Most app owners are highly concerned with user retention.
It’s a well-known fact that it costs much less to retain a good customer than it does to acquire a new one, so no one wants to be constantly attempting to plug “leaks.”
There are a number of strategies you can put in place to improve retention, many of which we’ve discussed here before, but one we’d like to take a closer look at is predictive analytics. How can we better use the tools we have available to encourage retention?
Let’s take a look.
How Do Predictive Analytics Work?
Predictive analytics is the practice of using historical data and advanced analytics to make predictions about future behaviors, preferences or outcomes. We have increasing amounts of data at our disposal now and more accurate methods of gathering it, predictive analytics helps us apply it to making decisions which will steer us (hopefully) toward our preferred goals for apps.
Retention is, of course, a big goal for most.
Until recently, predictive analytics were something that mostly existed in the somewhat mystical realms of advanced data scientists, but now, various analytics programs have created an opening for any business owner to be able to access and use data.
Essentially, predictive analytics can show you potential incidents of correlation, although you need to be careful about the difference between causation and correlation. For example, your analytics might be able to show you that “people who complete X action twice each week are more likely than any other user to still be with us in a year’s time.” That’s a correlation only.
The key here is that you’ll usually look at a sample of cohorts which make sense, such as by segmenting your users by type. This means that you now understand whether promoting that action identified should be a priority.
Predictive Analytics and Retention
Of course, predictive analytics have almost endless applications, but we’d like to focus on how this data can help with retaining your app users. The challenge is to be able to accurately form a hypothesis based on data available as to whether churn or retention of the user is likely. You have access to a lot of potential data, so it can be difficult to know which behaviors to start investigating.
The predictive analytics tools that continue to be developed can, fortunately, help with nudging you in the right direction. More advanced models are good at analysing data across specific cohort groups and telling you which correlations are more likely to result in retention.
These analytics tools are by no means a quick fix. They rely on machine learning, which means they become more accurate over time. Training data (historical data) is used to aid mathematical algorithms to identify data patterns. The more data, the better the results will be.
Let’s look at some practical ways predictive analytics can inform you so as to boost retention:
#1. Marketing Communications
Predictive analytics can alert you as to which behaviors are correlated with better retention, therefore should be a focus for you to encourage. You can use this data to segment customers based on actions or behaviors and send out marketing communications that are relevant to the segment.
For example, if you have an app where a certain feature must have been set up during on-boarding to ensure the customer sticks around afterward, you could send out push notifications, in-app messages or emails to those customers who haven’t set up the feature.
Another side to this is using predictive analytics for targeted offers. For example, you might be able to identify a segment of customers who are likely to be power users or a most profitable segment going forward. You might choose to send out an offer, say an exclusive feature to enhance their experience with your app.
#2. Keeping Your App Relevant to the Individual
Predictive analytics provides a great opportunity to help your customers personalize their experience and generally get more from your app. Over time, you can have algorithms assessing your customers’ preferences so that they’re able to provide them with suggestions of other items they might like.
For example, you can see this at work on Spotify when the app suggests new songs for your playlists. You can also see it on e-commerce apps like Amazon when it recommends other items based on your previous choices.
If you can find ways to help your users personalize their app use and assist them with that decision, then the chances are you can encourage them to remain as users for longer. Predictive analytics can help boost the value and utility of the app for them.
#3. Identifying Accurate Segments
You might have a basic idea of user segments for your app, but predictive analytics can provide you with segmentation options you may not have even thought of. You can gain a deeper understanding of customer behaviors and preferences so that you can create more accurate segments with more relevant messaging.
#4. Providing Decision Support
A practical application of predictive analytics in the retail world has been to provide users such as merchandisers with the information they need to make stocking or ordering decisions. They can be used in any other situation where a user might need to make a decision too. “Based on this data we recommend X decision”, as long as analytics provide accurate information, this would be a good user case for retention.
#5. Target Users Who Are Unlikely to Return
Predictive analytics can provide you with a very good idea of which first-time users are unlikely to return. This means you can choose to target them more with enticements to stay (for example, extended trials or discounts.
Some apps have used this as a way to more aggressively target these users, but of course, it’s a good idea to pay attention to whether this will really help you to achieve your goals in the long-term. If the user isn’t really within a group of people who you would describe as “ideal” for your app, then your retention efforts may be a waste of time long-term anyway.
#6. Identify Key Churn Causes
Of course, we can’t leave out that predictive analytics can help you to identify key churn causes in the first place so that you can work on mitigating those. IBM wrote an example recently of how its Watson Analytics might be used to identify churn causes.
Data might include factors such as the services the customer had, account data such as the type of account and payment details and demographic data such as gender, age range, and family situation. If possible, you could even dive further into app data, such as any reports of bugs, downtime or feature releases.
Retain Your App Users…
The possibilities for predictive analytics are really only beginning to be more fully appreciated. As analytics technology continues to develop, there may be more options than ever before for honing our use of big data.
For app owners, predictive analytics can play a key role in the retention of users. They can help you to identify key causes of churn, predictors of who might be likely to churn and accurate customer segments. You can also use this analytics to provide the user with a more personalized experience and to better target messaging.
Koombea builds apps that can integrate predictive analytics. Talk to us about your needs today.