RetailerIN, an in-store analytics and engagement solution, uses IoT data analytics and machine learning to provide retailers with suggestions to improve their processes and optimize store profitability.
The platform measures how shoppers move and interact with products in a physical store. It then connects with data sources such as cameras, WiFi, receipts and weather, and uses advanced algorithms to extract store-level KPIs from the raw data.
The KPIs are then used to feed a proprietary machine learning system that can recommend actions to potentially improve store profitability and performance.
With RetailerIN, store and marketing managers can:
Access store-level KPIs in near real time (shopping window conversion, returning customers, shopping basket mix and size, etc.);
Analyze trends and how store performance has evolved over time;
Measure the effectiveness of marketing activities and promotional campaigns;
Visualize and monitor the whole purchase funnel; and
Evaluate the potential effect of interventions (including changing opening hours, enhancing the shopping window organization or optimizing shifts of shopping assistants).