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How to Perform Retention Analysis To Grow Your Revenue

By Pete Kurkowski

While the term “retention analytics” spans multiple sets of analyses, the goal of this analysis is to identify which components of your business influence your customers to come back and continue to use your product or service time and time again.  


Retention analytics, and subsequent analyses, usually look at groups of users (segments) who share similar characteristics like: 


  • Demographics
  • Time they are onboarded
  • Length of time they have used the product
  • Consistency of use 
  • Preferred features
  • Points of friction
  • Churn


By observing patterns from each of these segments, it becomes easier to take action on product innovation, like:


  • Customer success teams retaining more existing customers 
  • Converting new customers into retained customers
  • Identifying when customers are most likely to churn 
  • Knowing what drivers will have the greatest impact in preventing churn

Why do retention analytics matter?

Did you know that 80% of your future profits come from 20% of existing customers? That makes the ability to understand what aspects of your product need innovation key to both keeping users loyal and sustaining revenue.


Companies that focus on their retention analytics:


  • Spend less money on retention
  • Increase annual recurring revenue (ARR)
  • Improve products and innovation with data
  • Spend less money on a revolving door sales model


Think of successful businesses like Facebook, Netflix, and Amazon. Each of these companies continues to succeed (and grow) because its model invites users to access its platform on a daily, weekly, and monthly basis.  


These organizations understand that having highly engaged and highly retained customers equate to a continuous revenue stream, and have been making product innovations for decades to capitalize on loyal users:


  • These users cost the company less to acquire
  • The return on profits, when compared to new or disengaged customers, tends to be higher


Conversely, companies with a leaky boat funnel or revolving new user funnel (who are obtaining and losing customers frequently) have a harder time sustaining revenue than organizations with a renewing customer base.  

Bundling retention analytics: How to compliment other growth metrics

Measuring retention rate on its own can help you understand the percentage of customers that generate revenue for your organization over a given period. Retention is particularly important for companies that generate revenue from advertising impressions on the page (like Facebook), or who pay for subscription services which are mostly dependent on renewals that can be influenced by creating a good user experience.


By coupling retention rate insight with other growth and user metrics, you maximize your findings for sustained revenue. 

Engagement analytics

Retention analysis often runs parallel to engagement analytics to help the process of defining segments of users who: 


  • Most frequently using the product
  • Continue to renew or buy more
  • Perform "desired actions" like clicking on a call-to-action during a session
  • Have the longest session duration
  • Perform the most actions, like clicks, video views, etc during a session


Observing retention and engagement at the same time helps organizations quantify product usage into a measure of success that can be attributed across the entire customer base. These measures of success should become objectives when considering what features and methods of usage should be adopted by new or potentially churning customers in an effort to prevent their churn. 

New user acquisition

When retention analysis runs alongside your new-user acquisition metric--sometimes referred to as survival analysis--you can determine the percentage of user growth that turns into a recurring and profitable customer base:


  • Find out why and how your customers are churning
  • Identify which products satisfy strong market demand
  • Analyze early adopters
  • Gauge interest in the product features that impact a long-term roadmap


By using retention analytics alongside other engagement metrics as a springboard to measure product and market fit, a business can more easily decide if product changes or a rebrand is necessary for survival.  

Retention analytics strategies

Some of the most common types of retention analyses, use cases, and questions enterprises seek answers to while conducting retention analysis might include: 


  • Measure how, when, and why customers churn or are retained and what services and features impact those metrics.

  • Analyze the customer journey and measure behaviors to determine where there is volatility and where there is frictionless interaction.

  • Identify features and behaviors and compare good and bad customer behaviors--what are the last actions a customer takes before they churn?

  • What actions are your most highly engaged customers performing, how much time are they spending, and how frequently are they doing the actions before they become converted to engaged users?

  • Measure active or engaged users versus disengaged users at different time windows.

  • Define segments of customers based on time-of-product-use (average session duration, length of time as a customer, frequency), and ask: Who uses your product for the highest number of days? When was their first day? What features do they use most often compared to users who don't?

  • Do users that take advantage of a certain feature have a higher churn rate?

  • Do customers who access three features each month have a lower churn rate?

  • Do users who contact customer service more than twice a month have a higher churn rate?


By finding the answers to these questions, and assessing where potential problems lay, you can address customers' needs when they are most likely to churn as a top priority.

Dive deeper with Scuba Analytics

Having the ability to run effective retention analysis in a timely manner gives organizations a competitive edge. However, for many, it's easier said than done.


According to research by FiveTran, 86% of organizations use multiple solutions to build new data pipelines, and 59% of organizations use 11 or more data sources to make data-informed decisions. These complex tech stacks can make it difficult to take action on data in a timely manner.


44% of businesses say that key data is not yet usable for decision-making, even after a pipeline is built, meaning for many companies, time to insights can take weeks or even months. When pipelines break, schema changes, source availability issues occur, or another data issue arises, time to insights can extend even further.

Drawbacks of business intelligence tools on the market

Many business intelligence, data visualization, or application analytics tools are prescriptive versus agnostic, which means they don’t often make it easy to scale large volumes of data and you’re prone to hitting a dead end when you want to scratch beyond the surface of high-level metrics. 


The most common drawbacks are:


  • You have to conform to a data model
  • Tools aren’t full stack 
  • Requires very technical people to get in-depth analysis 
  • Not flexible or agile in the variety of analysis
  • Inability to ingest high volumes of data
  • Built for one specific use case (journeys, transactions, retention)


With Scuba Analytics, clients can add data from any data source, of any size, of any schema, and conduct analysis, instantly. 

Scuba’s flexible agnostic schema builds cohorts against raw data on-the-fly

Because many analytics tools on the market are out-of-the-box, there are limitations on the level of depth for segment definitions when building and nesting cohorts into each other, or when performing advanced time-related calculations against them.  


Scuba Analytics’ on-the-fly capabilities mean more flexibility and faster results. For example, we can take quickly take:


  • Segment A: The first day a user is seen in our data set.  
  • Segment B: Calculate a literal day number in milliseconds (epoch time).  
  • Segment C: Nest segment A and B and subtract them from each other to get the literal number of days since that user's first day.


From there, we can quickly calculate how frequently that user has returned and used the product since their first day within seconds. 


No other product can do this against raw data, or do this well in any regard, which is the true power of Scuba Analytics.


Want to learn more? Schedule your free Scuba Analytics Demo today!


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