Returning customers receive recommendations based on what they've actually bought
Specialty home goods retailer, online-first, strong repeat customer base
The situation before
Repeat customers were receiving the same email campaigns as first-time buyers — same promotions, same product spotlights, same generic copy. The marketing team knew this was wrong but lacked the setup to run truly individual recommendations at any real scale.
What was built
An agent that monitors purchase events and identifies customers who have returned to browse after a previous purchase. For each returning customer, it reviews their order history and current browsing context, identifies the most relevant product recommendations, and generates a short, personalised email that references their prior purchase by name ("last time you ordered X, you might also like…"). Recommendations are reviewed against current stock levels before sending. Emails go out under the brand's email identity.
See it in action
Demo coming soon
A walkthrough of this scenario — how the agent runs, what the output looks like, and how the handoff to your team works. Content to be added before launch.
What changed
Repeat-purchase email click-through rates improved relative to generic campaign benchmarks. Several customers specifically referenced the personalised recommendation in post-purchase reviews. [Specific CTR and conversion rate — metric TBD]
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