Enabling personalization for Omnichannel Commerce (Internship / Master Thesis)

In 2 steps you will:

• First, set-up the principles and identify the tools of an architecture that: Aggregate in real-time high volumes of data (customers behavior, customer data, segments, products, inventory, promotions and incentive content)
• Uses a rule engine that compile this data and enables personalization behavior
• Ensure the integration with existing Omnichannel Commerce ecosystem (eCommerce, point of sales, social media,…), most probably based on a microservice logic.

Second, mature the solution from a rule engine to an AI engine:

• Gather feedbacks on the performance of the personalization
• Let the engine learn and perform better personalization

Objectives

• Analysis & research of current solutions & frameworks provided on the market
• Understand the key success factors of such personalization tools within a distributed environment
• Identify & validate the targeted solution Architecture
• Choose which use cases to work on, identify matching real business needs linked to them.
• Design and develop a POC of the solution