Maintaining a loyal customer base is essential for any successful business, yet especially for any business that relies on recurring revenue or repeat sales. Juxtaposed against this are today’s consumers, who are less brand loyal than ever before. With switching between services incredibly easy, retaining customers has become more critical - and challenging - for businesses in any industry. In our highly digital world, however, companies have a secret weapon to delight customers and keep them coming back for more: data.
That’s why one of the most important trends in analytics is understanding why customers leave one service provider in favor of another. Customer churn analysis – the process of analyzing data to identify trends, patterns, and correlations – is important to determine what causes your users and customers to keep using your service or depart. In this post, we discuss what customer churn analysis is, how best to conduct a customer churn analysis, metrics to monitor, and best practices to follow to reduce your customer churn.
Customer churn analysis is a process of analyzing and assessing customer retention levels in order to identify opportunities for improvement. It involves collecting data on customer behavior, such as how often they use the product or service and how satisfied they are with it.
Armed with this data, businesses can identify patterns that indicate loyalty or dissatisfaction with the company’s offerings, enabling businesses to take corrective action before customers defect or double down on high value customers to increase customer lifetime value (CLV). Companies can also use customer churn analysis to segment their customers based on churn likelihood and tailor their marketing efforts accordingly. By identifying which customers are at risk of leaving, companies can take action such as optimizing inventory levels more effectively to avoid customer dissatisfaction, run new campaigns to engage them, or provide special offers and pricing. Ultimately, this helps them improve retention rates by providing targeted offers to highly-valued customers who may be considering leaving. Customer churn analysis can be a valuable tool for businesses looking to increase customer loyalty and maximize their ROI.
Conducting customer churn analysis can help businesses identify why customers are leaving and develop strategies to improve retention, which inevitably drives the bottomline. Here are the five steps involved in conducting customer churn analysis:
You need to know where you are before you can assess how to improve, so the first step is to define your churn rate. This involves calculating the percentage of customers who have stopped using your product or service over a given period of time, such as one month or one year. To calculate this, divide the number of customers who left during that period by the total number of customers at the start of the period.
The second step is to identify the customers who have churned. This involves looking at customer records, such as purchase history, subscription status, and contact information, to determine which customers have stopped using your product or service. Ideally, you have this data in a cloud data platform, so information from disparate systems can be analyzed together for a single source of truth.
The third step is to gather and understand customer behavior and feedback. This involves collecting data on why customers are leaving, such as through surveys or interviews, or through scalable, low touch methods such as automated offboarding. It is important to consider both qualitative (subjective) and quantitative (objective) data when analyzing customer feedback.
Once you know which of your customers are leaving, you need to get to the real reason why by identifying the root causes of churn. This involves looking at the data collected in step three and identifying patterns or trends among customers who have left. These patterns can often point to specific issues, such as a lack of customer service or an outdated product. The most effective companies empower domain experts to engage with this data directly through self service business intelligence, so customer outcome owners can drill into the data as much as needed on their own to get to the critical root cause.
Once you know why customers are churning, the final step is to develop and implement strategies to reduce this churn. This may involve improving customer service, updating products, offering discounts or loyalty programs, or creating marketing campaigns targeted at retaining customers. It is important to monitor the effectiveness of these strategies over time, as they may need to be adjusted depending on changes in customer behavior.
Understanding the process of customer churn analysis is only the first step. It’s critical to use the right metrics to actually understand and measure this churn so you can effectively intervene. Some of the most common metrics used in customer churn analysis are customer churn rate, monthly recurring revenue (MRR) churn, customer lifetime value (CLV), customer acquisition cost (CAC), and net promoter score (NPS).
The customer churn rate is the percentage of customers who have stopped using your product or service within a given period, typically one month or one year. To calculate the customer churn rate, divide the number of customers who have left during that period by the total number of customers at the start of the period.
The MRR churn rate is the percentage of monthly recurring revenue (MRR) lost due to customers leaving. To calculate this, divide the total MRR lost due to churn in a given period by the total MRR at the start of that period.
CLV is an estimate of the total value a customer will bring to a business over their lifetime. It can be used to measure the effectiveness of strategies aimed at reducing churn and retaining customers.
The CAC is the amount of money spent by a business to acquire a new customer. This metric can be used to determine if it is more cost-effective for a business to focus on acquiring new customers or retaining existing ones.
NPS is a metric that measures customer loyalty and satisfaction with a product or service. It uses survey responses on how likely customers are to recommend the product or service to other people, with higher scores indicating a higher level of loyalty and satisfaction.
By understanding and tracking these metrics, businesses can deploy a variety of analytics strategies to better understand the effectiveness of their customer churn strategies and determine which areas need to be improved. Additionally, monitoring these metrics over time can provide valuable insights into customer behavior and satisfaction levels that can help companies identify new areas of opportunity to delight customers, such as embedding analytics into a product or app.
Now that we have covered the process of customer churn analysis and the metrics used to measure it, let’s take a look at some best practices for reducing customer churn.
Providing excellent customer service is one of the most effective ways to reduce customer churn. Make sure you are responding quickly to customer inquiries and complaints, offering helpful advice, and providing a positive experience. This can all be measured by looking at customer support tickets, call center response times, social media interactions, and staying active on WhatsApp Business across multiple devices. These data-driven metrics demonstrate customer service trends and effectiveness across various platforms.
Your customers are not all the same, so treating them as a monolith can stop marketing efforts in their tracks. Targeted marketing campaigns can be an effective way to retain existing customers and reduce customer churn. Consider using data from customer surveys or marketing analytics to create personalized offers or content that will appeal to specific segments of your customer base.
Personalizing service, products, and marketing to your users can only happen if you have a real 360-degree view of them, which is why having a data-driven understanding of the behavior of your customers is key to reducing customer churn. Keep an eye in your customer analytics on customer usage patterns to identify customers at risk of churning, and use this information to create strategies for addressing the issue.
Waiting until your churn indicators are flashing red about a customer may cause you to intervene too late. Engaging with customers regularly is an important part of reducing customer churn. Use email campaigns, social media posts, webinars, and other methods to keep in touch with customers and ensure they are aware of new products or services you may be offering. Additionally, consider using surveys or customer feedback forms or a customer advisory board to solicit customer opinions on how you can improve your products or services.
The other side of churn is retention, which is why developing effective retention strategies is an important part of reducing customer churn. Consider offering incentives such as loyalty programs, discounts, and rewards to encourage customers to continue using your product or service. These are especially powerful when they are part of automated campaigns that will be sent out when customers are at risk of churning in order to address their concerns and encourage them to stay with you.
Creating a positive customer experience is key to reducing customer churn. Consider implementing processes that will make it easier for customers to interact with your business, such as automated onboarding, self-service options, and personalized support. Additionally, use customer feedback to identify areas where you can improve the customer experience and address customer dissatisfaction proactively, instead of being forced to react.
Engaging with customers who are at risk of leaving can be an effective way to reduce customer churn. Consider using customer data to identify these customers and offering personalized support or incentives that will encourage them to stay with your business. Additionally, try to understand the reason for their dissatisfaction and address it quickly in order to prevent them from leaving.
By implementing these best practices, businesses can reduce customer churn, have more accurate sales forecasts, and improve customer loyalty.
Reducing customer churn should be a priority for any business, as it directly leads to long-term revenue benefits, improved customer loyalty, and a healthy, more stable business.
However, understanding customer churn requires data analysis and an understanding of different metrics that can help inform this decision. Effective customer churn analysis requires businesses to understand their overall customer turnover rate as well as to track different factors of attrition over time.
Keeping this information in the hands of data teams instead of the business owners who can take action to mitigate customer churn is a common mistake organizations make. Utilizing BI platforms such as ThoughtSpot can help correct this, giving every kind of employee the ability to gain insights into customer churn data quickly and accurately, thereby helping them stay ahead of their losses from attrition by understanding what is causing them prior. ThoughtSpot enables customers to analyze vast amounts of customer data with AI-powered search visualizations in minutes. So what are you waiting for? Sign up now for a free trial of ThoughtSpot and experience the power of self-service analytics at your fingertips!