The RevOps Playbook for Mastering Sales Forecasting A conversation with Navin Persaud, VP of RevOps at 1Password. Sales forecasting isn’t just about making numbers stretch. It’s about cultivating the insights and systems that make those numbers believable. And in the current hyper-competitive market, reliable sales forecasting can distinguish a thriving business from one that’s treading water. Navin Persaud, Vice President of Revenue Operations at 1Password, has navigated these challenges firsthand. With over 20 years of experience in sales, marketing, and operations, he shares how RevOps can transform forecasting into an engine for strategic advantage. Facebook Twitter Youtube From the Field to the Forecast: The Role of RevOps in Storytelling Picture a live sports broadcast. In that moment, you have the field (the systems and processes), the referee (enforcement and control), and the commentator (analysis and insights). Navin describes RevOps exactly this way: “We build the field of play, we referee what happens, and we provide commentary on the play-by-play.” This vivid analogy sets the tone for the rest of our deep dive: forecasting isn’t just data—it’s the dynamic interplay of infrastructure, discipline, and interpretation. The Forecasting Formula: Predictability vs. Accuracy Many teams chase predictable numbers, but accuracy is the real goal. Navin cautions: “Unless you have an agreed-upon process, reliable metrics, system controls, and CPQ in place, you can’t trust what’s being reported.” Without these, forecasts become little more than educated guesses. The Four Pillars of a Trustworthy Forecast: Standardized Sales Process – A clear methodology with defined stages. Reliable Metrics – Consistent data points that reflect real activity. System Control (CRM/CPQ) – Access restrictions to preserve data integrity. Cross-Functional Buy-In – Alignment among Sales, RevOps, and leadership. These ingredients form the backbone of a forecasting engine. Without any one of them, the forecast risks becoming chaotic rather than credible. https://www.youtube.com/watch?v=FNMSCBUVQuo&t=734s Building Credibility: Earning Street Cred from the Ground Up Forecasting isn’t just structural. It’s political. Navin explains that earning trust within the sales team is non-negotiable. He prefers a bottom-up approach: “If you’re struggling in ops because you can’t get alignment, it’s likely you don’t yet have the credibility with your sales process to guide and enforce change.” He starts by working closely with Business Development Reps (BDRs) and Account Executives (AEs), aligning on how the process impacts their day-to-day. This grassroots validation helps RevOps scale expectations upward without backlash, balancing collaboration with direction (“sometimes it’s a democracy… sometimes it’s ‘this is the way’”). Data Stewardship: Shared Responsibility, Shared Trust Clean data is the lifeblood of forecasting—but nobody owns it in isolation. Navin reframes “data ownership” into a more collaborative model: “I chafe at the word ownership. Data stewardship is shared. Marketing owns their set… but we all share responsibility to ensure it’s reliable and integrated.” Who Stewards What Data: Marketing: Lead and demand data Sales Ops (RevOps): Pipeline and forecast data Finance: Revenue recognition and margin metrics Customer Success: Renewal, retention, and expansion insights This distributed model ensures coherence across the Revenue Operations lifecycle, breaking down siloes and enhancing trust. Technology and AI: Elevating, Not Replacing, Process Forecasting flourishes on the foundation of good process—not glossy tech. Navin emphasizes: “Your CRM must remain the system of record.” Yet, modern advancements like AI add powerful enhancements—real-time pipeline alerts, context-aware insights, and automation of routine tasks: “I don’t need to bug reps anymore. I can now see real-time deal movement and build automation around it.” CRM vs. AI in Forecasting: CRM = structured inputs, stage control, unified pipeline. AI = signal detection, behavioral insights, proactive alerts. Together, they transform forecasting from reactive to predictive. Taming Data Chaos: The CRM Cleanup Checklist Even with systems in place, messy data can derail forecasts. Navin highlights the common pitfalls: Noisy activity capture from multiple systems feeding into CRM (calls, emails, engagement tools). Methodology misalignment, where reps interpret sales stages differently. Forecast Data Cleanliness Checklist: Are opportunity stages standardized and unambiguous? Is every feeder system integrated with clean deduplication? Do reps understand how their entries affect forecasting? Are renewals, expansions, and new business tracked distinctly? This fairness to data clarity is non-negotiable. The Back Door to Forecasting: Don’t Ignore Renewals In tight markets, chasing new pipeline is often harder. That’s why Navin champions the importance of existing customer retention: “Too often companies focus on the front door and ignore the back. Strong companies know growing and sustaining existing customers is the lifeblood of business.” He advocates for early preparation—tracking onboarding health, usage metrics, and expanding mindset long before renewal deadlines: Day 1: Capture analytics on onboarding and early adoption. 6 Months In: Proactively assess health and risks. 90 Days Pre-Renewal: Forecast renewal and surface growth opportunities. Forecasting Culture: Agile, Data-Driven, Evergreen Navin suggests RevOps adopt the rhythm of software teams: plan in sprints, release updates, gather feedback, repeat. “Strong RevOps teams should run like dev teams. Use Agile, release in sprints, test, deploy, monitor.” This continuous-improvement mindset fuels a forecasting culture centered on data—not chatter—and long-term credibility over quick wins. The Forecasting Flywheel: From Clean Data to Predictive Power Putting it all together, we arrive at a virtuous cycle: The Forecasting Flywheel: Clean, trusted data → 2. Reliable forecasting → 3. Leadership alignment → 4. Better planning & execution → 5. Process improvements → 1. Back to cleaner data Each loop reinforces the next, turning forecasting from an internal tool into a growth catalyst. Final Thoughts: Forecasting as Strategic Trust Forecasting isn’t just a report. It’s a signal of organizational maturity. Navin’s insights remind us that: You need process clarity before predictability. Credibility is built through empathy and collaboration with sales. Data cleanliness must be everyone’s responsibility. Technology empowers, but can’t compensate for human alignment. Renewals are not afterthoughts—they’re forecasting opportunities. Change management requires agile methodology and discipline. “Forecasting is the field we play on. If the rules aren’t clear and the commentary isn’t trusted, the game falls apart.” — Navin Persaud Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

Raising the Bar for RevOps Strategy & Planning A conversation with Dana Therrien, VP of Sales Performance Management & RevOps at Anaplan. We’ve all seen high-performing companies in action. They aren’t just efficient—they’re unified. Dana Therrien opens with simple clarity: “High-performing organizations share a vision, and everyone is aligned and committed to making it real.” But vision flies only as far as shared execution allows. Too often, organizations lack uniform standards for strategy and planning excellence—standards that would transform mere vision into operational reality. In today’s volatile market, RevOps leaders must not just facilitate plans, they must architect pathways. This blog unpacks how to raise the bar and why it matters. With real dialogue, frameworks, and practical tools for leaders aiming to elevate their strategic impact. Facebook Twitter Youtube Breaking Down Silos: Redefining RevOps’ Purpose Dana’s definition of Revenue Operations is rooted in years of observation and design. What began as a vision in his 2015 SiriusDecisions research—consolidating operations across sales, marketing, and customer success—has now become the cornerstone definition of RevOps. “I define RevOps as combining operations resources across go-to-customer functions—marketing, sales, customer success—and now HR, legal, finance—to eliminate silos, reduce friction, and deliver a single view from lead to renewal.” What’s changed is not just organizational charts, but how strategy flows across them—starting from how teams hypothesize, plan, and measure. That seamless line of sight from demand planning to renewal isn’t an abstract efficiency; it’s a competitive advantage.   Planning Isn’t Optional—It’s the New Execution LinkedIn recently ranked RevOps as one of the fastest-growing professions. Many leaders, Piper-like, focus on execution—but Dana urges a shift: planning is the frontier of competitive advantage. Consider these jarring data points: 60% of companies guess quotas, territories, and plans. 90% of those plans are revised after they launch. A large Silicon Valley telecom found that on-time comp delivery leads to 20% better annual performance. These aren’t numbers—they’re symptoms. Guessing plans isn’t strategic—it’s loaded dice. https://www.youtube.com/watch?v=WvA3U7Ikzto Define “Best.” Not “Better.” Dana’s challenge to the RevOps community is fundamental and psychological. “Better” is nebulous; “best” is directional. Without high standards, the brain stalls. He offers standards anchored in clarity: Timeliness: Deliver quotas and comp plans before or at sales kickoff. Accuracy: Keep redos below 5%—far better than the norm. Ownership: Clearly document dependencies—product, brand, demand, renewals. Collaboration: Build plans with marketing, CS—not after them. RevOps Planning Scorecard Template: Attribute Poor (Red) Average (Yellow) Best-in-Class (Green) Our Status Timeliness 1–3 months late Delivered but delayed Delivered at or before SKO   Accuracy >25% adjusted Some post-launch corrections <5% redo rate   Ownership Sales only Shared with Finance Fully cross-functional   Collaboration Siloed Limited alignment Fully joint GTM planning   This transforms strategic ambition into a practical dashboard, usable by teams and leaders alike. Dynamic Planning: The New Rhythm Static annual planning doesn’t fly in today’s environment. Organizations Dana works with at Anaplan choose agility: A SaaS firm revises GTM strategy and comp plans twice per year. A telecom company does it four times a year, adapting territory and quotas to market shifts in almost real time. “The most successful companies have instituted dynamic sales planning… modifying compensation and territories multiple times a year without disrupting the sales force.” Static vs. Dynamic Planning:  Static: One-time, lagging, rigid → RiskyDynamic: Quarterly or biannual adjustments → Strategic resilience From Insight to Automation: Analytics Maturity in RevOps Dana introduces a four-stage analytics maturity model (via Dr. Michael Wu, PROS): Descriptive: What happened? Predictive: What is likely to happen? Prescriptive: What should we do about it? AI-Driven: The system executes—no need for prompts. “Think of moving from Waze to self-driving cars. In RevOps, AI should reduce repetitive tasks, freeing leaders for strategic insight.” Yet he adds a critical caveat: automation should empower, not micromanage. This reflects a modern tension: intelligence must not become surveillance. Change Management: The RevOps Leader’s X-Factor RevOps leaders today often bring sales ops experience, but Dana argues that tomorrow’s leaders will be cross-pollinators: fluent in sales, marketing, customer success—and adept at change leadership. He urges: Hiring from marketing ops to enrich RevOps’ breadth. Integrating CS Ops into the strategic fold. Embracing change frameworks to move teams, not just processes. Executive Alignment: The Reverse-Failure Exercise Inspired by Paul Rolkens, Dana outlines a simple but powerful workshop: Imagine the worst: the company missed growth targets by 8%. Ask executives to anonymously list why. Surface risks before they become reality. “It’s a non-threatening way to surface honest concerns. When the organization says ‘this might cause failure,’ that becomes a critical alignment moment.” Reverse-Failure Workshop Board: A Template Missed Target By… Concern Raised Preventive Action Owner Product delay CRO Align roadmap with GTM CPO Low demand CMO Unified segmentation strategy CMO/CRO Renewal shortfall CS Lead Renewal playbooks & process CS Ops Why We Still Tolerate Bad Planning Dana closes with a piercing observation: 74% of companies rate their planning as poor, yet most normalize it like a chronic headache. “Poor planning is the migraine we’ve learned to live with. But migraines are treatable once you name them.” Key Takeaways for Transformation Demand clarity: define “best,” not “better.” Prioritize timeliness and accuracy—essential for performance. Instill agility: dynamic planning is a competitive must. Mature your analytics from descriptive to AI-automation. Lead change: integrate ops across functions and drive teams forward. Align leadership early via structured exercises. By treating planning as a strategic product—one that’s measured, iterated, and aligned, RevOps leaders move from enablers to architects of performance. Dana’s insights offer more than a playbook. They light a path away from opacity and inertia, toward clarity, precision, and competitive growth. Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

Crafting an Effective Governance Framework for Business Applications A conversation with Bill Vanderwall, VP of Business Applications at Cision. In the SaaS-driven world, businesses rely on dozens, sometimes hundreds, of applications to run sales, marketing, finance, HR, and customer success. From forecasting platforms to customer success dashboards, SaaS powers every corner of go-to-market. But there’s a hidden cost: sprawl. Too many tools. Too many overlapping features. Too many silos. Bill Vanderwall, former VP of Business Applications at Cision, has seen this story unfold at companies like Netscape, Marketo, and Malwarebytes. He’s helped organizations tame chaotic tech stacks, rationalize spend, and align SaaS with business outcomes. In this episode of The Revenue Lounge, Bill breaks down what a SaaS governance framework really looks like, why companies need one, and how to make it stick. Facebook Twitter Youtube Why Governance is No Longer Optional Bill recalls a time when business units could buy tools without involving IT. Sales teams bought engagement platforms. Marketing bought automation. Customer success bought onboarding solutions. Each tool solved a problem, but together, they created a fragmented landscape. “At Marketo and Malwarebytes, I walked into companies where SaaS applications were being managed by the business. Data didn’t flow. Processes broke down. IT had to step in to bring some order.” Governance, Bill explains, isn’t about slowing people down. It’s about creating balance. A good framework ensures: Tools align with business strategy. Data flows across departments. Costs are rationalized. Priorities are set at the right level. The Two-Tier Governance Model The heart of Bill’s playbook lies in a two-level governance structure — one that balances strategy with execution. 1. C-Level Steering Committee (Quarterly) At the top, governance happens at the C-suite. This steering committee meets quarterly to set priorities and allocate IT resources. Bill recalls his time at Malwarebytes, where the CFO drove discipline with a three-year plan: “We’d identify the company’s top goals for the year — say, improving retention — and then align technology investments to achieve them. The C-level team agreed on the priorities, which eliminated politics and kept us focused.” Governance Flow: Company Strategy → Annual Operating Plan → C-Level Priorities → IT & SaaS Execution 2. Operational Committee (Monthly) Beneath the steering committee sits a monthly operational group. This team handles smaller projects, integrations, and SaaS purchases that don’t require C-suite oversight. Example: When a business unit wanted to adopt Gong, the committee vetted it to ensure fit with Salesforce architecture and estimated IT effort. Once approved, the business team owned administration while IT ensured smooth integration. This layered approach creates agility without losing alignment. https://www.youtube.com/watch?v=LUPp1J3R2io&t=1s Buy vs. Build: Choosing the Right Path For years, companies wrestled with the “buy vs. build” debate. Bill says the equation has shifted: “With so many best-of-breed solutions available today, buying is usually smarter — unless the capability is core to your product strategy.” Buy vs. Build Decision Matrix Criteria Buy Build Speed to Value ✅ ❌ Cost Efficiency ✅ ❌ Strategic Differentiator ❌ ✅ Core Business Function ❌ ✅ Maintenance Burden ✅ ❌ Taming SaaS Sprawl: Rationalization in Action When Bill joined Marketo, he discovered four separate survey tools in use. Governance turned that chaos into a deliberate choice: one enterprise-grade tool, better aligned with business needs. “Having a lot of applications isn’t automatically bad. But you need to ask: who’s using it, what’s it costing, and is there overlap? Rationalization is about making those calls — ideally before renewal cycles.” Best Practices for Rationalization: Inventory applications bi-annually. Track usage vs. spend with monitoring tools. Consolidate overlapping tools. Align consolidation with strategic priorities. The SaaS Sprawl Funnel: 270 Requests → Group by Themes → Prioritize by Impact → Approve via Governance → Rationalized Roadmap Measuring ROI: Beyond Vendor Claims Every SaaS vendor promises sky-high ROI. Bill is skeptical. “Vendor ROI models are fine directionally, but they’re packed with soft dollars. You need to separate hard savings from fuzzy benefits — and focus on time-to-value.” Key ROI Factors: Hard Dollars: License savings, reduced churn, FTE efficiency. Soft Dollars: Productivity gains, collaboration, user satisfaction. Time-to-Value: How quickly can benefits show up? “A little revenue growth can cover a lot of expenses. Time-to-value often matters more than a perfect ROI model.” Data Quality: The Hidden Governance Layer Bad data is every company’s silent killer. Bill has seen organizations simply “live with it.” But governance frameworks can elevate data quality through: Preventing duplicates at the source. Using enrichment vendors like ZoomInfo. Creating data lakes (Snowflake, Redshift) for cleansing and harmonization. Data Quality Levers: Front-End Governance → Enrichment Tools → Data Warehouse → Analytics → Business Impact Managing Change: From Chaos to Control Introducing governance to a free-for-all environment isn’t easy. Bill’s advice: Start with quick wins (fix broken data flows, automate manual processes). Build trust through partnership (don’t be draconian). Celebrate outcomes (show how governance accelerates, not blocks). “At Marketo, we allowed certain groups autonomy as long as they communicated and aligned. Governance isn’t about control — it’s about achieving the same objectives together.” AI, Legal, and the Future of Governance AI adds a new dimension: vendors want access to customer data to train models. Legal wants to block everything. IT sits in the middle. Bill’s advice: Get legal, vendors, and engineers on the same call to negotiate. Clarify data usage rights, anonymization, and safeguards. Accept that not every situation is black and white. AI Vendor Risk Checklist: Will my data train your models? Is data anonymized? What security guarantees exist? Can we opt out? Best-of-Breed vs. Platforms: The Consolidation Tradeoff With budgets tightening, platform consolidation is tempting. But Bill is cautious: “Most companies are still best at the thing they started with. Platforms may catch up, but best-of-breed usually wins — unless platform efficiency outweighs the feature gap.” Template: Platform vs Best-of-Breed Scorecard Criteria Platform Best-of-Breed Cost Savings ✅ ❌ Feature Depth ❌ ✅ Integration Ease ✅ ✅ Scalability ✅ ✅ Innovation Pace ❌ ✅ Vendor Maturity: Balancing Risk and Innovation Should companies bet on a young, innovative vendor?

Forecasting Strategies at Different Company Sizes A conversation with Keith Rabkin, Chief Revenue Officer at PandaDoc. Revenue leaders are constantly under pressure to deliver predictability despite economic headwinds, fluctuating buyer behavior, and increasingly complex deal cycles. In this episode of The Revenue Lounge, we sit down with Keith Rabkin, the Chief Revenue Officer at PandaDoc, to understand how he builds a forecasting engine rooted in operational excellence, strategic RevOps partnership, and deep customer-centricity. Keith’s journey spans two decades, including tenures at tech giants like Google and Adobe, where he mastered the intersection of data, GTM strategy, and customer satisfaction. At PandaDoc, he’s applying that same blend of analytical rigor and human insight to build predictable, scalable revenue engines. Facebook Twitter Youtube From Strategy & Ops to CRO: Keith’s Unique Journey “I think I’m a little bit of a non-traditional CRO.” Keith didn’t take the usual route into the CRO chair. Instead, he rose through strategy and operations roles at Google, where he honed his ability to use data as a decision-making compass. At Adobe, he led GTM strategy and operations for their $9B digital media business, where he first saw how customer-centric data patterns could be used to optimize every element of a go-to-market motion — from self-service sales to channel revenue. What hooked him? The satisfaction of being “obsessed with a number,” he says. Forecasting, for Keith, is a game of patterns and puzzle-solving — and the payoff is not just in numbers, but in the satisfaction of delivering a great customer experience. Public vs. Private Forecasting: Two Worlds, One Philosophy Forecasting at Adobe looked very different than forecasting at PandaDoc. Yet the underlying discipline — inspecting deals, applying data, and aligning cross-functional teams — remains the same. Forecasting Element Public Companies Private Companies Pressure Intense, stock-driven High, but more flexible Methodology Automated, bottoms-up, segmented Manual inspection, rep-specific Timeline Sensitivity Quarterly commitments drive urgency More customer-aligned pacing Discounting End-of-quarter incentives common Long-game view prioritized “At PandaDoc, I’d rather let a deal slip than pressure a customer to close just to hit a quarter.” This mindset allows PandaDoc to prioritize relationships over revenue timing — which, ironically, improves long-term deal value and trust. https://www.youtube.com/watch?v=1cqYn4a_nsU&t=1s The Forecasting Playbook: A Blend of Art and Science Keith emphasizes that while forecasting may appear like a data-driven function, it’s equally about human judgment. Here’s what his playbook looks like: Forecasting Component Description Deal-by-deal inspection Analyze every key opportunity through rep/manager reviews. Historical trends Weigh pipeline based on historical stage conversions, seasonal patterns, and rep accuracy. Manager alignment Collaborate with frontline managers and VPs to roll up forecasts with realism. Gut check Understand rep behavior: who’s conservative, who sandbags, who needs pushback. “You get to know who’s sandbagging and who’s just optimistic. That’s the art.” He holds weekly meetings with his GTM leaders to walk through the forecast. But it’s not a top-down call — it’s a collaborative build-up, followed by his own adjustment based on trend recognition and leader context. The Domino Effect of Dirty Data Bad Close Rate Data → Inaccurate Pipeline Weighting → Forecast MissesUnreliable Pipe Gen Tracking → Overconfidence → Poor CoverageLack of Stakeholder Mapping → Underestimated Risk → Deal Slippage “Bad data is a huge obstacle to an accurate forecast.” Keith’s solution? A world-class RevOps team and weekly “D-DOM” meetings (Data Driven Operating Model), where every GTM leader sees and aligns on the same datasets. This operational cadence makes data central to every action — not just a reporting afterthought. RevTech Isn’t the Answer. But It Helps. While Keith is bullish on RevTech, he’s also cautious. He notes that no tool can replace fundamentals like deal inspection, rep performance analysis, and buyer engagement tracking. That said, RevTech tools have made his life easier in the following ways: RevTech Function Forecasting Value Call & Email Data Reveals real engagement and momentum. PandaDoc Engagement Tracks opens, page views, and forwards. CI Tools Helps uncover true multi-threading. Forecast Software (Under evaluation) Competing tools for visibility and roll-up support. “Forecasting tools are helpful, but the magic is in the intersections — rep + stage + seasonality.” He predicts that AI will soon help revenue teams process these complex intersections more effectively. Deal Inspection Checklist for Complex Sales Champion Validated? Is it a true champion or just a coach? Economic Buyer Identified? Can they sign off? Discovery Depth: Have we uncovered real pain and urgency? Multi-threaded? Are 2–3 stakeholders from different teams engaged? Next Steps Documented? Is there clear mutual action? PandaDoc Activity? Has the proposal been opened and reviewed? “We’ve locked down our stages with no judgment calls — just concrete criteria.” If a deal regresses — for example, the buyer leaves or restarts the evaluation — Keith prefers to roll it back rather than falsely keep it in an advanced stage. Revenue Doesn’t Stop at Closed-Won: Expansion and Renewals PandaDoc’s post-sale strategy mirrors its new business motion — highly structured, data-backed, and RevOps-enabled. Function Role Customer Experience (CX) Drives adoption, feedback, early risk signals. Account Management (AM) Owns renewals, expansion, and commercial negotiations. “We separate value delivery from commercial transactions. That builds trust.” Forecasting on the renewal side follows the same rigor — stage tracking, risk reviews, and AM-manager forecasts. High-risk or high-value accounts get escalated for deeper review. Winback Motion: Closed-Lost ≠ Lost Forever Keith and team recently launched a Closed-Lost Nurture Program: Targeted drip campaigns Scheduled rep check-ins Competitor contract timeline tracking Stakeholder remarketing (when contacts weren’t captured in CRM) “Buyers come in 6–9 months before a competitor renewal. If we stay top-of-mind, we win the re-evaluation.” This motion ensures that timing—not interest—is the only reason a deal is lost. Killing the MQL Debate: Pipeline is the Real Metric Keith doesn’t ignore MQLs, but he’s shifted PandaDoc’s GTM model toward pipeline accountability. GTM Metric Marketing Role Sales Role Shared Outcome MQLs Generate quality leads Qualify accurately Feedback loop only Pipeline Source and nurture Accept and close Joint ownership “Both teams are goaled on pipeline — and both refuse to blame