Product Recommendations, Done Properly
Showing shoppers products they may like, based on what they are viewing, what others bought or their own history.
Marcus Adeyemi
Why does it matter?
Recommendations help shoppers discover products and raise order values, but they depend on good data and clear placement.
What are the numbers?
- Types related items, frequently bought together, recently viewed and personalized picks
- Data product attributes, purchase history and browsing behavior
- Privacy personal data use must follow privacy laws and notices
- Measurement clicks and incremental revenue
What should I do?
- Start with related products and frequently bought together
- Exclude out-of-stock items
- Explain recommendation labels clearly
- Measure incremental impact
- Respect privacy choices
What should I avoid?
Avoid:
- Recommending unavailable products
- Personalization without clear privacy notices
- Too many recommendation blocks
- Recommending items already purchased
When should I get help?
Short answer Bring in help when choosing recommendation tools or measuring their impact.
Where this comes from
- Baymard Institute — Product page UX research
- California Privacy Protection Agency — California Consumer Privacy Act
The figures and practices above come from the sources listed.
Working on something like this?
We take on E-commerce Development work for teams who want it done once, properly. Tell us what you are building and we will tell you honestly whether we are the right studio for it. Start a project.
Where to go next
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