Recommendation Engines: Test Whether Suggestions Actually Help
Recommendations should make a choice easier, not merely fill a screen with personalised content. Start with one moment where a user has too many options and a better next choice would create measurable value.
Define What Help Means
For a marketplace, help may mean a relevant enquiry. For a workflow tool, it may mean completing a task with fewer steps. Establish a baseline and test a simple manual or rules-based suggestion first. AI product validation is stronger when it measures behaviour rather than clicks alone.
Keep Choice and Feedback Visible
Show why a suggestion appears when practical, make alternatives easy to find, and capture dismissals or corrections. Avoid forcing a recommendation into a high-impact decision. A small AI proof of concept can prove the interaction before model complexity grows.
Launch One Useful Loop
Choose one audience, one recommendation surface, and one success measure. Expand only when users repeatedly act on suggestions and the result improves.
Test the decision before building the engine
MVPHUB helps founders scope AI features around useful customer outcomes.
Book a free consultation with MVPHUBFrequently Asked Questions
How do you validate a recommendation engine?
Compare the user outcome with and without the suggestion, using a narrow workflow and a clear measure of help.
