Bouy - Profile

We love our power users and their enthusiasm to push Buoy to its full potential, but not everyone has the time to continually engage with the product. We had to deliver value for other low-engagement user types, and encourage them to interact with their data. We did that by merging the home profile and event recategorization into one experience using a scoring-based technique.

In the best-case scenario, where a user tells us precisely what fixtures they have in their household, the certainty is nearly 100%. Since it’s confirmed by the end user, we would assign an affinity score of 5 — the highest possible.

In the opposite situation, when we have zero confirmation by a user and thereby default to the algorithm’s guess work, we assign a score of 1 — the lowest possible. Based on this engagement scale, we could let users know where they currently are and what they can do to improve the machine learning algorithm’s accuracy.

We called this feature Improve Home Profile. It’s a way to take users through a flow interface and inform them about the fixtures and water usage events that Buoy is most uncertain about. This allows us to save users’ time by only asking them the most important questions to get the information we need to improve our algorithm.

Read the full case study here: https://www.mindtheproduct.com/helping-a-machine-to-distinguish-toilet-flush-from-kitchen-tap-a-case-study/