RevOps Lab

Weflow
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Welcome to the RevOps Lab - a podcast exploring the art & science of Revenue Operations. Every week, Philipp & Janis host RevOps professionals to discuss best practices and lessons learnt building scalable revenue engines. This show is for everyone interested in processes, tooling, enablement, and strategies to supercharge your GTM play. To find more episodes and resources on scaling your revenue engine, visit getweflow.com/revops

  1. 4d ago

    #123 Forecasting Consumption Revenue with High Accuracy – with Colin Gerber, VP RevOps at Socure

    Colin Gerber, VP RevOps & Strategy at Socure, returns for part two to unpack how his team forecasts revenue in a fully consumption-based business. He walks through the solution readiness document that substantiates every booking, the "BAR" (booked ARR realization) process that tracks ramp against reality, and why RevOps should own the forecasting system but not its accuracy. Plus: the comp structures that keep AEs and CSMs honest, and the two mistakes that blow the biggest holes in a consumption forecast. We cover: - How Socure prices ~35 modules by API-call consumption, not seats - Giving customers real-time visibility into what they're actually spending - The solution readiness document that turns a sales pitch into a forecastable number - Why AEs still call a bookings number — and when it gets challenged - Booking conservatively for pre-launch startups vs. mature, high-volume customers - Modeling seasonality into ramp schedules (BNPL, gaming, tax season) - Using live trials to close the gap between closed-won and go-live - The monthly BAR review with FP&A - Salesforce, JIRA, NetSuite and Tableau: the four-source forecasting stack - Booking discipline vs. realization — why bookings still matter in consumption pricing - Who actually owns the ramp: AEs, CSMs, and post-sales solution consultants - How 24-month payout schedules shape AE behavior on ramping accounts - The two mistakes that blow up a consumption-based forecast Colin Gerber: https://www.linkedin.com/in/colinsgerber/ Socure: https://www.socure.com/ Janis Zech: https://www.linkedin.com/in/janiszech/ Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Chapters: (00:01) Colin Gerber returns from Socure (02:37) How Socure prices consumption (06:08) Real-time visibility into spend (09:07) Why consumption pricing is spreading (10:49) Forecasting sessions & the solution readiness doc (15:07) Contract terms and seasonality (17:41) Live trials close the go-live gap (20:05) Ramp realization and revenue recognition (21:55) The four-source forecasting stack (25:05) Booking discipline vs. realization (29:14) Who owns the ramp (34:41) Two mistakes that wreck a forecast (39:07) RevOps shouldn't own forecast accuracy

    #123 Forecasting Consumption Revenue with High Accuracy – with Colin Gerber, VP RevOps at Socure

  2. Sep 21

    #122 What Leading Sales for 6 Months Taught Me About RevOps – with Nicole Bradshaw, Head of RevOps at PandaDoc

    Nicole Bradshaw spent six months as interim global sales leader at Eventbrite — and discovered how much she didn't know about her own job. She joins Janis to explain why RevOps teams know exactly what good selling looks like but never apply it to themselves, and why an order-taking RevOps function is the one most easily automated away. We cover: What changed when Nicole became her own stakeholder Why execs only want your top two problems, not all of them Balancing business impact against time to impact Using OKRs as a vehicle, not a solution Matching operating cadence to stakeholder level Strong opinions held loosely — showing up with a point of view Why order-taking RevOps is the easiest role to automate Bridging strategy and execution as RevOps' unique position Multi-threading inside your own company Picking the right champion for the right initiative Objection handling: anticipate, prepare, bring data Making people's lives easier before you need favors Framing every ask as a win-win Nicole Bradshaw on LinkedIn: https://www.linkedin.com/in/nkbradshaw/ PandaDoc: https://www.pandadoc.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Resource recommendation: Harvard Business Review — Nicole's habit of blocking 30 minutes every other week for professional development reading Chapters: (00:00) Intro & welcome to Nicole (00:37) Nicole's path into RevOps (03:08) Six months leading sales (04:43) Becoming your own stakeholder (07:34) Impact vs. time to impact (09:28) OKRs and operating cadence (13:08) Showing up with agency (17:19) AI-proofing the RevOps role (18:26) Bridging strategy and execution (22:56) Multi-threading and champions

    #122 What Leading Sales for 6 Months Taught Me About RevOps – with Nicole Bradshaw, Head of RevOps at PandaDoc

  3. Sep 14

    #121 How to Run a Global RevOps Org – with Dan Jiao, VP RevOps at Aircall

    Dan Jiao, VP RevOps at Aircall, has now moved two companies from a global functional model to regional GMs — at Signifyd and again at Aircall. He joins Janis to explain what actually changes when four business units each own their P&L, and why RevOps becomes the glue that keeps one company from quietly becoming five. We cover: Global functional vs. regional GM model — and what stays centralizedThe revenue scale where a regional split starts to make senseRunning RevOps as a product org: the global go-to-market operating systemWho owns segmentation vs. who co-designs territoriesEmbedding regional business partners as de facto chiefs of staffOne global scoring model, multiple regional calibrationsWhy capacity ratios differ market by marketRevOps as the neutral referee in budget and headcount debatesHolding one global forecasting standard across four business unitsBuyer-verified exit criteria instead of rep-filled fieldsSharing best practices when there's no global head of salesDan Jiao on LinkedIn: https://www.linkedin.com/in/danjiao/ Aircall: https://aircall.io Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/WeFlow: https://www.weflow.ai/Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Book recommendation: Switch: How to Change Things When Change Is Hard by Chip & Dan Heath Chapters: (00:00) Intro & welcome to Dan(02:33) Dan's path from sales to RevOps(04:46) Global functional vs. regional GM(08:12) When a regional split makes sense(11:18) Who owns segmentation and territories(14:04) The hybrid model and regional business partners(17:43) Account prioritization and scoring(20:49) Capacity planning across regions(25:53) RevOps at the budget table(27:52) One global forecasting standard(30:31) Buyer-verified exit criteria(32:43) Sharing best practices without a global sales lead(34:50) Book recommendation & close

    #121 How to Run a Global RevOps Org – with Dan Jiao, VP RevOps at Aircall

  4. Sep 7

    #120 How to become the AI Orchestrator of the GTM org – with Mollie Bodensteiner, VP RevOps at ZoomInfo

    Mollie Bodensteiner, VP RevOps at ZoomInfo and returning guest, joins Janis to break down how AI is reshaping RevOps roles, team structures, and career paths. RevOps used to report on the business — now it builds and governs the systems that run it. Mollie shares what the go-to-market engineer role actually is, why career ladders are turning into portfolios, and why she stopped doing case studies in hiring. We cover: From reactive help desk to building and governing the systems that run the business AI sprawl: why "give everyone access" now creates a governance problem What a go-to-market engineer really is — beyond the LinkedIn hype Agent ops: monitoring drift, QA, and cost like DevOps monitors uptime Why it's a skill set, not a new job title The analyst shift from firefighting to proactive intelligence Who should own AI: centralized, federated, or center of excellence Flatter orgs, wider spans, and horizontal layers replacing functional silos Career paths becoming portfolios instead of ladders Why Mollie killed case studies in her hiring process Skill vs. will — advice for early-career operators Mollie Bodensteiner on LinkedIn: https://www.linkedin.com/in/molliebodensteiner/ ZoomInfo: https://www.zoominfo.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Book recommendation: The Accountable Organization by John Marchica Chapters: (00:00) Intro & welcome back to Mollie (03:26) How RevOps jobs have changed (05:30) AI sprawl and the governance problem (08:43) The go-to-market engineer role (12:06) Agent ops and the digital workforce (16:25) What happens to the analyst (20:30) Who owns AI in the org? (24:19) Flatter teams, wider spans (28:20) Making RevOps impact measurable (31:43) Why case studies are dead in hiring (34:11) Skill vs. will: advice for operators (37:07) Book recommendation & close

    #120 How to become the AI Orchestrator of the GTM org – with Mollie Bodensteiner, VP RevOps at ZoomInfo

  5. Aug 31

    #119 CPQ in the AI era – with Colin Gerber, VP RevOps & Strategy at Socure

    Colin Gerber, VP RevOps & Strategy at Socure, breaks down 17 years of CPQ evolution — from Zuora's billing-wrapper days to today's consumption-ready, platform-agnostic tools. He shares how Socure pressure-tested its pricing in Excel for a year before building anything, and how a new entitlements object now ties CRM, CPQ, and billing into one source of truth. We cover: CPQ's three eras: Zuora → Salesforce CPQ → platform-agnostic tools like DealHub Pressure-testing pricing in Excel before implementing CPQ Socure's shift to à la carte, consumption-based pricing across 35 modules Common CPQ mistakes: over-complication and workflow bloat Scaling SKUs across ~170 countries RevOps' role: guardrails, deal desk, pre-deal margin modeling Using AI to auto-generate Solution Readiness Documents A bi-directional entitlements object unifying CRM, CPQ & billing Automated entitlement capture for PLG motions A preview of consumption-based forecasting Colin Gerber on LinkedIn: https://www.linkedin.com/in/colinsgerber/ Socure: https://www.socure.com Weflow: https://www.weflow.ai/ RevOps Chat Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Janis on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer/ Book recommendation: Team of Teams: New Rules of Engagement for a Complex World by General Stanley McChrystalChapters: (00:00) Intro & welcome to Colin (01:51) Colin's background (04:23) The three phases of CPQ history (10:00) CPQ challenges with consumption-based pricing (14:12) Common CPQ implementation mistakes (19:03) The role of RevOps in CPQ (21:19) Using AI in CPQ (25:21) Where's the system of truth? (32:26) Direct vs. self-service/PLG (34:44) Teaser: consumption-based forecasting (36:44) Book recommendation & close

    #119 CPQ in the AI era – with Colin Gerber, VP RevOps & Strategy at Socure

  6. Jun 29

    #118 Inside RevOps at a $100M+ ARR Consumption-Based Business – with Markus Jaensch, Head of RevOps at Aiven

    Markus Jaensch, Head of RevOps at Aiven, joins Janis and Philipp to unpack what consumption-based pricing actually means for a RevOps team running a $100M+ ARR business. Aiven — the open-source data platform behind managed Kafka, Postgres, OpenSearch, and ClickHouse — recently crossed $100M ARR, and Markus walks through how the forecasting model, comp design, and territory setup have evolved over 4.5 years to deal with the fundamental problem of consumption: a "win" doesn't equal revenue, and the next 12 months can swing either direction. We cover: Why consumption ARR is structurally harder to forecast than SaaS bookingsWhy Aiven moved away from pure ARR forecasting and back to bookings + finance-modeled ARRThe three forecast buckets: new business, add-on, and commit contractsUsing MEDDPICC with mandatory mutual success plans and CRM score as a red-flag signalT-shirt sizing (S/M/L/XL) with solution architects to anchor opportunity sizeThe two non-negotiables to close-won at Aiven: valid payment method + 3 consecutive days of consumptionWhy RevOps owns the close-won gate — and the tradeoff of being a controlling functionThe Farmer/Hunter evolution: from split, to mixed, to dedicated Inside Sales + named-territory Field repsComp design: bookings as a sanity metric, ARR as the paid metric, with new-ARR ramp-to-sizeStack: Salesforce + data warehouse, and why Aiven switched from spot ARR to a 30-day averageBuilding the process around the customer first, not internal convenienceMarkus Jaensch on LinkedIn: https://www.linkedin.com/in/markus-jaensch/ Aiven: https://aiven.io Weflow: https://www.weflow.ai RevOps Chat Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revopsletter Janis on LinkedIn: https://www.linkedin.com/in/janiszech Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer Book recommendation: The Café on the Edge of the World by John Strelecky Chapters: (00:00:00) Intro & Welcome to Markus(00:02:14) Why Consumption ARR Is the Topic — and Aiven Crossing $100M(00:03:39) Why Consumption ARR Is So Hard to Forecast(00:08:30) Bookings vs. ARR Forecasting — and Aiven's Evolution(00:12:53) MEDDPICC, Mutual Success Plans & Deal Hygiene(00:16:21) T-Shirt Sizing with Solution Architects(00:18:01) The 3-Day Consumption Rule for Close-Won(00:22:28) Modeling the ARR Ramp After a Booking(00:26:27) The Farmer/Hunter Evolution & Inside Sales Motion(00:29:37) Comp Design: Bookings, ARR, and Ramp-to-Size(00:31:00) Stack: Salesforce, Data Warehouse & 30-Day Average ARR(00:34:34) Final Learnings on Consumption Forecasting(00:36:29) The Customer-First Principle(00:38:02) Book Recommendation & Close

    #118 Inside RevOps at a $100M+ ARR Consumption-Based Business – with Markus Jaensch, Head of RevOps at Aiven

  7. May 11

    #117 Why Your Forecast Is Inaccurate (+ How to Fix It) – with Andy Smidmore, RevOps Leader (ex-Confluent, Cloudera, Docker, Ditto)

    In this episode of the RevOps Lab, Janis sits down with Andy Smidmore — a 15-year RevOps and SalesOps veteran whose résumé reads like a tour of high-growth West Coast SaaS (Confluent, Cloudera, Docker, most recently Ditto). Andy is also the author of a popular LinkedIn article series on RevOps fundamentals, including the piece that anchors today's conversation: Forecasting Is a Trust Problem Before a Math Problem. Janis and Andy unpack why the magic forecast number leadership wants is just the surface, why broken sales stages quietly leak forecast accuracy, and why mixing deal reviews into your forecast call destroys the very trust your reps need to be honest with you. We cover: Why forecasting is a trust problem first, a math problem second Why the "how much are you committing?" question on day one of a forecast call is the wrong starting point Building sales stages around the buyer's journey, not your selling process — and why misaligned stages are a leaking bucket The danger of performative deal progression and the false hope it creates for leadership Why deal reviews and forecast calls should be two separate motions "I'm not questioning you, I'm asking questions" — the subtle reframe that changes the whole room Crystal-clear definitions for pipeline vs. commit, kept short (5 bullets, not essays) What a true commit actually requires: technical sign-off, legal redlines, procurement engaged, stakeholder alignment Why a consistent week-in, week-out forecast format builds rep trust over time Treating forecasting as a team sport — and why punishing reps swings them toward concealment, not honesty Links: Andy Smidmore on LinkedIn: https://uk.linkedin.com/in/andy-smidmore-3aab0556 Andy's article series on RevOps & forecasting (LinkedIn) Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Book recommendations: Disrupted by Dan Lyons The Girl with Seven Names by Hyeonseo Lee Chapters: (00:00) Intro & welcome to Andy (00:51) Andy's 15-year journey through Confluent, Cloudera, Docker, Ditto (03:08) Why forecasting is the topic — and the article that started it (04:03) Why every forecast framework is unique to its company (06:15) Intelligence tools haven't replaced the foundational framework (08:13) "How much are you committing?" — the surface-level forecast call (10:01) The top forecast process problems Andy keeps seeing (11:33) Sales stages built around the buyer journey, not the seller (12:24) The POC stage problem: what does "done" actually mean? (14:25) When poor stage definitions destroy trust between reps and leadership (15:43) Creating an open, honest forecast call environment (18:17) From gut-based reviews to data-backed conversations (20:22) Why deal reviews and forecast calls must be separate motions (23:25) "I'm not questioning you, I'm asking questions" (25:00) The cadence: clean pipeline Thursday, deal review Friday, forecast Monday (28:32) Building a clear forecasting methodology — pipeline vs. commit (31:34) What actually constitutes a commit deal (35:21) Closing thoughts: stages, enablement, cadence, culture (39:35) Book recommendations & close

    #117 Why Your Forecast Is Inaccurate (+ How to Fix It) – with Andy Smidmore, RevOps Leader (ex-Confluent, Cloudera, Docker, Ditto)

  8. May 4

    #116 Sales Forecasting in the Age of AI – with Janis Zech & Philipp Stelzer (WeFlow)

    In this host-only episode of the RevOps Lab, Janis and Philipp take stock of what three years of building a forecasting tool — and hundreds of conversations with sales leaders, RevOps teams, and CROs — have taught them about getting to a reliable, repeatable forecast number. They unpack why forecasting is a process (not a number), why AI only works on top of a clean data foundation, and how the best companies combine roll-up, dynamically weighted, and AI-predicted forecasts into a single operating cadence. We cover: Why forecasting accuracy is the output of a well-run sales org, not the input The operating cadence: weekly meetings, deal reviews, and stakeholder alignment that make forecasting work Building the data foundation: activity capture, multi-threading signals, conversation intelligence, and CRM autofill Why deal hygiene and shared qualification criteria (SPICED, MEDDIC) are non-negotiable before you forecast Splitting the forecast into new logo, expansion, and renewal — and why bookings ≠ consumption The three-pillar forecast: dynamically weighted + bottom-up roll-up + AI prediction (with corridors, not single numbers) How to run a roll-up motion: baseline vs. best case, rep forecast vs. independent manager forecast Why running roll-ups in spreadsheets breaks down at 50+ reps Calculating dynamic stage probabilities by rep tenure or team — and when it's worth doing Why AI predictions are only as good as the data foundation underneath them Links: Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.getweflow.com WeFlow RevOps resources: https://www.getweflow.com/revops Join the RevOps Chat Community: https://www.getweflow.com/community Subscribe to the RevOps Letter: https://www.getweflow.com/revops-letter Operating Cadence master deck: ping Janis or Philipp on LinkedIn to request Chapters: (00:00) Intro: Has AI fundamentally changed forecasting? (01:34) Why forecasting is a process, not a number (02:25) What an operating cadence actually looks like week-to-week (04:23) The data foundation: why CRM alone isn't the system of truth (06:53) Anchoring deal conversations on a shared qualification methodology (08:42) How AI adds an objective layer through automated capture and CRM autofill (09:29) Stage entry/exit criteria and deal signals for deal health (13:06) Why "comparable deals" matters as you scale past 50 reps (14:23) Forecasting as the end result of a well-functioning sales org (16:05) Splitting forecasts: new logo, expansion, renewal, bookings vs. consumption (17:22) The three-pillar forecast: dynamically weighted, roll-up, AI prediction (18:50) Roll-up forecasting: baseline vs. best case, rep vs. manager numbers (24:07) Anatomy of a roll-up: hierarchy, gap-to-quota, pipeline coverage, deal-by-deal (26:43) Why spreadsheets break down for roll-up forecasting at scale (28:07) AI prediction models: aggregate vs. deal-by-deal scoring (29:02) Dynamically weighted forecasts and rep-level stage probabilities (32:44) Why AI predictions only work on top of clean data foundations (34:27) Book recommendation & close

    #116 Sales Forecasting in the Age of AI – with Janis Zech & Philipp Stelzer (WeFlow)
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About

Welcome to the RevOps Lab - a podcast exploring the art & science of Revenue Operations. Every week, Philipp & Janis host RevOps professionals to discuss best practices and lessons learnt building scalable revenue engines. This show is for everyone interested in processes, tooling, enablement, and strategies to supercharge your GTM play. To find more episodes and resources on scaling your revenue engine, visit getweflow.com/revops

Information

  • Creator
    Weflow
  • Years Active
    2023 - 2026
  • Episodes
    125
  • Rating
    Clean
  • Copyright
    © Weflow
  • Show Website
    RevOps Lab

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