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.
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.
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 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 each other.”
He calls this alignment “the maturity signal of a true GTM engine.”
Operating Rhythm: Weekly Forecasting with Discipline
Every Monday, Keith sits down with his VPs to:
Inspect the forecast
Track closed-won vs. commit
Dig into high-risk deals
Calibrate assumptions and push for clarity
He also inspects call notes, listens to recordings, and personally engages with large or strategic deals — especially if there’s a co-sell opportunity or long-term strategic value.
“I follow my curiosity. Sometimes I see a logo and think: wait, this could be huge for us.”
Final Word: Forecasting is Decision-Making Discipline
Keith leaves us with this:
“Forecasting is just decision-making under uncertainty. The tighter your ops, the cleaner your data, and the more customer-first your mindset — the fewer surprises you’ll face.”
In a world chasing predictability, leaders like Keith prove it’s possible to forecast with confidence — not just by looking at dashboards, but by embedding forecasting into every conversation, every inspection, and every interaction across the revenue org.
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