Predictive Analytics
We train machine-learning models on your data to forecast churn, demand, and revenue — so you act on predictions, not hunches.
The Problem
Your sales team is reacting to churn after it happens. By the time a customer cancels, you have already lost them — and the six months of revenue it would take to replace them. You are spending marketing dollars on broad campaigns instead of targeting the accounts most likely to convert or leave.
Demand planning is a guessing game. You overstock slow-moving inventory and run out of what sells. Pricing decisions are based on what competitors charge, not what your data says customers will actually pay. Every quarter, leadership asks "what will next quarter look like?" and the honest answer is "we are not sure."
The data to answer these questions already exists in your CRM, POS, and transaction history. It is just sitting there, unmodeled and unused.
How We Solve It
We build statistical and machine learning models trained on your historical data to predict customer behavior, demand patterns, and revenue outcomes. Every model ships with a clear accuracy metric so you know exactly how much to trust it — and a monitoring dashboard so you see when it drifts.
Models trained on your data, not off-the-shelf templates
Forecast accuracy reported with confidence intervals
Production-ready scoring API + monitoring dashboard included
Drift detection + automated retraining pipeline
What You Get
Predictive Model Suite
Production-ready models for churn, demand, or lead scoring — trained, validated, and deployed to your infrastructure.
Real-Time Scoring API
An API endpoint your applications can call to get predictions on individual customers or products in milliseconds.
Model Performance Dashboard
A live dashboard tracking prediction accuracy, drift, and feature importance — so you always know your models are working.
Playbook & Training
A documented playbook for your team to interpret predictions and take action, plus a hands-on training session.
Frequently Asked Questions
For most models, 12-24 months of transaction or behavioral data gives us enough signal. We can work with less, but accuracy improves with more history. During discovery, we will assess your data and be upfront about what is feasible.
Accuracy depends on the problem class, the volatility of the underlying time series, and the data-quality posture of the source systems — anyone quoting a single number across all engagements is selling, not engineering. We commit to: (a) reporting MAPE / RMSE / AUC against held-out data per engagement; (b) publishing confidence intervals on every prediction so you can decide when to trust the model and when to apply human judgment; (c) being upfront during discovery if the data we see will not support the accuracy threshold you need.
All models drift over time as customer behavior changes. We set up automated monitoring that alerts you when accuracy drops below a threshold, and we include a retraining pipeline so your team (or us, on retainer) can refresh the model with new data.
No. We design the system so your existing technical team can operate it. The retraining pipeline is automated, the dashboard is self-explanatory, and we provide documentation for every component.
Ready to Predict Smarter?
Book a free 30-minute call. We will assess your situation and tell you exactly what is possible — no pitch, no pressure.