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Buying Group, RevOps

How Nektar Automates Buying Committee Engagement

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How Nektar Automates Buying Committee Engagement RevOps 11 min Updated: August 4, 2026 Salesforce’s data model has three objects relevant to a buying committee: Account, Contact, and Opportunity Contact Role. A Contact is a person. An OCR is a static label, “Decision Maker,” “Influencer”, attached to an Opportunity. That’s the entire model. It was built to store contacts. It was never built to map a buying group, and that distinction is architectural, not semantic. The average enterprise deal involves 6 to 10 decision participants. The average CRM opportunity has one or two OCRs. A rep who wants to log a third has to remember to create it, assign a role, and keep it current by hand. In practice, almost nobody does this consistently, and the gap between what’s actually happening in a deal and what’s recorded in Salesforce quietly compounds. Get our latest insights into your inbox Why This Gap Exists, and Why It’s Getting Worse An OCR captures a label at a single point in time. It doesn’t track whether that person is engaging, disengaging, newly arrived, or gone. A contact tagged “Champion” who stopped responding three weeks ago looks identical in Salesforce to one who replied yesterday. The label exists. The signal behind it doesn’t. Every system that reads CRM data inherits this blind spot: forecasting models, ABM platforms, customer success tools, attribution engines, and increasingly, AI agents. When a human reads an incomplete CRM record, they can sense-check it, ask a follow-up question, or lean on institutional memory. When an AI agent reads the same record, it takes it at face value. If Salesforce shows one contact on a deal, the agent plans around one contact. Salesforce’s own April 2026 Headless 360 initiative made every core Salesforce capability available as an API, specifically so agents can read, write, and execute workflows without a human opening a browser first. That’s a real shift in what an incomplete buying group actually costs. It used to produce a slightly-off report a manager could catch. Now it can produce a wrong decision made by software, at a speed nobody catches in time. What Breaks Across the Business When Buying Groups Go Unmapped The gap above doesn’t create one problem. It creates a different failure in every function that touches CRM data. In sales, a rep who inherits an account from a departing colleague gets three logged contacts on a key opportunity when the real buying group was nine. Months of relationship context, who the real blocker was, which VP had a back-channel, evaporate the moment the previous rep leaves. A closed-lost re-engagement trigger fires on the one contact still in the system, while the five or six other people who actually shaped that decision were never captured at all. In sales leadership and forecasting, a CRO’s mandate to “multithread every deal above $200K” has no way to be measured, since there’s no data showing whether a labeled “Executive Sponsor” has attended a meeting in the last month or gone quiet. Forecast models score deals on the activity they can see, which typically represents a fraction of what’s actually happening in the account. In marketing, a VIP re-engagement campaign gets built from CRM contacts that cover maybe one or two people per account, while the procurement lead and technical evaluator who actually shaped the original decision never make it onto the list. A prospect’s CFO can attend a webinar six weeks before close and never show up in an attribution report, because nobody added them as an OCR. In customer success, a champion leaves for a new role three months before renewal, and the team discovers there’s no fallback relationship, because nobody ever recorded who else in the account cared about the outcome. QBR invites go to the two or three operational contacts CS already knows, while the executives who cared about strategic outcomes were never mapped in the first place. In RevOps, a meaningful share of the team’s time goes to chasing and cleaning contact data that was incomplete by design from the start, since adding more contacts manually doesn’t solve a problem that’s actually about missing engagement context, not missing names. How Nektar Automates Buying Group Engagement Nektar’s approach starts from a different premise than “get reps to log more contacts.” The data already exists, it’s in the emails, the calendar invites, and the meeting attendee lists generated by normal sales activity every day. What’s missing is the layer that captures it, structures it, attributes it to the right opportunity, and writes it into Salesforce automatically. Four capabilities do this end to end: 1. Automated Opportunity Contact Role Creation Nektar automatically identifies stakeholders from real email and meeting activity and creates the corresponding Opportunity Contact Role, with no rep input required. Every relevant person gets documented and correctly associated with the opportunity as the relationship develops, not just the one or two contacts a rep remembered to add manually. Why it matters: Eliminates the manual entry that made OCR data unreliable in the first place, keeps contact roles accurate and consistent rather than dependent on rep memory, and gives sales and RevOps leadership a real, current view of who’s actually involved in a deal. 2. Conditional OCR Contact roles get created based on specific, predefined conditions your team actually cares about, not a one-size-fits-all rule. Different sales teams or business units can tailor exactly when and how a contact role gets assigned. Why it matters: Adapts to your specific sales process rather than forcing a generic structure onto it, keeps contact-role creation focused on genuinely relevant people rather than every name that appears in an inbox, and scales to complex sales environments with large, varied buying committees. 3. Intelligent Meeting Tagging Meetings get automatically tagged with relevant context, using AI to identify key details and associate them with the right contacts and opportunities, closing the gap between what happened in a meeting and what’s actually recorded about it. Why it matters: Surfaces real insight into what a meeting actually

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Buying Group

The Buying Group Playbook

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Playbook The Buying Group Playbook​ A practical guide to adopting opportunity-based marketing.​ Stop chasing MQLs. Start tracking the committee that actually buys. Only 1% of MQLs convert to revenue. Meanwhile, the average B2B deal runs through 6-10 stakeholders who never show up as a lead score. This playbook pulls together conversations from The Revenue Lounge with GTM leaders at Palo Alto Networks, Reltio, and G2 on why they walked away from lead-centric marketing and rebuilt their funnel around buying groups instead. What you’ll take away: Why coverage, campaign-to-opportunity influence, and buying group completeness are replacing MQL volume, lead scores, and email opens as the metrics that matter. The four buying signals to check for any account. A stakeholder role map and buying signal tracker template you can put to use immediately. Real numbers: 2.4x larger deal sizes, 22-23% faster sales cycles, and 60% less pipeline fallout when buying groups are fully mapped. How to handle the objections you’ll get internally with pilot-first responses that don’t require new budget. Get the full breakdown, templates, and results included. Turn Buying Group Theory Into Revenue Impact​ Explore Buying Group Usecase See how leading GTM teams build complete buying groups without relying on manual CRM updates with Nektar Download The Playbook From The Revenue Lounge Podcast​ Related Webinars Ready to Turn your Data into Revenue Outcomes? Book a Demo

Buying Group, GTM, Marketing

Diversifying GTM with Buying Groups, ABM, & Operational Excellence

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Diversifying GTM With Buying Groups, ABM & Operational Excellence A conversation with Maria Thibodaux, Vice President, Marketing Strategy & Operations at Solarwinds. Executive Summary SolarWinds’ VP of Marketing Strategy & Operations, Maria Thibodaux, outlines how the company is modernizing B2B marketing without discarding proven levers. The MQL remains useful, but only as one node in a diversified system that also includes ABX and buying-group engagement, events and channel routes to market, and brand building to earn a spot in the consideration set long before form fills. The operating model leans on governed processes, interoperable tech, and data storytelling to connect disparate signals into decisions sales can act on. AI is applied pragmatically—first to reduce manual load in lead management and routing, then to pilot agents that support expansion and renewals. Throughout, sales and marketing co-own platforms and outcomes, trading “handoffs” for shared accountability. Key points at a glance MQLs evolve, not vanish: Treat them as demand capture while ABX and buying-group motions drive depth and coverage. Portfolio GTM: Balance always-on digital with targeted ABX, buying-group plays, in-person events, and channel to reach specific accounts and regions. Brand + demand balance: Protect brand investment so you are in the top-three consideration set when buyers self-educate. Data storytelling over dashboards: Custom schemas and connected data (CRM, MAP, intent, events) mirror the real GTM motion and explain outcomes credibly. Pragmatic AI: Start with high-impact efficiency (lead management, routing, follow-ups), then test agents for expansion and renewals. Tight sales–marketing loop: Marketing runs key platforms used by sales and stays engaged post-launch to ensure adoption and results. Governance matters: Standardized codes, budgets, and processes make multi-touch measurement and experimentation possible at scale. Facebook Twitter Youtube Introduction Marketing leaders today are redefining how they measure success, allocate budgets, and engage buyers. For Maria Thibodaux, Vice President of Marketing Strategy and Operations at SolarWinds, the key isn’t in abandoning legacy metrics like MQLs — it’s about integrating them into a broader, more intelligent revenue ecosystem that reflects how modern buyers actually behave. In this in-depth conversation with The Revenue Lounge, Maria shares how SolarWinds is evolving its marketing operations, blending ABX and buying group strategies, optimizing its tech stack, and leveraging AI to create a seamless bridge between marketing, sales, and customer success. From Corporate Communications to Data-Driven Marketing Leadership Maria’s career began in financial services, where she managed corporate communications before pursuing an MBA at the University of Texas at Austin. That move — and a growing fascination with data-driven storytelling — led her to SolarWinds 14 years ago. “I’ve always loved how data tells a story,” Maria reflects. “It’s not just about reporting numbers; it’s about connecting dots that influence go-to-market strategy and customer experience.” Today, Maria oversees four critical pillars of marketing operations at SolarWinds: Marketing Technology: Managing the GTM tech ecosystem and ensuring alignment with sales systems. Marketing Operations: Driving governance, budget management, and process efficiency. Marketing Analytics: Measuring digital performance and campaign outcomes across the funnel. Strategy: Leading annual planning, QBRs, and strategic alignment across the 200-person global marketing organization. The MQL Isn’t Dead. It’s Just One Piece of the Puzzle There’s an ongoing debate in B2B about whether the MQL (Marketing Qualified Lead) is obsolete. Maria doesn’t see it that way. “MQLs aren’t dead. They’re just one part of a diversified demand portfolio,” she explains. “The real challenge is not choosing between MQLs, ABX, or buying groups — it’s figuring out how to make them all work together.” At SolarWinds, the marketing team treats MQLs as the demand capture mechanism — the “always-on digital storefront” that continues to attract individual buyers. But diversification comes from layered strategies designed for deeper engagement. https://www.youtube.com/watch?v=tKUoI32hsMw Diversifying the GTM Playbook SolarWinds’ go-to-market model is primarily inside sales-driven, but diversification extends through channels, global events, and localized marketing. “Our in-person events have been a big bet,” Maria says. “They help us meet customers where they are — and that’s especially effective in global markets.” The company’s “World Tour” series brings together customers and prospects in regional hubs, driving face-to-face engagement that digital channels often can’t replicate. These events strengthen both the brand and buying-group engagement. Capturing Buying Groups Beyond the Click For Maria, the key to engaging buying groups lies in understanding their internal dynamics. “In our business, practitioners are usually the advocates,” she explains. “They drive the conversation internally, but we still need to appeal to finance, security, and leadership stakeholders.” SolarWinds uses ABX programs to engage each layer of the buying team — from hands-on IT professionals evaluating product functionality to C-level decision makers focused on cost, security, and scalability. ABM and ABX: Same Concept, Smarter Tools Maria believes the concept of ABM hasn’t fundamentally changed — what’s transformed is the tooling. “ABM itself hasn’t evolved as much as the tools that support it,” she notes. “What’s changed is how we touch buyers — more channels, more personalization, more opportunities for engagement.” The rise of intent data and signals has made ABX smarter, allowing SolarWinds to identify readiness earlier and fine-tune engagement sequences. But Maria cautions against over-reliance on automation. “Signals can get noisy,” she says. “The magic is in combining automation with human interpretation to make those signals actionable.” Buyer Fatigue and the Rebirth of Brand Building With inboxes overflowing and LinkedIn feeds saturated, buyer fatigue is real. “We see it in unsubscribes and opt-outs,” Maria says. “The automation overload has made outreach less personal, and buyers are tuning out.” SolarWinds is responding by rebalancing brand and demand efforts. While digital demand capture continues, there’s renewed focus on brand marketing, PR, and event presence — the levers that strengthen recall and trust. “Being present where customers already participate matters,” Maria adds. “It’s about showing up consistently, not just showing up when you need something.” Tech Stack Evolution: From Manual to Intelligent Maria’s team is evolving their tech stack around efficiency, automation, and interoperability. The essentials remain — CRM, ERP, automation tools — but the focus is on AI-driven optimization. “Every year it’s

Buying Group, Sales

The Evolving Role of SDRs: Navigating Outbound Fatigue & Embracing Buying Groups

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The Evolving Role of SDRs: Navigating Outbound Fatigue & Embracing Buying Groups A conversation with Kelly Lichtenberger, VP Sales Development at HiBob. Sales development is at a crossroads. What once worked—mass sequences, endless dials, and a “spray-and-pray” approach, is now leading to diminishing returns. Buyers are fatigued, inboxes are overflowing, and SDRs are burning out. Yet, building top-of-funnel pipeline is still one of the most critical levers for revenue growth. So, how does the modern SDR team succeed in an environment defined by noise, automation, and shrinking buyer attention spans? In this episode of The Revenue Lounge, Kelly Lichtenberger, VP of Sales Development at HiBob and author of Prospect Like a Girl: Winning in Sales Using Your Emotional Intelligence Over Artificial Intelligence, shared her perspective on building authentic connections, leveraging emotional intelligence, and balancing technology with personalization. Here’s the detailed breakdown of her insights. Facebook Twitter Youtube From Phonebooks to AI: Kelly’s Journey Through Sales Development Kelly’s career started long before SDR platforms and LinkedIn existed. She recalls flipping through phone books, driving past office buildings, and tracking down numbers to call. “When I started my career, there was no Google, no AI. We literally had a phone book. If I drove down a highway and saw a new sign on a building, I’d try to figure out how to call them.” – Kelly Lichtenberger Her path took her from running her own outsourcing company to consulting, and ultimately to leading HiBob’s 60+ global SDR team. That breadth of experience shapes her philosophy today: technology should enhance—not replace—the human connection in sales. The Great Ignore: Why Outbound Fatigue Is Real Kelly calls today’s prospecting environment “heavy.” Before COVID, it took ~11 touches to reach a prospect. During COVID, that ballooned to ~18. Today, SDRs need 25–27 touches across 45 days to break through. And prospects are more sophisticated than ever: They recognize templated, generic messages instantly. They consume information across fragmented channels (email, phone, LinkedIn, mobile). They’re trained to hit “delete” on irrelevant outreach. “If you keep doing the same thing over and over with zero results, it’s the definition of insanity. You have to change it up. Personalization and creativity are the differentiators now.” – Kelly Lichtenberger https://www.youtube.com/watch?v=a8MVl8JiFiE Quality Over Quantity: Rethinking SDR KPIs For years, SDR success was measured in sheer volume—calls made, emails sent, meetings set. But Kelly warns that volume alone creates diminishing returns. At HiBob, her team focuses on: Meetings completed (not just scheduled) Pipeline acceptance rate (ensuring quality over filler opportunities) Multi-threading impact (how many stakeholders they can influence in a buying group) Personalization at Scale: The New SDR Playbook Kelly believes personalization isn’t optional anymore—it’s the SDR’s competitive edge. And it goes beyond “Hi {FirstName}” tokens. Some tactics HiBob SDRs use: LinkedIn signals: tracking job changes, posts, and shared connections. Video outreach: short, phone-friendly videos to stand out in a crowded inbox. Creativity tests: A/B testing creative messages, then templatizing winners into sequences. “Please keep saying the phone call is dead—because that’s where I win. But it’s not about feature dumps on voicemails. It’s about elevating your game and being interested, not interesting.” – Kelly Lichtenberger Emotional Intelligence > Artificial Intelligence Kelly’s book, Prospect Like a Girl, argues that emotional intelligence (EI) is more important than artificial intelligence (AI) in modern sales. While AI helps SDRs save time (e.g., autodialers, transcription, sequencing), the differentiator is still human connection. EI Builds Trust: Asking, “Maybe you can help me?” opens doors faster than product pitches. EI Reads the Room: SDRs must listen, not bulldoze. Prospects already know a lot before taking the call. EI Creates Curiosity: The goal isn’t to close in the first message—it’s to spark interest and earn the next touch. “No is as powerful as yes. Maybe is what kills the deal.” – Kelly Lichtenberger The Buying Group Motion: Moving Beyond MQLs Kelly echoes what many modern revenue leaders believe: the era of the individual MQL is over. HiBob’s team is experimenting with buying group strategies: Creating early-stage opportunity “containers” for accounts showing swarming signals. Engaging multiple champions instead of betting on one lead. Aligning with marketing to ensure SDRs aren’t just chasing scores but confirming initiatives with multiple stakeholders. What Traits Make a Successful SDR in 2025? Interestingly, HiBob often hires SDRs without sales experience. Kelly looks for traits over résumés: Coachability – willingness to be trained. Curiosity – ability to teach even their leaders new tools or perspectives. Resilience – grit to handle rejection and keep evolving. “Sales isn’t Friday golf and making money. It’s really hard work. But if someone shows me their why and willingness to be coached, I’ll give them a chance.” – Kelly Lichtenberger Will AI Replace SDRs? Kelly Says No The elephant in the room: will AI make SDRs obsolete? Kelly’s answer: absolutely not. AI is a productivity booster, not a replacement. Just like Netflix didn’t stop us from watching movies—it changed how we consume them—AI will change how SDRs prospect, not eliminate them. “If you as a human don’t learn how to work in both worlds—AI and human—you’re the one who will get replaced.” – Kelly Lichtenberger Key Takeaways for Modern SDR Leaders Outreach requires 25+ touches—design for persistence. Shift KPIs from activity metrics to pipeline quality. Personalization is a non-negotiable—test, learn, templatize. Emotional intelligence builds trust where AI cannot. Adopt buying group motions—multi-thread every deal. Hire for traits, not résumés. Coachability wins. AI will augment SDRs, not replace them—unless they refuse to adapt. Final Word Sales development isn’t dying—it’s evolving. The SDRs who embrace creativity, curiosity, and emotional intelligence will thrive, while those who cling to outdated, volume-heavy tactics will struggle. HiBob’s Kelly Lichtenberger reminds us that the human touch is still the ultimate differentiator in sales. “Be interested, not interesting.” – Kelly Lichtenberger Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

Buying Group, Marketing

Beyond the MQL: A Blueprint for Buying Group Marketing, ABM Evolution, & AI-Powered Growth

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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

Buying Group, Marketing

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

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From MQLs to Buying Groups: How Palo Alto Transformed its Funnel & Drove 15x Pipeline Impact A conversation with Lauren Daley, Director of Marketing Operations at Palo Alto Networks. “We all knew MQLs weren’t working. But we were still being measured by them. Something had to change.”— Lauren Daley, Director of Marketing Operations, Palo Alto Networks In an era where enterprise B2B buying is driven by committees, not individuals, most marketers still operate in a lead-centric, MQL-obsessed model. But at Palo Alto Networks — one of the world’s largest cybersecurity companies — a transformative shift has been quietly reshaping how demand generation connects to pipeline. Lauren Daley, Director of Marketing Operations, alongside Jeremy Schwartz, spearheaded one of the most impactful GTM transitions in recent memory: abandoning individual MQLs in favor of a buying group-driven strategy. This shift didn’t just improve pipeline metrics — it won Palo Alto Networks Forrester’s 2025 Demand and ABM Program of the Year and led to double- and triple-digit improvements in pipeline performance. Let’s walk through the detailed steps of this transformation, the cultural and technical pivots it required, and how you can apply Palo Alto’s approach to your organization. Facebook Twitter Youtube Why MQLs Failed to Deliver — And Why Buying Groups Matter For years, marketing has been measured by how many MQLs it can generate. But most B2B enterprise purchases aren’t made by individuals — they’re made by buying committees. At Palo Alto Networks, this was especially evident: they were selling multi-product, high-stakes cybersecurity solutions to government, healthcare, and large enterprises — all of which involve multiple stakeholders in the buying process. “We weren’t doing a good job of connecting all those signals, those buying group members, and packaging it in a way sellers could take action on. That was the disconnect.”— Lauren Daley Marketing teams were doing the hard work of engaging the right personas, but those efforts weren’t translating into revenue. Why? Because individual leads weren’t enough. A shift to buying groups was long overdue. The Journey Begins: From Pilot to Playbook The transformation started not with tech, but with people. Lauren and her team began small — launching a pilot focused on Business Development Representatives (BDRs) and enabling them to associate more stakeholders with each opportunity. “We didn’t boil the ocean. We started with the friendlies — people who immediately bought into the vision.”— Lauren Daley The early results were compelling enough to draw interest from other teams across the company, and that’s when momentum truly started to build. 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 Blog: How

mqls to buying groups
Buying Group, Marketing

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

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

buying group model
Buying Group, Marketing

Decoding the Buying Group Model: Strategies for Success

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

Buying Group, Marketing

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

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

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