If you are following the Men’s T20 World Cup, you would surely know about the fantasy sports platforms that allow you to predict your team’s best bet and reward you for getting it right. You select your players and build your dream team on the platform. If your players perform well, you win. Simple, isn’t it?

What if you could iterate this approach in your business ecosystem? Except, this time around, you’ll be in greater control of your predictions for accuracy and results.

Sounds interesting? Keep reading as I dig deeper into how you can make this happen!

Imagine, if you could:

  • Predict accurately the time when a customer will leave you
  • Understand your customers’ behavior deeply
  • Keep your customers engaged based on their likings and preferences

Artificial Intelligence (AI) and Machine learning (ML)

Well, you can translate all these stated objectives into reality by leveraging new-age tools—AI/ML.

With companies and conglomerates, like yours, sitting on massive data sets, technology infused with AI/ML offers different ways to develop predictive models (more on this later) that can correctly predict customer churn rates.

But first, let’s address the basics

What is Customer Churn?

It is the proportion of customers who stop using your services or products during a specific period. In simpler words, customer churn (customer attrition) occurs when customers stop doing business with a company or do so much less frequently than they did previously. It is one of the most important metrics for determining how well a company performs in terms of customer satisfaction and retention.

For example, if you got 1,000 customers last month and lost 100, then your monthly churn rate is 10 percent.

What is the Significance of Churn Prediction?

According to Fred Reichheld of Bain and Company’s research, “a 5% increase in customer retention can increase profits by more than 25%.[1]

Besides this, different studies reveal that the cost of customer acquisition is 5 times that of customer retention. Understanding how to retain customers and predicting churn is, therefore, a critical business metric.

Being able to predict churn probability in time to take preventative measures represents a significant revenue saving for any business. Also, the probability that the acquisition cost has already got recovered by the customer’s spending may be very low.

Moreover, retaining an existing paying customer is always easier and less expensive than acquiring a new one. As a result, predicting churn with high accuracy on an ongoing basis is critical not only for avoiding revenue loss but also for improving customer engagement and developing new revenue streams.

Now that you understand the need for predicting churn, the next question is:

What Should be the Approach for Predicting Churns?

Back in the day, customer retention research and execution was an expensive process. Therefore, there were more intuitions-backed decisions. However, with AI/ML coming to the fore, successful organizations are addressing customer churn by building predictive models and leveraging data and algorithms. The predictive models can be used to identify behavioral patterns of potential churners, segment the “at-risk customers,” and take appropriate actions to retain the trust.

An ideal approach to build an AI/ML-powered system i.e. capable of forecasting customer attrition rate may look like the following:

  • Define churn and understand the problem
  • Collect data
  • Prepare, clean, and pre-process data
  • Build predictive models
  • Productionize the models
1. Define Churn and Understand the Problem

Churn as a problem statement varies from business to business. While it can be different across industries, it can also differ within the same company depending upon the specific business unit. For example, in the case of a bank’s customer closing their accounts, reducing the account usage could be one type of churn and for their digital platform, customers uninstalling the application from their mobile could be another type.

2. Collect Data

Data scientists understand the story hidden in the data and are able to make inferences from it. But for that, data must be rich enough to tell a story. Some organizations adopted the route of data science early and are already sitting on large data sets collected from different sources. However, organizations that have recently started their journey may not be having that historical perspective.

For a start, it is necessary that you understand the kind of data being collected regularly and ensure that no required data source has been missed. While the minimum data required to predict churn is simply some form of customer identification and a date/time of that customer’s last interaction, the reality is that adding additional data on top of this minimum data set is highly encouraged and recommended.

You need to also note and keep in mind how the data is getting collected, in what formats, the frequency of collection, their storage assets, and how these can be consumed.

3. Prepare, Clean, and Pre-process Data

Data in most organizations reside in different source systems in different formats. Combining/Sinking the entire data set from different sources in single storage is critical so that different models can be built and tested on this data. This will help get a singular view of the entire data set. It is recommended to have a storage repository such as a data lake that holds a vast amount of raw data in its native format until it is needed.

This step should be followed by data enrichment by joining different data sets and data exploration and preparation by understanding different data variables and cleaning up the data to ensure everything is homogenous. Data collected about individual customers from different channels offer a clear understanding of their preferences, behavior, contexts, and affinities.

4. Build Predictive Models

Building a predictive model is not just a skill but an art that separates a good data scientist from a better one. It is essential while building the predictive model, you must make sure that it will eventually learn what is expected. A data scientist needs to pre-empt what will happen when the model will be deployed into production: What data will be available? When would the prediction be: for next week, next month?

Another important aspect of the entire churn prediction process is the iteration and interaction between feature engineering and predictive modeling. And to find the best model, a critical step is choosing how to evaluate which model is best. It is better to choose an evaluation metric that fits the business’s needs.

5. Productionize the Models

The next step is to figure out how to optimally provide predictions to the team that will consume it. This can be achieved in two ways:

a. assigning a score to each customer depending upon their probability of getting churned

b. segmenting the customer base into three categories: high-risk, medium-risk, and low-risk

Another important point that needs to be considered is how to feed this information into different communication channels and loop back the feedback so that system should keep on learning and improving.

In Conclusion

We are living in the ‘want now’ reality, mandating you to understand your customers’ likes, dislikes, and preferences. Customers value the ability to make their purchases instantly and retaining “at-risk customers” is the key to success.

Click here to learn more about how your organizations can effectively predict churn risks, get end-to-end control of your entire data science lifecycle, and accelerate your journey from data to decisions by leveraging a single AI/ML-based data science platform.

[1] https://media.bain.com/Images/BB_Prescription_cutting_costs.pdf