As product usage analytics deliver a growing stream of data and insights into product team dashboards, user retention metrics have become an increasingly reliable KPI to measure customer, product, and company health. While this has long been the case for SaaS it is becoming increasingly important for B2B on-premise software, too.
Customer retention is “vital to the growth of your business since the cost of acquiring a new customer is much greater than maintaining and growing an existing account.” Research shows that “increasing retention by 5% actually increases profits anywhere from 25% to 95%,” so let’s define user retention, how to measure it, and what to do to improve it.
User Retention is the continued use of a product or feature by your customers. While measuring user retention for feature usage is relatively straightforward (using that feature at least once in a specified period of time), measuring it for a product requires more definition. It is important to lay out the usage factors that will be considered to be “product usage” before beginning your measurement efforts. For example, does simply running the application count as usage or is there a higher threshold based on time, number of key features used, etc.
User Churn is a subset of user retention metrics that looks at the number of users who have stopped using a product over a specific time period. Defining that time period is important. Is your application used daily? A shorter time frame makes sense (especially where daily retention rates are critical). Is your application used monthly? A longer time frame is more appropriate. Understanding the context of how your application is used and setting a relevant time period for analysis will yield more fruitful insights.
Once you decide on the relevant time period, you can measure user retention by taking the number of active users at the beginning of the time period and subtracting it from the number of those users who are still using your application at the end of the time period. To calculate the retention rate for that time period, simply divide the beginning number by the end number.
At the most basic level, churn analysis is measuring how many users leave your application in a given time period. Usage Intelligence provides a few options that allow you to look at churned, or lost, users from a few different angles.
Get insight into the product details, OS profile, and hardware specifications of users who churn away from your product so you can begin to build the profile of what typical churned users look like and start finding out the root causes.
Understanding the lifetime behavior patterns churned users had before they walk away from your product can help you identify variations from your more loyal customers. Analyzing overall product engagement and retention or drilling down into feature usage will give you a more specific picture of churned user interaction.
Once you have identified the factors leading to churn, you are in a better position to take steps to improve retention. If users are churning after the first of three steps in your “getting started” onboarding wizard, it makes sense to reexamine it and identify how and why users are getting hung up on step 1. If your user flow analysis reveals patterns leading to churn, it may make sense to focus on usability and UX efforts to improve the customer experience before churn becomes a bigger issue.
Perhaps you are seeing that key features associated with customer success and value are not being discovered or have limited use. In-application messaging can improve your retention efforts by educating users at risk of churning on the best ways to use those features and pointing them to how-to videos and knowledgebase articles.
“Instead of waiting until something goes wrong and reacting through customer support, customer success aims to ensure customers are gaining value from the product or service at every step.” With usage analytics guiding the way, both Customer Success and Product Teams are well-positioned to proactively improve customer lifetime value and retention by focusing on these metrics and the underlying causes.
Keith is Revulytics’ VP, Software Analytics and was the co-founder and CEO of Trackerbird Software Analytics before the company was acquired by Revulytics in 2016. Following the acquisition, Keith joined the Revulytics team and is now responsible for the strategic direction and growth of the Usage Analytics business within the company. Prior to founding Trackerbird, Keith held senior product roles at GFI Software where he was responsible for the product roadmap and revenue growth for various security products in the company's portfolio. Keith also brings with him 10 years of IT consultancy experience in the SMB space. Keith has a Masters in Computer Science from the University of Malta, specializing in high performance computing.
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