Customer recommendations fuel revenue rise for Argos
What’s the story?
With a Single Customer View in place, Argos had a deep understanding of each customer’s transactional and engagement behaviour. With a huge inventory of products across lots of different categories, Argos wanted to make full use of this asset by sending emails with highly intelligent cross- and up-sell messages to increase basket sizes and encourage conversions.
What we did
We built a machine-learning predictive model that offers intelligent, customer- and product-level recommendations and that is trained to make cross- and up-sell specific suggestions.
Hugely powerful and self-optimising, Recommender analyses the correlation between interest and purchase for more than 45,000 products and trillions of basket combinations. Making full use of customer data, it also tailors recommendations to individual customer’s price sensitivity and previous behaviour. What’s more, it’s channel agnostic and can be used to tailor all marketing channel and online experiences.
The Impact
We built a machine-learning predictive model that offers intelligent, customer- and product-level recommendations and that is trained to make cross- and up-sell specific suggestions.
Hugely powerful and self-optimising, Recommender analyses the correlation between interest and purchase for more than 45,000 products and trillions of basket combinations. Making full use of customer data, it also tailors recommendations to individual customer’s price sensitivity and previous behaviour. What’s more, it’s channel agnostic and can be used to tailor all marketing channel and online experiences.
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