Forecasts, not hunches
Demand, churn and revenue projections with measured accuracy and honest uncertainty ranges.
Data Analytics
Predictive analytics
Churn, demand and scoring models on your warehouse data: the shortest path from reporting into machine learning.
What we do
Predictive analytics answers forward questions with the data you already collect: which customers are likely to churn, what demand next month looks like, which lead or claim deserves attention first. Built on a governed warehouse, these are focused models wired into daily work, not a research program.
We start from a decision someone makes weekly, then build the simplest model that beats the current guesswork, validated on your history before anyone trusts it live. The first scored output usually lands in weeks, inside the tools your team already uses: CRM, dashboards or a plain API. No platform migration is required if your warehouse is in reasonable shape.
Every model ships with monitoring for accuracy and drift, a retraining plan and an honest measure of lift over the baseline. If a heuristic beats the model, we say so. This is also the natural bridge into wider ML and AI work, on foundations that are already governed and tested, which is exactly where most AI initiatives stall.
Why it pays off
Scores where work happens
Predictions land in your CRM, ERP or dashboards, not in a notebook nobody opens.
Validated before trusted
Every model is backtested on your history and compared against the current way of deciding.
Monitored in production
Accuracy and drift are tracked live, with retraining planned rather than improvised.
A bridge into ML and AI
The same features, pipelines and governance carry your first models into the wider AI roadmap.
Data Analytics
How we work
01
Pick the decision
We choose one recurring decision where a better prediction has clear, countable value.
02
Baseline and features
We measure how the decision is made today and build features from your warehouse data.
03
Model and validate
Simple models first, backtested on history, promoted only when they beat the baseline.
04
Deploy and monitor
The model ships into your tools with accuracy tracking, drift alerts and a retraining cadence.
Deliverables
Scoped use case with success metric
Feature pipelines on warehouse data
Validated model with backtest report
Integration into CRM, ERP or BI
Monitoring, drift alerts and retraining plan
Questions, answered
How much data do we need?
Less than the myth says. A few thousand labeled examples often support a useful churn or scoring model, and the feasibility check in week one tells you concretely whether your history is enough.
What if the model performs poorly?
Then you learn that early and cheaply. We validate against your history before production, and if the lift over the current baseline is not worth the complexity, we recommend stopping. That answer costs weeks, not quarters.
Is this machine learning? Do we need data scientists?
It is applied machine learning on tabular warehouse data, and no, you do not need to hire first. We build and hand over models your analysts can monitor, and hiring can follow the proven value.
Start predicting instead of reacting
Pick the decision that hurts most and we will scope a model for it in one call.
Book a discovery call