What is predictive analytics?

Estimating what happens next from what happened before — with the uncertainty stated rather than hidden.

What is predictive analytics?

Predictive analytics uses historical data to estimate future outcomes — how much stock will sell, which customers are likely to leave, which equipment will fail, which applicants are likely to default. Useful implementations report a confidence range alongside the estimate and connect directly to a decision.

A prediction without a decision is decoration

The common failure is a churn model that is 87% accurate and changes nothing, because nobody defined what happens when a customer is flagged. Before building anything, establish what action the prediction triggers, who owns it, and what acting on a false positive costs.

Point estimates mislead

'Demand will be 1,240 units' invites a decision no forecast can support. 'Between 1,100 and 1,400, with 80% confidence' lets a buyer plan properly. Any forecast presented without a range is hiding information the decision-maker needs.

Beat the simple baseline first

Experienced planners with good heuristics are hard to outperform on stable products. We benchmark against the existing method before building a model, because the honest answer is sometimes that the spreadsheet is fine and the win is in scale rather than accuracy.