CHURN MODEL
ON STRUCTURED DATASET

Colruyt is the third largest Retailer in Belgium and one of the most technically advanced.
They asked us to do a churn model . After 2 days of work, we got a pretty decent model with TIMi:

36 additional models in 2 days

Colruyt was also interested in predicting a “partial churn”: A “partial churn” means that the customer continues to buy some product but there exists some categories of products were the customer has stopped buying anything. There are 83 product categories at Colruyt. Amongst these 83 categories, there are 47 categories that are very small and it makes no sense trying to predict anything for them. After two additional days, we got 36 additional models “per categories of product” (i.e. models for “partial churn”). Here are some of these models:

graph2
graph3
graph4
graph5

Data-transformation

To build these models, we received a table (as a text file) where each row is: “a customer X bought the product Y at the date D”. This table has 1.4 billion rows (2 years data). Since the Anatella scripts are developed, the total computing time required to update the 36 models (starting from raw data) is around 1 hours. The data-transformation graph that was developed to create these 36 models from raw-data was:

graph7

Note  1 

graph8This box runs inside a loop a complex sub-procedure located in a sub-graph. At each iteration of the loop, the sub-graph computes a different Target Column. We have 36 targets to compute. The 36 computations are running in parallel on 16 cores, Anatella using multithreaded computing.

Note  2 

graph9This box runs inside a loop a sub-procedure located in a sub-graph. Each run of the sub-graph computes 26 variables that are representing the consumption behaviours of each customer in a specific product category. Since, there are 36 product categories, Anatella produces as output 36 tables with 26 variables in each.

Note  3 

This makes a left-join between the “main table” (40 socio-demo variables), the 36 tables containing the 36 targets (computed during “note 2”) and the 36 tables computed during the previous step (“note 3”). This left-join involves thus 1+36+36=73 tables. The output table has thus 40+36×26+36×1=1012 columns and 660K rows. On an ordinary laptop, the computation time for the left-join is 9 seconds. In opposition to other tools, Anatella has no limitation on the number of tables inside a join operation and on the number of columns involved inside a join operation.

Note  4 

This box is a model factory: it automatically creates in batch the 36 required models based on the unique table computed during note 3.

RETAIL CUSTOMERS

delhaize

Delhaize

mediamarkt

Media Markt

colruyt

Colruyt

loreal

L’Oréal

makro

Makro

redpharma

Redpharma

Tom & Co

Tom & Co