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Predictive analytics and forecasting

Forecasting, churn, propensity or pricing models built around a specific decision, deployed with monitoring and a route back to retraining.

Typical timing
10–14 weeks
Engagement
Fixed scope
Delivery framework
User researchDiscoveryAlphaBetaLive
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Objective

Improve a specific, valuable decision with machine learning — demand forecasting, churn prevention, lead scoring, pricing, fraud or maintenance — and put the model into the workflow where the decision is made, with monitoring for drift.

You may need this if

  • Forecasts are built by hand in spreadsheets and often wrong
  • Churn or fraud is spotted only after it happens
  • Data science prototypes never reach production
How it runs

Activities, step by step

The plan follows our delivery framework. Steps that do not apply to this kind of work are left out rather than padded.

  1. 01 · User research1 week

    Decision framing

    • The decision, its owner and its value defined
    • How the prediction will be used in the workflow
    • Success metric and baseline agreed
  2. 02 · Discovery2 weeks

    Data and feasibility

    • Data sourced and explored
    • Baseline model built to test feasibility
    • Go / no-go on expected value
  3. 03 · Alpha3–5 weeks

    Model development

    • Features engineered and models compared
    • Validation against holdout data and bias checks
    • Explainability for business users
  4. 04 · Beta2–3 weeks

    Deploy

    • Model deployed with MLflow or equivalent
    • Integration into the operational workflow
    • A/B or shadow test against the current approach
  5. 05 · LiveOngoing

    Monitor

    • Drift and performance monitoring
    • Scheduled retraining
    • Value reported against the baseline

Deliverables

What you keep at the end.

  • Production predictive model
  • Feature pipeline and model registry entry
  • Model documentation and bias assessment
  • Monitoring dashboard
  • Value report against the baseline

Outcomes

What it is built to change.

  • A measurable improvement in one important decision
  • Models that stay accurate over time
  • A repeatable path from prototype to production

Often combined with

Every engagement starts with a 45-minute working session with the SME who would own the work. No deck.

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