Personalize and enrich the shopping experience

Date: 2021-11-10

[Customer issue]- How to capitalize on my buying process to enrich my offers?

In order to cope - and even hyper-personalize (unique, differentiated, and personalized customer relations on all media and without delay) - companies must rethink their customer journeys and the processing of their data.

The challenge is to ensure a quick and easy shopping experience, combined with an offer adapted to individual needs.

Several solutions have been implemented in recent years, such as chatbots or platforms entirely dedicated to customer relations (before and after sales). Nevertheless, these exchanges are often highly targeted and the wealth of information shared is often not used or only slightly used due to the difficulties encountered in processing and segmenting the key data from these conversations.

[Pain Points] Time consuming tasks - Customer diversity - Data classification

[Our Solution] Use shared data to personalize and enrich your offer

In order to differentiate themselves, companies rely on the personalization of their offer, hoping to keep their customers and increase their average basket size. Therefore, it is essential to know your customer well.

Our skills in Automatic Natural Language Processing allow us to identify and extract key information shared by a user in all telephone conversations, emails, chatbots, etc. Lettria's offer facilitates the collection and use of this data to personalize your offer (insurance for the client's watch collection) and enrich it (by offering to insure the client's pets).

The benefits of our solution are threefold:

  • Expand the average customer basket (with additional offers)
  • Improve the personalization of the offer thanks to the personal data transmitted
  • Automate data processing (improved usage/archiving)

NB: Lettria also offers GRPD compliance services to avoid any financial, image or cybercrime risks related to the misuse of customer data.

[Solution advantages] Enhanced personalization - Increased average customer basket - Automated data processing

Usecases:underlined text

  • Insurance: Propose a unique insurance basket for each user through the analysis of formal and informal exchanges with customers. The objective is to propose personalized and complete offers (adapted to specific requests: animals, second home, jewelry, business, etc.).
  • Banking: Identify customers' consumption habits and their financial/financing needs (consumer loans, rental investment loans, business financing, etc.).
  • Retail: Analyze customer interactions to propose complementary/additional products adapted to their needs to increase their purchases.

5 minutes

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