Using Artificial Intelligence to Enhance Personalization in Software Design

?? Merry Christmas! Welcome to #GetUX 23, where we discuss about using artificial intelligence to enhance personalization

Opening an app that you use daily only to be greeted by irrelevant suggestions and settings — that is frustrating. For many users, this is unfortunately the norm: a one-size-fits-all approach to user experience that just doesn’t cut it anymore.

In our digital world today, personalization is paramount. People are getting tired of software that just doesn’t seem to know their choices right away. Social media algorithms has probably played a big part — their algorithms know just how to entice people to open their app and keep scrolling.

A report by Segment indicates that 71% of customers feel frustrated when their shopping experience is impersonal. McKinsey & Company also found out that 71% of buyers expect companies to deliver personalized interactions, and 76% of them get frustrated when this doesn’t happen. These statistics highlight the importance of, the growing demand for, tailored experiences in software applications and design.

Let it be clear: incorporating AI-driven personalization into software design is no longer a luxury. It is now a necessity. It is required to meet user expectations and enhance overall satisfaction. Fortunately, there is a way out for software companies willing to enhance their user experience out of the outdated mess — that is, AI, and its role in greater software personalization.

Here are five tips on how to leverage AI for greater personalization in design.

Understand the users’ context

Personalization, and software design in general, starts with a deep understanding of users and end-users (the actual market of people who are going to be your customers). You need to know who they are, what they need, and the environment in which they interact with your product. This can be started by doing simple market research, with things like focus group discussions and interviews.

But, you can also dig deeper with AI. After you’ve requested their permission and collected their contextual data, you can leverage machine learning models to examine behavioral data, environmental factors, user preferences, and temporal contexts. Early-stage startup founders need to learn as much as possible about the users, and through this data you can learn much.

This matters: a travel app suggesting sunny destinations when a user is clearly browsing for winter getaways will obviously feel tone-deaf. The same as an e-commerce platform promoting large home appliances to someone living in a small apartment, based on shipping address. AI can read all of these and combine them with contextual understanding into insights about the user.

Leverage predictive analytics

Predictive analytics is one of the specialties of artificial intelligence. It is like having a crystal ball for understanding your users’ needs, even before they realize what they want. The way it works is through analyzing the user’s behaviors and preferences, and anticipating them to provide timely and relevant suggestions that feel almost magical.

Predictive systems are contrasted with reactive systems which respond to user input. It delivers content, recommendations, or solutions tailored to what the user is likely to need. An obvious benefit is that it saves time for the users of your product. Another way that these systems benefit your software is that it builds trust — users become more likely to feel that your product cares about them.

Practical examples of these systems abound particularly in e-commerce sites. Based on previous browsing and purchasing history, these sites suggest items that users will likely want to buy next. Another way that it can be used is to personalize dynamic content. News sites can select and curate headlines based on past reading behavior, current events, and even user mood. Sports trackers can help remind users of past good behavior.

Calibrate trust & add user feedback loops

Even though artificial intelligence is getting more and more sophisticated these days, it can still make mistakes. Hallucinations are a feature of large language models — chatbots — where the model makes up information as it goes along. It’s a risk in these systems.

To help counter adverse effects from these, make sure your users know what kind of help they’re getting and from where. Include disclaimers like “this chatbot may make mistakes” — and let users to proceed at their own control.

An even better way of controlling this is to include feedback loops in the process. After the user gets a suggestion from the system, let the system know whether their suggestion is good or not. This is often implemented in the form of “thumbs up” or “thumbs down” ratings, which provides feedback to the system about their suggestions.

Gradually onboard users

While personalization is powerful, it can also feel overwhelming or intrusive if introduced too quickly. When users are bombarded with too many features or requests at once, it can lead to confusion, skepticism, and abandonment. Users may not fully understand how the personalization works or why it’s beneficial; skepticism as to why there’s too much data collection upfront; and abandonment, because they feel overwhelmed.

This is a basic tenet of user experience design — contextual onboarding. When introducing a feature to users, best start with the basics: begin with straightforward options like allowing users to set preferences manually. As they begin to get more comfortable with your app, introduce more.

Also, give users control. Offer them to customize their onboarding experience. Allow them to skip revisit steps, adjust settings later, or opt out of certain personalization features entirely. Give them feedback in milestones — as users interact with personalization features, acknowledge their process. For example, after completing a profile setup, show a message like “Great! Your recommendations are now tailored to your preferences”.

Conclusion

Personalization is important in software design these days. Users have largely come to expect a degree of personalization that is increasingly greater. This personalization, however, cannot be done recklessly — because the costs of doing so is also great. It is crucial to understand the users’ context in which AI help will be provided, to leverage predictive analytics, calibrate users’ trust, and to gradually familiarize users with the processes and systems involved.

Maturis is a UI/UX design studio geared towards making better #ArtificialIntelligence apps. Get in touch with us through LinkedIn Messages to take your idea to the next level!

Liked this article? Subscribe for more weekly UX insights related to artificial intelligence business and startup growth ??