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Predict Customer Churn: A guide to using Churn Models


Predicting customer churn is the process of identifying the number of consumers or subscribers that are likely to switch from your company to another business. This is a critical activity for any business, as losing customers takes a significant toll on bottom lines. 

If you can predict which set of customers are going to leave and more importantly, why, you can address their concerns and make a concerted effort towards retaining them.

While browsing the internet, customers are virtually exploring options to replace a current brand. Loyalty in the digital world can be fickle unless your brand offers a compelling and irreplaceable option.

We are living in an aggregated world where websites exist just to provide customers with options for their current choices: be it in insurance, automobiles, fashion, healthcare, or a host of other industries. 

Customer Churn

Predict Customer Churn: Why do customers switch?

A McKinsey study highlights that only a few customers can be called loyalists. Most customers look around and explore options online for better features and prices. Only a few industries like investment, cellular network companies, and automobile insurance enjoy high levels of loyalty. Listed below are some of the reasons why customers shift brand loyalty. 

Churn Data

Churn happens as customer expectations are not met

Simply put, nothing is more off-putting than being promised something and not getting it. The product or service that you offer simply has to be of a high standard. No amount of SEO and social media marketing can make a bad product or service acceptable to consumers. If a bad product or service is advertised and popularized, it will create a bad reputation even faster.

Stages of Customer Journey

Churn happens if the value doesn’t exist

Consumers will buy products and services even at a higher price if they deliver value. For example, people will pay higher for a mobile network simply because it offers better connectivity in their area, regardless of the price. The other thing to consider is the price that competitors offer. If your competitors are offering a nearly identical product or service at a lesser price, then it is a rational choice to consider revising your prices too.

Churn happens due to inconsistent quality

It takes many good experiences to make up for a bad one. So effort should be undertaken to avoid a bad experience. Just imagine, if a product has just one rating, and it is a bad one, would anyone buy it? In some cases, the brand will simply not get another chance if its product was not up to mark when the consumer tried it for the first time. Beauty and healthcare products are prime examples of this.

Why Customer Service Matters

What is a churn model?

It is a predictive model which tells us at any given point of time about the chances of a customer leaving. The model divides customers into two groups: those who are likely to stay and those who are likely to go. It is based on the propensity of the customer to leave.

Churn Model

What are the benefits of a Churn Model?

Once you know which customers are at the risk of leaving, their concerns can be addressed. They can be targeted with special promotions or nudged with reminders to repeat their purchase. The Churn Model also helps to estimate how worthwhile it is to try retaining the customer as it makes more sense to invest in retaining a high-value client. If a client is not bringing much revenue, an effort of around 5% of his previous expenditure could be spent on retaining him.

Benefits of Churn Reduction

How do you design a Churn Model?

Like any supervised Machine Learning model, we need training data with explanatory variables and the target response. Based on the training data, the model is fine-tuned to explain the relationship between the variables and the responses.

Usually, this is based on historical data where we know which clients have left (positive target) and which were retained (negative target). Features define the propensity to leave and include demographic data such as age, gender, occupation, education, etc, as well as factors such as customer interactions, feedback, buying behavior, and value of transactions.

Churn Prediction Model

What do you need to watch in the Churn Model?

In the Churn Model, you need to see how many clients identified as those who were probably going to leave, actually left. Also, the parameters based on which decisions are made, need to be understood. For example, if relative discounts matter more than absolute discounts, then a new pricing strategy can be evolved accordingly. 

Events that increase the propensity to leave need to be monitored daily and responded with real-time solutions. Unforeseen events can also play a part, such as the emergence of a new player in the market. If these factors are not taken into consideration, the results will not be accurate.

Churn Data Set for Machine Learning
How do you reduce customer churn?

Once the model has given you the reasons why customers are leaving, you need to work out ways to address their concerns. These include: 

  • Interacting with them through phone and email to conduct surveys and gather more data. 
  • Fixing quality issues at the earliest and targeting the right set of clients.
  • Delivering value to users who have signed up for free trials to make them permanent customers.
  • Offering regular incentives in terms of freebies, coupons, discounts, rewards and sweepstakes. 

The frequency with which the customer is raising support tickets is another key indicator. It can mean either of two things. If interactions are continuing seamlessly, it means there are no issues and the customer is satisfied. Instead, if there are no interactions or engagements in terms of support tickets, this means that the interest is missing and expectations were not met.

It is also advisable to keep an eye on what your competitors are doing to ensure that your offerings exceed theirs. If they are experimenting with new pricing strategies, you will need to counter them to ensure they do not take away your customers.

Customer Churn Reduction Techniques
Predict churn management proactively

All companies that want to race ahead in terms of market share need to implement Churn Modeling into their digital analytic systems. In an age where data is all prevalent, it becomes absolutely imperative to leverage it to your advantage.

The main takeaway for CSMs (Customer Success Managers) is that they have to be proactive in analyzing data with Churn Prediction Models. The news of a customer leaving should never come as a surprise. Efforts should be made to retain clients beforehand with the appropriate incentives and remedies. So let’s start watching the churn and reduce the burn on our profits by using the right tools!

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