RevOps transformation that unlocked predictable growth
Strong product-market fit, broken operating model
This Series B company had everything you want at their stage: a product customers loved, a growing sales team, and fresh capital to scale. What they lacked was the operating infrastructure to scale efficiently.
Their HubSpot instance had been set up hastily two years earlier and never properly architected. Each sales rep had their own way of logging deals. Marketing was generating leads but nobody could tell which ones converted. The forecast the CRO presented to the board was built on gut feeling and spreadsheet arithmetic, and it was consistently off by 30-40%.
Revenue per rep was declining as the team grew, and nobody could pinpoint why. The company was adding headcount to solve a process problem.
- Forecast accuracy below 60% — the board was losing confidence
- No standardized sales methodology or stage definitions across the team
- Marketing and sales alignment was a standing argument, not a process
- Revenue per rep declining with each new hire
- 7 different tools with no unified data layer — HubSpot, Salesforce (legacy), Intercom, Stripe, Notion, Google Sheets, and Looker
- Customer expansion and churn tracked manually by the CS team in a spreadsheet
Design the operating model, then build the system to run it
We embedded with the revenue leadership team — CRO, VP Marketing, and Head of CS — for 16 weeks. This was not a CRM cleanup. This was a RevOps operating model design from first principles, followed by a full technical implementation in HubSpot.
Discovery
Ran a full revenue process audit: interviewed 12 team members across sales, marketing, CS, and finance. Mapped every handoff, every data flow, and every reporting gap. Assessed the existing HubSpot instance against best practice benchmarks and identified 47 configuration issues.
Design
Designed a RevOps operating model covering lead-to-cash. Built a data architecture spec: unified contact model, standardized deal stages with entry/exit criteria, lead scoring framework, lifecycle stage definitions bridging marketing and sales, and a forecasting methodology based on weighted pipeline with historical stage conversion rates.
Build
Rebuilt HubSpot from the ground up. Consolidated the legacy Salesforce data. Implemented MEDDPICC stage validation via required properties. Built 23 automation workflows covering lead routing, deal stage progression alerts, renewal tracking, and expansion signals. Connected Stripe revenue data, Intercom product usage signals, and built a custom integration for their usage-based pricing model.
Launch
Rolled out the new operating model in phases: sales team first (week 1-2), marketing alignment (week 3), CS and renewal workflows (week 4). Each phase included live training, updated playbooks, and a certification quiz. Built a deal inspection dashboard for weekly pipeline reviews.
Optimize
Ran the first board-quality forecast in month two. Iterated on lead scoring weights based on 90 days of conversion data. Added predictive deal scoring using HubSpot’s AI tools calibrated against their actual win patterns. Designed the quarterly business review cadence the leadership team now runs independently.
From hiring to fix problems to scaling what works
Within one quarter of launch, the company stopped adding sales headcount and started getting more from the team it had. Revenue per rep climbed because reps were spending time on the right deals, not chasing unqualified leads or duplicating effort.
- 4.2x revenue per rep — up from a declining trend, driven by better lead quality, deal prioritization, and automated admin work
- 92% forecast accuracy — the board went from questioning every number to trusting the model, replacing gut-feel projections with data-backed weighted pipeline
- 45% reduction in sales cycle — standardized stage progression, automated follow-ups, and multi-threading signals cut average time-to-close from 62 days to 34
- Marketing-sourced pipeline attribution went from unmeasurable to 38% of total qualified pipeline
- Customer expansion revenue became visible and trackable — CS identified 2.1x more upsell opportunities in Q1 post-launch
- The CRO cancelled the standing "data cleanup" meeting that had been running weekly for 18 months
We were about to hire two more reps to hit our number. Checkpoint showed us we did not have a capacity problem — we had an operating model problem. The RevOps transformation paid for itself in the first month, and our board finally trusts the forecast.
Scaling revenue, not headcount?
Let’s talk about building a RevOps operating model that makes your existing team dramatically more effective.
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