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Marketing, Sales

How to Drive Sales and Marketing Alignment With Unified Data

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How to Drive Sales and Marketing Alignment With Unified Data Sales, Marketing 13 min Updated: August 14, 2026 Sales and marketing alignment is a huge, ongoing problem. Roughly 9 in 10 sales and marketing professionals say their teams are misaligned, worrying in a business environment that only moves faster every year. Because sales and marketing performance gets measured differently, both teams end up using different approaches and systems, producing disjointed content, maintaining a passive relationship, and setting disconnected goals. All of it leads to real revenue leakage. Get our latest insights into your inbox What Is Sales and Marketing Alignment? Sales and marketing alignment is the ongoing set of processes both teams use to collaborate seamlessly across the full range of revenue-generating activity, not a single initiative with a defined end date. It matters more in a digital, distributed business environment, where teams are scattered across geographies and increasingly remote. Alignment isn’t an outcome you reach once, it’s a continuing joint effort, and a large majority of sellers and marketers feel poor alignment actively hurts both the business and the customer experience. Misalignment most commonly shows up when marketing hands off leads without complete or current contact information, when both teams set disconnected revenue goals based on their own separate strategies, or when communication between the two is simply muddled. Cross-functional collaboration smooths these bumps out, and the first real step toward it is putting unified data at the center of the process. A Peek Into Unified Data Data volume keeps growing every year, and it’s growing faster than most estimates from even a few years ago suggested. The real challenge has never been generating data, it’s creating value from it. Nearly half of employees find it genuinely difficult to share information across teams because of poorly integrated systems, and the result is the same dirty data pattern showing up everywhere: incomplete, inaccurate, non-compliant, outdated, inconsistent. This doesn’t just hurt rep performance, it hurts the customer experience directly, a majority of reps believe organizational silos negatively affect how a prospect experiences their team. Unified data solves this by combining data from disparate sources and disconnected systems into a single, central view, capturing data across the sales and marketing tech stack so everyone works from the same picture. It also drives better customer engagement, reduced churn, and higher ARR. Forrester’s research highlights how much buyer engagement now happens before a deal closes: buyers commonly contact sellers five or more times before closing, expect instant answers to complex questions to shorten the cycle, and a large share do substantial independent research before ever talking to sales. That journey leaves behind data breadcrumbs with real insight in them, if a team is actually set up to see it. Making the most of that engagement means sales and marketing need continuous data exchange, not a periodic handoff. New prospect information keeps arriving, and it needs to be accumulated, managed, and kept current so both teams have it at their fingertips. That’s the specific problem unified data solves, and automation, paired with AI for contextual insight, is what makes it work at real scale. Marketing data sales can use: lead and organizational data, lead scoring and qualification results, customer behavior and intent analysis. Sales data marketing can use: connecting campaign objectives and performance directly to revenue, nurturing leads with genuinely relevant content, and building lookalike models of sales-ready leads for new prospecting. The Need for Sales and Marketing Alignment Misalignment between sales and marketing is estimated to cost businesses more than $1 trillion annually, a figure tracing back to Harvard Business Review and IDC research that’s still actively cited in current studies on this exact topic. Sales and marketing have conventionally operated as separate worlds, with different objectives, revenue goals, and processes, creating friction that keeps both from operating at their best. In today’s dynamic environment, where events like economic instability can shift the landscape quickly, a disconnected sales-marketing relationship simply isn’t viable for sustained growth anymore. The buying process itself keeps getting more complex, too. Businesses need to build trust and real relationships, and the buyer today is a buying group, key stakeholders across multiple departments, not one person. Gartner’s research puts the current average at 6 to 10 stakeholders per deal, with enterprise deals frequently reaching 17 or more, and a large majority of B2B deals now involve at least three buying-group members. That buying group wants to engage with multiple people on the seller’s side too, evaluating the company, its process, and its offering as a whole, not just one rep’s individual pitch. As the buying process evolves, so does the customer journey. Traditionally, that journey ended at deal closure. In the modern, bow-tie-shaped funnel, it continues well past close, into onboarding, driving real impact, and sustaining growth, and for subscription businesses specifically, post-sale service and retention matter as much as the initial close, sometimes more. Most SaaS businesses rank customer retention as a genuinely high priority, and for good reason: happy customers are highly likely to purchase again, and companies with strong post-sale support see meaningfully higher repeat-customer rates. At the same time, buyers increasingly want less direct involvement from a rep during the process itself. A majority prefer a rep-free purchase experience where possible, and while selling has become more remote than ever, virtual sales execution still tends to fall short of the win rates teams actually expect. Sales and marketing alignment is a real part of solving that gap, but it has to be more than an exchange of information between the two teams, it has to be a genuine exchange of ideas. Best Practices for Sales and Marketing Alignment With Unified Data Organizations with genuinely well-aligned sales and marketing functions see measurably higher customer retention and higher sales win rates, and strong cross-functional collaboration has been linked to significant increases in key customer spend. Here’s how to actually get there. 1. Define Shared Goals and Strategies Start by establishing common ground through shared Objectives and Key Results,

Marketing, RevOps

10 Best Account Based Marketing Tools for 2026

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10 Best Account Based Marketing Tools for 2026 Marketing 10 min Updated: July 16, 2026 Account-based marketing tools have gotten better at execution but are not easier to run at scale. Multi-channel ABM campaigns still require real coordination between sales and marketing, and the tools in this category exist to make that coordination less painful, whether that means better targeting, better personalization, or better reporting on what’s actually working. Two things are worth knowing before you evaluate anything on this list. First, this category has consolidated meaningfully in the last few years, several tools that used to be independent are now part of larger platforms, and it’s worth knowing which is which before you sign a contract expecting the standalone product. Second, ABM’s underlying data problem has gotten a new dimension: as more marketing and sales tools add AI features on top of account and contact data, that data has to be genuinely accurate, not just directionally useful, or the AI layer amplifies whatever gaps are already there. Get our latest insights into your inbox What is ABM? Account-based marketing flips the traditional funnel. Instead of casting a wide net and qualifying leads down to a smaller set, marketing and sales work together from the start to: Identify high-value accounts that fit ICP criteria Engage them with personalized content Find and map the actual decision-makers involved Move them toward closure together Stay engaged post-sale to capture expansion opportunities ABM isn’t one-and-done selling. It’s built around customer lifetime value, which is also why the data behind it has to stay accurate well past the initial close. Marketing Attribution Usecase Uncover hidden first-party contacts to drive your ABM efforts with Nektar Map buyer group intelligence hidden in sales conversations Create personalized ABM Campaigns Discover hidden pipeline from first-arty contacts 10 ABM Tools for 2026 6sense Revenue AI, predictive account intelligence and journey orchestration HubSpot Marketing Hub, omnichannel personalization for HubSpot-native teams Demandbase, account-based experience across three integrated modules Terminus (now part of DemandScience), multi-channel ABM with native email-signature marketing RollWorks, ABM built around paid ad execution Foundry Intent (formerly Triblio), intent data and web personalization bundled with Foundry media Vainu, sales intelligence and account data for list building Apollo.io, prospecting, engagement, and ABM in one platform Uberflip, content personalization and distribution for ABM Alyce by Sendoso, AI-personalized corporate gifting for account engagement Overview of the 10 Best ABM Tools 1. 6sense revenue AI 6sense helps marketing teams identify high-value accounts, predict where they are in the buyer journey based on account activity, and engage them with the right message at the right touchpoint. Features: automatically updates contact lists with additional firmographic information, segments accounts into behavioral cohorts, tracks activity across channels and attributes it back to the account. Pricing: Custom, based on users and use case. 2. HubSpot Marketing Hub HubSpot Marketing Hub is an omnichannel marketing solution with particularly powerful personalization tools. You can use them to set up and automate hyper-targeted messaging across multiple touchpoints to reach and engage with specific prospects. Features: automatically segments contact lists based on customer criteria, spots prospects who mirror your top customers through lookalike lists, personalizes messaging across landing pages, emails, socials, and more. Pricing: Marketing Hub Starter: $7/user/month Marketing Hub Professional: $800/month Marketing Hub Enterprise: $3,600/month Free marketing tools with limited features are also available 3. Demandbase Demandbase runs on the Account-Based Experience concept across three connected modules: ABX Cloud for ABM strategy, Advertising Cloud for campaign management, and Data Cloud for integration support. Features: account-level insights for campaign execution, support for multiple ad formats across global markets, straightforward integration with existing stacks. Pricing: Custom, based on use case and team size. 4. Terminus (now part of DemandScience) Terminus merged into DemandScience in November 2024. The product continues to operate under the Terminus name, now backed by DemandScience’s broader B2B data and demand-generation assets, and still includes the native email-signature marketing (via its earlier Sigstr acquisition) that differentiates it from most other platforms on this list, turning every outbound employee email into an addressable ABM surface. Features: in-depth segmentation including buyer intent, multi-channel campaign support (ads, chat, email signatures, web personalization), a built-in B2B CDP. Pricing: Quote-based; third-party buyer data puts mid-market packages around $40,000 to $80,000 annually, with enterprise tiers higher. 5. AdRoll ABM (formerly RollWorks) RollWorks was fully rebranded to AdRoll ABM in August 2025, when parent company NextRoll unified its AdRoll and RollWorks brands into one platform. It’s the same product, team, data, and pricing as before, just operating under the AdRoll name, and remains a good fit for marketers who rely primarily on paid ads for account-based lead generation. Features: targeting recommendations based on historical campaign performance, account-to-decision-maker mapping with contact information, contextual account signals like org changes, mergers, and acquisitions. Pricing: Starter plan around $975/month; contact sales for other tiers. 6. Foundry Intent (formerly Triblio) Triblio was acquired by IDG, now Foundry, back in 2020, and the product has been sold as Foundry Intent for several years. If you’re evaluating this expecting the independent Triblio product, know upfront that pricing and packaging now tie more closely to Foundry media-spend commitments than the standalone product used to. It’s a strong fit specifically for enterprise tech and IT vendors already buying Foundry/IDG content syndication, since its intent data draws from IDG’s own editorial coverage areas (security, cloud, enterprise software) and is noticeably weaker outside them. Features: visual, drag-and-drop campaign builder, intent- and activity-based conversion probability scoring, web personalization bundled with Foundry’s media inventory. Pricing: Tied to media-spend commitments; contact Foundry directly. 7. Vainu Vainu is a sales intelligence tool for finding high-value accounts from its global company database, speeding up list-building with contextual account information. Features: targeted contact lists built from ICP filters, automatic updates as new contacts are found, a single consolidated view of contacts stored across other tools. Pricing: Free trial available. Team plan around €4,200/year, Business around €9,900/year, Global around €12,000/year, custom Enterprise pricing. 8. Apollo.io Apollo.io combines prospecting, campaign orchestration, and sales engagement, letting you

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

scaling gtm teams
GTM, Marketing

Scaling GTM Teams with Data-Driven Insights & Inclusive Leadership

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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)

data challenges in marketing
Marketing

Overcoming Data Challenges in Marketing: Navigating Privacy, Silos & Insight

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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

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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