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 is clear that luxury brands must develop the processes to adjust their pricing models to match demand and maximize sales, while maintaining their brand identity.
Retailers and brands struggling with surplus stock should look to the latest technology, such as artificial intelligence (AI) and machine learning, which could play a vital role in enabling these businesses to sense key indicators of demand from changing market conditions and external sources data – not only sales and promotions, but also news, weather and events – in order to set the optimal price.
Dynamic pricing, dynamic sales
The destruction of unsold merchandise not only has a significant impact on a brand’s bottom line, but it also does no favors 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 optimization 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 optimization doesn’t just give 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.
Despite the challenges that it, as well as 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 optimization could offer Burberry, and other luxury brands, the opportunity to manage their 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, maximize sales and improve profitability.
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