From MQLs to Opportunity-Centric Revenue: How Reltio Transformed Its GTM Strategy A conversation with Joel Jacob, Director of Marketing Operations at Reltio. For years, marketing teams have been evaluated by MQLs (Marketing Qualified Leads). But as B2B buying behavior has evolved—where decisions are made by buying committees and involve longer, more complex journeys—the MQL metric no longer serves its original purpose. One person filling out a form doesn’t indicate true intent, and one lead doesn’t equal one deal. The Shift: From Leads to Opportunities Reltio, a leading B2B SaaS platform that unifies data for enterprise clients, realized this shift early. With a sales cycle averaging nine months and involving multiple stakeholders, their traditional lead-based funnel was no longer sustainable. Joel Jacob, Director of Marketing Operations at Reltio, shares how they transitioned from a legacy MQL-based model to a modern, opportunity-centric buying group strategy. This wasn’t just a process tweak—it was an end-to-end transformation of their go-to-market engine, completed in just 60 days. Facebook Twitter Youtube Why the MQL Model Failed Reltio Joel and his team began by diagnosing the inefficiencies of their MQL-centric process: 1% Conversion Rate: Only 1 out of every 100 MQLs was turning into closed-won revenue. Single-Threaded Opportunities: BDRs would often pursue individual leads without context, while AEs had to manually identify and involve the broader buying group. Misaligned Processes: Marketing, BDRs, and sales were working in silos, tracking separate KPIs and speaking different languages. High Customer Expectations: Their enterprise clients required a tailored, consultative approach, not generic drip campaigns and lead scoring. “We weren’t solving for how we sell. We needed to solve for how our customers buy.” What Changed: The Opportunity-Based Revenue Engine At the heart of Reltio’s new model is the concept of an opportunity container that is tracked from the very start of the buying journey. Key Components: Stage 0 Opportunities: Created proactively for cold target accounts to align all GTM efforts from the get-go. Buying Group Identification: Progress only happens when at least three relevant personas are identified within the opportunity. Unified Funnel Ownership: Marketing, BDRs, and AEs jointly own and advance each opportunity. Real-Time Intent + Historical Data: Powering personalized campaigns and outreach using platforms like 6sense and LeanData. Persona-Based Targeting: Ads and outreach are aligned with opportunity stage and key personas, not just job titles or industries. This model allows for marketing to target ads based on opportunity stage, for BDRs to tailor messaging using real-time insights, and for AEs to focus on qualified, committee-led opportunities.   https://www.youtube.com/watch?v=NPhOjO54wac&t=1s Overcoming Operational Hurdles Implementing this new strategy wasn’t without challenges: Time Constraint: The entire shift had to happen in just 60 days, before the start of the fiscal year. No New Tech: Reltio opted to re-architect their existing stack (Salesforce, Marketo, LeanData, 6sense) rather than buy new tools. Zero Downtime: The transition had to happen without interrupting live sales or BDR workflows. Team Alignment: Joel and team had to overcome deeply entrenched habits and misaligned incentives. “We stopped calling ourselves marketing or sales ops. We were just ‘operations’—unified behind a common goal.” Data Quality: The Real MVP Joel emphasized that none of this would have been possible without clean, connected data across marketing and sales systems. Years before the switch, Reltio had invested in data unification and intent platforms. That foundation paid off. Historical Data: Enabled predictive modeling via 6sense. Account-Centric View: Powered by LeanData and Salesforce to track buying group activity. No More Attribution Wars: Everyone works the same opportunities, making marketing influence clear without the blame game. “60 days gets the headlines, but that was only possible because we invested years into getting our data right.” The Role of AI in a Data-Ready World Joel’s team now uses AI to increase efficiency in key areas: BDR Enablement: Automating research and outreach so reps spend more time engaging and less time preparing. Predictive Signals: Using AI to model when an account is likely to move into an active buying cycle—based on engagement and historical patterns. Campaign Optimization: Automating content and ad delivery based on opportunity stage. But he warns: AI without good data is meaningless. “There is no AI without clean data. If you feed bad data into AI, you’ll just get bad results faster.” The Payoff: Faster Velocity, Better Pipeline Stickiness Reltio’s transformation delivered results fast: Pipeline Stickiness: Opportunities are more likely to progress and less likely to go dark. Faster Velocity: More deals now close within the same fiscal year, despite a 9-month average cycle. Better Alignment: GTM teams operate from the same playbook, improving efficiency and morale. Clear Attribution: Marketing and sales share credit instead of competing for it. Advice for Teams Looking to Make the Shift Joel’s parting advice for RevOps and marketing leaders: Let the Data Lead: Start with facts, not opinions. Use historical conversion rates to make the case for change. Collaborate Cross-Functionally: Ditch the silos. Align Ops, Sales, and Marketing under shared goals. Don’t Wait for Perfection: You don’t need a perfect tech stack. Use what you have and iterate. Train and Align Mindsets: It’s not just a systems change—it’s a mindset shift. Over-communicate and retrain internal teams on the new model. Stay Customer-Centric: Build your process around how your customers actually buy—not around your internal comfort zone. Final Thoughts Reltio’s journey proves that moving beyond MQLs is possible—and impactful. But it requires more than new tools or campaigns. It takes executive buy-in, operational discipline, and a deep commitment to aligning every team around opportunity creation and customer value. “We don’t talk about ABM anymore. We just call it the process. Because it’s how we work now.” Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

From MQLs to Buying Groups: How Palo Alto Networks Modernized Its GTM Engine A conversation with Jeremy Schwartz, Sr. Manager, Global Lead Management & Strategy at Palo Alto Networks In a rapidly evolving B2B landscape, where multiple stakeholders now shape buying decisions, relying solely on traditional MQL-based models no longer cuts it. At Palo Alto Networks, Jeremy Schwartz, Senior Manager of Global Lead Management & Strategy, has been spearheading a transformation—shifting the company from an outdated lead-centric model to a buying group-focused motion. This move hasn’t just modernized their go-to-market strategy; it’s delivered tangible business results. In this blog, we break down Palo Alto Networks’ journey, the challenges they faced, and the playbook they followed to build a scalable, revenue-generating buying group engine. Facebook Twitter Youtube The Problem: A Funnel Full of Waste Jeremy had a front-row seat to the inefficiencies of the MQL model. From his experience as a campaign strategist and now as a lead management leader, one thing was clear: MQLs were often vanity metrics. “You drive great MQLs that either don’t convert or get thrown back. The lowest person on the totem pole is often the MQL—and sales doesn’t want to waste time on them.” – Jeremy Schwartz Campaigns generated leads, but many never matured into opportunities. Even when they did, sales would frequently reject them, seeing little value in a lone networking admin reaching out. The funnel was leaking at every stage. The Aha Moment: Forrester’s B2B Revenue Waterfall The real turning point came when Jeremy attended a Forrester conference and learned about their B2B Opportunity Waterfall model. It flipped the focus from individuals to buying groups. Inspired, Jeremy returned and pitched the idea internally. His leadership responded with: “Run a pilot and show us what you find.” https://www.youtube.com/watch?v=Dx74q_tiIGg&t=4s Phase 1: Building the Pilot Palo Alto’s pilot kicked off with a 3-month research phase. The team mapped out what people, processes, and systems would be impacted, then aligned with Forrester to tailor the buying group model to their environment. People First They recruited BDRs across multiple GTMs (product go-to-markets), geographies, and segments to get a representative pilot group. At the same time, they analyzed two years of closed-won data to identify real buying group personas. “You don’t need to hire a consultancy to identify your buying groups. Look at your closed-won data—it’s all there.” – Jeremy Schwartz Process Discovery They identified two key BDR motions: Create new opportunities with multiple stakeholders. Add new engaged personas to existing opportunities. Both processes, however, were painfully manual—10+ steps each. Phase 2: Launch and Learn They ran the pilot for a full quarter. Initial triggers still came via MQLs, but BDRs were trained to: Check intent platforms (like Demandbase) Identify other engaged personas at the same account Multi-thread their outreach This approach led to: More meetings booked Better response rates (especially when referencing colleagues) Higher acceptance by AEs (thanks to meetings involving multiple roles) “Mentioning a colleague in an outreach email is real personalization. And it worked.” – Jeremy Schwartz The kicker? Deals with multiple stakeholders started closing—faster and at higher values. They presented the early pilot results to their CMO. The response? “That’s cute.” So the team partnered with data science to extrapolate the results across all opportunities. The model predicted a 13% revenue lift—assuming full buying group coverage. That got attention. “Suddenly, our CMO said, ‘Do more of that.’” – Jeremy Schwartz Phase 3: Automation and Scale To make the process scalable, they built automation in their internal cloud app: When a lead was accepted by a BDR, the system automatically identified other engaged personas from that account. These individuals were assigned to the same BDR for follow-up. Adding someone to an existing opportunity became a one-click process that even notified the AE. They also created custom dashboards to track metrics like: Number of opportunities with buying groups Deal size and velocity Incremental pipeline created Coverage across accounts and products By the end of their fiscal year, these automations were live globally across all BDR teams. Results That Mattered Here’s what Palo Alto Networks achieved by moving to a buying group model: Metric Improvement Deal Size 2.6x increase (in certain segments) Conversion to Pipeline Significant lift over MQL-only opps Closed-Won Rates Higher for opportunities with buying groups Pipeline Quality Larger, multi-threaded deals Coverage 12% of Q2 opps had buying groups; target = 15%   They also introduced new marketing metrics: Campaign-to-Opportunity: Replacing MQL-to-Opportunity Buying Group Coverage: How many personas per deal Buyer Representation Spread: Ensuring campaigns target multiple personas, not just admins “Our leadership is still MQL-obsessed, but now we’re reporting incremental pipeline and seeing influence in closed-won deals.” – Jeremy Schwartz Building the Future: A Signal-Based Scoring Model Palo Alto’s next frontier? Replacing lead scores with signal-based models using four dimensions: Fit: ICP match Intent: 1st, 2nd, and 3rd-party signals Engagement: Website visits, downloads, event participation Completeness: Buying group coverage per account “If three or more people are showing up from an account, with different titles, that’s a signal. You don’t wait for an MQL to act.” – Jeremy Schwartz Lessons Learned: What Jeremy Would Do Differently Push for executive alignment earlier Involve campaign marketers sooner after pilot results Don’t overdesign—start small, learn fast, course-correct Accept the reality of system complexity (especially in older enterprises) “Martech stacks are like Rome—layers upon layers built by different people over time. Nothing is clean.” – Jeremy Schwartz Advice for Companies Starting the Journey Start small with a controlled pilot. Use your own data to identify buying groups. Get BDRs involved first—they’re closest to pipeline creation. Automate before scaling. Show revenue impact, not just lead volume.   “Even if your leadership still chases MQLs, show them better conversion, deal size, and real revenue impact. That’s what moves the needle.” – Jeremy Schwartz Final Thoughts Palo Alto Networks didn’t just adopt a trendy new model. They operationalized a seismic shift in how revenue is created—by recognizing buying groups as the real unit of conversion in B2B. Whether you’re just starting

Welcome to The Revenue Lounge How to Influence Buying Groups with Data, Intent, and ABM A conversation with Sydney Sloan, Chief Market Officer at G2. B2B buying has transformed. What was once a one-on-one sales conversation is now a team sport, spanning roles, departments, and even geographies. Today’s buyers are informed, autonomous, and collaborative. They’re forming buying groups long before sales ever enters the conversation. And if your go-to-market (GTM) team isn’t aligned to this reality, you’re already playing catch-up. In this episode of The Revenue Lounge, Randy Likas sits down with Sydney Sloan, Chief Market Officer at G2, to unpack how marketing and sales teams can evolve to influence modern buying groups. She is a 4X CMO, board member and advisor with decades of experience in driving transformative growth and innovation for high-tech companies. Sydney offers a masterclass in using data, intent signals, and segmentation to win complex deals. Here’s a breakdown of the conversation—and why it matters. Facebook Twitter Youtube 🚨 Why Buying Groups Matter More Than Ever The traditional lead-based model is failing. As Sydney puts it, “MQLs are noise.” They flood sales with contacts that aren’t ready to buy—leading to frustration, wasted time, and missed opportunities. Instead, modern revenue teams must focus on identifying buying groups—clusters of stakeholders from the same account showing interest in your solution. These signals can come from downloading content, comparing vendors, visiting your pricing page, or just quietly researching on review platforms. A single lead might lie. But a buying group rarely does. “When you have executive alignment and more than three people in the buying cycle, close rates are 44% higher.”– Sydney Sloan, CMO, G2   🧠 Data Is the Foundation. But it Needs to Be Smart Sydney breaks down three types of intent data: Third-party: Activity across the open web (e.g., searches, keyword trends). Second-party: Data from trusted ecosystems like G2—category views, comparisons, reviews. First-party: Visitor behavior on your own website, CRM engagement history, and sales activity. The magic happens when you triangulate these data sources. For instance, if someone downloaded your whitepaper (first-party), compared your product with a competitor on G2 (second-party), and searched relevant terms online (third-party)—you’ve got a red-hot buying group signal. But here’s the catch: if your CRM is a mess or your systems are siloed, you’ll never connect those dots.   “There’s no excuse not to have tier 1 and 2 accounts built out with clean, up-to-date contacts across buying personas.” https://www.youtube.com/watch?v=NkYTDVKx5Eg 🔁 The New GTM Playbook: From Leads to Stakeholders Moving to a buying group strategy requires more than good data—it requires GTM alignment. Instead of chasing individual MQLs, Sydney recommends: Scoring accounts, not contacts. Tracking signals at the account level to prioritize outreach. Rethinking SDR metrics: focus on meetings with multiple personas, not just any meeting. Partnering marketing, sales, and product around a shared account strategy. Sydney shares how G2 moved to an account-based model where the sales team gets tailored engagement strategies based on segment (SMB, mid-market, enterprise). Every team member—from demand gen to product marketing—knows who their core personas are and how they relate to each other. 🧩 Operationalizing Buying Groups at Scale At Forrester’s recent event, a key theme emerged: evolving from “buying groups” to “buying networks.” This includes partners, peers, analysts, and ecosystems that influence buyer decisions. Sydney highlights a few scalable tactics to work with buying groups: Persona Workshops: G2 ran hands-on workshops using real Gong quotes to help every department internalize customer personas. Segmented Campaigns: Instead of generic ABM, G2 builds micro-segments like “Security companies using 6sense, not yet G2 customers,” and tailors messaging accordingly. Pipeline Meetings: Marketing, sales, and SDRs review the same data together bi-weekly to troubleshoot stuck opportunities and improve velocity. Deal Acceleration Programs: Everyone in stage 2 of the pipeline gets invited to bi-weekly virtual events to deepen relationships and drive conversion. ⚖️ Brand vs. Demand: It’s Not Either/Or Many companies struggle with where to invest: long-term brand or short-term pipeline. Sydney makes it clear: do both, early and often. Brand earns you a seat at the table. G2’s Buyer Behavior Report shows average vendor shortlists are down to just three. Demand capture turns that attention into pipeline. “Brand is giving something away with no ask. Demand is giving something away to capture a contact. Different plays, both essential.” 📈 Rethinking KPIs for Buying Group Success MQLs are out. So what’s in? Sydney advocates for shared KPIs across marketing and sales focused on: Pipeline creation Closed-won revenue Retention Internally, marketing can track velocity, lead-to-meeting time, and program-level cost-per-lead. But in cross-functional pipeline meetings, everyone should speak the same language: revenue. 🧹 The Data Problem: Why RevOps Must Lead One of the biggest blockers to activating buying group strategies is messy, siloed data. Marketing tools hoard information. Sales tools don’t sync well. And critical insights never make it to the opportunity record. The solution? A strong Revenue Operations team. “I’ve surrendered. Marketing Ops now sits in RevOps—and that’s a good thing. RevOps should own the data foundation.” Clean data doesn’t just support GTM alignment—it powers AI and automation. And as Sydney warns, “Bad data trains bad agents.” 🚀 Final Takeaways: Winning with Buying Groups Buying groups are real—and they convert better. Track and engage multiple stakeholders early. Use intent signals across data types. Build workflows that treat G2 comparisons and pricing page visits as bottom-of-funnel signals. Go beyond ABM. Focus on micro-segments to tell sharper, more personalized stories. Align GTM with shared KPIs. Eliminate the MQL silo and focus on revenue outcomes. Fix your data. Clean, enriched CRM data is essential for sales, marketing, and AI. Want to build a buying group motion that works? Start by getting your GTM teams aligned, your data house in order, and your content strategy laser-focused on each persona in the buying network. And if you’re still chasing MQLs, it might be time to hit pause—and rebuild for the way B2B buying actually works today. Want to hear more stories from revenue leaders? Subscribe to The