Riddle me this: what is the common link between highly successful organizations (no matter the industry) and highly successful individuals (no matter their field)?

They all have an extremely clear vision of their journeys: where they were, where they are, and where they’re headed. 

Suppose your goal today is taking your business to new heights, getting out of the slump, and moving the needle toward a brighter future. In that case, you need to understand where you are and where you’re going

In eComm & Retail, this translates into a crystal clear perception of your customer data. Even if third-party data is history, your store still owns an abundance of zero and first-party data. 

Out of all the insights you can gather from your data, one of the most useful ones is predicting customer churn

This post examines customer retention through a churn predictive model – showing you how to use this model to predict customer attrition, address red flags inside your organization, and retain your high-value customers.

What Is Churn Propensity?

Churn Propensity represents the likelihood that a consumer will stop using (and buying) your products. 

This metric takes into account customers’ behaviors, such as the payment history or buying frequency, as well as zero and first-party data about the customer, to predict whether or not there’s a probability of a customer leaving you. 

Other factors influencing customers’ propensity to churn are customers’ satisfaction, the effectiveness of customer services, and the competition. 

Over the past couple of years, eCommerce businesses (but not exclusively) capitalized on the potential of predictive analytics, ML, or data science. They used these methods to predict and prevent Churn, grow their retention rate, and ultimately healthily grow their businesses. 

Churn propensity models are based on the factors that will most likely cause customer churn and a company’s ability to address them.

What Is a Propensity to Churn Model?

A customer churn predictive model is the tool deployed to predict a customer’s likelihood of leaving your brand and stopping buying your products. 

In the beginning, there was the acquisition stage. 

Organizations would bend over backward to acquire new customers but didn’t care much about retaining them. There was plenty of fish in the sea, so to speak. 

Today, the situation is as different as night from day. When the acquisition isn’t sustainable anymore, innovative companies become more defensive and focus on retention.  

Companies seeing their churn rates be like.

Here’s where the churn model comes in handy, acting as a “crystal ball,” predicting high churn risks inside your customer base. 

A churn propensity model analyzes your historical data, investigating customers who have already stopped purchasing your products. 

The churn prediction model searches for patterns or shared attributes in these customers, based on which it predicts which current customers have the highest propensity to leave. 

Propensity to Churn Model Example

Suppose you’re operating in the Health & Wellness Industry, selling proteins and other supplements through a monthly subscription. 

(the logic of the propensity model example is the same in any industry)

Now, let’s say you want to deploy a propensity-to-churn model that predicts which customers will cancel their monthly subscriptions. 

Your first task will be gathering historical data on customers who previously unsubscribed. Look for information such as:

  • How long was the customer subscribed
  • How much would the customer spend monthly
  • The customer’s age and gender
  • The type of subscription the customer had
  • Any complaints from the former customer

After gathering all this data, the next step is feeding it to a churn scoring algorithm and predicting the probability of a customer canceling their subscription. 

The model might look like this:

A 25-year-old customer who has been with the company for three years. The monthly order revolves around $40, and there have been no customer complaints. This pattern suggests this customer segment is unlikely to churn.

On the other hand, you might identify a customer segment that shows churning signals. 

For example, you can find a 55-year-old customer, who has been with your company for half a year, spends around $12/ month in your store and has regular complaints. This model might predict a high propensity to leave in the immediate future. 

Tl;dr

If you want to see which customers are most likely to stop coming to your store, you first look at the data on customers who have already left. Look for patterns and similarities in the data and identify current customers who share the most characteristics with those who left. 

How to Create a Propensity-to-Churn Model 

Propensity-to-churn models are used to improve customer retention and as a prevention tool for customers with high lifetime value. 

In any field, prevention is cheaper than treatment, so it’s worth knowing how to build such a model since it empowers you to act before it’s too late.

There are roughly seven steps to creating a Propensity-to-Churn Model. Still, the process might vary according to the software you’re using or other data specifics & exceptions. 

  • Collect historical data.

The first step is gathering information on your customer base. Include data for customer segments who left your business and active customers. 

Segment your data, including demographic info, purchase history and buying patterns.

  • Create a training set

Split your data into a training set and a test set. 

The first will be fed to the algorithm and used to train your churn prediction model. 

As for the test set, you should use it to assess the model’s performance and effectiveness.

  • Pick a machine learning model.

Data Science teaches us about logistic regression, the decision tree, or random forest as methodologies for training your data algorithm. 

Decide on the model that best suits your needs and feed it the necessary data, so it learns to predict the likelihood of Churn.

  • Test the model

After you gathered & segmented your data and decided on a predictive model, use your test set to predict customers’ propensity to churn. 

The results will return as a 0 or 1 – each customer is given a score based on their churn probability. 

  • Evaluate the model. 

After you run the test, look at the results through the lenses of accuracy and precision. 

This analysis should reflect whether or not the model is capable of predicting customer churn.

Run multiple tests until you’re satisfied with the results.

  • Run the model on your customer base.

When the training period is over, you can run the model on your customer base and identify customers at the risk of churning. 

  • Adjust the model regularly

This last step is an ongoing process. As customers come and go (which is natural), you’ll never find yourself short of new data to feed your algorithm. 

Continue gathering and segmenting your data, test different prediction models, and fix any inaccuracies in your models.

​​Machine Learning algorithms adapt and improve as they’re used, and new data samples become available. 

Treat your propensity to churn model as you would treat a Tamagotchi – leave it on its own, but remember to check in from time to time.

How Do You Calculate Propensity to Churn?

An important disclaimer before we go into propensity scoring.

Remember that any propensity to churn model is based on an algorithm. Therefore, you’ll need both qualitative and quantitative data to train this model. 

Your model can only predict propensity if you pay attention to the training phase and feed the algorithm enough data to reach relevant conclusions.

The propensity to churn is calculated using a binary classification method. In layman’s terms, each customer is given a 0 or 1 value according to their churn probability.

Afterward, each customer goes into a segment: 

  • High churn risk
  • Or low churn risk.

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How to Reduce Customers’ Propensity to Churn

Churn modeling empowers you with an obvious ace in the hole, enabling you to monitor customer behavior more accurately than any other model. 

This means you can pinpoint signals and actions before a customer dumps your brand and recognize these indicators in current customers

If you can clearly distinguish the signs of departure, you can quickly set in motion prevention tactics and minimize customer churn. 

Another essential disclaimer for this chapter: your churn model doesn’t identify the segments most likely to be persuaded to stay. 

In other words, even if churn analytics reveal customers at risk of leaving you, they won’t show you who’s the most receptive to your retention strategy. 

However, you can use a propensity-to-churn model to evaluate whether or not your churn reduction strategies are effective. 

Use the knowledge of your customers’ propensity to churn to create control groups for your retention strategies. 

A/B testing can work wonders here – it doesn’t need to be reduced to website personalization or email subject lines alone. 

If you have a customer segment with a high risk of churning, you can split this into multiple sub-groups and test your prevention techniques. 

This way, when all is said and done, you can ensure you’re comparing apples to apples in your debriefing sessions and don’t get sidetracked by subjective interpretations of your actions.

Churn prediction models are insanely helpful in experimentation. Before you reach a retention strategy set in stone, you can use multiple sub-groups to control and measure the effectiveness of your system. 

When it comes to reducing the propensity to churn, you want to take the same approach as you would toward preventing Churn inside your company. 

Strip down the buzzwords and lowering the propensity to churn comes down to keeping an eye on the little (but crucial) things in customer experience. Always strive to improve your business and surpass yourself.

Here are a few quick ways to prevent & reduce customer churn in eCommerce, Retail – or any other business.

  • Turn customer service into a priority.

No matter how fancy and high-tech you get with your churn analysis and prevention, nothing beats fast & efficient problem-solving on your part. Offering the best user experience weighs significantly into customer retention.

Never underestimate customers’ need to feel seen, heard, and valued. Invest in training your customer service representatives, provide live chat support, and prioritize tickets from high-valued customers. 

  • Learn from complaints. 

Customer feedback is your mirror. It shows your strong and weak points without vanity metrics, ego-boosting, or pampering. It’s up to you what you want to do with the image reflected in that mirror. 

Take action on customer complaints and bad reviews, but also move towards fixing repeat problems forever. 

This way, you won’t frustrate customers experiencing the problem over and over again while also proving you’re invested in providing better, unique customer experiences.

  • Turn the customer base into a community.

Humans have evolved to live in communities. Those who survived only did it because their tribes protected them.

Fast forward to today, when humanity still craves the sense of belonging and being part of something bigger than ourselves. 

Use your communication channels to create a community around your brand. Give your customers a sense of identity they’re comfortable with, and monetize their sense of belonging into churn reduction strategies. 

  • Provide personalized experiences. 

Data doesn’t stop at churn modeling. Instead, you can use it to provide tailored experiences that feel relevant and valuable for your customers. 

For example, you can analyze buyer behavior, look for buying frequency patterns or product assortment patterns, and send out personalized product recommendations. 

Churn reduction isn’t a one-time project, something you’d tackle today, then forget about it come next Quarter. It’s a continuous effort to keep your customers happy and engaged with your brand.

Wrap-up

Thus we conclude our journey through the futuristic world of churn propensity. 

It used to be difficult to predict when a customer would leave, but AI and machine learning help us bridge the gap between this moment and the future. Technology allows us to identify patterns, spot red flags, and even predict customer behavior.

A propensity-to-churn model gives accurate insights into customers’ reasons for leaving and highlights the signs of departure. So you can prevent customer churn instead of dealing with the consequences. 

Use the insights given by a well-trained algorithm combined with the personal touch of human interactions to create an unbeatable retention strategy. 

Good luck, and remember to have fun!

Frequently Asked Questions about the Propensity to Churn

What does churn mean in Analytics?

Churn represents the act of a customer leaving you. Churn analytics refers to measuring the rate at which consumers leave your brand. Churn Analytics’ purpose in uncovering the reasons behind customer churn.

What is the churn process?

The Churn process represents customers or clients leaving a business. Usually, the reason behind this process is customers lacking to see value in a product, or achieve their desired result. To prevent churn, you must make a priority from helping your customers reach their goals.

How do you develop a churn model?

To develop a churn model, you first need to gather your historical data of former customers, then feed this data into an algorithm. The algorithm then looks for patterns to identify churn signals and prevent churn in your current customer base.

How do you calculate propensity to churn?

The propensity to churn is calculated using a binary classification method – each customer gets a 0 or 1 value according to their churn probability. After, customers are divided into two segments: high churn risk, and low churn risk.

What does propensity Modelling mean?

Propensity modelling refers to using AI or machine learning to predict customer churn, based on your historical data on customers who already quit your brand.