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My team was asked to overhaul an existing mobile application that was rapidly losing users to make it collect user data to drive sales for over the counter allergy products and grow market share.
Behavioral analysis of allergy sufferers through secondary clinical research
Technical landscape analysis of the available pollen trackers, weather stations, and other related data sources
Observing and interviewing patients in a trial group
Surveying 10,000 people who suffer from frequent allergy attackes
Most users don't understand what a pollen forcast will mean for their symptoms, and if they do it isn't really providing the context they need to know what to do.
After interviewing some severe-allergy suffers, we found that they needed help understanding how their symptoms would be affected by hourly changes in the forecast and what to do about it.
From here we mapped weather and population data from 3rd party sources and symptom data from anonymized users to our proprietary intervention plan.
Combining these with standard ENT treatment protocols allowed us to understand which data would be effective in creating useful insights for a user.
We determined that tracking symptoms, environmental data, and particular determinants to user success would give us a picture of a user that we could compare to the population to recognize patterns.
Those patterns became the triggers we would use to serve content. The only things left to do before designing the user interface were to figure out what content would be meaningful, and test the scenarios.