igital Luxury Group (DLG) has announced its acquisition of Re-Hub, a Chinese technology company, set up in 2017, that specialises in AI-driven business intelligence solutions for luxury brands. “This acquisition represents the first step in our ’buy and build’ strategy, which will rapidly create strong opportunities centred around AI for our clients in China and globally," says David Sadigh.

Re-Hub provides a cloud-based SaaS platform featuring three key modules for brand performance measurement and e-commerce optimisation, grey market tracking, and pre-owned goods monitoring. It tracks more than 150 luxury brands for clients that include Breitling, Ferragamo, Tory Burch and Zegna. DLG says it remains committed to pursuing further data and AI acquisition opportunities as part of its long-term strategy. Read on for our interview with David Sadigh.

Max Peiro, founder and CEO of Re-Hub, and David Sadigh, founder and CEO of DLG.

Europa Star: You just acquired Re-Hub. What are its main services?

David Sadigh: Re-Hub applies artificial intelligence to analyse luxury brand sales in China. An example: the platform can precisely track price discrepancies between several primary or secondary marketplaces for the same luxury good. It uses machine learning to semantically analyse what internet users are saying about a brand or product. And it can correlate these factors to study the connection between social media discourse and a product’s commercial success or failure.

Going forward, what are your ambitions for the platform?

I’m convinced that artificial intelligence has huge potential for the luxury segment, hence it is a savoir-faire that we needed and which usefully completes the range of services we provide. We will also develop this expertise outside China, Re-Hub’s main market. Already, we are operating an international pilot project for the watch sector.

Luxury brands aren’t falling over themselves to take advantage of artificial intelligence. What makes you think this will change?

So far, whatever claims it may make about data analysis, the luxury industry is still largely instinctive in its functioning. Marketing, sales and finance divisions are too often siloed and communication between them is anything but fluid. This can work in a buoyant economy, which has been the case in recent years. Luxury hasn’t had to adapt in the way other industries such as FMCG have, and because it had no fundamental reason to change its processes has been slower to adopt AI. Now that margins are tighter, using artificial intelligence to make efficiency gains becomes a more pertinent solution.

For example?

One sticking point concerns the price increases that certain luxury brands have implemented in recent years. Artificial intelligence can analyse a mass of data to establish whether or not there is a correlation between these price hikes and a potential drop in brand value, in particular through new product resale. These tools can also identify if certain collections are losing ground and whether they should be discontinued. In contrast, we can also identify which brands are gaining ground even in a more challenging economic climate, as is currently the case for Messika or APM Monaco in jewellery, or Jacob & Co. and Norqain in watches, which we believe are underrated. Another example is to identify models or collections that are no longer in the current catalogue and could be relaunched.

Many luxury brands have opened their own boutiques and e-commerce sites in order to collect customer data. Why would they need a third party to process this data?

Luxury brands generally find that their e-commerce doesn’t realise the potential they had hoped, whereas marketplaces such as Amazon and Tmall generate huge volumes of exchanges. Brands used to complain about not having access to data gathered by their third-party physical retailers and the same thing is happening on-line. A holistic approach that includes data from these marketplaces is therefore important in producing an accurate picture of the market and its potential. Artificial intelligence is a crucial part of this analysis.