Three-Project Series

Fighting Churn with Manning's liveBook Data you own this product

prerequisites
basic Python • basic PostgreSQL • basic machine learning
skills learned
design and calculate customer metrics • analyze different customer behaviors • machine learning model churn forecasting
Carl Gold
4 weeks · 10-15 hours per week average · INTERMEDIATE

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team

5, 10 or 20 seats+ for your team - learn more


Churn is the bane of any subscription business, with hard-won customers vanishing every month and taking their fees with them. In this series of liveProjects, you’ll explore techniques for counteracting churn using data derived from Manning’s own liveBook reader’s platform. You’ll analyze engagement data to find out what keeps customers coming back, and how to spot signs a customer is about the leave. Each project in this series covers a self-contained technique for battling churn, so you can pick and choose the skills most relevant to your career.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Customer Behavior
In this liveProject, you’ll clean and process data from Manning’s liveBook platform and prepare it for investigatory analysis to discover causes of customer churn. You’ll load a raw data dump of customer behavior data into PostgreSQL for analysis, categorize and check the event records for bad data, and calculate a basic set of interpretable customer metrics. Some SQL skills and a little Python is all you need to produce a clean and easy-to-interpret data set of customer behavior.
Project 2 Churn Analysis
In this liveProject, you’ll analyze a data set of customers metrics to uncover patterns and behaviors that indicate customer churn. You’ll determine key details like rate of retention, and analyze your data to determine which metrics are associated with customers leaving and customers staying loyal. You’ll analyze how customer behaviors are related to one another, and see if you can expand on your datasets functionality to find new metrics that indicate churn.
Project 3 Churn Forecasting
In this liveProject, you’ll develop a simple machine learning algorithm that can determine from data metrics which of your customers are likely to churn out of an online business. You’ll develop an XGBoost machine learning model and explain its predictions, then build a logistic regression statistical forecasting and analysis model to also predict churn. By the time you’re done, you’ll have two reliable and automated tools for spotting when customers are likely to leave so that your marketing team can easily intercede.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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team

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only $33.33 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • exclusive 50% discount on all purchases
  • Fighting Churn with Manning's liveBook Data project for free

project author

Carl Gold
Carl Gold is the senior data science manager for financial startup Migo.money. He has previously worked as chief data scientist for Zuora, the industry-leading subscription management platform. He has a PhD from the California Institute of Technology.

Prerequisites

This liveProject is suitable for anyone with a basic background in programming and data analysis, who wants to really make a difference to a business's churn. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Basic Python
  • Basic PostgreSQL

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.