Customer Retention Analytics: How Data Can Help Reduce Churn

Customer retention analytics is crucial for helping you supercharge your business. By analyzing data about your business’s customer retention, you can reduce churn. And as it’s much easier to retain your existing customers than to gain new customers, it’s worth finding out more about how analyzing this type of data can help boost your business. 

Having access to data related to customer retention and customer satisfaction can help your business make key developments to drive strategy for business growth. Customer retention analytics can help you retain your valuable customers. And as finding new clients will nearly always be more expensive than keeping current ones, it’s in your business’s best interests to do everything you can to retain the clients you already have.

So, without further ado, let’s find out everything you need to know about using customer retention analytics to reduce churn.

What Is Customer Retention?

Customer retention is the use of campaigns and other activities to increase the likelihood of a customer purchasing your goods or services again. It’s a nuanced concept where you expect an increase in profitability with each repeat purchase because you haven’t spent the same amount of money as on customer acquisition. Retention can be a short-term or long-term strategy that focuses on building loyalty among customers.

Customer retention should be your priority as a 5% increase in retention can increase profits by 25% to 95%. Customer retention affects growth and profitability just like customer acquisition. Although from a long-term view, customer retention may not give faster results, your revenue and profitability will depend on existing customers more than on new customers.

Churn is the number of customers that do not return to your business after making a purchase. Churn rate is the most important metric for analyzing customer retention. 

You can adopt several customer engagement strategies to ascertain which customers keep buying your product or service. Data can help you define these techniques and help you establish deep relationships with customers by working on weak spots in your customer journey.

What Is Customer Retention Analytics?

Customer retention analytics are the metrics you measure to ensure you’re doing all you can to keep customers. These metrics help you decide how satisfied your customers are and if they’re on their way to becoming brand loyalists.

If you have sharp insights related to customer retention, you can use them to draw conclusions by comparing empirical data and develop an optimal customer retention strategy. If like most businesses, 80% of your profits come from only 20% of your current clients, you can use data to ensure these customers don’t leave.

You can use machine learning on your customer retention data to predict customer behavior. Customer retention analytics will draw conclusions and correlations from data like purchase history and demographics. 

Let’s now take a closer look at the different types of customer retention analytics.

Descriptive Analytics

Descriptive analytics give you detailed insights based on historical data. These include tracking past purchases, complaints, customer service satisfaction, and product reviews. Descriptive customer retention analytics would need your cloud engineers to ensure all customer-related data is stored on the cloud and regularly updated and backed up. 

Since it uses historical context to devise retention tactics and style a personalized customer experience, all historical data needs to be available for analysis. 

Prescriptive Analytics

Prescriptive analytics find solutions based on insights gained from descriptive analytics. You can gain data on remedial solutions to increase retention and see how they fared. Prescriptive analytics makes you evaluate all strategies in hindsight so they can be improved. 

An example of prescriptive analytics is the use of Fraud Detection in a bank. An algorithm will assess historical data after a purchase has been made to see if it fits in with the typical levels of expenditure. If it finds an anomaly the bank will be alerted and a course of action will be recommended, such as canceling the bank card. 

Predictive Analytics

This works along with descriptive analytics to predict customer behavior based on historical data. This can help you anticipate certain customer interactions and increase customer retention by preparing for them. 

An example of this would be to use the historical transactions to determine how likely someone is to continue their subscription, for example at a yoga class. Next time this customer enters the studio, staff will receive a prompt to offer extra incentives to the customer to convince them to resubscribe. 

Diagnostic Analytics

The data gathered and analyzed around a single problem or event and to try to understand the reason why it occurred falls under diagnostic analytics. Maybe you had a huge fall in customer retention during a specific month. Figuring out why this happened and how to avoid it happening is the key to diagnostic analytics.

An example of Diagnostic Analytics could be the food subscription company HelloFresh determining the reasons behind customers not renewing their subscription service, which could include budget, diet, or not fitting with the customer’s schedule. HelloFresh can then use this data to improve the customer experience and try to prevent other subscribers from canceling for these same reasons.    

Consumption Analytics

Consumption analytics is similar to diagnostic analytics but instead helps understand customer behavior. Why did they choose you over a competitor? Why did they stop supporting you? This information is key to understanding changes in customer behavior, especially when it hits your retention rate.

How Does Data Analytics Benefit Customer Retention?

It can be incredibly beneficial to have a data-driven customer retention strategy. Leveraging customer retention analytics to its full potential can help reap rewards that are not just commercial but can also build your reputation and provide a competitive advantage. Here are the top advantages of customer retention analytics.

1. It Minimizes Customer Acquisition Costs (CAC)

Identifying your target audience, lead generation, inbound or outbound marketing, and pushing leads down the conversion funnel costs money. When your customer finally buys something, you’ve already invested a good deal of capital. This is the customer acquisition cost (CAC).

It’s extremely costly to acquire a brand-new customer. Defining customer engagement and retention strategies based on data analytics are best to reduce this loss and reduce churn.

Investing a fraction of the cost into an existing customer through engagement and world-class customer service makes more financial sense. Your existing clients allow you to upsell or cross-sell without any major CAC, meaning they can be more profitable than new customers.

2. It Creates an Opportunity for Sustainable Growth

It’s easy to sustain growth when you know what factors affect it. When you have data to support your retention strategies and business is mostly recurrent, it allows growth patterns to repeat. You provide high-quality goods and the best customer service; customers reciprocate by being loyal to you. Analytics doesn’t just give you a competitive advantage. It tells you exactly which customers are most likely to recognize it.

3. It Provides Quality Insight Into Customer Experience 

The customer experience (CX) and customer journey are critical for any business looking to retain its customers. Customers won’t just leave you if your product is faulty. They’ll also not return if your customer service isn’t top-notch. Analyzing the data of where you went wrong with a customer and removing faults from multiple points in the customer journey can help create a flawless customer experience.

Data gives you real-time information about your customers. It can help you create personalized landing pages, email newsletters, and mobile apps. Customer analytics can help create a personalized customer experience and also help you send offers unique to each customer.

4. It Establishes Customer Loyalty

When you show a customer that you are grateful for their patronage, they often reciprocate with loyalty. Analytics can give you the data necessary to identify why customers leave and at what point. You can leverage this information and intervene at this point to be thankful and offer a customized shopping experience. Cultivate a strong relationship with your customers and invest in incentives for them to increase brand loyalty.

Instead of inundating clients with dozens of offers, narrow down your offering and use data to suggest what interests the customer. You can easily expect customer loyalty and revenue to increase when you listen to what the customer wants.

Strategies to Reduce Churn

Follow the actionable tips below to reduce churn and retain your valuable customers: 

Prioritize Leads That Are High Quality

Analytics can give you important information about your customers based on business intelligence data. You can identify the most profitable customers based on purchasing preferences, value, and frequency. 

Also, the customers less likely to churn are similar to your primary target customers. Identify such customers by applying algorithms to your data and focus your energies on retaining them.  

Make a Data Roadmap 

Consider data to be an integral driver of success. You have to understand that data if analyzed properly, can bring monumental changes. Once you use customer retention analytics to find areas of improvement, make the changes, and track results. 

Determine Which Machine Learning Methods to Use

Predictive analytics require the use of machine learning. You cannot manually create a predictive model if you want to have the best prediction possible based on historical data. Not only is it labor-intensive, but big data also makes it impossible. Iterative machine learning algorithms based on math, probability, and statistics can provide predictions and insights to increase retention and reduce churn.

Set Key Performance Indicators (KPIs) to Track Your Efforts

Part of your data roadmap will incorporate automated, scalable, and repeatable KPIs. Having KPIs ensures that the entire organization gives data analytics the importance it deserves, and KPIs will also provide crucial information about the success and failure of data drive changes. KPIs ensure that changes are monitored, and processes with negative outcomes are eliminated. 

Use Segmentation to Identify Your Customers

You can use analytics to segment your customers into different groups. It’s easier to draw insights from smaller groups than from one large data pool. Segmentation also helps identify more valuable customers and allows you to change your communication strategy to suit their individual needs. Segmentation can be based on demographics, purchase history, purchase value, and purchase frequency.

Wrapping Up

An efficient retention analysis can help you identify which clients are more likely to churn and the most volatile customer journey points. You can adjust your marketing strategy accordingly by tracking customer behavior and studying important metrics. You can immediately implement churn prevention strategies to engage the customer and use data analytics to improve customer service and boost your revenue.

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About the author

Patty is the EMEA Product Marketing Manager for RingCentral Office, the leader in cloud communications and web conferencing solutions. Patty is passionate about creating value and differentiation, ensuring a better experience for customers and partners. She gained a wealth of international product marketing, product management, GTM, and market development experience, across a range of high-tech SaaS in a fast-paced, hyper-growth environment that assumes both strategic and tactical execution. She is not new to UC, starting in Tandberg, then Cisco, driving the launch of video collaboration and services, and Enghouse with global responsibilities for hosted CCaaS.