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Sruthi Malla
Sruthi Malla June 3, 2021

Analytics is one of the top reasons to choose omnichannel user engagement tools for a competitive advantage. There is much to do with user analysis defending product-market fit and market proposition with the available trends for a particular timeline. On the other hand, customer retention is a trending topic in any discussion forum. It is hard to retain customers today who are five times more profitable and easy to convert for any business compared to new users. So how do analytics and retention work together?

Why Are Retention Rates Important?

Retention rates help you understand how well your app is performing over time per user. A higher retention rate is better overall because users who stay active longer provide more engagement and a more significant monetization opportunity.

Retention rates are great for understanding why many users disengage from apps and where you stand in the grander scheme of app performance. This can help pinpoint what you can improve.

With your analysis, you could look at how retention develops in your app over 30 days. If retention drops at a certain point, a goal may be to increase the number of interested and engaged users to retain past that date. In gaming, this is often the end of the onboarding period. In verticals like e-commerce, you could find out when purchases occur and optimize for that point in time.

In general, retention rates give you a potential insight into the longevity of your app.

How to use analytics to retain customers?

Predictive analytics: When using analytics for customer retention, predicting the users who are about to churn is of great importance. Just like the saying goes – “Prevention is better than cure.” It is always good to prevent certainly a few essential things in business before they tend to happen. Retention has that emergency lift in business scenarios because once a customer is lost or churned, it is nearly impossible to get him back or make him loyal again.

Instead,  predicting churning users and converting them into loyal customers is where the fun lies. Analytics plays a crucial role in finding out the churn metrics and many more actionable insights for customer retention. 

Segmentation analytics: Based on different business models, users are segmented and targeted with personalized messaging. No matter what, it is hard to convince customers to stay even after giving your best. So, we often find the difference in the number of existing customers that qualify for defined segments. If your loyal customer count is decreasing, that’s quite an alarm to take action right away. Segmentation is a hidden gem when it comes to analyzing how customers are moving in their life cycle.

Funnel analytics: Funnels are again valuable tools to find the process drop-offs and act as visual analytics to figure out the drop-offs at a single instance. Funnels are helpful to figure out the initial stages of churn and thus provides a chance to hold them tight for better retention. Accordingly, segmenting users dropping off at each step of the funnel and channeling them towards conversion with retention strategies is where the customer success manager or user engagement strategist comes into action.

Screen flow analytics: Screen flows are a hidden insight to figure out the initial stages of churn. Suppose the majority of the users are found exiting the app/website at a particular screen. In that case, the design and functionality of the screen should be revisited to see the major friction points that are forcing users to leave. If the design issues prevail for a more extended period, it increases customer dissatisfaction which eventually leads to customer churn. Hence, make sure there are no design liabilities for a better retention rate. 

Retention analytics: Everything mentioned above is an indirect way of measuring churn or retention, but most of the omnichannel tools and platforms are equipped with retention analytics that is worth pinning to any business dashboards.

With the ease at which these reports show insightful analysis, anyone can figure out what’s happening at first glance itself. Below are different types of retention analytics reports and what they are used for.

Regular retention report: 

The regular retention provides the percentage of users who come back on a specific day. Let us understand this with a general example.

Imagine you have ten fresh users who first use your app on Monday, the first day of the month. Three of those users come back the next day on Tuesday, the second of the month, and two come back the following day on Wednesday, the third of the month. Your Day 1 retention rate is 3/10 or 30%. Your Day 2 retention rate is 2/10 or 20%. If five people were to come back 89 days from Monday the 1st (not shown), your Day 90 retention rate would be 5/10 or 50%.

Note that the two individuals who came back on Day 2 could be all, some, or none of the three that came back on Day 1. That is to say, each day is calculated independently.

What is Return Retention?

Sometimes you want just one simple answer. Rather than combing over how many users are coming back today vs. tomorrow, or in two weeks vs. in three weeks, you just want to know how many customers you’ve successfully built a long-term relationship with. Return retention gives you that number.

To understand how return retention is calculated, let’s go back to our example of 10 new users on the first of the month. On the 7th, user A comes back. On the 10th, user B comes back. On the 15th, user C comes back and again on the 16th. Your Day 7 retention is 3/10 or 30% because users A, B, and C all came back on Day 7 or after. Your Day 14 retention is 1/10 or 10% because only user C came back on or after Day 14.

Note: It doesn’t matter if a user comes back one time or 100 times after the day you’ve selected. At the same time, if you select Day 7 as the day to measure against, it doesn’t matter if the user comes back on Day 7 or Day 700.

Since last used report:

The custom reports are where we can write our queries to be showcased as reports. Creating a report of users who last used the app/website provides us with a downloadable/segmentable list of users to retarget and onboard again. Thus retention is just not limited to existing users. Every user who is lost is equally essential as the existing user/new user. 

Wrapping up:

There are many ways to calculate retention/churn using analytics provided by omnichannel tools like Upshot.ai. Above anything, the one who is looking for insights should be able to think and build a hypothesis of possible reasons for churn and figure out how to find those users who are about to churn. The business mind should be equipped with an analytical quest, and it’s like a treasure hunt game when it comes to finding the correct patterns and metrics with faulty dips and exciting peaks in the analysis. 

Build an exhaustive list of hypotheses, try to find them in existing reports, or build custom reports that fit your analysis. The end goal should be the treasure; in our case, it is our valuable retainable customers.

About Upshot.ai

Upshot.ai is an omnichannel, user engagement, and gamification platform that helps digital product owners and marketers improve their product adoption and conversions. Fortune 1000 companies such as GE, UHG, Puma, Sony, ITC, Tenet healthcare are using Upshot.ai and observed a massive increase in product adoption YoY increment in revenues.

Book a demo of the most comprehensive user engagement and gamification platform to achieve your digital product goals. Join our communities for more insights, guides, and best practices that keep you updated with proven strategies and tips to supercharge your campaigns.

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Also read : Gamification strategies guide of 2021

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