CIO-online

On October 12th, at the opening of the 5th edition of Fashion Tech Week, ESCP Europe’s “Mode et Technologie” chair, led by Lectra, organized a debate about data in the fashion industry and its distribution.

France, October 16th, 2017
CIO

In the field of American fashion, when something “is the new black” it means that it has become very fashionable. At the opening of the Fashion Tech Week, as in each of the past editions of the event, the “Mode et Technologie” Lectra-ESCP Europe chair, founded four years ago to deal with the fashion / technology intersection, organized a debate. This debate took place on the opening day of the 5th annual Fashion Tech Week (12th – 20th October, 2017).

The theme of this year’s debate was “customer data”, which many are calling the “new oil”. The speakers did not come strictly from the fashion sphere, but more from fashion distribution, without forgetting the sponsor, Lectra, which works both in software and in the material  needed to cut the fabrics (with the necessary optimization).

Reduce returns
After an introduction by Valérie Moatti (co-director of Lectra-ESCP Europe Chair), the round table brought together Gulnaz Khusainova (CEO of Easysize), Fabrizio Fantini (Evo Pricing CEO), Elise Beuriot (Director of the Fashion Market at Amazon Europe), Olivier Dancot (Lectra Vice President of Data) and Céline AbecassisMoedas (co-director of Lectra-ESCP Europe Chair). Summarizing the debate, data is seen as a means to improve fashion distribution and optimizing distributors’ profitability.

For EasySize, the first problem to be solved for online fashion retailers is the return of goods. While this is an unpleasant situation for the consumer, who is mainly disappointed with the product and therefore disappointed by the brand, returns are very expensive for the distributor. The management of purchasing history allows retailers to advise consumers of the right options and, consequently, to reduce the percentage of returns.

Optimize your assortment
Fabrizio Fantini pointed out how purchasing history data makes it possible to optimize the assortment of products. Amazon is obviously working on this exact point, as the online distributor is far behind in this specific sector compared, for example, to the cultural sector. Historical data also allows us to understand consumer tastes from the beginning, as Olivier Dancot explained. For him, data collected and processed quickly enables “Fast Fashion”, that is, the collections change every month, taking into account the trends observed through the data.
And, depending on the production loading plan, it is possible to determine when maintenance on a cutting machine will be more profitable.

Lectra thus allows you to switch from predictive maintenance (knowing when to change a piece before a failure) to prescriptive maintenance (knowing when it is more profitable to change a piece before a failure). Historical data can be crossed across several stores, as proposed by Easysize. The difficulty is therefore the integration of data that are not necessarily homogeneous every time a new store enters in the system. But, of course, data sharing benefits everyone thanks to richer information. And machine learning, where the computer learns by just crossmatching the data initially in random state, can overcome the traditional decisionmaking limits that normally requires you to know what you are looking for.

Better service in exchange for personal data
The real issue raised – especially during this period of compliance according to the General Data Protection Regulation (GDPR) – concerned the consumer’s acceptance of the processing of their personal data. A distributor like Amazon only needs a few pieces of information: identity, email address, physical address, and payment method. The rest is collected from the consumer’s online behaviour, without asking them directly. These data, intersected with those coming from other consumers and with the available stocks, allow you to recommend dedicated offers.

Data allows you, therefore, to optimize the service provided to the consumer.
As a consumer, Fabrizio Fantini said he is ready to provide personal information to benefit from a better service, closer to his expectations. Gulnaz Khusainova confirmed this point of view, but for her, it is absolutely essential that everything is done in the most perfect transparency, so that the consumer can check their data before sharing them. In this way, they know what service they can get in exchange for what data.