For companies, understanding and continually improving the customer experience has become as important as product development, innovation or marketing. To help partner brands better understand their customers’ expectations, Galeries Lafayette has in-house developed Custom Insights, a data-driven customer experience analysis and personalization platform.
Galeries Lafayette has 19 of its own outlets in France and 38 affiliates. Like all stores, they have been severely affected by the Covid-19 pandemic, but they are doing their best to return to their pre-crisis level.
The Custom Insights platform will enable the company to accelerate its end-to-end transformation by leveraging 5.4 million visitors per month to its merchant website and store network.
Retail media platform “Custom Indicators”
The media retail market is now experiencing very strong growth, with Galeries Lafayette claiming to be the first French multi-division player to develop an initiative of this size.
Aiming to provide partner brands with access to a more detailed understanding of Galeries Lafayette customers’ expectations in order to offer them personalized experiences at every stage of their shopping journey, the platform has been developed using Agile Service Design and Lean Startup methodologies, enabling Make it possible to quickly improve them while reducing costs.
Custom insights are designed into a co-building approach with partner brands, with strategic or operational issues in mind. With its secure management and the reliability of the data analysis it provides, brands will have access to accurate management of their performance and customer profiles, to suggest a more appropriate product offering and anticipate trends for upcoming seasons to create more effective and measurable marketing devices. For their part, Galeries Lafayette will be able to improve customer knowledge.
The platform was created in accordance with the regulations on the protection of personal data, it is based on unique AI algorithms, optimized for the worlds of fashion and home, and does not develop any “unhelpful” functions. Thus it limits the number of data processing operations to what is absolutely necessary and reduces carbon emissions.