90 Days, 2 AI Use Cases Live, 23% Reduction in Customer Churn. A Real Sprint.

The CFO on this engagement asked a reasonable question at kick-off: "What do we actually get in 90 days?" The answer at the time was an honest estimate — two AI use cases in production, data infrastructure capable of supporting them, and a measurable impact on at least one business metric. The actual outcome exceeded the estimate on every dimension.

90 days from signed agreement: two use cases live, 23% reduction in customer churn, and the data infrastructure to run four more use cases without additional engineering investment. This is the full story — the decisions made, the methodology used, and the places where the sprint nearly went differently.

The Starting Condition: What We Walked Into

The client was a B2B SaaS company, ~200 seats, with a churn problem they knew about and a data infrastructure that couldn't tell them why customers were leaving. They had product analytics (Mixpanel), a CRM (HubSpot), and a support platform (Intercom) — three disconnected data sources with no unified view of customer health. AI was on the roadmap but hadn't been defined beyond "we should be doing something with AI."

Week 1 of the sprint was a data audit. Not a surface-level review — a systematic mapping of every data source, schema, quality level, and connection status. The audit identified 47 data quality issues (covered in detail in a separate post) and established the data readiness baseline. Week 2 was use case prioritization based on the audit output.

The Two Use Cases We Chose and Why

Use Case 1: Churn Prediction Model. The data audit showed that product usage patterns in the 30 days before a customer churned were consistently distinct from the patterns of customers who renewed. That's a trainable signal — which means it's a solvable use case given sufficient data quality. With the data quality fixes from the audit applied, we had 18 months of clean usage data, enough to train a reliable model.

Use Case 2: Support Ticket Auto-Triage. Intercom data showed 40% of support tickets were variations of the same 12 issue types. A classification model could route and prioritize them without human intervention, reducing first-response time and freeing the support team for genuinely complex issues. This use case had a faster implementation path — the data was clean, the problem was well-defined, and the success metric (first-response time, ticket volume per agent) was already being tracked.

The Churn Model: How 23% Actually Works

The churn prediction model assigned a weekly risk score (0–100) to every active account based on 14 usage signals: login frequency, feature adoption depth, support ticket frequency, seat utilization, billing inquiry history, and nine others. Accounts above a risk threshold of 72 triggered an automated HubSpot workflow: the customer success manager received a notification with the specific signals driving the score, and a templated outreach was sent if no CS activity occurred within 48 hours.

The 23% reduction is measured against the 12 weeks before the model went live, adjusted for seasonality. Of the 47 accounts flagged as high-risk in the measurement period, 31 received proactive outreach and 24 remained customers. That's a 77% save rate on flagged high-risk accounts — higher than the team's historical save rate on accounts they identified manually.

"The churn model didn't change the customer success team's job. It changed what they knew before making a call. Every intervention they made was informed by 14 data signals rather than a gut feeling about which accounts felt quiet. Better information produces better outcomes." — Pavel Siddique, CEO, Indpro AB

What Nearly Went Wrong: The Data Quality Gap

The churn model almost wasn't built. The initial data audit found that Mixpanel event tracking had an 18-month gap where a key usage event wasn't firing correctly — corrupting the data for that feature's usage history. We spent 8 days in weeks 2–3 reconstructing the historical data from server logs. Without that reconstruction, the training dataset would have been insufficient for a reliable model. Data quality is almost always the real timeline risk in AI sprints.

Frequently Asked Questions

Q: What's the minimum data history needed to train a reliable churn prediction model?

12 months of clean usage data is the practical minimum for a B2B SaaS churn model with reasonable predictive accuracy. 18–24 months is better because it captures multiple renewal cycles and seasonal patterns. Below 12 months, the model can still be useful but will have higher uncertainty in its risk scores and will need more frequent retraining as more data accumulates.

Q: How much does an AI Readiness Sprint cost, and what's the ROI framework?

Our AI Readiness Sprint is priced between 300k–800k SEK for 90 days, depending on scope and data infrastructure complexity. The ROI framework: calculate the annual revenue impact of the business metric improvement (in this case, 23% churn reduction × annual churn cost) and compare it to the sprint investment. On this engagement, the 23% churn reduction represented a multiple of the sprint cost in year-one retained revenue alone.

Q: Can this sprint be run for companies with less mature data infrastructure?

Yes — the sprint adapts to the starting condition. For companies with minimal data infrastructure, weeks 1–4 focus entirely on data collection and pipeline setup before use case development begins. This may reduce the scope of what's achievable in 90 days, but the sprint still delivers a defined outcome. We scope the use case ambition to the data readiness level.