Business Intelligence Solution to Increase User Engagement

“How can we make users return to our app?”
“How frequently should we send push notifications?”

Many SaaS leaders face these challenges.

If you’re managing a digital product, like an app or a platform, this might be your situation as well: 

Users are logging in, but not returning frequently enough. If you want to create a sticky app, one that becomes an integral part of a user’s daily habits, digital product analytics can provide invaluable support.

Digital product analytics provide invaluable support in such situations, offering actionable strategies based on data-driven analysis.

However, while working with data, many struggle with deriving insights:

“We’re having all this data, how can we get insights out of it?”

If you’re also interested in becoming data-driven, here’s an example of a business intelligence solution helping you make more informed decisions to reduce the drop-off rate of users.

Report for reducing drop-off rate

A user is considered to have dropped off if they have not logged in within a specific period (e.g.30 days). The lower the drop-off rate, the better.

Drop-Off Rate = Number of users who dropped off in the respective week / Total Number of Users.

Engagement Buckets

Engagement is the number of consecutive days each user logs on. The higher the number of days, the higher the engagement. A user logging in 7 days in a row is more engaged than one who logs in less frequently.

How engaged should you aim to make your users to reduce the drop-off rate? This report created in Power BI shows how business intelligence helps you answer these types of questions.

Drop-Off Rates Report

The report works in the following way:

Data collection: The report pulls each user’s subscription start and end dates, as well as every day the user logged on.

Data model: In the back-end, the report calculates for each week the drop-off rate, and groups each user based on their engagement level: starting from users logging just 1 day, to users logging 12 days in a row.

Here is how you can use such a report:

Analyse Engagement to reduce drop-off rate: In the top chart, the red line indicates drop-off rate. The green bars show the number of users from every engagement bucket. The chart is ordered from left to right starting from the least engaged users – logging for just 1 day – to the most engaged users – who logged on 12 days in a row. At 10 days of repetitive usage, the drop-off rate decreases significantly from 11% to 4%. So, if a user logs in for 10 consecutive days, their chances of dropping off are significantly reduced. 

This analysis, facilitated by Power BI and digital product analytics, allows the product team to set a strategic goal to achieve a 10-day login streak. To support this goal, they can, for example, build  gamification elements to encourage 10 days of repetitive usage.