Improve Customer Churn prediction using analytics in the subscription economy
What are the types and challenges in customer churn prediction and how to reduce churn with data analysis. This is how the customer churn analytics work.
May 12, 2020
Research studies show that customer retention is the main growth pillar for products and services in the subscription economy. We all know that there is intense competition in the SaaS market.
In such a market, customers are free to select any product or service from a wide range of providers. Customers may quit even due to one bad experience. Likewise, if there are many unsatisfied customers, you can suffer from both material losses as well as damage to reputation.
When it comes to customer churn prediction, it is important to work with churn rate prediction. You must analyze the required data and find ways to implement a successful analytics strategy.
As with analytics, your data science specialists require data to work with. It depends on your company goals and you must know the type of data needed. You then need to select data, prepare it, preprocess it, and then transform it in a suitable form for building data analytics models.
Talking about customer churn analysis, you need to find appropriate methods to train your models and select the best performers, which is another vital part of the work. After choosing a customer churn prediction model with the highest accuracy, you can put it into production.
What is Customer Churn Prediction?
Many companies use customer churn analytics to calculate the rate at which their customers quit the product, service, or site. You must ask yourself “are you losing customers?” If you are losing them, then you must ask yourself “how are you losing them?”
When you answer these questions successfully, you would be able to take significant actions and reduce churn rates. Doing so leads to happy customers, large margins, and high profits. So, to prevent churn, you need to measure it using analytics.
Customer churn analytics allow you to plug the leak in your customer bucket. The purpose is to borrow a common analogy. If you are running a business and prioritizing to attract new customers over existing ones and not noticing the toll churn, it can seriously affect your profits.
Research shows that over 95% of customers silently quit the product and do not leave any clues or feedback as to why they have quit the product or service. With customer churn analytics, you can quantify your customers’ value – as well as the price at which you acquire them and develop innovative ideas to increase customer retention.
Simply put, Using machine learning algorithms and models to work on customer churn prediction dataset, you can prevent revenue loss, reduce the costs of marketing and sales, lower customer acquisition costs, and improve the quality of customer service.
How does customer churn analytics work?
Customer churn analytics use data to predict the causes of churns. You can integrate this type of tool into your company’s existing CRM or support systems. This allows you to measure the number of customers you have lost or about to lose.
Most churn analytics enable you to track individual user events to show the journey of the user and the steps they took before quitting the product. Using this method, you can compare this behavior with retained customers and find out what went wrong.
Challenges in Customer Churn Prediction and Prevention
It is not always easy to measure churn – particularly when you try to measure it based on previous data. The future might resemble the past – however, you need to know that nothing is certain.
Finding a pattern
For instance, there may be unforeseen events – such as the emergence of new competitor companies in the market to a wide range of fluctuations in the market. So, your old models can predict wrong customer churn rates, which eventually cause you to take wrong actions.
It is likewise difficult for your data analytics team to apply the finding of analytics to individuals. Therefore, many companies apply churn analytics to a limited dataset – for instance, when has the last time customers interacted with the company.
Finding the root cause of churn
So much so, if you have come across a customer who has called your customer support number and wanted to cancel his or her subscription, then you need to know that the customer is not canceling because they called the customer support. In fact, they are calling your customer support department because they have collected several grievances for the last couple of months.
If you want to get to the bottom of your customers’ churn, you must view the entire journey of the customer – and focus on both higher and lower points so that you can determine the true cause of churn.
Using the right tool
In this regard, it is important to choose the customer churn prediction software wisely. Make sure it integrates with your company’s CRM and support systems. The tool must offer you a central repository to store the customer data. The interface must be simple enough so all members of your team or your employees who are not experts can access it easily and use it without any trouble.
What is retention with churn analytics?
You can’t simply prevent churn without identifying it. You can use customer churn analytics to view the actions of the users throughout their lifespan. This way, you can develop hypotheses regarding the causes or reasons, which led them to quit your SaaS product.
So, you can use follow-up questionnaires and surveys to know the details and suggest your team to take vital actions for preventing the churn. You can also segment your churn dataset for greater clarity.
Keep in mind that if you have thousands of customers – you can’t predict churn rate for each of them. It is because no two customers quit your product for the same reason precisely. However, you need to look at cohorts, which are classes of users that often behave similarly.
Remember, sometimes, churn is not bad at all. For example, uncooperative and unprofitable users churn is good for your company. You can use customer segmentation and related methods to discover your valuable customers – so that you can seek to retain them while avoiding unprofitable customers.
This helps you consider whether the customers you would like to keep from churning can be retained. Let us clarify this by example.
When the election is over and there are no more political campaigns for five years, users will expire after it no matter what – in this regard, churn is inevitable. Anyway, you can also segment churned customers for the amount of time they were your customers.
If you want to find why new users are churning at higher rates than long-term users and if this has caused by changes in your services, you can use data to develop hypotheses. Then, you will test the data using different predictive models or customized machine learning algorithms to reduce churn.
What are the types of Analytics for customer churn prediction?
There are different types of analytics for customer churn prediction. However, to give you the idea, most companies use 5 types of customer churn analytics in the subscription economy.
The first one is known as prescriptive analytics, which allows you to focus on answering certain questions. Using prescriptive analytics, you can find the best solutions and suggest different options for taking advantage of future opportunities. So, in this way, you can make informed decisions. For customers churn, you can use the next-best-action and next-best-offer analytics.
Likewise, another type of analytics used by companies to predict churns is “predictive analytics.” It is a common method that uses models to predict events in the future. Examples include churn risks and renewal of risk analysis.
If you want to uncover patterns for a certain segment of customers, you can use descriptive analytics. Remember, this type of analytics is time-consuming and does not provide the best value results.
This method offers you to take on insights in measuring what happens in the past and using it, you can predict patterns in detail for why customers have quit your product. Examples of descriptive analytics are statistical summaries, clustering, and the use of association rules in customer churn analysis.
Moreover, diagnostic analytics is a sophisticated method where you analyze data to find out why something happened such as measuring churn indicators as well as usage trends among your customers.
Churn reason analysis is a popular example of diagnostic analytics. Remember, using this method, you can look into past events and focus on the different relationships or associations and even sequences to predict customer churn in the future.
Last but not least, outcome analytics is another significant method or tool to gain insights on the existing customer behavior while aligning the patterns with the data from the churned customers. You can use this method to focus on previous consumption patterns and associate it with your business outcomes to reduce future churns.
You can use it to find out why customers quit your SaaS product and predict the churn rates for the existing customers. This also allows you to make changes in your product or service or apply any new strategies to retain your customers.
How to reduce churn with data analytics?
If you want to improve your customer retention, then you need to focus on reducing churn rates through customer churn prediction. One way to do it is through data analytics. Next, we tell you how to do it. Continue reading!
Create a Data Roadmap
Many companies say that they don’t have a clear and sophisticated strategy to embed data and analytics. Some research reports show that when a company takes an integrative approach, it uses analytics as a key strategic driver of growth rather than using it solely for IT. This, eventually, leads to accomplishing the desired results.
If you want to thrive in the market, you must do things differently. For instance, it is important to get the most out of your data. Likewise, you need to implement necessary changes on the organizational level after comprehending what the data explains to you.
So much so, you have the data collected already – now you need to ensure you use it and then enforce certain changes, which are required in the business. In this regard, you need to develop a data roadmap, which is a significant approach.
After making the roadmap, you must follow it up. The best way to create a data roadmap is to make sure your corporate key performance indicators (KPIs) are automated. They must also be scalable and repeatable.
The next step is to focus on stakeholders and describe the key issues you want to resolve. In this regard, you need to categorize the problems into data and compare them with system issues. Most often, you will find that the problem is not with the data but how your employees are using and managing it.
You must prioritize your tasks and evaluate the technical feasibility of the plan you have created for predicting churns. You must re-evaluate the progress every 2 or 3 months to stay on track.
Emphasize On High-Quality Leads
Your customers won’t churn if you treat them like your primary target customers. When you collect data on both your existing customers and potential customers, you can focus on customers that will less likely to churn.
Is this possible? Of course, it is. You need to apply algorithms, which can compare the characteristics and features of your customers to your potential customers. For instance, customers with similar characteristics are probably those who want to use your product.
They will find it interesting and thus stick around with it. Next, you need to focus on segmentation, which by now has become important for you. Each customer segment will allow you to determine distinct features, which will help you easily identify your next customers.
Create Predictive Models
The best way to do customer churn prediction is by using machine learning and artificial intelligence-based algorithms. You can analyze data using different forms of analytics – such as predictive analytics to look at the association among different metrics.
If you want to create sophisticated customer churn prediction strategies, you can predictive analytics because it allows you to predict the future. Remember, this is done based on looking at past datasets – to determine what your customers may like or dislike. This way, you are one step forward in reducing churn rates.
Sometimes, while creative predictive models, you might feel overwhelmed by a large number of variables. Often, it is difficult to manage and analyze all variables simultaneously.
No matter if you highly skillful data analysts, most of the time, you will face problems because looking at data manually can be troublesome and time-consuming. To creative predictive models of customer churn, the best solution is to rely on machine learning algorithms, which can accurately and quickly uncover the reasons why your customers are churning or why they still like your product.
Machine learning is a broad field that focuses on using statistics, mathematics, and probability methods to find relationships among variables, which optimized significant outcomes – such as churns and retentions. You can apply such models to the customer data and make predictions.
The interesting thing about machine learning algorithms is that they are iterative and can learn continually. For example, you have two datasets – one is training data and the other one is testing data.
The datasets must have the same variables. The training data is used to make your machine learning model learn to make predictions. The testing dataset is used to make the actual predictions. Examples of machine learning algorithms are logistic regression and multi-layer perceptron.
Get Data-driven Insights
You can get data-driven insights by analyzing free-text responses, which exist at the open-ended survey questionnaires. The best way to do it is with text analytics solutions, which use sentiment analysis. This can help you determine your customers’ grievances.
Besides, data collection is of no use if you don’t know how to gain insights from it. Some research studies have found that less than 20% of companies use customer data to make informed business decisions.
You can find machine learning or AI-based algorithms and use them to automate the analysis of free-text feedback. Natural language processes and machine learning can simplify the procedures of getting insights from the customer data.
Use Segmentation to predict churn and retain customers
You can use data analytics to segment customers into different groups. Doing so will allow you to find out how each segment interacts with your product or brand. You can likewise look at each sub-groups and focus on gaining insights. The next step is to adopt different interactive and servicing strategies to improve churn prediction and increase retention for the most wanted customers.
So, how to do segmentation? The easiest way is to analyze data including customers’ lifestyle, demographics, SaaS product purchased, types of customers, the purchase value, and the frequency of purchase. The analysis of data and the right type of variables allow you to discover the type of customers who are driving the most revenue. In this way, you can predict customer churn for future days.
Lastly, it is difficult to analyze a large volume of data – however, using machine learning algorithms, models, tools, and data analytics, you can easily structure the data, do analysis for customer churn prediction and improve it. Good Luck!
Rakhin has over 10 years of experience driving business development and client services. In his prior roles, he stayed close to customers to understand their requirements and help them achieve their business goals. He is passionate about customer success.