- Bundle attach rates on their website was lower than in-store
- Initial product attachment effort was a manual process (no personalization)
- Dixons built a generic collaborative filtering recommendation model but were unable to productionize it on their website
- Adobe analytics data was very complex and difficult to view at an individual level
Syntasa plugged natively into Dixons Carphone’s existing technologies and within their GCP environment to synthesize behavioral data so that it could be available for analysis right away. To produce personalized recommendations, Syntasa built a Nearest Neighbor model to generate a neighborhood of similar customers, based on browsing behavior and products purchased together by similar customers.
- AI-Assisted Merchandising
- Product Recommendations
- Identity Resolution
- Adobe Analytics Adapter
“Syntasa has been really invaluable in speeding up our time to value by architecting our Adobe Analytics data and productionizing data science and machine learning modeling at scale, and in such a way that we can confidently pass that into production systems to drive the user experience.”
“What we had a challenge with was the ability to look at the granular detail of the user-level data, so that we can start to look at what an individual customer is doing (and might do) on our website.”
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Read the full case study to hear more about the challenges, solution and results experienced by the Data Science team at Dixons Carphone.