How retailers are unlocking data-driven strategies with AI

Senior data executives from top consumer products and retail brands share how data and AI are transforming their business at Big Data LDN London as part of the Capgemini-hosted panel discussion, in partnership with Women in Data®.

The best retailers are like mind-readers. They know what their customers want to buy and how much they will spend. But companies don’t need to be telepathic to grow their profits: they only need to put the right data to work in the best way.

That might mean something specific such as using AI to price match competitors, or something as general as improving an organisation’s data literacy, according to four senior data executives at this year’s Big Data LDN conference. They were being quizzed by Claire Williams, Vice President, Analytics and AI, Retail and Consumer Product at Capgemini Invent.

For best results, every employee needs to become a data champion, according to Steph Bell, Director of Analytics at J Sainsbury. “We need to be bringing data closer to the business and bringing business closer to the data,” she said.

Leaders must also be “data fluent” if they want their businesses to be competitive, said Emma Duckworth, Head of AI and Data Science at healthcare company Haleon, which owns brands such as Panadol and Sensodyne toothpaste. “The most impressive managers are those who make data-driven decisions,” she told the audience. “They understand it and they prioritise how data is being used.”

Real time, competitive pricing

A key factor in improving commercial performance as John Lewis Partnership (JLP)  enters the Golden Quarter (Black Friday, Christmas and January sales) is the relaunch of its “never knowingly undersold” price promise, based on an AI-led assessment of what 25 competitors are charging for 30,000 products, in store and online.

“The power of AI has enabled us to make sure the John Lewis team can factor in internal and external data, plus other factors, at the same time, in real time,” said Barry Panayi, Chief Data and Insight Officer at JLP. “People in the commercial team will make commercial decisions on where we should sit versus competitors. AI enables them to do that quickly without lots of manual effort and risk.”

At Sainsbury’s, data has long been at the heart of its Nectar loyalty programme. Last year’s introduction of personalised pricing for Nectar cardholders changed how the retailer rewards its customers, said Bell. “In the Nectar app, you get hyper-personalised offers driven by algorithms that understand how you shop and what offers you’ll want.”

Attention to customers remained paramount, she added: “Amid all the technical brilliance of AI, you have to be obsessed with your customers so you can make the right decisions about where to best deploy your technology.”

Clearer language, safer products

At Haleon, AI is being used to enhance inclusivity and representation in digital advertising content “One barrier to health inclusivity is health literacy,” said Duckworth. “We are using AI to make sure information about our products is accessible and of the appropriate reading age. Our Health Inclusivity Screener tool evaluates content on health literacy and representation criteria.”

Meanwhile, GenAI is also helping Haleon to be more efficient when it comes to translations. “We built an AI powered internal translation product that is really making a difference to us as a global company,” she added.

AI needs well-structured data

For all the enthusiasm about the transformational impact of AI, participants cautioned that organisations need to structure their data for AI applications to work.

Simon Jury, vice-president of data and analytics at Asda, said there was a certain naivety among executives about what it means to use data as a decision-making tool. “It’s incumbent on us [in the data team] to have the right, clean data available in the right format at the right time for the people who will be making those decisions,” he said. “Training, data literacy and data culture are part of that. But it has to be top down and bottom up.”

Duckworth from Haleon agreed. “If you want to get the value from this amazing AI, you can’t avoid having clean, well-structured, well-organised data,” she said.

“Otherwise, something big will blow up because no-one has bothered to fix their data,” warned Panayi of JLP. “AI is going to make that much worse. When that happens, data management will get its renaissance.”

Data literacy for all

While Asda continues to migrate its data from Walmart’s IT systems to its own, Jury is most excited about what the future could mean in terms of non-technicians being able to interrogate complicated data without help from experts. “Having an English interface into complex datasets would mean non-techies could follow a meaningful train of thought with the data,” he said.

Everyone agreed that excitement over the arrival of AI was helping to change attitudes towards data. “AI – and even a lot of data and analytics – historically felt quite removed [from the rest of the business], a black box people couldn’t tangibly understand,” said Bell. “But GenAI is opening the door to a much wider conversation about data and all applications of AI.”

A huge thank you to Women in Data® for the opportunity to host this truly insightful panel discussion! To learn more, read our latest report exploring how organisations are harnessing the value of generative AI.