Personalization has entered every aspect of consumers’ lives. In fact, they see it daily in the targeted messages from their favorite websites or apps. And as these websites and apps learn more about the consumer’s behavior, the more tailored the messaging becomes.
This constantly evolving level of personalization drives consumers to engage daily with these apps or sites, so they can receive the latest suggestions on what to watch, listen to, read and more.
For example, the popular music service, Spotify, learns about a user’s musical tastes when they choose a song or artist to listen to. Then, as they engage more with the app, it feeds them more suggestions for songs they may like in the interface. And as their taste in music changes, the recommendations change.
Pretty soon, they’re not only going back to the app listen to their favorites, but also to discover music they may not of heard before — making this their go-to destination for all things music related.
Smart Personalization Makes Health Benefits Programs Better
Smart personalization can drive that same level of engagement in health benefits programs, when applied correctly.
And using this information, the program can deliver personalized incentives, content, and more to the user, spurring them to further engage with the program and ultimately adopt healthy behaviors.
Jiff excels at driving engagement and healthy behaviors through personalization by using a unique engagement optimization system that’s always learning about the user.
Here’s How It Works
First, Jiff aggregates various types of data from various sources such as our platform partners, employers’ benefits information, in-app behavior, and health goals, and more. We do this while following the highest security and privacy standards.
Jiff’s engagement optimization system learns and grows with the user, in order to drive health behaviors and outcomes
Then, using that data we send recommendations to the user based on their current health-goals, demographics, and behaviors. And as the user engages more with the program and the app, we learn more about their preferences and goals.
Using best practices in game mechanics and behavioral economics, we prompt the user to engage with the app more in a fun, intuitive, and engaging way so we can learn, and remember, the individual user’s likes and interests in order to motivate healthy behavior.
For instance, Jiff knows that Kate is a 35-year-old new mother. Jiff also notices she frequently engages with team challenges, and has demonstrated curiosity in stress relief and mental resilience.
Based on our understanding of Kate and people like her, we’ll serve her additional stress and resiliency-related content and new challenge notifications at a cadence that is most likely to stimulate a positive response.
Finally, as Jiff learns more we’ll personalize the incentive type, content, events and alerts to the user based the the data we’ve aggregated about the them to predict the desired outcome.
And, the engagement optimization system is constantly learning and growing with the user in an effort to give them the recommendations, content and incentives they desire to accomplish their health-related goal.
For more information about how we’ve mastered Personalization for health benefits programs, read our post on how we do it.