Predictive model consulting

What are predictive models?

A predictive model uses historical data to estimate something not yet known: how many units will sell next week, which customers are likely to cancel, when a pump is likely to fail, whether a transaction looks fraudulent, or what price will clear the market. Its output is a number or a probability that feeds a decision.

That decision is the point. A forecast nobody acts on has no value, and a churn score with no retention action attached is just a report. Every predictive model we build starts from the decision it supports, the cost of being wrong in each direction and the lead time the business needs to act.

The family includes time-series forecasting, classification models that estimate the probability of an event, survival models that estimate when it will happen, and anomaly detection. They follow the discipline of general machine learning delivery, with one addition: they must be tested on the past as if it were the future. They are part of our AI consulting practice.

Signs you need predictive models

Prediction pays when the business makes the same forward-looking decision many times, and when acting earlier is worth something. These are the signals we hear most often from operations, finance and customer teams.

• Planning runs on last year plus a percentage

  Stock, staffing or capacity are set from simple rules, and the business either runs short or carries too much.

• You learn about churn after the customer leaves

  Retention teams react to cancellations instead of early signals, when it is too late to change the outcome.

• Maintenance happens either too early or too late

  Equipment is serviced on a calendar, wasting parts, or after breakdowns, losing production time.

• Fraud review queues grow faster than the team

  Analysts check transactions in arrival order rather than by risk, so the riskiest ones wait the longest.

• An existing forecast is trusted by nobody

  A model exists, but planners override it so often that it has become a formality.

The last sign matters more than it seems. A forecast that planners override is usually missing something they know: a promotion calendar, a supplier constraint, a local event. Bringing that knowledge into the model, or into the process around it, is often the fastest gain available.

What our predictive modeling engagement covers

The deliverables are designed so that the business can judge the model before relying on it, and keep judging it afterwards. Each one is written for the people who will use the predictions, not only for the people who build them.

• Target and decision definition

  What exactly is predicted, over which horizon, at which level of detail, and which action follows each prediction.

• Historical data audit

  Length and consistency of history, missing periods, changes in definitions, and the external signals worth adding.

• Baseline and candidate models

  A naive forecast or simple score first, then models that must beat it on the metric the business cares about.

• Backtesting and validation report

  Performance measured on past periods the model never saw, by segment and by horizon, with the errors explained.

• Shadow deployment and drift monitoring

  Predictions logged against real outcomes before going live, then tracked for drift with a retraining trigger.

Predictions come with a measure of uncertainty whenever the method allows it. A demand forecast given as a single number hides the risk; a range lets planners decide how much safety stock to hold, and a probability lets a retention team choose whom to call first.

How we build forecasts: four phases

The sequence is built to fail cheaply. A model that cannot beat the baseline in backtesting never reaches production, and that decision is made with numbers rather than impressions or the enthusiasm of a demo.

Backtesting uses rolling windows: the model is trained on data up to a date, predicts the following period, then the window moves forward. This reproduces how the model will be used in practice and exposes the periods where it fails, such as a holiday season or a supply disruption, before the business discovers them live.

Which prediction approach fits your problem?

The right technique depends on what you predict and how the prediction is used. The table summarizes the main families we work with and the points that most often decide whether they succeed.

Fraud and pricing models share a difficulty: the model changes the behavior it measures. Fraudsters adapt and customers react to prices, so these models need shorter retraining cycles and closer monitoring. We design that feedback loop from the start rather than adding it after the first surprise.

Risks and drift in predictive models

Predictive models fail differently from other software. They keep running and keep producing numbers, even when those numbers are wrong. The risks below are the ones we design against from the first phase.

• Look-ahead bias

  Using data that was not available at prediction time makes backtests look better than reality. We rebuild features as of each date.

• Concept drift

  The link between inputs and outcome changes, after a pricing change or a new competitor. Monitoring compares predictions with outcomes continuously.

• Self-fulfilling predictions

  A churn score that triggers discounts changes who churns, which corrupts the next training set. Control groups measure the effect.

• Automation without a fallback

  If the model or its data feed fails, the process needs a safe default. Every deployment defines one.

When a model drifts, the response is planned: an alert, an investigation by a named owner, retraining on recent data and validation before the new version replaces the old. That routine is documented at handover so your team can run it without us.

Predictive model pricing

Every engagement starts with a diagnosis and ends with a fixed quote. Durations are typical, not promised.

What changes the price: length and quality of history, number of targets and segments, prediction frequency, and integration with planning or operational systems.

The quote covers senior consultants only, a defined scope and the deliverables of each phase, including the backtesting report and the drift monitoring setup. Compute, external data subscriptions and licensed software are billed separately and estimated during the feasibility check.

Predictive models FAQ