Intelligent pricing could help to win back customers and drive sales, says Blue Yonder

FacebooktwitterredditpinterestlinkedinmailFacebooktwitterredditpinterestlinkedinmail

This week’s news of Marks & Spencer’s pre-tax profits falling 62% is yet another sign of the challenging conditions that many UK retailers are currently facing. The impact of the most difficult trading conditions since the 2009 recession is further underlined in M&S’s plans to close 14 stores this year, taking the planned closures to over 100 by the end of 2022. Commenting on the results, Uwe Weiss, CEO of Blue Yonder, suggests that M&S, along with many other high street retailers, is finding it challenging to strike a balance between sales and margins, and that applying innovative technology, such as Artificial Intelligence, to product pricing can help to optimise the relationship between consumer demand and price sensitivity, enabling retailers to regain control of their balance sheet and offer a much-improved customer experience.

Weisscomments: “M&S has carefully cultivated a reputation for quality and has been able to charge a premium for its products on the back of it, but new market entrants have sprung up and eroded its market share. It has a loyal customer base that is happy to pay more for the quality product that it offers, but the question is, how can it win back more cost-conscious shoppers without sacrificing its hard-fought for reputation and margins? The answer may lie in price optimisation.

“Using AI, retailers can adopt a dynamic pricing model that can prevent stock from going unsold, left on the shelves – charging full rate for the season, and adapting pricing strategy towards the end of the season, or when there is a lot of stock that needs to be shifted. Margins are everything in retail but mean nothing when there’s stock left on the shelves.

“Price optimisation solutions powered by AI can accurately predict customer demand and automate pricing decisions for a retailer, across every product category and every store, learning the relationship between price changes and demand while incorporating a retailer’s business strategy. However, truly automated price optimisation doesn’t just mean giving a retailer insights into what the best price might be. It uses these insights to automatically set the optimal prices to deliver the best bottom line, while rapidly sensing vital demand signals from changing market conditions and data such as sales, promotions, weather and events.”

Weiss concluded: “Despite the challenges that it, and the vast majority of UK high street retailers face, M&S remains one of Britain’s best-loved brands, with significant market share and revenue of over £10bn. Innovative AI and machine learning-based price optimisation could offer M&S the opportunity to transform how it manages the relationship between price and consumer demand, helping it to improve the experience for its loyal customers and drawing back in those who may have been tempted away by discount rivals and online competitors.”