Customer churn analysis

Customer acquisition is expensive. Understanding which customers are likely to churn and why allows product managers to identify potential pain points and friction within the digital experience that may be contributing to customer churn and for customer success to work to retain those customers most at risk of churn.

Overview

Based on a customers product and website usage, along with data about the customer, predict which customers are likely to churn and identify the key factors that are driving this churn.

TARGET

Will a customer churn in a given time period?

Challenge

The first challenge in churn prediction is defining what the business means by churn. 

Action
  • Cancel a subscription
  • Last purchase more than a month or quarter ago
  • Last used the product a month or quarter ago
Time Frame
  • In the next month, quarter or year.

The next challenge is joining and transforming the data contained in multiple different tables to create the training dataset. This includes data that defines if a customer has churned from the CRM, data that describes how a customer interacts with the product from the product usage and web analytics DB, and enrichment data such as demographics for third party customer behavior and economics data for macro socio-economic influences. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.

Modeling techniques and libraries

Multiple regression analysis

Allow more than the two choices across multiple dimensions. Models the target as a function of the independent variables.

Package: 
  • Sklearn - linear_model
  • Statsmodels

Machine learning analysis

Build machine learning models to predict the target as a function of the independent variables. Use model interpretability packages to evaluate the impact of the independent variables on the prediction.

Packages:
  • Sklearn
  • ELI5
  • LIME
  • SHAP

Data features

# of Blogs Viewed
Web Analytics DB
Data Type
Continuous
Target
No
Yes
# of Product Features Used
Product Analytics DB
Data Type
Continuous
Target
No
Yes
# of Product Features Used per Day
Product Analytics DB
Data Type
Continuous
Target
No
Yes
# of Support Tickets
Customer Support DB
Data Type
Continuous
Target
No
Yes
# of Videos Viewed
Web Analytics DB
Data Type
Continuous
Target
No
Yes
# of Website Sessions
Web Analytics DB
Data Type
Continuous
Target
No
Yes
# of Website Views
Web Analytics DB
Data Type
Continuous
Target
No
Yes
# of Whitepapers Viewed
Web Analytics DB
Data Type
Continuous
Target
No
Yes
Avg Resolution Time per Ticket
Customer Support DB
Data Type
Continuous
Target
No
Yes
Avg Sessions per Day
Product Analytics DB
Data Type
Continuous
Target
No
Yes
Avg Time per Session
Product Analytics DB
Data Type
Continuous
Target
No
Yes
Common Words from Support Interactions
Customer Support DB
Data Type
Categorical
Target
No
Yes

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