This a simple Customer Lifetime Value analysis using Buy Till You Die Modelling With PyMC Marketing library https://www.pymc-marketing.io (and Lifetimes too! https://lifetimes.readthedocs.io/en/latest/lifetimes.html)
This repo is accompanied by a Medium article availabe here:https://zhijingeu.medium.com/estimating-customer-lifetime-value-with-buy-till-you-die-modelling-python-pymc-marketing-85bc64fce8a6
The code is accompanied by a series of videos where the PPTX is also available in this repo
- BTYD Models : https://youtu.be/SngwCEt_2MI?si=v3XgjNE_I0eCLx4A
- PyMC Marketing - Bayesian Inference, MCMC Sampling and Hierarchical Models https://youtu.be/SngwCEt_2MI?si=vK2SKhNgTZTa5kCx
- Pratical Code Along https://youtu.be/K_LmvZPPWek?si=_dCnhky4aIdyNjzj
The data used is from the Online Retail II data set which contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011 available here: https://archive.ics.uci.edu/dataset/502/online+retail+ii.
Consider checking out my other repositories too ! :
- https://github.com/ZhijingEu/Cohort_Retention_Analysis - This is an implementation of a custom Customer Retention Analysis class with a number of helpful methods to generate customer churn insights frequently used for marketing analytics to understand the growth and change of an organisation's customer base (new vs retained vs lost)
- https://github.com/ZhijingEu/RFM_Analysis_KMeans - This a simple RFM Analysis Using K Means Clustering On A Publicly Available Brazilian e Commerce Dataset on Kaggle