6 Ways App Developers Can Use Predictive Analytics To improve User Experience

Think about the last time you felt you should have been more careful before taking action. Maybe you signed up for a course or approved a product idea only to find out it isn’t for you. What if you knew before you started? You could have saved all your time, energy, and resources. Right?

The predictive analysis aims to do the same for apps.

It deploys statistical techniques and historical data on previous tasks to forecast the impact of your current actions in the future. It enables the app development team to optimize their software delivery and helps to predict velocity and capacity with precision.

The patterns of the earlier transaction and design data help to identify upcoming opportunities and predict possible behaviors.

It won’t be wrong to state that predictive analysis allows app developers to become proactive. They anticipate the actions of the users based upon evidence rather than relying on hunch and assumptions.

According to the World Quality Report, one of the leading challenges in the development cycles is the inability to decide what to test. However, the growing presence of cloud-based computing will help to prioritize. You can come up with various models and choose the best course of action.

Now that we have looked at how this process applies in app development, let’s switch to discuss how it improves the user experience:


1. For Personalized Marketing

For marketers, customer intelligence is the name of the game. The more they understand the customers, the easier it becomes to cater to them.

Personalized marketing through apps is an incredible way to lure and entice customers. Take the example of Amazon and Netflix. One is an e-commerce giant, and the other is a pioneer of streaming services. They both recommend products and shows based on their customers’ behavior and choices.

It is all the outcome of using predictive analysis. If you integrate it into your mobile app, you can suggest a personalized list to the users or show them messages and offers according to their taste.

A Salesforce report reveals that 91% of top marketers are fully committed or already using predictive analysis. The anticipation of customer behavior allows them to craft a better marketing strategy.

2. For content modification

Running the predictive analytics tool on your app will help you to identify which element of the app is turning users away. These insights will help your sales team to concentrate on improving their tactics in that specific area.

A detailed representation of the predictive models can help you to come up with content that befits your audience. The content shown to the audience based on the predictive model will improve the user experience. It will also engage the user for a longer time on the app.

A predictive engine will help to forecast which segment of the users is likely to be stirred from specific content choices. Acting upon that data and curating personalized content suggestions for your users will improve app retention.

You can also make changes to your app’s user interface (UI) according to customer feedback and predictive analytics.

3. For identifying the time to switch devices

When using predictive analysis in mobile apps, you can put your finger on the operating system widely used by your audience. This information is like the goldmine, as it allows you to design an app based on users’ preferences.

4. For risk/fraud prevention

Predictive analytics are useful to forecast risk levels for a security breach, fraud, and theft. You can monitor the app usage trends and keep a close eye on every activity. These analyses can identify unauthorized attempts to break into the app and even freeze the accounts that are compromised.

Developers are using predictive analysis in many ways, and these possibilities are only expanding.

5. For an improved notification system

Predictive analysis helps a developer to understand the reaction on notification messages. They can examine if they receive a different response when they tweak messages or content. All such data enables the developer to plan their notification push in a manner that brings maximum positive responses.

Users can be divided into the following segments based on their actions:

  • Users most likely to abandon the app
  • Users who interact the most
  • Users who install and forget

When you have precise data, you can then segregate your users based on response to push notifications. It makes things easier for the users as well. Those who frequently login would appreciate regular alerts. Whereas, those who rarely use the app would like the low intrusive nature of your app.

For example, airG managed to improve its app notification system based on predictive analysis — too many notifications spammed their users. The predictive analysis enabled airG to identify this problem, and they fixed it by updating their app. Now, for the users of AirG spam isn’t a concern anymore.

6. For user retention

Statista forecasted that mobile app revenue would hit $188.9 billion in 2020. Of course, every developer will want a piece of this revenue. In this case, user loyalty will be the key to determine how big a share an app can get.
Predictive analytics will help to boost user loyalty. As you continue to identify the pain points of the user and fix them, more people are likely to stick with your app.

These analyses precisely point all the weak areas in your app. When users notice that the developer responds within a short span to fix those issues, it makes them feel valued.
If your customers are satisfied with your app, the conversion rate stays the same or goes above. It barely goes below your standard purchase bar.

Such patronage gives an edge to the app as compared to its competitors. Not to mention, it improves your reputation in the market due to positive word of mouth.

Final Thoughts

Predictive analytics in the context of mobile applications brings a plethora of benefits. If used appropriately, you can perk up the overall experience of every user that downloads your app.

If predictive analysis helped you in improving the overall user experience of your app, do tell us in the comments section below.

Author BIO:
Alycia Gordan is a freelance writer who loves to read and write articles on healthcare technology, fitness, and lifestyle. She is a tech junkie and divides her time between travel and writing. You can find her on Twitter: @meetalycia

Leave a Comment