Recommendations about a product or service delivered straight to your mobile device can be a boon or a burden. If the recommendations aren’t relevant to your interests then they’re just deemed to be noise and will go largely ignored.
But if recommendations are personalized and deliver relevant information, then they can be a compelling feature.
For example, Netflix learns more about you the more you use the service. And as your tastes in movies change, so do the personalized recommendations they send you.
Those personalized suggestions are driven by a recommendation engine — a feature found in many consumer applications — that learns more about you the more you use an app or online service, and aggregates items based on that information. And as a result, the engine is able to predict what you may like and how best to connect you to that desired item.
These smart recommendations help remove the guesswork associated with finding your desired selections, and allows the product to deliver a service in a more targeted manner to its preferred target audience.
Jiff’s recommendation engine targets participants of a health benefits program using the same techniques, and helps employers deploy smart recommendations that will drive improvements in adoption, behaviors, outcomes and satisfaction.
How Jiff Knows What’s Right for the User
Jiff knows that each employee has different healthcare needs and goals that can change overtime. And, a traditional corporate wellness program won’t provide a solution for those disparate needs. For example, a 40-year-old smoker with heart disease has different health goals than a 30-year-old runner who is relatively healthy.
In order to deliver the right solutions at the right time to drive healthy behaviors, Jiff relies on its recommendation engine that uses digital exhaust from other health-related apps integrated in the platform, claims data, as well as eligibility files.
Using that information, we’re able to learn and grow with the employee and send personalized recommendations for activities or programs that will help them achieve their personal health goal at that time.
For example, the 40-year-old smoker may have quit smoking and is managing their heart disease, but now they want to lose weight. In short, our recommended programs for this person will change, as their actions change.
What’s more, Jiff’s recommendation engine frequently adapts to these changes, adjusting its recommendations to best match employees’ current situations.
This enhanced level of recommendations helps drive engagement and overall satisfaction in health benefits programs. And, it will help the user adopt healthy behaviors that they’ll continue to use long after the program has concluded.
Read more about how Jiff has mastered personalization.