How to build with Limbic
The appeal of building products that feel natural (rather than artificial) is the holy grail of product (and UX) design. At Limbic, we believe that the way to accomplish this is not just to focus on the speed and efficiency of number-crunching, but to give computers an entirely new input: emotion. My co-founder, Ross Harper, discussed this idea in a recent post.
Limbic’s first product — a stress-detection SDK — has found good traction in the health and wellbeing space. I’d like to take a moment to highlight three particularly interesting use cases that emerged to date.
- Use Limbic to provide valuable new insights to your end users
This one is a (relatively) simple use case… yet powerful. We have seen end users delight in tracking and measuring their own stress levels to inform their choice of lifestyle. Fitness trackers today monitor physical activity. Steps, calories burned, etc. Although physical exercise is used to improve mental health¹, and the topic itself is something people are increasingly concerned with², fitness trackers today cannot monitor mental fitness. Limbic enables your product to play into that need.
As an added benefit, our stress insights are present even when users are not actively engaged with your app — adding value even when you’re not around.
2. Use Limbic to better time notifications and reactivate churned users
What’s better than a friend who offers you help right when you need it, without having to ask?
For the first time, app developers building in the health and wellbeing space can now reach out to their users when they are feeling a particular way. You can now empower your meditation or mindfulness app to nudge your users when they are having a stressful day and let them know you are still there to help them.
3. Use Limbic to inform your own machine-learning models and product analytics
As machine learning becomes more and more ubiquitous in every day applications, it’s touching a lot of wellbeing applications as well. A great example of this is the growing femtech space. Applications like Flo and Clue are using machine learning to predict when a user’s next cycle is going to start³.
Other examples can be seen in meditation apps A/B testing different courses for certain user segments. If the goal of your app is to (partially) relieve stress, you now have an easy-to-track, quantitative metric to optimise.
The applications of Limbic are massive (not that I’m biased). The examples above are the first ripples in a wider splash that we’re making. Stay tuned for the next post on how Limbic is being used.