With the personalization process, a system identifies a user as a specific type of individual and then delivers relevant and individualized content and functionality to them. Personalization aims to enhance the experience of the users by anticipating and meeting their unique needs to guide them through a custom conversion funnel.
Waze is a traffic and navigation app, and a great example of personalization. It monitors its users’ usage patterns so that it can make things easier for them. If a user leaves work every day at 5 pm, then the app identifies it as a pattern and asks them at 5 pm if they are heading home.
How does this work for the users? Users are normally tired by the end of the day and just want to get home as quickly as possible. When the app asks them if they are ready to head home, it helps users in skipping steps such as repeatedly putting in the same data. Waze just made its users’ life easier. That’s what people want, someone looking out for them.
Personalization is dynamic. It learns and adapts. When you personalize a user experience, you are offering something useful based on users’ characteristics, behaviors, attributes and/or data analysis. UX personalization is about creating an individualized experience that is relevant to and targeted towards a user’s needs.
There are many brands that are doing this successfully and in real-time. Google keeps track of your upcoming flight, train and bus reservations, appointments, interviews, etc., and notifies you before the scheduled time, given that this information is stored on your phone.
Personalization can only matter to businesses if it matters to users. It is an obvious but important fact. If your app’s UX personalization is done right, it will have a major impact on the success metrics in the following ways:
An AI content personalization system essentially reads all the text and other metadata associated with the content to identify what it’s about, how long it is, and what format it’s in. It then classifies all of it by labeling it with different topics. Much of this is driven by AI. But, depending on the solution, a significant amount of human work may also be needed to coordinate labels and feed the machine system information.
The system’s AI classifies all this data, then learns on its own what to look for in the data. From there, using a number of sophisticated machine learning models and techniques, the system determines what content to recommend to each site visitor, including modeling how content topics relate to one another.
Amazon Personalize enables you to improve customer engagement and conversion by powering real-time personalized product and content recommendations, and targeted marketing promotions. It is based on over 20 years of recommendation experience and research in machine learning at Amazon. It’s like having your very own Amazon.com machine learning recommendation system 24 hours a day.
You can get started with no prior machine learning experience using simple APIs to easily integrate sophisticated personalization capabilities into your systems and platform. Amazon Personalize automates the complex steps required to build, train, tune, and deploy a machine learning recommendation model so you can deliver personalized user experiences faster.
All of your data is encrypted to be private and secure, and is only used to create recommendations for your users. You pay only for what you use, and there are no minimum fees and no upfront commitments.
Personalization is a tool that can make your app more powerful, but only if it’s done right. When it comes to personalization, empathy plays an important role. Understanding your users’ needs and preferences can go a long way in creating an enriching UX.