Customer Lifetime Value Prediction

CLV Prediction Documents

Benchmark Results

Benchmarked against a naive baseline (avg order value x purchase frequency x horizon) on synthetic transaction data (381 customers, 1yr train / 30d holdout). Full methodology and numbers: docs/benchmark.md.

The naive baseline can look competitive per-customer, but the trained pipeline is dramatically more accurate at the portfolio/aggregate level — the level most CLV decisions (budgeting, cohort value) are actually made at. Hyperparameter tuning narrows the per-customer gap further, though it isn't guaranteed to improve the aggregate number since tuning optimizes each model's own loss, not portfolio-level total error.

This framework we generate 2 main predictive model per customer. First, Next Purchase (Frequency) Model will be trained. This model will help us to predict the day of nex purchases per customer Second, Customer Value Model will be trained. THis model will help us to predict what will be the amount of next purchases per customer. There will be customers can not be predicted by those models above because of lack historical informations. Those customers are NewComers. This platform allows us to predict NewComers' total lifetime values as well.

Installation

Tool can be used any other package by install it via pypi or git command

poetry add clv_prediction

OR

poetry add git+https://github.com/caglanakpinar/clv_prediction.git

Project layout

clv/
    docs/   
        - configs.yaml
        - test_parameters.yaml
    confgis.py
    dashboard.py
    data_access.py
    executor.py
    functions.py
    main.py
    newcomers.py
    next_purchase_model.py
    next_purchase_prediction.py
    purchase_amount_model.py
    utils.py