Predictive Analytics

Predictive analytics is probably the part of AI that's been working in production the longest, and it's also where the gap between the vendor pitch and the actual reality tends to be smallest. Done well, it's a steady-burning utility. Forecasts that beat the spreadsheet. Churn signals that beat hunches. Demand curves that hold up against the season.

Done badly, it ends up being a model trained on the wrong data, predicting the wrong thing, with the wrong number of decimal places attached to make it look impressive.

Where it works for our customers

• Demand and revenue forecasting. At SKU, lane, or segment level, depending on where you have the necessary history.
• Churn and retention. Predicting which customers are about to leave you, with enough lead time to actually do something about it.
• Lead scoring. Working out which prospects are genuinely likely to convert, based on behavior rather than demographic guesswork.
• Maintenance and reliability. Predicting failures from sensor and operational data before they turn into outages.
• Risk scoring. Credit, fraud, and operational risk in the cases where the data actually supports it.

How we figure out if it'll work for you

We look at your data. Specifically, we look at how much history you have, how clean it is, how stationary the underlying process is, and how many examples you have of the thing you're trying to predict. If the data won't carry the model, we'll tell you, and we'll usually suggest what to fix first.

What you get when it does work

A model deployed in production, integrated into the systems where your team is already making decisions, retrained on a schedule, and monitored for drift over time. Plus the calibration data, so you know how much to trust the output. Get in touch.