Performance bot
Mendix enables app building with low-code, making it easier and faster to build what users need. App users have little patience for slow load times and connection issues. Application performance is a key factor that impacts a pleasant user experience. Performance Bot offers suggestions for improving app performance, based on machine learning and anonymous usage data.​
Collaboration
What made this project unique was the collaboration that happened. My aim and role was to guide them towards and through a truly collaborative process, with PM, Devs, Design and UXR. People were hesitant at first to do all activities as a team, doubting the added value and worried about time investments. But I insisted and it paid off.​​​
Starting from OKR's
We started from an OKR goal to increase adoption of Performance Bot with 10%. Based on this goal we defined hypotheses, and then planned qualitative research to test them. We investigated with 5 users who were using the Bot, as well as with 5 who weren't, and spent one-hour with each of them.
Valuable outcomes
The shared sense-making of all our insights brought clarity on the issues with Performance Bot:
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lack of awareness and lack of trust
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confusing Logic Bot with Performance Bot
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not aware that anti-patterns and best coding practices were powering the Bot.
Product direction
Fueled by our insights we brainstormed on problem oriented capabilities:
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adding a clear place for 'Best Practices' in a new tab
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renaming Mendix Assist (which later became Maia - Mendix AI Assistance)
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improved explanation about the AI -powered performance best practices, both on the Mendix website and in the documentation.​​​
The collaborative approach lead to fewer roadmap and planning discussions afterwards, because everyone had been involved from the start.
But this project also brought other gains: It increased team cohesion, gave the team more focus, and increased accountability.