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eCommerce / Recommendation Tech

Discover: Proving a Personalisation MVP Could Drive Revenue Before Building the Full Engine

Recommendation engine MVP with paying users in 8 weeks

Client: Discover
Discover: Proving a Personalisation MVP Could Drive Revenue Before Building the Full Engine

The Challenge

What Discover Was Facing

The founders of Discover had spent years in eCommerce and believed personalised product recommendations were being under-invested in by mid-market retailers who could not afford enterprise solutions. The hypothesis was that a lean, easy-to-integrate recommendation tool would win on simplicity, not sophistication. The MVP had to prove the integration was fast enough that an eCommerce manager could do it without an IT team.

The Solution

What We Built

We built the MVP around a single integration method: a JavaScript snippet. Drop it into any Shopify or WooCommerce store, and recommendations appear on the product page in under 15 minutes. The recommendation logic was intentionally basic — collaborative filtering on purchase history. The sophistication was in the ease of setup, not the algorithm. We shipped in 8 weeks and ran the first live test on a real store.

Discover: Proving a Personalisation MVP Could Drive Revenue Before Building the Full Engine – solution

Results

Measurable Outcomes

First live store integration completed in 11 minutes by a non-technical eCommerce manager — validated the setup hypothesis
3 paying merchants in week one, all acquired through a founder-run cold email campaign
Average revenue uplift of 6.4% across pilot stores in the first 30 days — enough for merchants to expand their subscription tier

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Discover: Proving a Personalisation MVP Could Drive Revenue Before Building the Full Engine | SaaS Development Agency