THE CHALLENGE

One of the core pillars of AI product design is building robust feedback loops since. Unfortunately, Lucid’s current in-app feedback pattern was too limited and ineffective – users couldn’t share context, share positive experiences, and was generally unintuitive. As a result, we received little to none actionable feedback and missed the insights needed to iterate meaningfully and shape the future of our product.

GOALS

Increase the rate of submitted feedback and the number of positive, qualitive input.

RESEARCH

Since I wasn’t a subject matter expert in feedback patterns, I conducted a deep dive into existing patterns in other products and explored the psychology behind giving feedback.

I found that users are more likely to give feedback when it doesn’t feel like filling out a form, the tone is action-oriented and personalized, and they believe their input will be valued and acted on.

THE SOLUTION

More control leads to better feedback

Users have a more robust and flexible experience that allows them to toggle between options, opt-out at any point of the flow, and provide both closed and open feedback.

Less form, more flow

The callout replaces the traditional form feel – no radio buttons or checkboxes. As the user moves from top to bottom, the experience feels less like a form and more fluid and conversational.

Additionally, the callout dynamically adjusts its language and options based on whether the user selected thumbs up or thumbs down. In fact, the three options listed were the top adjectives users consistently mentioned in conducted research and feedback from the past few months.

Every voice counts

When a user submits feedback, we go a step further by assuring them that our team is actively notified and will take a look at their feedback as soon as they can. Furthermore, for negative feedback, users have the ability to access Lucid Software's support page.

IMPACT

Before and after

The left showcases the old in-app feedback pattern while the right is the refreshed experience.

Results

After implementing the in-product feedback pattern, Lucid saw a 40% surge in feedback submissions – across both positive and negative sentiment.

Additionally, the depth and clarity of feedback drastically improved, empowering teams to uncover key user insights and iterate with greater speed and accuracy. In fact, the AI teams at Lucid Software were able to double the rate of feature updates within that quarter.

NOTES

To maintain the confidentiality of the project, I omitted particular pieces of information and artifacts. All information in this case study is my own and does not necessarily reflect the views of Lucid Software.

For a more in-depth walkthrough, feel free to reach out!

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