Recommending Groceries

One of the world’s biggest retailers, with 7,000 stores worldwide and 80 million weekly customers.


While recommendations and product matchups are a common feature inside physical grocery stores, retailers often struggle to harness the potential for suggesting additional products to consumers online. Many of the recommendation systems currently used online are slow and lack the power and flexibility required to take full advantage of the data on hand.


ASI developed two models for making smarter, quicker recommendations based on the sequence in which people shop and common pairings of items. Both models made use of a technique called collaborative filtering.

Firstly, a Just Added model was developed, to operate when consumers are in the early phase of their shopping and have few items in their basket. When a new item is added further items are suggested based on data relating to items that are usually bought in sequence (such as milk after cereal, tomatoes after carrots, biscuits after tea).

The second model was developed to analyse a full basket and ask the shopper What’s Missing? Towards the end of an online shopping session, prior to checkout, the recommendation system was developed to be able to make suggestions of items that a shopper may have overlooked.

Recommending Groceries
In collaborative filtering, the preferences of many users are used to predict the preferences of others who make similar choices. Here, shopper 1 and 3 have choices in common.


The project has been able to deliver a sevenfold revenue increase from sales attributable to the recommendation engine compared to the previous recommendation program.

This translates into a potential increase in online sales of £25 million per year. The project makes online shopping easier, introduces an explorative element by suggesting items that might not have been bought before, and closes the gap between online and in-store marketing.

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