Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Customer lifetime value predictive model with Python

Name: Customer lifetime value predictive model with Python
Rating: 3.6 (71 reviews)

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

Created byDatagist INC

Last updated 5/2020

English

English [Auto],

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

Course content

6 sections • 42 lectures • 5h 24m total length

Introduction of customer lifetime value model and applications4:43
Transaction data to create CLV model by example (check resources,data, code)3:51
You will learn the transaction data and Python codes used in this course. Check the attached files in the resource of the section and download them!
How CLV model is working in retail business6:58
Explain CLV concept , calculation and application in intelligent sales7:55
Understand the customer lifetime value concept, how historical data and machine learning predict future purchases and active periods, and how to rank customers for targeted promotions.
Walk through the agenda of CLV modeling7:44

Explain the features in CLV modeling (1)4:34
Learn features used in customer lifetime value modeling with Python, from transaction data to aggregation, decide on unit price and quantity, and aggregate records to customer level.
Explain the features in CLV modeling (2)3:55
lecture on data analysis with Python10:59
Python programming environment, data sets, codes and installation (1)7:39
Install the Anaconda distribution on Windows, Mac, or Linux, then install essential packages for data exploration, data preparation, and CRM model building, using Spyder or notebook and handling third-party data.
Python programming environment, data sets, codes and installation (2)7:38
Learn to download and install the Anaconda software, use Spyder as the editor, and install lifetime and LightGBM to build customer lifetime value models with data preparation.

Lecture on data analysis practice for CLV modeling (1)7:29
Lecture on data analysis practice for CLV modeling (2)6:32
Lecture on data analysis practice for CLV modeling (3)10:20
Use pandas to inspect data types and missing values, assess missing-value proportions, and apply imputation or removal. Prepare customer-level data by filtering and aggregating for CLV modeling.
Lecture on data analysis practice for CLV modeling (4)6:31
Explore data preparation for clv modeling in Python: check data shape, handle missing values, filter by positive quantity, compute monetary spend, convert invoice dates, and run describe for statistics.
Create features and modeling data set for CLV model8:19
Generate clv features from transaction data using the lifetimes package and pandas. Build a dataset with customer id, end date, and t, focusing on frequency, age, recency, and monetary value.

Understand Geometric / Negative Binomial method (BG/NBD) for CLV model6:06
Lecture on Poisson distribution and relationship with CLV modeling3:48
Explore how the Poisson distribution, with lambda as the transaction rate, models purchase frequency and repurchase probability for customer lifetime value in Python CLV modeling.
Explain Gamma distribution for the customers’ transaction rates3:04
Explore how gamma distribution models customer transaction rates, deriving lambda via likelihood estimation to predict Poisson-based repurchases and future purchase counts.
Explain Geometric & Beta distributions and relationship with CLV modeling4:53
Summary on the CLV model’s statistical background6:33
Explore the CLV model's statistical background by linking Poisson, geometric, and gamma distributions to a likelihood framework that estimates churn probability and future spend from purchase frequency.

Lecture on creating BG/NBD model using lifetime package in Python7:27
Learn to build a BG/NBD model with the lifetimes package in Python, fitting the model to data using frequency, recency, and time since first transaction to predict future purchases.
Create and explain the frequency-recency matrix diagram with CLV model11:09
Create and explain the probability_alive_matrix with CLV model4:55
Explain the probability_alive_matrix within a customer lifetime value model, focusing on churn risk, active probability, and how recency and frequency drive purchase likelihood.
Apply CLV model to predict future transaction number5:50
Apply the CLV model to forecast daily purchase quantities for each customer using frequency, recency, and age, and create a 'predicted_purchase' feature to rank customers by expected purchases tomorrow.
Create period_transaction plot for CLV model’s validation7:42
Performance tracking with calibration_purchases_holdout_purchases plot6:45
Partition the data by time into calibration and holdout periods, add validation columns, and fit the BGF model on pre-date data; then compare predictions to holdout purchases using calibration_purchases_holdout_purchases plot.
Plot the historical probability of survival based on CLV model8:21
Subset the customers with repurchase behavior4:07
Select customers with at least one repeat purchase using pandas to create a returning customer summary and focus analysis on this targeted group.
Gamma-Gamma modeling and the relationship with CLV model3:56
Fit Gamma-Gamma model with Python lifetime package5:24
Fit the gamma-gamma model with the Python lifetime package to estimate each customer's average transaction value, then use the predicted value to inform the customer lifetime value calculation.

Build CLV model using other AI algorithms8:04
Lecture on the additional data sources and data analysis13:21
Explore enriching customer lifetime value modeling by integrating score and discount data with transaction data, creating features like recency, frequency, and spend in Python, and comparing parametric versus nonparametric approaches.
Explain Python codes for creating CLV model using other AI algorithm (1)10:49
Explain Python codes for creating CLV model using other AI algorithm (2)8:53
Understand the theory of gradient boost tree algorithms and applications13:58
Explore how gradient boosting builds tree models to predict residuals and refine forecasts, using Python implementations like XGBoost and LightGBM for customer lifetime value modeling.
Apply the xgboost for creating CLV model in Python (1)14:54
Apply xgboost and gradient boosting to build a customer lifetime value model in python, using features like recency, frequency, customer age, and external data, with train-test split and mse evaluation.
Apply the xgboost for creating CLV model in Python (2)10:58
Apply the xgboost for creating CLV model in Python (3)16:39
Explain the gradient boost tree algorithms’ framework and implementation11:34
Use Lightgbm algorithm to create CLV model in Python (1)6:13
Learn to build a CLV model with LightGBM booster in Python, comparing LGBM regressor and classifier, tuning max_depth and learning rate, using fit with MSE objective.
Use Lightgbm algorithm to create CLV model in Python (2)5:36
Partition the data into training and testing sets (70 percent training, 30 percent testing) with X1, X2 features and y target, and monitor performance with a watchlist during Lightgbm model training to build a customer lifetime value prediction.
Use Lightgbm algorithm to create CLV model in Python (3)8:32

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.

Who this course is for:

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

Customer lifetime value predictive model with Python

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 31min

Preparation for CLV modeling5 lectures • 35min

Data exploration and feature generation practice with Python5 lectures • 39min

Explain BG/NBD for CLV models5 lectures • 24min

Create CLV model with lifetime Python package10 lectures • 1hr 6min

Create CLV model Xgboost and Lightgbm Algorithm12 lectures • 2hr 10min

Requirements

Description

Who this course is for: