Introduction
AI is turning the retail industry on its head. Retailers see the incredible opportunities that AI can present if they're able to seize the moment. But the foundation of AI is data, and without a more comprehensive data strategy built into their architecture, retailers get only limited insights.
Every retail business owner and store manager want to understand how their customers' shop and how they engage with the store environment. They want insights on how customers interact with products and which products they pick up and proceed to purchase. Insights like these can be gleaned through the partnership of Microsoft and AiFi.
AiFi is a leading autonomous store operator with more than 80 locations for some of the top retailers worldwide. Microsoft has partnered with AiFi to create an end-to-end Microsoft Power Apps solution for smart stores called Smart Store Analytics. AiFi handles store rollout, operations and customer support, while Microsoft Cloud for Retail delivers analytics and insight on AiFi store data using Smart Store Analytics.
Together, AiFi and Microsoft Cloud for Retail equip businesses with the insights they need to operate the stores of the future. This collaboration aims to reduce deployment time and costs for autonomous stores.
With Smart Store Analytics, retailers can predict customer and operational needs, monitor and interpret engagement, and maximize the value of their data. By tracking customer behavior with heat maps and sales data, retailers can offer the right products at the right price in the right location.
Retail store managers can benefit from continuously optimizing fleet performance, store layout, product catalog, and shelf placement by using this data. With the user-friendly interface, retailers can access analytics within and outside the UI as a Microsoft Azure Data Factory pipeline for advanced users.
Smart Store Analytics can help store managers with insights into the following areas:
Store layout - Identify areas of the store where customers spend the most and the least time. Learn about the shopper's journey from store visitor to a purchasing customer.
Product catalog - Learn about the growth in unit sales and share of overall sales for products in the catalog and distinguish the best-selling items from those items that need more attention.
Product placement - Identify combinations of products that are often purchased together to discover cross-selling opportunities and optimize product placement in the store.
Promotions - Empower the store manager to make data-driven decisions on product placement and promotions based on closely related products.