Case Study: Moonpig Increases Conversions by Optimizing Product Recommendations

Moonpig is a UK-based retailer specializing in unique and personalized cards and gifts. With over 10,000 card designs along with a large selection of gifts, such as food, drink, flowers, personalized mugs, and much more, continually optimizing the digital experience is crucial in order to surface the right products to the right customer at the right time. A/B testing and personalization is thus an integral part of Moonpig’s brand strategy. 

Moonpig is very development-driven, and invests heavily in internal machine learning and product recommendations capabilities. Having an optimization tool that both integrates with their existing ecosystem and allows them to A/B test all of the site features is therefore very important to them. 

While they used to use a client-side A/B testing solution, they found it was difficult to integrate, and ran into problems with bucketing, data accuracy, and site performance.

They selected SiteSpect because of its server-side A/B testing capabilities, which allows them to A/B test all of their site features, including algorithms and product recommendations. Further, Moonpig uses SiteSpect’s Engine API implementation, meaning their traffic does not flow directly through SiteSpect. 

James Huppler, Head of Product at Moonpig explains, “Using SiteSpect’s Engine API is really valuable for us. It allows us to group and cohort users accurately, our engineers find it straightforward to implement A/B tests, and we don’t have any of the flicker we had with our client-side tool.”

By optimizing their algorithms, Moonpig was able to take their product recommendations to the next level. Read the full Moonpig case study to see the impact of their product recommendations optimization. 

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