Benefits of Customer Churn Prediction Using Machine Learning

Increasing churn, or attrition, could be a nightmare for any marketer, business analyst, Head of Sales, or CEO. Obviously, when customers don’t extend contracts or stop regular purchases, it affects not only revenue but also reputation. Since the cost of client acquisition is usually much higher than retention, it brings difficulties to businesses, especially when the cause and the remedy are unclear.

According to Michael Redbord from HubSpot, even a small-scale churn can affect businesses significantly — ‘If we take a pay-for-subscription model, we’ll see that a low rate of monthly churn will heighten dramatically in quarterly/yearly reports. Since it takes more money and effort to obtain new users than to retain existing ones, companies with growing churn rates venture to fall into a money pit, just because they have to spend more resources on the new client acquisition.’

Fortunately, the most effective methods for identifying factors of attrition, as well as prescriptive analytics for combating it, can be provided by customer churn prediction software using machine learning.

Benefits of Customer Churn Prediction Using Machine Learning

Identify at-risk customers

Different companies lose their clients for different reasons. In most cases, there are numerous “pain points,” which remain unknown for product owners. From the bad quality and absent features to unpleasant design and poor customer service — there are a lot of details that you do not take into account that your clients do. Even if your product is almost perfect, you can still reward your new customers with some attractive discounts and offers and ignore your loyal ones. When a business applies churn prediction, machine learning can do analysis and forecasts based not only on customer behavior but also on the brand’s.

Identify methods to implement

After the root cause of client churn has been identified, companies can reconsider and rebuild their products and change their business strategy accordingly. Transformed data and automated flow can be used in CRM and marketing automation systems. However, this doesn’t mean that using machine learning for churn prediction is about building a certain model for a certain task. It is more about domain knowledge and an ability to deliver the best possible solution based on learning data, processes, and behavior.

How Does Machine Learning for Customer Churn Prediction Work?

Basically, the main task here is to forecast customer churn with the help of machine learning and define its cause. If businesses do this in time, they can decrease churn rate (number of customers who decide to cancel renewal/subscription, stop purchasing or switch to competitors), and increase retention rate (number of customers who continue using services or buying goods).

This is where data science engineers step in. First, they have to specify what data should be collected. So they prepare, preprocess, and transform everything they gathered in a way that is understandable for the machine learning model. Then they search for the most appropriate methodology to train algorithms, tune/adjust the models, and, in the end, vote for the best performing variants. Eventually, a model with an outstanding accuracy rate (in terms of prediction) is chosen. This is the one that will go live.

Basically, all the tasks that data scientists have to complete while building AI-powered solutions to forecast client attrition are included in the next five stages:

1. Defining the problem and the goal

To ask the right question is already half the solution to a problem’ – Carl Jung
At the very beginning, data scientists need to figure out what questions to ask and, as a result, which type of ML issue should be solved: classification or regression.

In the first case, researchers decide which class or category data points (buyers/users/partners) belong. Due to historical data, normally, churn/non-churner labels are set. These labels are the answers which will be forecasted during the ML training. The classification model allows companies to get answers to the questions below:

• Will a certain client attrit?
• Will a subscription be renewed?
• What are the chances a user switches to a cheaper plan in the nearest months?
• What are the signs of uncommon customer behavior?

Keep in mind that the last question refers to discovering anomalies and is used to search for outliers — the points that are not related to any other data.

If you qualify customer churn prediction as regression, then machine learning estimates how a target variable depends on other data values. Unlike classification, which works with categories, regression works with numbers. For example, you can predict when a certain customer will be close to churning or request probability estimates of churn per client.

2. Creating a database

The next stage is about specifying data sources to be used for the following stage of modeling. In most cases, you get data from:

• CRM platforms (Salesforce, Pipedrive, Microsoft Dynamics 365),
• Marketing/Analytics services (Google Analytics, AWStats),
• Comments on social media/reviews/blog pages,
• Feedback provided on demand.

3. Data preparation and preprocessing

At this stage, historical data needs to be transformed into a format that suits machine learning best. The main goal here is to ensure that all discrete units of information are gathered through the same logic, and the whole dataset is consistent.

Then a set of input features reflecting different behavior patterns is created. Behavior characteristics may vary from industry to industry; however, approaches that help identify at-risk customers are general.

4. Modeling and testing

This is when a churn prediction model for machine learning is developed. Normally, several models are trained, tuned, and tested to define the best one in terms of speed and accuracy. They can represent logistic regression, decision trees, random woods, or any other suitable algorithm.

5. Deployment and monitoring

This is the final stage of the development of machine learning for churn rate forecasts: the chosen model is ready to go into production. It can be either integrated into existing software or become a core for a newly built application.

Although machine learning is known as an AI subset and quite a smart technology, you can’t just deploy it and forget. Model accuracy tracking should be handled by data science experts regularly so that any adjustments or improvements could be implemented as soon as possible.

The Five Best Machine Learning Use Cases for Churn Prediction

Some businesses determine machine learning as a ready-made technology; however, this is not correct. Although similarities between companies operating in the same niche can be easily found, it doesn’t matter they have similar business processes, flows, pricing policies, marketing strategies, and designs.

Normally, ML algorithms are developed from scratch, specifically for each company’s needs, goals, and expectations. However, there are universal principles to follow in each industry.

Customer churn prediction in Telecom using machine learning

Because of a highly competitive market and a wide range of products/services (Internet, television, mobile networks, etc.), such giants as AT&T, Sprint, Vodafone, and T-Mobile have already utilized machine learning for reducing churn rate. Today even smaller companies and startups try AI applications right after their services enter the market.

In the wireless network segment, the average monthly rate of customer churn is 2.2%, whilst the yearly one is 27%. Interestingly, the annual cost of client attrition is $4 billion in the U.S. and Europe and around $10 billion globally.

Supposedly, not less than 1.5 million customers would keep doing business with the same companies if the correct prediction was increased by the rate of 1%. It would also mean a $54 million benefit annually.

Customer churn prediction in Retail using machine learning

This is the case when financial institutions will be interested in a position under the curve. If you already have a machine learning model that is able to forecast customer churn with an accuracy of 85%, it may seem to be good enough. But what if that accuracy rate was 87% or 88.6%? Don’t underestimate data scientists who train machines by assigning different classification tasks.

For example, UBS, the largest Swiss bank, utilized ML algorithms to avoid client churn after HSBC, its main European competitor, launched several ads campaigns aimed at billionaires who were already with UBS for a long time.

Customer churn prediction in Marketing using machine learning

Churn rate is one of the key performance indicators for subscription-based businesses. Back to the roots, this model was invented by the book publishers in the UK in the 17th century. Tech giants such as Google, Amazon, Apple, and Netflix have been using the same strategy until today, yet with the help of machine learning for customer churn prediction. For instance, when Netflix finds out segments of at-risk subscribers, the company starts proactively engaging them with special discounts or offers. There are also hundreds of middle-sized companies that just started implementing AI for attrition forecasting.

The companies, utilizing subscription business models, used to implement machine learning to identify users who are not satisfied with the current services. This is how they get an opportunity to address customer issues before it’s too late. It is important to find at-risk users ten or eleven months before their renewal. In this way, marketers and salespeople will have time to contact those users, specify the pain points, and create a flow to help them realize the value they get from the service.

Customer churn prediction in Banking using machine learning

This is the case when financial institutions will be interested in a position under the curve. If you already have a machine learning model that is able to forecast customer churn with an accuracy of 85%, it may seem to be good enough. But what if that accuracy rate was 87% or 88.6%? Don’t underestimate data scientists who train machines by assigning different classification tasks.

For example, UBS, the largest Swiss bank, utilized ML algorithms to avoid client churn after HSBC, its main European competitor, launched several ads campaigns aimed at billionaires who were already with UBS for a long time.

Customer churn prediction in Marketing using machine learning

In marketing, machine learning can assist in segmenting customers into different groups of attrition risk. Once algorithms work, a particular churn ratio will be forecasted for each data point in each group.

Thus, marketers can focus on certain groups and certain customers, identify the same pain points, and consider solutions based on the ML analysis.

Our Experience

We are proud to create outstanding and effective solutions to boost businesses in the U.S., Europe, and Asia. But when it comes to AI, and, especially, ML, there are no borders. One of our recent case studies is about Australian chain stores specialized in auto parts. Thanks to our ML model with the stores’ historical data and ROC AUC = 0.71, our client was able to take appropriate measures to decrease the churn rate.

Having expertise in AI and ML, we are ready to offer you an impressive stack of technologies that are most suitable for your specific business needs. Since our team includes experts who have PhDs in artificial intelligence and experience in training machine learning algorithms for customer churn prediction, we would be happy to discuss your query and provide the best solution possible.

Final thoughts

It is crucial to see the whole picture of customer interaction, starting from the first steps of their journey. The more valuable features for an ML model you select and the more qualitative dataset you provide, the more precise predictions you can count on.

Businesses with an extensive client database and tons of offerings would benefit from customer segmentation. However, the number and variations of the ML models depend on segmentation results. That’s why data scientists should not only deploy models but also monitor, revise, and tune features if needed to maintain the requested level of prediction accuracy.

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