The Challenge
A product-led SaaS company with 15,000 active accounts had a churn problem they could see but couldn’t act on. Their data team had built sophisticated behavioral models in their warehouse — usage trends, feature adoption curves, engagement scores — but none of it reached the teams who could actually do something about it.
The customer success team relied on gut feel and manual account reviews to decide who to reach out to. They checked product usage dashboards when they had time, but with 300+ accounts per CSM, systematic monitoring was impossible.
The result:
- Churn signals went unnoticed. Accounts would go dark for weeks before anyone flagged them — by then, the customer had already decided to leave
- Outreach was reactive, not proactive. CS only engaged when a customer raised a support ticket or explicitly asked about cancellation
- Rich data, zero action. The warehouse had everything needed to predict churn — login frequency, feature adoption, support ticket velocity, billing signals — but it was trapped in dashboards nobody checked daily
The VP of Customer Success knew the data existed. She just couldn’t get it into her team’s workflow.
What We Found
The data was already modeled. The company’s data team had done excellent work building clean, tested models in dbt. Customer health scores, product usage metrics, and engagement trends were all computed daily. The gap wasn’t data quality — it was data delivery.
The CRM was the action layer. CSMs lived in HubSpot all day. They managed their book of business, tracked interactions, and prioritized outreach there. Any solution that required them to check a separate dashboard would fail — they’d already proven that.
No existing reverse ETL infrastructure. Data flowed one direction: from operational systems into the warehouse. Nothing flowed back out. The concept of syncing warehouse data to CRM was new to the team.
What We Built
- Churn risk scoring model — enhanced the existing dbt health score model to produce a 0-100 churn risk score based on declining login frequency, feature abandonment, support ticket spike patterns, and billing signals (failed payments, downgrade inquiries)
- Reverse ETL pipeline via Census — synced churn risk scores, last login date, feature adoption status, and account health tier to HubSpot contact and company records, refreshed every 6 hours
- Automated alert workflows — HubSpot workflows that triggered Slack notifications to the assigned CSM when an account’s churn risk crossed the “high risk” threshold (score > 75), plus automated email sequences for medium-risk accounts
- CS prioritization dashboard — a HubSpot custom view sorted by churn risk score, giving CSMs a daily action list instead of a 300-account wall of noise
The Result
Churn dropped 18% in the first quarter. CSMs were reaching out to at-risk accounts 2-3 weeks earlier than before — while there was still time to intervene. The automated email sequences for medium-risk accounts re-engaged 23% of accounts that had gone quiet.
CSM efficiency improved dramatically. Instead of manually reviewing accounts, CSMs started each morning with a prioritized list. Time spent on account research dropped by roughly half — the scores and context were already in HubSpot.
The team expanded to three more activation workflows within the quarter:
- Product-qualified lead scores synced to sales for prioritized outreach
- Expansion readiness signals (approaching plan limits, exploring premium features) triggering upsell alerts
- Onboarding completion tracking synced to CS for accounts falling behind the ideal activation path
The ROI was immediate and measurable. The 18% churn reduction on a base of 15,000 accounts translated to significant ARR retention. The engagement paid for itself within the first month.
Talk to Us About Data Activation