Predictive projects rarely fail on the mathematics. They fail because nobody agreed which decision the model was meant to change, or because the data behind it could not be trusted once it left the analyst’s notebook.
We run data mining work through CRISP-DM, the open industry-standard process. Data, business and domain experts define the objective together, and each model is judged on whether it improves that decision in everyday use.
What this includes
Business and data understanding
Business goals translated into a data mining problem, an inventory of the data available, and an early read on its quality and gaps.
Data preparation
Selection, cleaning, integration and feature construction - scripted and repeatable rather than done once by hand.
Modelling and evaluation
Candidate techniques tested against a clear baseline, with results assessed for business value as well as statistical accuracy.
Deployment and monitoring
Models moved into everyday use with reporting on performance and drift, and a plan for when they need retraining.
Customer and marketing analytics
Segmentation, propensity, churn, pricing and marketing effectiveness models that feed directly into campaigns and offers.
Forecasting and risk
Demand, financial and workforce forecasting, plus fraud and risk scoring for claims, credit and supply chain.
What you get
- A defined business decision behind every model
- Forecasts that draw on more than last year’s history
- Repeatable data preparation instead of one-off analysis
- Models monitored in production, not forgotten after launch