From traditional retailer to digital champion
How AI platforms bridge the gap between brick-and-mortar retail and e-commerce

AI-based data analytics in brick-and-mortar retail is more present than ever in the global retail market, resulting in a rich but also fragmented offering of multiple stand-alone solutions, each with their own interfaces. In order to nevertheless enable deep cross-use case insights, correlations and automations in this structure, a central data management and execution platform is required. Using an exemplary project report, this presentation shows how a globally operating retail giant succeeds in collecting, correlating and acting on various in-store data using one single platform and user interface.
As a result, the client receives a data-driven understanding and data basis for value improvement in core metrics such as:
o Customer Insights - Who is my customer?
o Age and gender insights
o Group size (Single, Couple, Family)
o Length of stay
o Customer behavior - What interests does my customer have?
o Dwell time in product areas
o Most viewed product categories/products
o Shopping Analytics – How does my customer move in the store?
o Heat Maps
o Individual paths and customer routes
o Store Optimization - How to optimize shelf management and avoid out of stock?
o Ensuring 100% product availability
o Detecting misplaced products
o Checkout Analytics - How can the Checkout process be optimized and made more customer-friendly?
o Cashier waiting time
o Pass through detection
In the 15-minute presentation, we want to show how our customer uses a scalable use case portfolio on a central platform to break down data silos and turn raw data into profitable knowledge.