Case Study: SAIT Pilot

Case Study: SAIT Pilot

This case study reports a pilot by Italian grocery retailer SAIT to test how RetailerIN in-store analytics provides insight into layout and display optimization. The net result was an increase in sales of high-margin products.

Challenge and Opportunity

SAIT is an Italian grocery retailer with strong commercial presence in northern Italy. Founded in 1899, it currently runs approximately 100 supermarkets, generating 300+M€ yearly revenues. Thanks to its innovative technology, in the past months RetailerIN has worked with the company, providing analysis of shoppers’ movements inside one of SAIT stores. The aim was to understand how layout and display organization could be improved to enhance the store’s sales and profitability.

Solution

To achieve such a goal, we deployed our RetailerIN technology in one of their stores, a mid-size one (~1,000sqm) located close to Trento, Italy.
The deployment included the installation of 14 ceiling mounted antennas and attaching tags to shopping carts and shopping basket. The indoor positioning system used was provided by our technology partner Quuppa.
The deployment was fast. It took about 2 hours for deployment design and planning, 4 hours for cabling and electrical work and 4 hours to install and fine-tune the actual localisation infrastructure. In less than 10 hours overall data started to flow through our cloud-based processing pipeline, and information started populating a Web-based dashboard providing actionable KPIs.

Results

The pilot lasted 6 months.
During this period the RetailerIN service was used by:

• the store manager to validate specific decisions on the operations of the store, especially with respect to the placement of promotional areas and the overall flows of customers.
• the retailer’s marketing department, to take informed decisions on the management and positioning in-store of specific brands,
• category managers to gain insights on the value of the store space based on the number of customers visiting the various areas of the store.

RetailerIN highlighted the presence of some cold areas – areas with low foot traffic – in the store. Mapping categories and products to the actual layout, we discovered that some of the high-margin products (in particular organic food products) were positioned in a cold area. By moving these products to a high-traffic area, as identified by RetailerIN heatmaps, suddenly things changed, and in particular:

Shoppers started spending more time in the organic food area (16% increase in area dwell time with respect to the initial layout), 7% more customers had at least one organic food product in their basket, and more important the sales of such products grew by more than 5% over a 45 days time period. And this was not a temporary effect due to the new placement: after three months this increase in sale still persists!

Conclusions

By analysing how shoppers move inside a store RetailerIN provides store managers with actionable insight on how to improve the store’s performance. In this case study a notable profitability enhancement was achieved by reorganising the store’s layout in order to drive more foot traffic to a high-margin product category. This intervention, guided by RetailerIN analytics, was sufficient to provide a positive ROI to the customer.

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