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How to Run the Churn Probability Analysis for your SaaS or Subscription Business?

Author

Chintankumar Maisuria
August 07, 2023

Read time: 4 minutes

Happy Monday!

In today's essay, we'll explore how to run the Churn Probability Analysis for your SaaS or B2C subscription business.

Background

In the bustling world of Ai and modern business, where customers have hundreds of options, retaining their loyalty has become challenging.

Let’s say you're the founder of a rapidly growing SaaS or consumer subscription startup, and your core product has gained significant traction in the market.

But amid the celebratory air, a concerning question looms: how do you ensure that your hard-earned customers don't slip away unnoticed? How do we identify whether this new feature made a meaningful impact on their users' experience or was it merely a passing trend?

The answer to this question lies in churn probability analysis. Let's dive in and understand the intricate steps that empower businesses and teams to, ensuring sustained growth and unwavering customer loyalty.

What is Churn Probability Analysis?

Churn probability analysis, also known as predictive churn analytics, is a pivotal practice for businesses striving to retain customers and boost overall growth.

Businesses can proactively engage users and maintain satisfaction by reviewing the factors contributing to user churn and predicting its occurrence.

Let's understand how to perform churn probability analysis effectively:

Step 1: Data Collection and Preparation

Gather Relevant Data 

Start by collecting a comprehensive dataset; it will be crucial for analysis and could be in the form of events that could be brought into analytics software, for example, Mixpanel. This could include:

  • user interactions

  • behaviours

  • demographics

  • firmographics

  • any pertinent information.

Data Cleaning

Ensure the data is accurate and reliable by eliminating the following:

  • duplicates

  • errors

  • inconsistencies

Step 2: Define Churn Metrics

Churn Definition

Establish a clear definition of churn for your business:

  • Is it a lack of engagement

  • subscription cancellation

  • another indicator

Time Window

Determine the time frame within which you'll monitor user behaviour to identify churn:

  • a weekly

  • a monthly

  • custom timeframe

Step 3: Feature Engineering

Identify Key Features

Select features likely to influence churn, such as

  • usage frequency

  • session duration

  • feature utilization

  • customer support interactions

Step 4: Data Analysis and Exploration

Exploratory Data Analysis (EDA)

Visualize data to identify that might correlate with churn:

  • trends

  • patterns

  • outliers

Correlation Analysis using Cohorts

Build cohorts of churned and retained users to analyze the correlation between individual features and churn. This helps identify features impacting user retention and churn.

For example, an Analytics SaaS business could build a hypothesis:

  • Users connecting less than three data flows are more likely to churn.

  • Users reviewing analytics daily and weekly basis are more like to retain.

Step 5: Predicting Churn Probability

Score and Rank Users

Tag users using cohort data to predict churn probability for each user. Let’s take the same example of an Analytics SaaS business:

  • A user connected less than three data sets.

  • A user connected to more than three data sets.

  • A user reviewing analytics daily and weekly basis.

  • A user reviews analytics every month.

Score and rank these users based on their predicted churn probabilities. For example:

  • high-risk churn

  • medium-risk churn

  • low-risk churn

Step 6: Developing Interventions

Segmentation

Segment users based on churn probability and other attributes. The best start is grouped as high-risk, medium, and low-risk churn.

Intervention Strategies

Develop targeted interventions for each segment.

For example - high-risk churn users consider sending personalized offers, recommendations, or notifications.

Step 7: Monitoring and Iteration

Real-Time Monitoring

Implement a system to monitor churn probability in real time. Continuously update models and user segments with new data.

Iterate and Improve

Regularly assess intervention effectiveness. Adjust strategies based on outcomes to enhance churn management efforts continually.

Mixpanel outlined a few of the reasons for customers churning:

  1. Product Value: Customers are not getting value (or finding success) from your product.

  2. User Activation: The onboarding funnel is too complicated, or there are too many steps.

  3. Usage Frequency: The product doesn’t encourage a usage frequency that’s regular enough (e.g. daily, weekly, monthly), causing customers to forget about it.

  4. Subscription Cost: The cost of your product is too high relative to competitors, or its real or perceived value.

  5. Overhyped marketing: The product doesn’t align with the message customers were sold, or they had a negative experience with it.

  6. Technical Issues: A bug or a broken U.I. element doesn’t let users complete a critical action.

Conclusion

Churn probability analysis is a dynamic process demanding a keen understanding of data analysis and user behaviour.

It certainly helps uncover the mysteries of user churn and helps predict it. Once known, another challenge is implementing and testing effective interventions to boost customer retention, user stability, and positive user and revenue growth.

That's it for today's article! I hope you found it insightful and valuable.

Wishing you a productive week ahead!

Thanks,
Chintan Maisuria

P.S. Come say hi on Twitter and Linkedin 👋🏼

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