The recent news that luxury brands have destroyed millions of pounds worth of unsold merchandise to avoid their products falling into the wrong hands and being sold on the “grey market” at knockdown prices, illustrates the challenges that these brands face in matching pricing and customer demand. With Richemont, owner of Cartier and Mont Blanc, and Burberry among those who have implemented these measures, it clear that luxury brands must develop the processes to adjust their pricing models to match demand and maximise sales, while maintaining their brand identity.
This is according to Uwe Weiss, CEO at Blue Yonder, who suggests that the latest technology, such as artificial intelligence (AI) and machine learning, could play a vital role in helping luxury brands to sense key indicators of demand from changing market conditions and data such as sales, promotions, weather and events to set the optimal price.
Uwe says: “The destruction of unsold merchandise not only has a significant impact on a brand’s bottom line, but it also does no favours for that brand’s environmental credentials or PR image. Burberry appears to be taking positive steps to address these issues, with executives announcing plans to reduce the cost of its products in China by 4 per cent, as higher prices in Asian markets had been cited by some analysts as one of the key reasons for the company’s surplus stock. However, there is also an opportunity here for all luxury brands, whatever market they operate in, to take a more refined approach to their pricing than broad price cuts.”
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 excess stock that needs to be sold.
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.
Uwe concludes: “Despite the challenges that it, and the vast majority of UK retailers face, Burberry remains one of the world’s most iconic brands, with annual sales of over £2.7 billion. Innovative AI and machine learning-based price optimisation could offer Burberry the opportunity to manage its pricing with greater accuracy, avoiding the necessity for broad price cuts and matching price more accurately with customer demand. This strategic, data-driven approach could help to significantly reduce waste, maximise sales and improve profitability.”