{"id":26089,"date":"2024-12-02T10:26:55","date_gmt":"2024-12-02T10:26:55","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=article&p=26089"},"modified":"2024-12-02T10:27:36","modified_gmt":"2024-12-02T10:27:36","slug":"personalisation-at-scale-sainsburys","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/personalisation-at-scale-sainsburys\/","title":{"rendered":"Sainsbury\u2019s personalisation at scale for actionable benefit customer health"},"content":{"rendered":"

Points for personalisation<\/span><\/b>\u00a0<\/span><\/h4>\n

Loyalty points for Sainsbury\u2019s and its partner businesses are earned through the Nectar programme, and these points are then redeemed for discounts, special offers, and similar incentives. By utilising the data, Jardine and his team were able to identify areas of growth and influence shopping decisions for millions of people across the nation that would positively impact their health and habits through personalisation. <\/span>\u00a0<\/span><\/p>\n

In the UK, nine in ten customers now have at least one loyalty card, with many more owning multiple loyalty cards. For Sainsbury\u2019s and its customers, the Nectar programme earns over \u00a3200 million in value via points annually, with a Nectar card being used 32 times every second \u2013 this puts Sainsbury\u2019s in a unique position to identify real-time shopping habits throughout the year with a high degree of accuracy.\u00a0<\/span>\u00a0<\/span><\/p>\n

Through investment of \u00a370 million in personalised pricing algorithms, Sainsbury\u2019s utilised machine learning to tailor product recommendation personalisation and pricing suggestions for their customers. <\/span>\u00a0<\/span><\/p>\n

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Award-winning personalisation<\/span><\/b>\u00a0<\/span><\/h4>\n

Sainsbury\u2019s has gained a lot of experience in building algorithmic machine learning customisation engines, and the data team wanted to take that experience and knowledge that is traditionally commercially focused and apply it to another significant challenge: national health.<\/span>\u00a0<\/span><\/p>\n

In the UK, two-thirds of adults are classed as overweight, which is a serious concern for long-term health and the impact on the NHS which spends millions every year tackling weight-related health issues. Jardine explained that it was a challenge that Sainsbury\u2019s felt obligated to tackle, with the company making the commitment that 85% of its food sales on volume would be on items that are healthy or better for consumers.\u00a0<\/span>\u00a0<\/span><\/p>\n

Jardine and the data team explored the choices available around health options when it came to customers selecting their groceries, noting that the existing system was relatively impersonal. People make different food choices for different reasons, and this meant Sainsbury\u2019s needed to take a very granular approach to personalising shopping experiences for customers through automated machine learning algorithms.\u00a0<\/span>\u00a0<\/span><\/p>\n

The first challenge was identifying what Sainsbury\u2019s customer guidance journey looked like, followed by getting customers connected to multiple touch points to help guide the journey further. Despite sounding straightforward, Jardine explained the process was far more nuanced than expected. One major hurdle was understanding what constitutes a healthy product \u2013 the UK government has a classification system, but it was considered a binary system by Sainsbury\u2019s wanted a detailed approach; for example, many fruits have high sugar content, so what metrics should be used to determine healthiness?<\/span>\u00a0<\/span><\/p>\n

The Sainsbury\u2019s data team worked closely with the nutritional team and existing healthy eating guides to understand the existing data and identify areas of improvement. This was then developed into a scale of healthiness and the healthy eating automated machine learning algorithm for personalisation. There are more than 25,000 products across 17 different categories that are in a constant state of evolution, with products being added and removed, that require classification.\u00a0<\/span>\u00a0<\/span><\/p>\n

Due to the scale of the project, this could not reasonably be handled manually, so automated pipelines needed to be built to make the process seamless. This was designed in such a way that as soon as a product is added, it is scored and placed to sit in the healthy eating programme.<\/span>\u00a0<\/span><\/p>\n

Once this was achieved, the data team were then able to apply the project across the customer base to better understand what they are buying, their basket compositions, key food groups, and how Sainsbury\u2019s could provide the opportunity for better food choices. This led to a stronger understanding of what was preventing customers from certain choices and how Sainsbury\u2019s could bring better balance to customer options.\u00a0<\/span>\u00a0<\/span><\/p>\n

Three main areas were uncovered:<\/span>\u00a0<\/span><\/p>\n