Cases, models, and the questions behind them.
Each investigation follows the same structure: a question, a hypothesis, the data, the analysis, the decision, and the outcome.
- № 01
the tree that could explain itself
75,000 synthetic customers, one depth-4 decision tree, and an uncomfortable question: do you present the model that scores best, or the one the board can follow?
11 minpythonscikit-learnpandasRead → - № 02
what is a customer actually worth?
a walk through descriptive stats, correlation, simple and multiple regression on 25,000 synthetic customers — and the confounders hiding in plain sight.
8 minpythonnumpypandasRead → - № 03
who's about to ask for a mortgage?
training a random forest on 100,000 synthetic customers to spot the quiet signals of intent — and finding that behaviour beats demographics, every single time.
Propensity Modelling9 minPythonscikit-learnpandasRead → - № 04
The £2 Million Marketing Mistake
A fictional retailer spends heavily across paid search, social and email. Which channels actually caused the growth?
Marketing Measurement12 minPythonPyMCMeridianRead → - № 05
not all customers are equal
a subscription business treated every customer the same. the top 8% were paying for the other 92%.
Segmentation11 minpythonscikit-learnlifetimesRead → - № 06
who will leave next?
a telco was firefighting churn after it happened. a propensity model moved the fight forward by 90 days.
Propensity Modelling12 minpythonlightgbmshapRead →