Our air quality app is used everyday by users all around the world. It also ranks as one of the highest in the air quality app categories on both the Apple and Google Play stores. So how did we do it?
Building an Air Quality App for Consumers
When we started our air quality app journey, many businesses and governments were not ready to answer the public demand for practical solutions that protected them from harmful air quality.
In 2020, a lot changed, and more businesses are approaching us for help and advice on incorporating air quality data into their own consumer apps. We thought we'd share some of our mobile app experience insights and learnings with you, and how we came to realize that air quality data without insights or context is totally meaningless!
1. How We Found Our Audience & Validated Our Assumptions
When you’re in the business of improving individual lives for the better, you also need to understand the personal use cases and motivations that bring people to look for the solutions you provide.
We’re not going to explore all the fundamentals of product research and user personas here, but these are some strategies that particularly worked well for us:
— Reverse Engineered Personas By Exploring Companies in our Ecosystem
One thing that really worked for us at the start was to explore the assets and messaging for companies within our eco-system, such as weather and pollen tracking apps, and work backwards from that: What were their main features? How did they position themselves strategically? Were there any insights we could obtain from their websites and apps and their direction for the future?
Say a company offered a mobile app experience providing informational and educational content — we could deduce that they were targeting educators or those interested in self-learning. If another player provided a considerable amount of governmental and legislative information, this gave us yet further insights into a separate group of potential user group interests. This method helped us to validate some of our own assumptions as a first, basic step in the early days.
— Used social networks to learn our target customer’s pain points
Social media provided an invaluable tool for becoming intimately acquainted with our would-be users and the solutions they felt were currently missing in the marketplace.
In addition to product development inspiration, social media helped to identify the gaps in information out there, and a strategic backbone to our content planning. We’re lucky to have the full gambit of atmospheric scientists, product experts, marketing and technical specialists at BreezoMeter, meaning we’re well equipped to answer the public’s burning questions in an informed and thorough way.
2. Why People Need Insights, Not Data
Raw data, even if it’s personalized to the user, gives no value in the long-term, it’s the insights and recommendations you provide during your mobile app experience that do this.
Here’s an example: Say you just bought a new fitness watch that tracks your sleep. You check the watch and see some numbers that basically tell you slept for 8 hours or 6, or 7.
This kind of information can hold interest for a while, but we’ve found that for prolonged user retention and interest, individuals really want insights into how well they slept and advice for how to improve their sleep over time in light of their personal activities and habits throughout the day.
Simply reporting on ‘how’ or ‘task completion’ will never fully address your users’ needs in the long run.
Maslow’s hierarchy of psychological needs goes some way to explaining why this is: At the top of the pyramid is the self-actualization so many of us are striving to reach: Much of the drive behind personalization is connected to habit change and optimal living in today’s day and age.
Consumers today want to feel as though they’re improving their lives and solutions that provide raw data without insights don’t really help them to do this.
3. How to Support Tiny Habit Changes
We are big fans of Fogg’s behavior model for habit formation at BreezoMeter. The idea behind this approach is that long-term behavior change occurs not by pure motivation or willpower, but rather by making small changes to behavior that evolve over time.
For these behavioral changes to begin, there needs to be the initial motivations for doing so, the actual ability to complete a task — (if it’s too complicated, people will give up) — and initial trigger for wanting to change a particular behavior.
For Breezometer, this model starts with encouraging our users to become more air-quality aware: Simply checking air pollution levels where they are at any given time — and making small choices as a result such as choosing to walk on the opposite side of the road, heading to a different park or shutting the windows when they receive a low air quality alert.
Are you Building a Consumer App with Environmental Data?
Think about the real motivations and needs of your users, and what will drive engagement long-term with your product: Insights & recommendations rather than raw data.
Changing behavior requires a combination of motivation, triggers, and the ability to complete an activity.