, tools like Microsoft’s Copilot have changed the equation. Sellers no longer need to spend hours keying in updates; AI copilots automate much of the work, giving back valuable selling time while improving the accuracy of organizational data. That shift doesn’t just make life easier for sales reps — it directly influences how finance builds business plans, how marketing targets campaigns, and how leadership decides where to invest. The Ripple Effect of Bad Data: Inaccurate CRM → Misaligned forecasts Misaligned forecasts → Wrong hiring decisions Wrong hiring → Poor investment allocation Poor allocation → Broken GTM execution Sales Data Hygiene Checklist: Ensure executive sponsors are logged in CRM after every client interaction Capture loss reasons consistently and in detail Centralize meeting notes and avoid “email-only” knowledge Use AI copilots to automate repetitive updates AI as a Force Multiplier For Barbara, the biggest breakthrough of the last few years is the way AI has reshaped sales leadership. Preparing for business reviews once required combing through dashboards for half a day. Now, AI copilots can generate a consolidated view of sales and finance data in minutes. “AI isn’t replacing us. It’s enabling us to move faster, remove administrative burdens, and focus on client conversations.” — Barbara Pawar This is where technology becomes a force multiplier. AI tools are not about replacing the art of selling but about amplifying it. They allow leaders to identify anomalies in pipeline health, monitor week-over-week forecast growth, and spot at-risk opportunities before it’s too late. For frontline sellers, AI takes the administrative burden off their shoulders. For leaders, it provides context-rich insights that shape better coaching conversations. Where AI Transforms the Sales Cycle: Lead Qualification → Scoring and prioritization Deal Execution → Real-time insights on next steps Forecasting → Anomaly detection and accuracy improvement Post-Sale → Predictive churn analysis and nurture triggers https://www.youtube.com/watch?v=eBz2IU5E2pk&t=2282s The Evolution from Seller to Leader Perhaps the most relatable part of Barbara’s story is her reflection on moving from individual contributor to sales manager. As a high-performing seller, she controlled her own outcomes, built deep client relationships, and defined success in personal quota attainment. Transitioning to leadership meant letting go of that control and scaling through others. “High-performing sellers often struggle when promoted because they coach others to sell like they sold. But selling is an art—each seller succeeds differently.” — Barbara Pawar That realization reshaped her leadership philosophy. Rather than cloning her own selling style across the team, she emphasizes understanding each individual’s unique strengths. Some sellers need frequent guidance and coaching, while others only need a manager to step in when blockers arise. Barbara believes that true leadership lies in adapting your style to the motivations and personalities of your team — and in creating an environment where every seller can thrive. Weekly Coaching Framework Template: Monday → Pipeline review with a focus on deal blockers Mid-week → Coaching sessions on strategic opportunities Friday → 1:1s to align on motivation, growth, and support Building Context Through Data Barbara’s own daily routine as a sales leader underscores the importance of consistency. Every morning begins with a dashboard review — not just to check pipeline numbers but to spot trends. Is the forecast growing week over week? Are certain industries expanding faster than others? Where is pipeline coverage falling below the 3x quota threshold? She points out that data isn’t only about a sales leader’s own targets. Sometimes the most critical insights lie in the metrics of their boss or executive leadership — such as cost of sale or efficiency ratios. Leaders who only focus on their own dashboards risk missing the bigger picture. The Sales Leader’s Dashboard: Forecast trend line (week-over-week, month-over-month) Pipeline health by industry or region Win/loss breakdown Cost of sale vs. revenue efficiency Turning Losses into Learnings Not every deal can be won, but every loss can be valuable. Barbara has institutionalized the practice of loss reviews — structured sessions that involve not just the sales team but also marketing and other stakeholders. “Over 50% of lost deals are not to competitors—they’re to inaction. Reviewing those deals immediately creates learnings and opportunities for re-engagement.” — Barbara Pawar These sessions are about more than assigning blame. They’re about capturing insights when they’re fresh: What worked well? Where did the client stall? What signals could we have caught earlier? The results often feed nurture campaigns or trigger future re-engagement plays. 📌 Loss Review Agenda Template: Deal summary (from AE and SE) Reasons for loss (competitor / inaction / budget)

Overcoming Data Challenges in Marketing: Navigating Privacy, Silos & Insights A conversation with Liana Dubois, Chief Marketing Officer at Nine. Marketing has always been about knowing your customer. But in today’s digital-first world, marketers are drowning in more data than ever before—spread across platforms, governed by shifting privacy laws, and often trapped in silos that make it impossible to see the whole picture. “Just because you can doesn’t mean you should.” That’s how Liana Dubois, Chief Marketing Officer at Nine, frames the challenge. With over 12 years at Australia’s largest locally owned media organization, Liana has lived through the industry’s transition from siloed datasets to a unified, privacy-first strategy built on first-party data. In this conversation, she breaks down the realities of marketing data today: how to extract insight from information overload, why first-party data is the cornerstone of personalized marketing, and why creativity—not just algorithms—remains the beating heart of growth. Facebook Twitter Youtube The Data Dilemma At Nine, the challenge is on a massive scale. With television, radio, publishing, marketplaces, and a streaming service (Stan, Australia’s answer to Netflix), the company has touchpoints with nearly every Australian. In fact, 22 million of the country’s 27 million residents are signed in to one of Nine’s platforms. That scale is a marketer’s dream—and nightmare. “Having a 22 million-person dataset is wonderful,” Liana says, “but it doesn’t give me all the answers. It tells me who I’ve got, how many I’ve got, and what they’re doing on our platforms. But it doesn’t tell me why they’re with me, or what they do when they’re not.” Here lies the trap many marketers fall into: mistaking data points for insights. Numbers can tell you what is happening, but not why. And if you don’t understand the why, you can’t design strategies that deepen loyalty or attract the next wave of audiences. From Data to Insight: Data = The What (e.g., “1M users watched Nine Now last night”) Insight = The Why + How (e.g., “They watched reality TV for social connection—so let’s design campaigns that tap into that human truth”). Breaking Down the Walls Nine didn’t always have this holistic view. The company was once four separate businesses—TV, publishing, radio, and streaming—each operating with their own datasets. The turning point was implementing an Adobe Customer Data Platform (CDP), which allowed Nine to collapse silos into a single customer view. “The CDP has been paramount,” Liana explains. “It’s the only way we could truly see audiences moving between the Sydney Morning Herald, Nine Now, and our other brands. Without it, we’d still be flying blind.” For organizations still wrestling with siloed data, her advice is blunt: make a CDP your first investment. The Power of a CDP: Before → Four isolated businesses, fragmented data. After → Unified customer view, enabling personalized journeys and smarter monetization. https://www.youtube.com/watch?v=1dEQKoC61Bc&t=10s The Rise of First-Party Data If data is the fuel of modern marketing, then first-party data is the premium grade. Nine made a strategic choice years ago: requiring logins across platforms. At the time, it felt risky. Today, it feels visionary. “Whether or not cookies sunset doesn’t matter,” Liana says. “First-party data, treated ethically and with a privacy-first lens, will only become more important.” For brands relying heavily on third-party data, this is the wake-up call. Consumers are increasingly selective about who they share data with, and governments are tightening regulations. Only those who build trust and collect data transparently will thrive. Why First-Party Data Wins: ✅ Owned & durable ✅ Privacy-compliant ✅ Higher accuracy ✅ Stronger personalization ❌ No reliance on third-party cookies The Privacy-Personalization Balance Marketers are obsessed with personalization. But done wrong, it crosses into “creepy” territory. “If I’ve bought a polka-dot blouse, why am I still stalked around the internet by polka-dot blouse ads?” Liana laughs. “That’s not helpful. That’s just lazy targeting.” Her recommendation: avoid over-indexing on micro-targeting. Hyper-granular personalization may squeeze short-term gains, but it fails to nurture long-term demand. Instead, Liana advocates for cohort-based targeting at scale—big enough to avoid creepiness, broad enough to capture future demand, yet precise enough to feel relevant. Targeting Spectrum: ❌ Micro-Targeting → Creepy, short-term ROI ✅ Cohort Targeting → Balanced, scalable, future-proof Measuring What Matters With over a century of legacy across publishing, radio, and TV, Nine doesn’t just measure clicks or impressions. It measures brand equity and audience trust—metrics that can’t be captured in an overnight ratings report. “We fall victim to treasuring what we measure,” Liana warns. “Instead, we need to measure what we treasure.” Her approach mirrors the brands that advertise on Nine. McDonald’s, Uber, Audi—they don’t just measure transactions. They measure growth in customer base, frequency of engagement, and emotional resonance. Balanced Marketing Scorecard: Metric Type Example Why It Matters Audience Growth New viewers, subscribers Expands reach Engagement Time spent, repeat visits Builds loyalty Brand Health Awareness, trust, salience Long-term equity Commercial Outcomes Ad revenue, conversions Ties marketing to business goals AI, Ethics, and the Future Like most CMOs, Liana is excited by AI—but cautious. “AI will only ever be as good as its tradesperson. We’re probably in peak hype cycle now. Eventually it will normalize, like the internet or data once did.” She sees ethics becoming a dominant theme in marketing tech’s future. In fact, she predicts the rise of a new role: the Chief Ethics Officer. Back to the Heart of Marketing: Creativity Despite the hype around data and AI, Liana’s closing message is simple: marketing is still about humans. “Humans buy on emotion and justify with fact. Let’s bring back a renaissance of creativity—storytelling that makes people feel something. Because that’s what drives growth.” In her view, data should inform creativity, not replace it. The best campaigns are powered by insights but carried by emotion. 📑 Template: Creativity + Data Playbook Use data to uncover insights (the “why”) Translate into human truths Build campaigns rooted in emotion & storytelling Measure both brand impact & performance metrics Final Word In a world obsessed with dashboards, data lakes, and martech

Beyond the MQL: A Blueprint for Buying Group Marketing, ABM Evolution, & AI-Powered Growth A conversation with Leslie Alore, SVP Marketing at Flexera. In B2B marketing, traditional lead-based funnels are no longer sufficient to capture the complexity of modern buying behaviors. Decisions are increasingly made by groups of stakeholders, each with unique priorities, influence, and timelines. This has rendered the singular MQL metric inadequate. Leslie Alore, Senior Vice President of Marketing at Flexera, has taken a bold stance on rethinking marketing performance metrics, aligning go-to-market teams, and leveraging AI to better engage buying groups. In a recent episode of The Revenue Lounge, Leslie outlined how she has redefined what marketing success looks like, how she operationalizes ABM for platform sales, and why AI is central to the next evolution of buyer engagement. Facebook Twitter Youtube Rethinking the Role of MQLs Leslie begins with a candid admission: marketers have done themselves a disservice by elevating MQLs to the primary measure of marketing’s contribution. At Flexera, she has radically narrowed the definition of an MQL to focus only on true ‘hand-raisers’—prospects who explicitly request a sales interaction, whether that’s a demo request, a meeting with a product expert, or a direct booking with a sales rep. “An MQL is somebody who requests something that results in a sales meeting. They ask for a demo, they ask to talk to an expert, they book a meeting. That’s it.” – Leslie Alore By tightening the definition, her team was able to dramatically improve response times, sharpen SDR focus, and boost conversion rates. This approach doesn’t discount other engaged contacts—such as those who download content or attend webinars—but these interactions are used to warm accounts for future outreach rather than being sent immediately to sales. The goal is to avoid SDR burnout and focus resources where buying intent is real. Moving from Vanity Metrics to Business Impact To ensure marketing’s performance aligns with business priorities, Leslie implemented a three-tiered scorecard: “Metrics matter, but they should reflect how marketing drives the business forward—not just how many activities we can check off.” – Leslie Alore https://www.youtube.com/watch?v=L8AuFPnUmog ABM Beyond Marketing Leslie is quick to point out that ABM should not be viewed as a marketing initiative alone—it’s a holistic business strategy. In platform-selling scenarios, where multiple point solutions target different stakeholders, understanding and mapping buying groups is essential. Her process starts with: Defining the Ideal Customer Profile (ICP) for each solution. Identifying users, buyers, and influencers for each product. Analyzing overlaps across solutions to reveal the best platform-fit accounts. “Sometimes, the influencer might not be involved in saying yes, but they can absolutely say no.” – Leslie Alore Balancing Demand Capture and Generation Applying the 95-5 rule, Leslie notes that only a small fraction of target accounts are actively in-market at any given time. Flexera’s strategy is to: Capture Demand Aggressively for in-market accounts through coordinated “swarming” of stakeholders by marketing, SDRs, and sales. Generate Future Demand with out-of-market accounts through thought leadership, education, and brand reinforcement until they’re ready to buy. This ensures short-term pipeline health while building long-term growth momentum. Harnessing AI for Speed, Scale, and Insight Leslie identifies three vectors for AI in marketing: Improving Marketing Productivity – Using generative AI tools like Writer to reduce content production timelines from weeks to hours. Enabling Customer Outcomes – Embedding AI-driven capabilities in Flexera’s own products. Adapting to Buyer Behavior – Responding to how buyers themselves are using AI to research and evaluate solutions. Predictive analytics tools like 6sense help Flexera interpret first-, second-, and third-party buying signals, enabling the team to prioritize accounts with greater accuracy. “If you’re not great at capturing demand, you have no business trying to generate it.” – Leslie Alore Key Lessons from Leslie Alore’s Approach Redefine MQLs to prioritize genuine buying intent and improve SDR efficiency. Align metrics in tiers to connect marketing measurement directly to business impact. Treat ABM as an enterprise-wide strategy, not just a marketing program. Balance demand capture with long-term demand generation for sustained growth. Leverage AI both to optimize marketing execution and to respond to shifting buyer behaviors. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

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

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

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