Churn Prediction

Question

-Using the attached dataset (dataset.csv) analyze the data and visualize the most important aspects using your preferred method.

-Furthermore, share three ideas on how to decrease the churn rate. Document steps where needed.
-Split the data into train and test sets. Predict whether a shop will churn or not.

Please document your steps and method used. The csv,“sample_submission_file_churn” will help with the format.

The objective of this case study is to create a machine learning model that will predict whether a customer will churn or not.

mboroto_89 · Answer

@AlteryxMarco  many thanks, this helps a tone! i created a workflow for day of the week analysis for days with most orders and also days most orders are delivered, ofcouse for orders requested on a weekend have a  longer TAT i.e 2 days given weekends are non working days esp Sunday.

AlteryxMarco · Answer

@mboroto_89 I would not remove duplicates on client ID (just don't use the ID itself as a feature to train the model). Like you said there are multiple interactions per customer, and it's likely ONE of those interactions if not multiple contributed to the actual churn. Example: Let's say I buy a single quantity of something and have no issues. Maybe I go back now and buy another 50 items in bulk, but I experience longer wait times for my order (which puts me off and I leave). If you were to remove one of those factors from the data then you lose that observation. This is kind of oversimplifying it, but the short of it is your customer ID is just your primary KEY ID and since you do have many observations per customer, you'll want to retain them so the model can learn from them.

Some other helpful tips - maybe explore feature engineering. One Example - is the delivery date a weekend? Weekday? Holiday? etc,, these could all play factors into how the products show up, when they show up, etc..

Hope this helps!

mboroto_89 · Answer

Thanks @AlteryxMarco , will follow the steps provided.

One thing am struggling with is whether to remove duplicate clients based on client ID. However given that this is a classification problem identifying clients likely to churn, there are multiple data points that represent different interactions, I suspect these may be valuable for predicting churn.