Business Intelligence

A Tutorial for Churn Prediction Using Machine Learning

by
Craig Wisneski
,
October 19, 2021

Not sure why customers are abandoning your product? Customer churn is a ubiquitous phenomenon in the business world. The average company loses 61 customers out of every 100 it acquires, per year. 

After all, as a consumer, you probably don't exclusively use the same toothpaste brand or only one streaming music service. We tend to shift between brands depending on our mood, the prices and features offered by a product, or even whether we're just bored of using it.

This doesn't mean you should ignore customers leaving your brand. In fact, the opposite is true. At Akkio, we've seen firsthand how companies can use AI to understand and predict customer behavior, and use those insights to win them back.

In this article, we'll look at how companies can use machine learning to understand and predict customers’ actions. We'll look at the most common reasons that customers churn, and how you can prevent churn with predictive analytics.

What is customer churn and how does it affect your business?

Customer churn is a tendency of customers to abandon a brand and stop being a paying client of a particular business. The customer churn rate is the percentage of customers that discontinue using a company’s products or services during a particular time period (typically a month or year).

Customer churn has a significant impact on a company’s financial performance. Accenture reports that customer churn costs businesses an estimated $1.6 trillion a year, and this is only going to go up as customers become more demanding and companies scramble to retain their loyalty. 

Typical causes of customer churn

Customer churn has many potential causes, including:

  • Bad customer service
  • Failure to meet market quality and standards
  • Not enough value
  • Bad customer-fit
  • Customers have found other alternative solutions

Let's look at each of these in detail.

Bad customer service

Customers stop doing business with companies when they experience poor service. A recent survey titled “Achieving Customer Amazement study” found that 96% of customers will leave businesses for bad customer service. This means that investing in solid customer service is crucial for success.

Failure to meet market quality and standards

Customers stop doing business with companies when they don't meet the expected standard of market players. Even in the 80s, around half of consumers felt that the quality of U.S. products was declining. More recently, the New York Times reports that we’re seeing inflation through the worsening quality of products and services.

As businesses aim to cut costs, many reduce the quality of their offerings, which has negative impacts on customer retention.

Not enough value

Companies fail to retain customers because they offer too little in return for their money.
As written in The Telco Churn Management Handbook, “consumers sometimes react more strongly to the reputation [of a business] than the facts.” It’s important that both the real and perceived value of your business is high in order to retain customers.

Bad customer-fit

Customers stop doing business with companies when they don’t feel a strong connection or match between the brand and their needs. Naturally, customers that perceive strong customer-brand fit will be less likely to churn. 

That said, this is the only potentially positive cause of churn, as bad-fit customers are bad for business. However, if your business has bad-fit customers in the first place, then you’re targeting the wrong audience.

Customers have found other alternative solutions

A common reason for customer retention dropoff is when customers find alternative solutions to a problem. If your brand fails to compete by offering winning solutions, then customers will naturally gravitate to the superior products and services out there.

How do companies prevent this? Offer more relevant products and services! When customers switch providers, they're likely to stick with the brand that provides them with what they need or want most.  

How it affects your business

Churn negatively impacts every area of your business, including your valuation, your monthly revenue, your Customer Acquisition Cost, and your growth rate.

When your customers churn, you’re not only losing money in the short-term, but also losing market shares to competitors — especially if those customers are repeat purchasers who have more loyalty. This means that as a business, you have less market share and less room for growth. The obvious result of this is that your company's valuation plummets. 

Of course, when you lose an existing customer, you’re losing future revenue. This, in turn, reflects on your business’s long-term viability and profitability. A customer that you lose today may have been a repeat purchaser who is loyal to your brand, and thus represents a significant loss. 

Further, they might be replaced by a new customer who has no prior relationship with the company, meaning that acquisition of this new client would cost more in terms of resources and marketing spend than retaining an existing client. In other words, your Customer Acquisition Cost increases.

Finally, churn decreases your scalability and growth. If a certain number of customers are leaving your product or service every month, you won’t be able to scale at the same rate as your competitors. You'll also find that it will take longer for you to achieve critical mass and become profitable, which will limit your growth potential.

What is churn prediction, and why is it important? 

Churn prediction is a predictive analytics technique that predicts when customers are likely to leave your company. 

It's an important tool for businesses for several reasons:

  • It helps identify potential risks 
  • It enables businesses to take preventative action
  • It helps to understand customers better, thus making it easier to maintain beneficial client relationships  
  • It helps to make better business decisions and increase your ROI

Let’s look at each of these reasons in detail.

Churn prediction identifies risks before they happen. If a business knows when it will lose customers, it can take action before the customer abandons the product or service. This is especially true for startups and smaller businesses, where every dollar counts — time spent identifying risks early on could be time well spent preventing those risks from materializing altogether.

By predicting churn, a business can plan ahead and prevent unnecessary expenses while also preventing problems that might arise later on if action isn't taken. For example, if a business knows that it will lose repeat customers because they have other options than with the company, it can invest in providing alternative solutions (or better ones!) before customers leave. 

Alternatively, if a business has identified that its customers will stop using certain features of its product or service due to their poor quality level, then the business needs to improve aspects of the product until users are happy. In all cases, anticipating customer churn means taking proactive steps now rather than later when problems arise later on down the line.

In other words, churn prediction helps businesses increase their return on investment (ROI). This also helps increase customer loyalty — if a business has identified that customers are leaving due to poor quality or an issue with user experience, then the company needs to improve those aspects before its users leave in droves.

Churn prediction with machine learning

Machine learning is transforming many aspects of our daily lives, from recommending songs to optimizing our travel routes.

Churn prediction is one of the most prominent applications of machine learning, given that churn rate is a make-or-break metric for businesses. Using AI for churn prediction can help you understand and address this costly problem.

How does it work?

It’s simple: you input data about your customers into a model and the model predicts their likelihood of churning. That data can include categorical features like customer satisfaction and demographic information, or numerical information like their spending. This data commonly comes from CRMs, and you don’t need big data for this.

These models can be trained using historical data to predict future churn, and they can also build a profile of each customer using his or her individual characteristics. For example, Akkio’s no-code AI can predict your customer churn rate and identify who might leave your company in the future so you can take necessary action.

You’ll start with a historical dataset of customers, which needs to include a column on whether or not that customer has churned, and columns that may be indicative of churn. Akkio already has a demo telecom churn dataset provided, so you can do some exploratory data analysis to see what it looks like, but you can also upload your own by hitting “Upload Dataset.”

You’ll then select the column you’d like to predict, such as “Churn,” and hit “Create Predictive Model.” Akkio will automatically create a series of machine learning models in the background, and select the best one for your dataset. In moments, you’ll have a churn model.


 The Akkio Flow Editor used to create a churn prediction workflow, showing a subset of predictions and prediction quality on the test set, with an accuracy over 80%.

This is an example of binary classification, which will predict which of your customers will be churners or non-churners. This is a common use case for telecommunications firms and Internet Service Providers, or really any business dealing with customer churn.

Now that you’ve built a churn prediction model, you can deploy it anywhere. Using our no-code integration with Zapier, you can send customer data to Akkio from virtually any source, and get the probability of churn back. You can also directly integrate with tools like Google Sheets, Hubspot, and Salesforce, and more technical teams can use Akkio’s API for fully custom integrations.

Traditionally, data science professionals would conduct all these steps manually, including data preparation, data preprocessing, feature selection, and implementing classifiers with tools like Python and sklearn, whether it’s logistic regression, neural networks, deep learning, random forest, or even just decision trees.

You can see this process through case studies on technical forums like Kaggle, where data scientists share the modeling process, from data visualization to deploying models like xgboost.

This process would be time-consuming and require highly technical talent, as the artificial intelligence modeling process includes things like hyperparameter tuning, even for a simple customer attrition classification problem. Now, service providers can easily plug their data into Akkio, select a target variable, and they’re off to the races. 

It’s both a useful tool for non-technical professionals, or even to help data scientists more quickly build and deploy models.

Tips to ensure predictions are accurate

Let’s discuss four tips to ensure your AI customer churn predictions are accurate:

  • Data should be labeled
  • Use enough training time
  • Use enough training data
  • Test predictions against the real world

Let's explore these four tips in detail.

Data should be labeled

There are two types of data in machine learning: labeled and unlabeled. Labeled data is data that has been annotated with information about its attributes or characteristics.

In contrast, unlabeled data refers to any piece of information that has not been given a label or category. Unlabeled data can come from many sources including social media posts, web pages, emails, documents, audio recordings, and so on.

Churn prediction is what's called a supervised learning task in machine learning, which just means that you'll need labeled data. Fortunately, most data sources for churn prediction, such as HubSpot and SalesForce, will have structured, labeled data.

Enough training time

Training is the process of teaching a machine learning algorithm how to perform a task by exposing it to examples of that task and providing feedback about its performance. The more examples it sees, the better it will get at performing the task in question.

In the case of churn prediction, we want our machine learning algorithm to be able to predict whether or not customers will churn based on their attributes and behaviors within your product or service. So we'll provide our algorithm with lots of examples so that it can learn from them and improve its predictive accuracy, which takes training time.

Enough training data

The more examples our algorithm sees, the better it will get at predicting churn. This means that it's worthwhile to invest in building a large and diverse training dataset.

The more examples you give your algorithm, the better it will get at churn prediction. However, if you have a large data set to work with, it might be difficult to find the time and resources to manually label all of your data. In this case, you'll need to use supervised learning techniques that can help automate the labeling process.

Enough test predictions against the real world

Once we've built our machine learning model and provided it with lots of examples of churning customers, we'll want to test its accuracy against some measure of how well it predicts churn for new customers. 

This is called testing because we're checking whether our model's predictions align with reality. We call this process validation because we're validating our model's performance against something outside of itself (in this case, actual customer behavior).

Win back at-risk customers

Once you’ve identified customers at risk of churning, it’s time to win them back.

Our first tip is to improve customer service. As we've seen, customers who churn are more likely to be dissatisfied with their experience than customers who stay. And the longer they stay, the more loyal they become. 

So improving customer service can go a long way towards keeping your customers and increasing retention rates. Here are some ways you can do this:  

  • Provide clear instructions on how to use your product or service (for example, by providing educational blog articles)
  • Provide an easy way to contact your team (with support tools like Intercom or Zendesk)
  • Respond quickly to inquiries and complaints (within 24 hours in most cases)

Another way you can keep customers from churning is by offering them additional products or services. This is known as a product upsell or cross-sell offer, and it's an effective way of getting new customers while also retaining existing ones. Here are some examples:      

  • A fitness app might offer users the option to buy a subscription for unlimited classes after a free trial period
  • A SaaS company might offer additional training sessions for users who want more help with their software application

Discounts and promotions are another way to keep customers from churning. In fact, a recent study found that offering a discount led to higher retention rates. Here are some ways you can offer discounts:  

  • Give away free products or services as a way of attracting new users and encouraging them to stay longer
  • Offer a loyalty program that gives out points or other incentives for referring friends and family members to your product or service
  • Offering a referral program is also a great way of increasing customer retention, since it allows you to reward existing customers who refer new ones (and vice versa).   

Finally, there's nothing wrong with sending out targeted marketing campaigns aimed at keeping your most loyal customers around. This could include sending out emails on specific days of the week when they're more likely to be online, or sending emails personalized based on customer location.

Product firms could also send out emails at certain times during the year when your customers might be looking for gifts for their loved ones (for example, around Mother's Day). By doing this, you'll be able to increase conversion rates, which means more revenue for your business!

Conclusion

Data is king when it comes to churn prediction. However, traditional methods of analyzing customer data can be time-consuming and expensive.

Enter AI. AI can analyze and find patterns in any type of data, including data to predict churn, such as customer records in Hubspot or Salesforce. With Akkio’s no-code AI platform, you can quickly build a model that predicts customer churn and use it to take action.

Akkio is an all-in-one AI solution, which means it can even be used for things like product testing, employee retention, lead scoring, forecasting, fraud detection, cost modeling, and sales funnel optimization. We’ve even explored the use of Akkio to help in achieving the UN’s Sustainable Development Goals.

Sign up for a free trial of Akkio to start preventing churn and optimizing your business.

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