What this solves
Questions your dashboards cannot answer.
Churn you cannot predict.
You know which customers churned last month. You do not know which ones will churn next month — until it is too late to intervene.
Segments you cannot see.
Your customers are not one group, but your marketing treats them like they are. You are missing the high-LTV segments that should be getting more attention.
Forecasts you do not trust.
Revenue projections are built on gut feel and adjusted spreadsheets. You need a model grounded in your actual data patterns.
What we build
Models built for decisions, not for demos.
Churn prediction models
Behavioural models that identify at-risk customers 30–60 days before they cancel. Integrated with your CRM or CS tooling so action can be taken.
Customer segmentation
Data-driven customer segments based on behaviour, value, and intent — not just demographics. Actionable for marketing, product, and retention teams.
Demand & revenue forecasting
Statistical forecasting models built on your historical patterns. Useful for inventory planning, headcount decisions, and investor reporting.
A/B test design & analysis
Statistically valid experiment design, running, and interpretation. Stop making decisions on underpowered tests with inconclusive results.
“Churn dropped from 4.2% to 2.8% in four months.”
A B2B SaaS company had 4.2% monthly churn with no early warning system. Customer success was reactive — they only knew about cancellations after they happened.
We built a churn prediction model using 45-day behavioural signals, integrated health scores into the CS team dashboard, and set up Slack alerts for at-risk accounts.