Predictive analytics
A forecast nobody acts on is a hobby. The prediction is the easy half; the hard half is what happens next.
Predictions need a destination
Plenty of organisations have built a churn model. Rather fewer have changed anything as a result.
The model gets built, validated, and presented. It's accurate. Everyone agrees it's impressive. And then the list of at-risk customers arrives in a spreadsheet each Monday, and it isn't anyone's job to do anything with it, and the intervention was never designed, and within a quarter nobody opens the file.
So we scope backwards from the action. Who sees this prediction? In what system? What are they expected to do? What's the intervention, and is it worth more than it costs? If those questions don't have answers, the model won't change anything regardless of how accurate it is.
What we build
- Churn and retention
- Who's likely to leave, with enough notice and enough explanation to do something about it.
- Demand forecasting
- Volume by product, location, and period, feeding purchasing, staffing, and stock decisions.
- Predictive maintenance
- Equipment failure anticipated from sensor and service data, before it becomes an outage.
- Risk and credit scoring
- Probability of default, fraud, or claim, with the explainability that regulated decisions require.
- Lifetime value and propensity
- Who's worth acquiring, who's likely to convert, and where marketing spend earns its return.
Getting it used
- We design the intervention alongside the model
- A churn prediction is worth precisely as much as the retention action attached to it. That gets scoped in the same engagement.
- We deliver into the workflow, not into a report
- The prediction appears in the CRM, the ordering system, or the maintenance schedule — wherever the person who acts on it already works. A prediction requiring someone to open a separate tool will be used for a fortnight.
- We make it explainable
- "This customer is 78% likely to churn" prompts the question "why," and if you can't answer it, nobody trusts the number or knows what to do. Every prediction comes with its main contributing factors.
- We tune the threshold to the economics
- The cost of a missed churn and the cost of a wasted retention offer are rarely equal. We set the operating point against your actual numbers, not against a default.
- We validate the way time works
- Backtesting on historical periods, never on data the model couldn't have had at prediction time. This mistake makes models look excellent in development and useless in production, and it's remarkably common.
Before you ask.
Depends on the signal in your data. We'll establish the realistic horizon early — a shorter accurate forecast beats a longer one you can't rely on.
Machine Learning Models
A model that scores 94% in a notebook and a model that earns its keep are different achievements. The distance between them is where this work lives.
Data & CloudData Science & Analytics
You almost certainly don't need another dashboard. You need an answer to a question, and then possibly to stop looking.
Tell us what you're trying to build.
A 30-minute call, no charge and no pitch deck. Describe the problem and we'll tell you how we'd approach it, roughly what it costs, and whether we're the right team for it. If we're not, we'll say so.
30 minutes · No charge · No deck