Understanding Churn Rate: A Comprehensive Guide

The business world focuses a lot on building Sales strategies to accelerate growth but surprisingly, less is talked about the importance of customer retention. While extensive Sales can help companies grow at a rapid rate, failing to retain customers could lead to revenue leakage. To mitigate such challenges, companies should devise a strategy to retain existing customers for an extended period. 

An effective customer retention plan can reduce the burden on the Sales and the Marketing team, which would lead to bringing in quality customers instead of hard-selling. In other words, considering customer retention strategy paves the way for improving the growth of the business. Initially, it takes time to implement new strategies, but once you get a hold of them, it will yield profits. Consequently, organizations should rethink their business models by optimizing customer Churn Rate for growth.

Table of Contents

• Understanding Churn Rate
• Understanding the Types of Churn
  • Voluntary
  • Involuntary
• Understanding the Calculation of Churn Rate
• Understanding the Significance of Churn Rate
• Steps to Identify and Optimize Customer Churn Rate
  • Collect User Information
  • Build an Analytics Team
  • Train Machine Learning Models
• Understanding the Challenges Faced while Implementing Churn Strategy
  • Data Collection
  • Right Talent
  • Data Literacy
• Conclusion

Understanding Churn Rate

Churn Analysis Rate, also known as the rate of attrition, is the percentage of service or product subscribers who end their subscription during a set period. Simply put, it is the rate at which customers end their association with a brand and stop bringing in revenue. This rate keeps track of lost customers, opposite to the growth rate that tracks the flow of new customers. The thumb rule is that the Churn Rate should never exceed the growth rate, as that will imply a declining business, thereby making this rate a more-than-ever vital metric to keep a check on a company’s financial health.

Understanding the Types of Churn 

Numerous types of Churn can be specific to the different business use cases, but it can be broadly categorized into two kinds — voluntary and involuntary.

Voluntary

Voluntary Churn occurs when a customer ends the subscription due to poor customer experience. Voluntary Churn is prominent because of factors like complex User Interface, poor onboarding, increase in the pricing, rise in competition/better alternatives, and ineffective customer support.

Involuntary

Involuntary Churn occurs when customers unintentionally lose access to services due to technical issues or problems like the hard or soft decline of payments. Involuntary Churn can be avoided by notifying the customer of the payment due date and optimizing the checkout page. 

Understanding the Calculation of Churn Rate

Suppose a company starts a quarter with 100 customers and loses 10 customers while gaining 20 customers in the three months. The Churn Rate for the quarter, in this case, will be 10%. This not only affects the revenue negatively but also diminishes the positive impact of gaining new customers. In other words, onboarding customers on one side with Sales and losing customers on the other with bitter customer experience stagnates the company’s growth. 

Understanding the Significance of Churn Rate

Monitoring this metric is especially important for SaaS-based businesses, whose large sum of revenue comes from subscription fees. An increasing Churn Rate indicates that your business process has potential flaws. The causes may vary from business to business—for some, it may be a bad user experience; for others, it can be a poor product/market fit; for the rest, the product price may feel a little steeper than what your competitors might be offering for similar entities.

Depending on the causing factor, Churn Rate can help businesses overcome challenges that might be falling on their way. First and foremost, the Churn Rate offers a holistic understanding of an organization’s customer retention statistics. Declining customers means they are unhappy either with your product or service.

As a business quality indicator, the Churn Rate alerts a business to determine and eliminate weaknesses in business operations. Studies suggest that retaining customers is far easier and cheaper than acquiring new ones.Therefore, businesses should put in active efforts to retain the acquired patrons to succeed in the longer run.

An annual Churn Rate of 5-7 percent is considered normal for SaaS companies, but it depends on the market you are serving with your solutions. A competitive market can lead to an increase in Churn Rate.

Steps to Identify and Optimize Customer Churn Rate

1. Collect User Information

As mentioned earlier, factors leading to Churn are unique for every business owing to the varying business models. For instance, if you own a food delivery business, a Churn may mean your customer ends your premium service, uninstalls your app, or is inactive for an extended period.

Whatever indications you consider, you need to amass customer’s behavioral data to train a Machine Learning model, which could predict potential Churn Rate based on the historical patterns. However, Machine Learning models can only provide better predictions if you have devised data pipelines to source necessary information into the models.

You have to pull users’ data from in-house touchpoints like websites, applications, forums, chats, and customer support calls. To go a step further, you can also extract data from third-party review websites and social media platforms to understand what users feel about your product or services. Today, there are several ETL service providers like Hevo Data that allow you to gather users’ information from different data sources without the need to code. Effectively accumulating data can assist you in pinpointing those who are at risk of opting out of your service. 

2. Build an Analytics Team

After obtaining users’ data, you need to build a Data Analytics team to leverage the information and gain insights. Analysts can either continuously monitor patterns in real-time or perform batch processing with the help of ETL practices. Generated insights can be shared with the customer support team, which can determine users’ problems. Such monitoring empowers the customer support team to serve unhappy customers by resolving issues in real-time.

While Analysis of structured data brings many benefits, you can use Data Analytics tools for sentiment analysis with unstructured data generated from social media. Social media has allowed users to seamlessly communicate with the brands, a place where a lot of talks happen. With real-time Data Analytics tools, you can keep an eye on the sentiments of customers who are engaging in a discussion about your products on social media platforms. A sudden change in sentiment can be determined to which the customer support team can respond to resolve users’ issues instantaneously.

3. Train Machine Learning Models

Ideally, no business goes without losing its customers. But the attrition rate can be kept to a minimum by adopting a few strategies that are enlisted below. To reduce customer Churn Rate, you can create Machine Learning models by training them with historical customer data. This will allow you to automatically segregate customers that are more likely to cut ties with your company. Machine Learning models can not only classify potential customer Churn but also determine what factors are influencing a bitter customer experience. Forecasting customer Churn Rate using Machine Learning models can assist companies in fixing the flaws by taking necessary steps.

A surge in the Churn Rate can affect key business metrics like Monthly Recurring Revenue (MRR), Customer Acquisition Cost (CAC), and Lifetime Value (LTV).

MRR – A high Churn Rate directly impacts the MRR since you stop generating revenue from numerous customers. 

CAC – According to a survey (2018), you spend 5 times more to acquire a customer than retaining an existing one. With more Churn, you not only increase your CAC but also reduce MRR.

LTV – Customer Lifetime Value is the total revenue generated by a customer over the lifetime of their account. If your CAC is rising due to attrition, LTV reduces since your spending increases while gaining new customers.

Understanding the Challenges Faced while Implementing a Churn Strategy 

Incorporating new strategies for customer Churn can be a challenging task for many businesses due to the absence of robust information collection practices, data literacy, and the right talents.

Data Collection

Gathering users’ data associated with natural interactions with applications or websites is relatively easy. But, you should make efforts to drive customers to provide additional information based on your business processes. For instance, if you own an e-commerce website, you can ask customers to rate their shopping experience soon after they make a purchase or ask for their feedback after they receive the ordered product. Such strategies will lead to more data accumulation, which would help in better data analysis for uncovering accurate insights.

Right Talent

While data collection from different sources has become more accessible due to no-code or low-code ETL tools, you need experts who can build Machine Learning models and monitor the efficiency of its predictions. Historically, Machine Learning has been prone to giving misleading predictions, which could negatively impact your business. Hiring the right talent for implementing Machine Learning can make a significant difference in how efficient you become with your customer Churn strategy.

Data Literacy

As you have to rely on a lot of insights into customers’ data either through Machine Learning models or Data Analytics, enabling data literacy across the organization is crucial for you to use the information for expediting decision-making to implement changes that could assist in retaining customers. Without data literacy, you would not be able to harness the potential of data to its fullest.

Conclusion

Identifying the Churn Rate has become more than crucial in this era, where there is no dearth of options. Today, customers do not struggle to seamlessly evaluate products and services to onboard with the best solution providers in the market. In the competitive world, reducing the Churn Rate will only get difficult for organizations. Still, with the right approach — Analytics and Machine Learning — you can lower the customer Churn with minimal efforts.

However, do understand that customer retention is no joke; it is an all-extensive process that keeps customer interest at the highest importance. Parallelly, knowing that customers are going to leave is a fact of business. And at times, it won’t even be due to your flaws.

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