# Product recommendation engine lifts email conversion by 25%

> UK menswear retailer · Retail & Consumer

A menswear retailer's marketing emails were sending repeat customers on long searches for products they had bought before. QuantSpark's recommendation engine personalised those emails and lifted conversion by 25%.

## At a glance

- **25%** higher email conversion rate

## What was the problem?

Analysis showed a large share of customers repurchased similar products year after year, but frequent changes to the range meant repeat buyers needed five or more clicks to find their preferred item after being reactivated by email. Emails were only partially personalised and rarely reflected prior purchases, creating friction that suppressed demand. Customer research found that more than 80% of repeat buyers wanted a similar item to their first purchase.

## What did QuantSpark do?

QuantSpark built a recommendation algorithm that suggests products to each customer based on their previous preferences, such as size, colour, fit and style, and targeted those most likely to buy habitually. Each repeat-buyer email now carries at least 50% creative directly related to the exact prior purchase. The model was automated by combining transaction logs, stock levels and CRM data, integrated with the retailer's existing CRM, and QuantSpark also supported the design of marketing content to make best use of the recommendations.

## What changed?

Personalised recommendations now reach more than 100,000 customers through fully automated data pipelines. The personalised emails achieve open rates close to double those of non-personalised emails and a 25% higher conversion rate, driven by reduced friction for repeat buyers.

---

Canonical page: https://quantspark.ai/case-studies/product-recommendation-engine-email-conversion
More about QuantSpark: https://quantspark.ai/llms.txt