Customer lifetime value predictive model with Python

Description

If you wish to start the data analytics career or apply machine learning expertise into business, this is the right course you must choose!

Here I will provide a series of lectures on a practical marketing AI model -- 'Customer Life Value Model', or CLV model. The method is also sometimes called the 'repurchase modeling'. I would say what you will learn is a very useful AI forecasting model for marketing campaign and promotion. Because the CLV models I am teaching in this course are currently widely used in retail banking, insurance, and other sales-related industries. Why? Since it helps business owners select the most valuable customers to get their business better and better!

The value of my course is mainly reflected in the following aspects:

1. The CLV models can be quickly created because the process and features for building models are very concise and efficient.

2. One can utilize the model to predict the customer's purchase behavior or purchase preference for a specific merchandise in a given future time period.

3. The CLV model can be used to predict the probability of customers' repurchase behavior.

4. The CLV model can be used to analyze the activity and loyalty of different customers -- help you solve customer retention problems.

5. Based on the output of the CLV model, business owners can calculate and rank the customer lifetime value.

The objective of the course is to let you master how to effectively use the big data and AI algorithms for intelligent marketing. For example, if you can successfully predict who will buy the commodities in the next month based on historical transaction data, then you would effectively apply some market strategies into these customers, like by launching advertisements, applying recommender systems for ‘cross sales’ or ‘cross recommendation’ At the same time, The business owners will also realize from the model's prediction who are not interested in the goods or services they are providing, perhaps they can adopt some other marketing strategies or promotion to make these silent customers become more 'active' or 'waked up'.

In addition to the business value you can absorb from the course, I also teach you some practical statistical, machine learning and AI algorithm knowledge and skills, combined with the Python programming coding. This mainly covers:

1. Various statistical distribution functions such Geometric / Negative Binomial used in the CLV models and interpretations.

2. Lifetime package in Python to create BG/NBD CLV model.

3. Different analytical and graphics tools in Lifetime package including implementation methods and interpretation.

4. Data exploration, cleaning and feature generation for CLV models with Python programming.

5. Model feature selection, feature engineering, cross validation and performance tracking.

6. Lecture on how to apply the third party data into CLV modeling.

7. Introduction of gradient boost tree algorithms’ framework and implementation including Xgboost and Lightgbm algorithm into CLV modeling.

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