Customer Lifetime Value Predictive Model With Python

A very useful marketing AI model course that enables you to master machine learning and application into business

What you'll learn
How customer lifetime value works in market strategy to promote business
How to use Python as a programming tool to perform data analysis and exploration
How to build customer lifetime value model using Python
How to conduct statistical analysis and feature selection
How to implement Xgboost and lightgbm algorithms
Requirements
Basic math background
Basic computer skills
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.

Overview

Section 1: Introduction

Lecture 1 Introduction of customer lifetime value model and applications

Lecture 2 Transaction data to create CLV model by example (check resources,data, code)

Lecture 3 How CLV model is working in retail business

Lecture 4 Explain CLV concept , calculation and application in intelligent sales

Lecture 5 Walk through the agenda of CLV modeling

Section 2: Preparation for CLV modeling

Lecture 6 Explain the features in CLV modeling (1)

Lecture 7 Explain the features in CLV modeling (2)

Lecture 8 lecture on data analysis with Python

Lecture 9 Python programming environment, data sets, codes and installation (1)

Lecture 10 Python programming environment, data sets, codes and installation (2)

Section 3: Data exploration and feature generation practice with Python

Lecture 11 Lecture on data analysis practice for CLV modeling (1)

Lecture 12 Lecture on data analysis practice for CLV modeling (2)

Lecture 13 Lecture on data analysis practice for CLV modeling (3)

Lecture 14 Lecture on data analysis practice for CLV modeling (4)

Lecture 15 Create features and modeling data set for CLV model

Section 4: Explain BG/NBD for CLV models

Lecture 16 Understand Geometric / Negative Binomial method (BG/NBD) for CLV model

Lecture 17 Lecture on Poisson distribution and relationship with CLV modeling

Lecture 18 Explain Gamma distribution for the customers’ transaction rates

Lecture 19 Explain Geometric & Beta distributions and relationship with CLV modeling

Lecture 20 Summary on the CLV model’s statistical background

Section 5: Create CLV model with lifetime Python package

Lecture 21 Lecture on creating BG/NBD model using lifetime package in Python

Lecture 22 Create and explain the frequency-recency matrix diagram with CLV model

Lecture 23 Create and explain the probability_alive_matrix with CLV model

Lecture 24 Apply CLV model to predict future transaction number

Lecture 25 Create period_transaction plot for CLV model’s validation

Lecture 26 Performance tracking with calibration_purchases_holdout_purchases plot

Lecture 27 Plot the historical probability of survival based on CLV model

Lecture 28 Subset the customers with repurchase behavior

Lecture 29 Gamma-Gamma modeling and the relationship with CLV model

Lecture 30 Fit Gamma-Gamma model with Python lifetime package

Section 6: Create CLV model Xgboost and Lightgbm Algorithm

Lecture 31 Build CLV model using other AI algorithms

Lecture 32 Lecture on the additional data sources and data analysis

Lecture 33 Explain Python codes for creating CLV model using other AI algorithm (1)

Lecture 34 Explain Python codes for creating CLV model using other AI algorithm (2)

Lecture 35 Understand the theory of gradient boost tree algorithms and applications

Lecture 36 Apply the xgboost for creating CLV model in Python (1)

Lecture 37 Apply the xgboost for creating CLV model in Python (2)

Lecture 38 Apply the xgboost for creating CLV model in Python (3)

Lecture 39 Explain the gradient boost tree algorithms’ framework and implementation

Lecture 40 Use Lightgbm algorithm to create CLV model in Python (1)

Lecture 41 Use Lightgbm algorithm to create CLV model in Python (2)

Lecture 42 Use Lightgbm algorithm to create CLV model in Python (3)

Anyone who wishes to learn how to apply machine learning and predictive modeling approaches into business,Anyone who needs to get started data science career in marketing, retail banking, insurance and intelligent sales related industries