The MQL was never a revenue metric it was an activity metric dressed up to look like one. This data-backed article examines why the Marketing Qualified Lead has lost its forecasting value in 2026 and lays out the three capabilities now replacing it across high-performing B2B revenue teams: AI propensity scoring, account-level attribution, and pipeline contribution.
For a rigorous, data-grounded breakdown of what comes after the MQL and what it takes to actually make the transition stick, read the full article here.
Most sales teams run on a quiet assumption: top performers are born, not built. Salesloft's analysis of 10 million opportunities challenges that directly and argues that the real gap between elite reps and everyone else is not talent but execution, and that AI now makes elite execution replicable. This playbook delivers five concrete, data-backed moves sales managers can act on immediately to systematically improve rep performance and revenue predictability.
The five moves are grounded in Salesloft's own research. The top 10% of reps drive 65% of all revenue, while the bottom half contribute less than 8% a gap the playbook treats as a solvable execution problem rather than a talent problem. The first move addresses large deals, showing that $500K+ opportunities close only 15 days slower than $50K–$100K deals, making deal avoidance an understandable but costly habit. The second targets expansion revenue, which contributes 13% of won revenue per quarter and closes roughly 40 days faster than new logos. Moves three through five cover how to build structured pipeline cadences (teams using them close 15% more deals), how to identify and seal the revenue leaks that 98% of companies don't track, and how to coach with diagnostic precision separating skill gaps from knowledge gaps and stalled-deal patterns from genuine close risk.
Take a look at the complete playbook here to access all five moves, the templates that support them, and the research behind them.
Four in five sales and finance leaders missed at least one quarterly forecast last year and the method is rarely the problem. This comprehensive guide covers the four main sales forecasting approaches, explains precisely why most forecasts fail despite access to CRM data and pipeline tools, and introduces buyer engagement signals as the missing layer that separates forecasts that hold up from those that don't.
The piece examines the structural limits of CRM-based forecasting: deal stages capture what reps enter into a system, not what buyers are actually doing. By the time a deal slips its close date, the miss was already weeks in the making. The guide introduces leading indicators stakeholder depth, activity recency, engagement decay, and mutual action plan progress that consistently predict close rates more reliably than stage labels. A worked example forecasts the same five-deal pipeline twice: once using standard stage probabilities, once weighted by deal room engagement data. The two numbers diverge significantly, and more importantly, the second pass identifies which specific deals need immediate attention rather than discovering it at quarter close.
If forecasting accuracy is a persistent challenge for your revenue team, explore the full guide here for a step-by-step framework that moves beyond rep-submitted data.
Consumption-based pricing breaks almost every assumption that traditional SaaS RevOps is built on and this episode delivers one of the most specific, technically detailed accounts of what that actually looks like in practice. Markus Jaensch, Head of RevOps at Aiven, joins hosts Janis and Philipp to walk through how their RevOps model evolved over four years as the business crossed $100M ARR on an open-source data platform.
Markus explains why a "win" in a consumption model doesn't equal predictable revenue, and how Aiven eventually moved away from pure ARR forecasting back to bookings plus finance-modeled ARR. He walks through the three forecast buckets (new business, add-on, and commit contracts), how MEDDPICC with mandatory mutual success plans functions as a red-flag system, and the two non-negotiables required to reach close-won status: a valid payment method and three consecutive days of consumption. The episode also covers how the Farmer/Hunter model evolved, how comp was designed using bookings as a sanity metric with ARR as the paid metric, and why RevOps owning the close-won gate creates real organizational tension but is worth it.
This is required listening for any RevOps leader operating in or moving toward consumption-based pricing models. Access the full episode here.
The MQL is B2B marketing's worst open secret everyone in the room knows the number is gamed, yet the entire GTM playbook still runs on it. This live workshop recording features Jon Miller, co-founder of Marketo and Engagio, in conversation with hosts Carolyn and Amber, walking through anonymized customer data that quantifies exactly what over-reliance on the MQL costs revenue teams.
Jon traces the MQL's rise from a sound operational idea to a volume game that sales learned to ignore, introduces the "gumball machine fallacy" to explain why more budget input no longer produces proportional pipeline output, and presents a three-tier lead model designed to preserve first-mover advantage without forfeiting future pipeline from demand that isn't ready yet. The data they present is direct: hand raisers in one account converted 83 times better than MQLs and qualified faster, while standard MQLs that didn't convert were worked for two months before anyone disqualified them. The workshop also covers the KPI shift leaders are making toward pipeline velocity, brand-question surveys, and post-sale revenue metrics and what the transition means for MarTech stacks as AI moves orchestration from rules to reasoning.
To see exactly what the shift away from the MQL looks like in practice with real data and a clear replacement framework watch the full workshop recording here.
RevOps career paths are still poorly mapped and this episode cuts through the ambiguity with a grounded, experience-driven account of what the function actually requires, what it pays, and how to position for it regardless of where you're starting. Host Chad Tabary interviews Lauren Mischke, Head of RevOps at Sunday, for a practical conversation that works equally well for aspiring practitioners and leaders trying to hire them correctly.
For anyone considering a move into RevOps or looking to understand the career landscape more clearly, listen to the full conversation here.
Marketing analytics is no longer a reporting function it's a strategic lever. This webinar, hosted by RevOps Co-op in partnership with CaliberMind, challenges marketing and RevOps leaders to reassess what actually matters heading into H2 2026, reframing analytics as the foundation for speaking the language of business leadership rather than producing dashboards no one acts on.
The session covers what has actually changed in marketing analytics AI during the first half of 2026, how to evolve from a reporting mindset to a data storytelling posture that drives executive alignment, and practical frameworks for measuring what matters while eliminating analytics work that generates activity without insight. Attendees will also explore what it takes to become an indispensable strategic partner to leadership using analytics and what "doing more by doing less" looks like when analytics is properly scoped and prioritized.
If your team is ready to move from analytics as a chore to analytics as a competitive advantage, register for the full session here.
Your AI is running. Your ROI is not. That tension is exactly what this live 20-minute session is built to diagnose. Mountainise CEO Jalil Nawaz and Director of RevOps Saqib Anjum walk through the five RevOps infrastructure gaps that are quietly killing enterprise AI returns in 2026 and deliver a ranked action plan that tells you exactly where to start fixing them.
Attendees walk away with a clear answer to which of the five problems is costing their organization the most, a ranked action plan based on which fix unlocks the most revenue, and the exact numbers to make the case to a board or CFO. The framework is diagnosed live on a real enterprise environment during the session, not explained in the abstract.
Don't miss this session if you've already invested in AI tools but haven't seen the returns. Reserve your seat here and walk away with a working diagnosis, not just a slide deck.