LVMH accelerates data contribution to its businesses to drive development

Data analytics has long been powering the fashion industry, from entire algorithms to predict sales, like at fast fashion giant Shein, to augmenting customer experience with personalised offers.

Luxury group LVMH has been expanding its use of data for over three years, partnering with Kaggle Days, an international competition for data scientists, in 2019, and in 2021 announcing a strategic partnership with Google Cloud.

This week the LVMH Data Summit from November 14-16 underscored the Group’s global strategy to accelerate the contributions of data to its businesses as a key lever to drive development. In a statement the company said “this digital acceleration is spearheaded by both the LVMH Group and our Maisons.”

Artificial Intelligence is omnipresent

LVMH said cloud-based artificial intelligence (AI) touches every part of its value chain, from product development and supply chain to engagement with employees, partners and customers. During the VivaTech 2022 show, LVMH launched its Data Academy, a training and certification program for teams at its Maisons aligned with the same objectives as the partnership with Google, while seeking to nurture a broad culture of data innovation and knowledge among staff.

LVMH stated over 900 of its employees have already acquired a deeper understanding of the challenges involved and the potential of future-facing data solutions. In October the Group rolled out an internal collaborative media platform called Forward to share and promote information and initiatives spanning all things digital among Group employees.

This data push is a fresh opportunity to expand the LVMH's spaces for sharing knowledge, use cases and best practices thanks to a series of special events:

The LVMH Data Summit welcomed senior executives from its Maisons, many of which rely on data for day-to-day activities encompassing retail, media, e-commerce, supply chain, digital marketing, CRM and more.

Talks centered on topics such as trusting AI for forecasting sales, balancing profitability with customer experience and using machine learning to discover factors affecting product returns.