From 7d7683b0a95a06cfc4b8ebab7f5e16a550c8ca46 Mon Sep 17 00:00:00 2001 From: tpique-ensae Date: Wed, 3 Apr 2024 19:21:06 +0000 Subject: [PATCH] test --- README.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index a3f0e73..3745681 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ -# Business data challenge 2023-2024 | ENSAE Paris +# Business data challenge 2023-2024 | ENSAE Paris + # Arenametrix : customer segmentation @@ -6,6 +7,10 @@

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+ ## Team 1 @@ -34,6 +39,14 @@ ### Description of the problematic The goal of this project is to create segments of customers from 15 companies belonging to 3 different types of activities (sports companies, museum, and music companies). +### More detailled instructions provided by Arenamtrix +- Definition of “marketing personae” that can be match with a probability to buy a future event +- Matching between future event and people in the database (with for instance a probability to buy a future event) +- And thus, a forecast of the quantity of ticket sold by event by “marketing personae” or by a segment of the database +- BONUS : What is the best timing to send a communication to each contact in the database and each “marketing personae” +- BONUS : What should we tell to each contact in the database and each “marketing personae”to make them come back + + ### Our approach We opted for a sector-based approach, which means that 3 segmentations have been performed (one for each type of activity). As the segments have to be linked to a probability of future purchase, we directly used the probability of purchase during the incoming year to make segments. The first step of the modelization is a pipeline that fits 3 ML models (naive bayes, random forest, and logistic regression) on the data to predict whether the customer will purchase during the year. We then use the probability of purchase estimated to split the customers into 4 segments. For each segment, we can estimate the potential number of tickets and revenue for the incoming year.