Case Study — Healthcare SaaS

From zero growth insight to a compounding infrastructure that predicts churn, drives experiments, and pays for itself.

$272K–$505K
Annual revenue impact
$900K–$1.6M
3-year projected impact
90%
Analytics cost reduction
30–60 days
Churn predicted before cancellation
118+
Decision-ready dashboards built

Before.

A B2B SaaS platform in healthcare with ~2,700 customers had built their analytics on an enterprise platform without a proper event taxonomy or compliance review. Events were firing but 0% of properties were being captured — months of data collection that couldn't be segmented or analyzed.

Their analytics weren't answering the questions that mattered: activation rate by practice type, feature adoption by user role, churn signals by customer segment. They had data. They had no decisions.

The CS team was working from a renewal date spreadsheet. No risk scores. No behavioral triggers. No early warning. High-value accounts churned with no prior indication. Meanwhile, analytics was costing $20K–$50K/year for zero growth insight.

The Situation
  • Analytics collecting data but not connected to any business decision
  • Analytics costing 10x what they should — $20K-$50K/year for zero growth insight
  • No churn prediction — CS team reactive, working from a renewal date spreadsheet

What we did.

A full DISCOVER audit across all 6 growth layers, followed by systematic remediation and infrastructure build.

Step 1 — Discovery
DISCOVER audit across all 6 growth layers: competitive intelligence, analytics & measurement, product experimentation, activation, retention & churn, and GTM & positioning.
Step 2 — Compliance
Analytics compliance gaps identified and resolved. Migration to compliant analytics stack completed. 5-layer data governance architecture implemented.
Step 3 — Analytics Rebuild
Full event taxonomy redesigned: 45 events redesigned around business decisions (vs. 12 previously). Migration to compliant analytics stack — $450/month vs. $20,000–$50,000/year for enterprise alternatives.
Step 4 — Decision-First Dashboards
118+ production charts built across 13 dashboards, all question-first. Every chart answers a specific business decision. Activation rate by practice type. Feature adoption by user role. Churn signals by customer segment.
Step 5 — Churn Prediction
85+ behavioral signals analyzed — login patterns, feature adoption trends, support ticket velocity. Churn prediction model trained with a 30-day prediction window. CS team saves accounts 30–60 days before they'd have cancelled — weekly at-risk list delivered every Monday.
Step 6 — Competitive Intelligence
Competitive intelligence database covering 35 competitors. 887+ pages of data analyzed. CS team equipped with competitive positioning guides for the most common competitive scenarios — your sales team knows exactly what to say.

After.

$272K–$505K
Annual revenue impact identified and addressed
90%
Reduction in analytics infrastructure cost
118+
Decision-ready charts built question-first across 13 dashboards
45
Events redesigned around business decisions (vs. 12 previously)
30–60 days
Churn predicted before cancellation — CS team intervenes, not reacts
$450/mo
Analytics cost (vs. $20K–$50K/yr for enterprise alternatives)

What you can do now.

Your CS team receives a weekly at-risk customer list every Monday — with the top 3 behavioral drivers of churn for each account. Proactive outreach, not reactive damage control.

Your growth team runs experiments against an instrumented funnel where every step is measurable — by persona, by cohort, by traffic source. No more guessing which segment the experiment moved.

Your analytics infrastructure costs a fraction of enterprise solutions — $450/month instead of $20K–$50K/year — while meeting enterprise compliance standards. Full behavioral visibility at a fraction of the cost.

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