Enhancing User Retention with AI-Powered Custom Recommendations: Best Practices

Expanding your customer base gets all the shine, but retention matters at least as much--regardless of the industry. If you retain your existing users at high rates, you gain consistent engagement and revenue streams that cost less to secure, become more predictable, and increase your chances of long-term business success.

That's not just a theoretical statement. Studies show that it costs 6x to 7x more to acquire a new customer than it does to retain an existing one, while 65% of revenue in the average business comes from repeat customers.  And yet, especially in the broad field of mobile app development, retaining existing users tends to be a struggle.

Research shows that the average user retention rate a single day after downloading an app is only 25%. In other words, only one-quarter of your users will continue using the app a day after they download it. By day 30, the average Android user retention rate drops below 5% for most industries.

To solve that conundrum, it's crucial to understand what users expect from apps and businesses they engage with in order to stay with them. Just as important is understanding how to build that retention, which is where technology like AI enters the equation. We'll discuss both of these areas in the guide below.

What Today's Users Expect the From Apps and Brands They Engage With

When users download an app, they want a seamless experience without technical issues or crashes. They expect to have it meet their basic needs, the reason they downloaded it. But don't underestimate the importance of personalization when it comes to modern user experiences.

Salesforce research has found that for 80% of modern users, brand experiences—especially digital experiences—are at least as important as the actual product or service those brands offer. The same research also found that 66% of consumers expect companies to understand and act on their unique needs and situations, while 52% expect all offers from brands to be personalized to those needs.

In other words, personalization matters, and it matters beyond simply adding a user's first name in communication. The entire experience has to be customized, which is consistent with other research as well.

According to McKinsey's Next in Personalization 2021 report, personalizing any connections with your audience continues to rise in importance. Among other things, it found that:

• 71% of consumers expect companies to deliver personalized interactions
• 76% of consumers get frustrated when they don't get those personalized interactions
• Companies that grow faster drive 40% more of their revenue from personalization

When consumers interact with a brand's online presence or app for the first time, McKinsey found that 67% expected personalized product or service recommendations. Meanwhile, 65% expect targeted promotions, while 72% said they expect businesses to treat them like they know them and their interests.

Related: Is Developing an App Worth It?

The takeaway, then, is simple: modern consumers expect deep personalization that offers a more customized experience. Brands and businesses that can satisfy that need will have a much easier time retaining their users for long-term business success.

How to Leverage Personalization Expectations Into More Effective Retention Strategies

When personalization goes deep, how can human-driven efforts keep up? The short answer: they can't. Especially for businesses with a broad current and potential user base, manually tracking preferences to build more relevant apps and digital experiences is simply impossible.

Fortunately, taking an entirely human-driven approach is far from the only possibility. Instead, as the Harvard Business Review points out, personalization is moving rapidly toward the age of AI:

"We are now at the point where competitive advantage will derive from the ability to capture, analyze, and utilize personalized customer data at scale and from the use of AI to understand, shape, customize, and optimize the customer journey."

To get there, the authors propose five pivotal best practices that can help businesses of nearly any size develop intelligence experience engines that drive a more customized and personalized user experience:

1. Connecting data signals and insights, turning unstructured data into smart data that allows for easy (and automated) analysis.
2. Reimagining the end-to-end experience as a seamless flow that doesn't need to wait for manual inputs but instantly and accurately personalizes every touchpoint between user and brand.
3. Activating the experience across channels, ensuring that no matter whether your audience engages through your website or your mobile app, they'll get the same personalized experience.
4. Fulfilling according to the customer's context, creating experiences based on the audience's perspective to ensure the ongoing relevance of any messaging.
5. Testing relentlessly, ensuring that personalization is firing as intended, and injecting new innovations into the user experience while testing that these innovations actually enhance the experience.

The complexity of these processes is also the most compelling case for injecting AI into them. In fact, each of these steps can be enhanced and accomplished by AI. For example, you can automate your app testing to iterate faster and ensure the successful personalized experience you're looking to create.

Related: 9 Tips to Improve Your Mobile Applications Testing Strategy

Think, for example, about how an AI-enabled chatbot could enhance your mobile app's user experience. If a user has a question, the bot can analyze user behavior data in real time, providing a more relevant and context-dependent answer as a result.

The longer the conversation goes on, the more the bot will learn, the more relevant the answers become--and the more likely the user will be to leave the conversation happy and ready to stick around.

More tailored and personalized messaging, of course, is just one way in which businesses can leverage AI technology to enhance their user retention. The other is so significant in what it can provide for both businesses and users, it deserves its own section.

Using AI-Enabled Custom Recommendations to Enhance User Retention 

Custom product and content recommendations are perhaps the most comprehensive and relevant example when it comes to connecting personalization with user retention. It builds on generative AI, which means processing existing data points to create new and original data points. 

In this case, the existing data points are multifold and can include: 

• Purchase and consumption history, like frequently purchasing baby clothes, frequently reading articles on a specific topic, etc.
• Browsing patterns in apps and on websites, which can reveal user interests, preferences, and the pain points they might be looking to solve.
• General user preferences, usually provided voluntarily through surveys or reviews.

A generative AI engine takes these inputs and folds them into more personalized recommendations for products (in e-commerce) or content (in the publishing industry). That, in turn, builds on all of the research shared above: more personalized experiences lead to greater user satisfaction, and ultimately better retention rates.

Some of the world's biggest online brands already use this basic process to engage their customers. Think Amazon's personalized product recommendations or Spotify's AI-generated playlists. 

But increasingly, these possibilities are being unlocked for other businesses and industries, as well. Gone are the days when only the biggest brands in the world could take advantage of custom recommendations.

Research firm Orbis projects the custom recommendation software market to grow more than 35% between 2020 and 2025. Smaller businesses may face computing or capacity challenges during the implementation, but technology is beginning to act as the great equalizer to make AI-driven product and content recommendations easier to implement and manage.

Getting there requires a few best practices that can help your business implement your own custom recommendations into your mobile app. 

5 Best Practices for Building AI Custom Recommendations into Your App Development Process

The relatively recent rise of artificial intelligence means that it's tempting to run before you walk. But especially when it comes to custom recommendations, you have to make sure they're both relevant and accurate. These 5 best practices can help.

1. Build AI Functionality Into Your App Development Strategy

First, and perhaps most importantly, any AI functionality within your app has to be strategic. Simply treating it as a delivery mechanism for new features like better recommendations is not enough. Instead, make it a core consideration point from the app idea forward, making sure that it fits within the fabric of your mobile app rather than just sitting on top of it.

2. Let User Experience Drive Your Strategy and Implementation

Modern app development is and has to be about the user experience above all. If your audience doesn't enjoy using the app and doesn't feel like it solves a core need, they will easily move on to other experiences. That's why all strategic and development decisions, from what AI functionality to include to where custom recommendations may be most relevant, have to be considered from that perspective.

Put differently, implementing custom recommendations just because it seems like a way to push more products to new or recent buyers may not be successful. Instead, consider how users could benefit from them and why they might appreciate the feature, and build in that direction.

3. Test Your Custom Recommendations Before Launch

Even the best theoretical functionality matters little if it doesn't work out as intended in practice. Custom recommendations are only as good as the products or content they surface, and how relevant they actually are to your audience. 

To get there, you'll need extensive testing of your AI models. The data it converts into recommendations has to be both comprehensive and accurate. It can learn over time, but the recommendations have to be relevant from the moment of the launch. Extensive testing, which can also be automated via AI-enabled processes, is key to successfully rolling out a feature like this.

4. Offer Feedback Opportunities for Your Users

Even after launch, your custom recommendation system likely won't be perfect. It will learn over time, but part of that learning can and should come directly via user feedback.

To get there, make it easy for your users to share their feedback on whether a recommendation was actually relevant to them. Simple thumbs-up and down buttons work because they can feed directly into your model, but you can also offer the opportunity for freeform entry to get more qualitative feedback.

5. Find the Right App Development Partner

AI tools like custom recommendations are exciting because they offer new opportunities for user engagement and retention. To get there, you have to get them right. That's where finding the right mobile app development company to partner with can be immensely beneficial.

At Milo Mobile, we believe in pushing the envelope of app development forward—as long as the products we design and build can still follow basic principles of solving core user needs. Ready to talk about a potential partnership? Reach out for a free 30-minute consultation today to discuss the ideal customized solution for your mobile app.

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