To prevent losing customers through customer attrition, companies turn to churn analytics. This type of analytics helps them measure, monitor and reduce the churn rate. The need for customer churn analytics is one of the reasons our clients turn to our BI implementation services. In this article, our BI experts summarize the main benefits customer churn analysis can bring and explain how to conduct it.
As churn analysis provides you with meaningful insights into how to retain your customers, there appears an opportunity for additional profit. Just look at these numbers: increasing customer retention rates by 5% can boost your profits by as much as 25% and even more. We believe that it is already convincing enough to start analyzing customer churn.
Effective churn analysis contributes to a deeper understanding of customer journeys. Considering the point where customers are likely to leave, companies can develop a set of retention activities to create a more comfortable customer experience and fulfill customer needs much better. This creates the conditions for growing a community of loyal customers who will share their positive experiences and become brand advocates.
Customer churn analysis gives companies a quite accurate prediction about customer preferences: key attributes they are looking for in products/services, features they are dissatisfied with, triggers that make customers more likely to churn, etc. Empowered with such insights, companies have valuable data, which contributes to optimizing the existing product or creating one anew.
Calculating customer (=subscription) churn alone is not informative enough for most businesses, as the percentage of all customers who choose to cease the relationship with your company does not reflect its impact on your bottom line.
To learn how customer churn affects business, you also need to calculate gross revenue churn (the percentage of revenue that is lost during a targeted period)
or employ more complex calculating methods.
Once you’ve rated your customer churn, customer data analytics and BI tools empower you to analyze it. To define triggers that cause customers to quit, you need to segment the leaving customers (through cohort analysis, analyses of churn rates by customer life cycle stages and behavior). The triggers empower you to define the likeliness of churn for every customer and set thresholds for defining at-risk customers. This way you can step in and take remedial actions for the sake of churn prevention. To create a predictive customer churn model, we recommend adding big data technologies into the analytical mix.
Stop your customers from turning their backs on you
By 2020, great customer experience is predicted to become the primary brand differentiator. And customer churn analysis allows businesses to continually improve customer experience and the overall brand image. Do you want to be among those companies? Drop a line to our BI implementation experts to stop giving out your revenue to the competition.