Do all your customers deserve the same offer?
The problem
An electronics chain treated all its customers the same way: the same offers, the same emails and similar discounts. With 6,000 customers and 26 variables, it needed to find behaviour patterns that would let it personalise its campaigns.
The solution
I selected 9 behavioural variables related to spend, frequency and channel. The analysis identified 4 customer profiles, from premium to at risk of leaving, and matched a strategy to each one.
The result
96.8%accuracy in the premium segment
What happens to sales when something unexpected happens?
The problem
A retail chain could forecast normal weeks fairly well, but its predictions failed when promotions, strikes or logistics problems came up. The history explained the past, but not always what was happening around it.
The solution
I compared two models over 156 weeks: one based only on historical sales and another that added five external variables. I validated them with time-based backtesting and simulated more than ten future scenarios.
The result
−32%error compared with the history-only model
Who are we about to lose?
The problem
An airline sorted its passengers into three tiers with rigid rules: Basic, Frequent and Premium. But those rules didn’t always reflect how people actually behaved, so it was hard to know who really needed a loyalty action.
The solution
I tested three models on 21 variables covering flights, spend and incidents, validated them against a baseline and analysed which variables helped tell the different passenger profiles apart.
The result
×2improvement over the previous rules
How much will we sell next week?
The problem
A retail chain plans stock and staffing mainly by looking at what it sold the year before. The problem: ordering too much creates excess stock; ordering too little means running out of product when demand rises.
The solution
I trained a forecasting model on 208 weeks of sales and validated it on another 52 weeks the model had never seen. The aim was to test how far the history could anticipate future demand.
The result
2.21%average forecast error (MAPE)
What makes a customer choose a flight?
The problem
An airline combines price, stopovers, luggage and flexibility, but doesn’t know how much each factor weighs in the customer’s decision. And that weight can change with the type of passenger.
The solution
I analysed 24 flight combinations rated by 1,000 customers. Using conjoint analysis, I broke down 24,000 ratings to measure how much each attribute contributes, both for customers as a whole and for each segment.
The result
69%weight of price and stopovers in the decision