Richemont Uses AI-Curated Suggestions To Deliver A Better Client Experience

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Luxury conglomerate Richemont is tackling the growing problem of delivering the right customer experience on different channels with the deployment of AI

Richemont is tackling the growing problem of delivering the right customer experience on different channels with the deployment of AI. Understanding which shoppers are likely to buy or repurchase, when to engage directly, and what creation to suggest enables sales associates to spend quality time with clients, engaging at the right time with meaningful advice. Richemont solves these retail challenges with an integrated Client Platform leveraging Google Cloud and its AI/ML capabilities.

Richemont began by posing two questions: which prospects or clients need extra attention, specifically, who is likely to convert or to repurchase; and what would be meaningful items to suggest to each client and prospect?

Both questions were addressed with machine learning algorithms. Their challenges included deploying and monitoring algorithms at scale for several brands across the globe, while addressing the specific business needs for each brand. For instance, it may be more relevant to recommend in-season items for fashion brands, while it is more about cross-fertilization across each brand’s iconic creations for watchmakers.

Engagement data such as email opened, clicked, SMS/MMS, website visits, was crucial to predict conversion of prospects for whom no transaction history is available per definition. For website interactions, Richemont leveraged the Google x Salesforce Connector and to deploy the Machine Learning algorithms and to monitor them, Richemont made use of Vertex AI, along with BigQuery, Cloud Functions and Google Storage, all orchestrated with Google Cloud Composer.

Richemont uses the deep learning library TensorFlow Recommenders to perform the product recommendation tasks. This library enables companies to build state of the art deep learning algorithms to achieve relevant and robust predictions. The company used in-store applications to invite people with a strong propensity to buy for boutique visits, while others at a different point in the purchasing journey were offered various options more suited to their tastes and inclinations. This solution was deployed across 11 brands in over 25 countries.