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.
What business question do you need answered?
Start with the decision you need to make. We will tell you whether data science can answer it and what it would take. We stay until it works. Not until we invoice.
Related: Advanced Analytics · Applied AI · View Case Studies