RevOps Foundation Case Study: Data Unification for Series B SaaS

## Snapshot - **Company**: Global SaaS platform (anonymized) — CRM + support + marketing tools - **Stage**: Series B, 5-10M USD ARR - **Motion**: Sales-led + PLG hybrid - **Timeframe**: 90 days - **Key levers**: CRM cleanup, data unification, RevOps dashboards - **Results**: Pipeline visibility +22%, CAC payback -18 days, NRR 92% → 108% --- ## 1. Client & Context The company is a global SaaS platform serving 50,000+ customers across 150+ countries. They provide CRM, customer support, and marketing automation tools — all in one suite. At Series B (5-10M USD ARR), they were struggling with: - **Three disconnected data worlds**: Sales in HubSpot, Support in their own product, Marketing everywhere - **No single source of truth**: Leadership couldn't answer "what's our real CAC?" in under 3 days - **Leaking pipeline**: Leads stuck between marketing automation and sales CRM with no routing logic What they needed wasn't "more leads" — it was **RevOps foundations** that could unify data across products. --- ## 2. The Problem When we audited their RevOps, four things were broken: - **CRM hygiene disaster**: HubSpot had 12,000+ leads with 40% duplicates, 30% missing firmographics - **No data unification**: Sales didn't know if a lead was also a support ticket - **Broken handoffs**: MQL → SQL conversion was 12% (industry avg: 20-25%) - **Zero dashboards**: Leadership was making decisions off 3 different spreadsheets **Baseline metrics:** - MQL → SQL: 12% - CAC payback: 14 months - NRR: 92% - Pipeline visibility: 60% --- ## 3. Our Diagnosis We ran a 14-day diagnostic across three lenses: **Funnel view**: Leads were dying at the "marketing → sales" handoff. **Data view**: No connection between support tickets, product usage, and CRM data. **RevOps view**: HubSpot was underutilized (20% of features), no automated routing. The diagnosis: **they had grown past their RevOps infrastructure**. --- ## 4. The Strategy Our 90-day RevOps sprint had four pillars: 1. **CRM Foundation**: Clean HubSpot, implement lead scoring, build routing logic 2. **Data Unification**: Connect product usage + support tickets → CRM 3. **Funnel Re-engineering**: Redefine MQL, implement lead lifecycle stages 4. **Dashboards**: CEO, RevOps, and Sales dashboards --- ## 5. Execution ### 5.1 CRM Cleanup **Week 1-2**: Exported 12,000+ leads, used Clay to identify duplicates, merged 3,400 duplicates. **Week 3-4**: Implemented lead scoring (0-100 points): - Demographic: Company size (20 pts), Industry match (15 pts), Funding (15 pts) - Behavioral: Product usage (25 pts), Pricing page visit (10 pts) - Threshold for MQL: 50+ points Result: 6,000 high-quality leads (from 12,000). ### 5.2 Data Unification **Week 5-6**: Built API integration: Product usage data → HubSpot (daily sync). Integrated Zendesk: Support ticket count + satisfaction score → HubSpot. Created "Customer 360" custom objects in HubSpot with MRR, product tier, and login frequency. ### 5.3 Funnel Re-engineering **Week 7-8**: Redefined MQL criteria (added behavioral): - Old: "Filled a form" → MQL - New: "50+ lead score OR 3+ product logins" → MQL Built automated nurturing sequences for "not ready" leads. Result: MQL → SQL jumped from 12% → 28%. ### 5.4 Dashboards **Week 9-12**: Built three dashboards: - CEO: ARR, NRR, pipeline, CAC payback - RevOps: MQL→SQL, CAC by channel, lead response time - Sales: My pipeline, next actions, at-risk deals --- ## 6. Results | Metric | Before | After | Benchmark (Series B) | |--------|--------|-------|---------------------| | MQL → SQL | 12% | 28% | 20-25% | | CAC payback | 14 months | 11 months | 8-12 months | | NRR | 92% | 108% | 100-110% | | Pipeline visibility | 60% | 82% | 75-80% | **What improved most**: The MQL→SQL jump (12% → 28%) created a cascade effect. **Benchmark context**: At Series B, typical NRR is 100-110%. We ended at 108%, in the "strong" band. --- ## 7. What's Next If we continued beyond 90 days: - **Month 4-6**: Implement predictive lead scoring - **Month 6-9**: Add expansion playbook - **Month 9-12**: Build churn prediction model The RevOps foundation was set. They could now scale to 20M USD ARR without guessing. --- ## 8. Takeaways for Other Founders - **Don't scale RevOps too early**: Start with HubSpot + clean data first - **MQL definition is everything**: Redefine "qualified" before buying more leads - **Data unification compounds**: Connecting product + support + CRM data unlocked 22% more pipeline visibility - **Dashboards drive behavior**: When sales reps saw "lead response time: 48 hours", they fixed it --- ## 9. How We Can Work Together If you're a 5-10M USD ARR SaaS struggling with RevOps foundations, this is exactly our 90-day sprint. We start with a **RevOps Audit** (3-5 days), then design a **90-day plan**. **Our RevOps sprints look like**: - Week 1-2: Audit + CRM cleanup - Week 3-4: Lead scoring + routing - Week 5-8: Data integration - Week 9-12: Dashboards + documentation Ready to stop guessing? **[Work with Vridhi Labs](/work-with-us)**. --- *This case study is anonymized. All numbers are real; specific details altered to protect client confidentiality.*