Most technology today is highly personalized. That means apps and services are designed for one person and change and adapt based on that person’s behavior, preferences, and the patterns they show as they use the technology over time.
When we talk about personalization today, it’s not just about the movies and products you want to buy anymore. Many of the ways that we deal with tasks in our daily lives are also getting more personalized. Think fitness tracking, budgeting, or even managing your household, to name a few examples.
Personalization Starts With Understanding User Behavior
Most personalization on your mobile device is based on data collection. First, an app asks for your input; then, over time, it learns from it. A music streaming service might notice you keep skipping the same song, while a fitness app might use your previous workouts to recommend alternative training to help you reach your goals faster.
A budgeting app, for example, might categorize spending based on past transactions and help users identify patterns over time. An AI financial planner can take that personalization a step further by helping users organize their financial information, explore different scenarios, and understand how multiple goals may fit into the same plan. This makes the technology more useful because it works with the user’s actual circumstances rather than relying on broad assumptions that may not reflect their needs.
To get the best results from a personalization system, the data used should reflect the user’s current situation as accurately as possible. Therefore, users should review the information entered into the app and the actions performed within it from time to time.
Smartphones Have Become the Center of Personalized Technology
Smartphones play a central role in personalization. A single device lets users access apps for daily life, such as their calendar, exercise, money, routes, messages, and entertainment.
This is also because apps reside on smartphones, which, as we have established, manage all aspects of a user’s life. Other apps on the same device can therefore also be considered by other apps when delivering personalized experiences to the user.
Behind the simple features of many apps and services are sophisticated automation and machine-learning systems. They scan huge volumes of data to identify repeat behavior, then find ways to act on it without constant human input.
AI Is Making Personalization More Flexible
Most previous attempts at personalization, particularly in recommendations, relied on predefined rules: A user watching X type of content would be shown Y type of recommendations. To create more meaningful recommendations, we can use AI to better understand the user’s needs and preferences.
For example, an AI-powered digital assistant can help users compare options, summarize longer texts, organize their schedules, and prepare for important decisions.
Although AI will rarely understand your needs perfectly (recommendations may be repetitive and inaccurate), it is still more flexible than a traditional rule-based system, thanks to the larger volume of context it can process.
Personalization Is Becoming More Predictive
The future of personalized technology is about anticipating user needs and routing them to the right place at the right time, before they even ask. Just as navigation apps alert drivers of traffic tie-ups on routes they normally take, and fitness tracking apps notify users of extra recovery time needed after especially grueling workouts, the user experience of personalized technology will be characterized by anticipating the user’s needs and presenting them with solutions and alternative courses of action before they even have to ask.
This can help save time by detecting things that the user may miss, but it must also remain transparent. In other words, users must understand why the app reached its conclusion and, importantly, whether it is correct and relevant.
When a product or service anticipates needs, it is working well, but users need to understand the basis for any forecast. They should also be able to switch things off if they become uncomfortable with directions suggested by their technology.
Privacy Becomes More Important as Technology Gets More Personal
The more personalized a service becomes, the more information a company might need to gather to serve up relevant information. So, there’s a big trade-off here between convenience and privacy.
Users should be cautious when an application requests access to data such as contacts, location data, financial accounts, or health data. Users should also know whether data is stored locally on the device or in the cloud and whether it is shared with third parties.
Personalization doesn’t require giving all your apps access to all your personal data. Users can generally choose the features that matter most to them and keep other information private.
