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Footfall Counting

Accubits collaborated with a prominent supermarket chain in Australia to develop an innovative retail heatmap analytics solution powered by computer vision. By harnessing the capabilities of CCTV cameras placed throughout the store, this solution efficiently collects valuable data on customer traffic.

THE CUSTOMER

Our customer, a leading supermarket chain in Australia, which has 20+ supermarkets located in different cities across the country. Supermarkets and Grocery Stores is one of the most competitive industries in Australia. Our client wanted to leverage cutting-edge technology to enhance their digital capabilities to differentiate their brand from the competitors. 

THE BUSINESS CHALLENGES

In-store product placement is a critical factor that determines how well a retail store can perform. A highly strategized product placement can be the difference between selling a few units and selling thousands of units of product. Our client followed conventional in-store product placement strategies available from decade-old research findings. They realized that these strategies are not sufficient to drive more sales. Today, data-driven decisions are helping businesses to perform several times better as compared to the results from their conventional modus operandi.

Our client wanted to leverage in-store customer traffic analytics to drive more sales. For example, if they know which section of the store has the highest traffic, they can reposition the product to a different shelf so that people would notice other products on their way or see more product offers that can help to increase more sales. The business challenge was to track the in-store customer traffic in real-time so as to strategize product placements based on the insights generated.

BUSINESS REQUIREMENTS

Our client put forward a set of business requirements for us. The major requirements are as follows;

  • Track the in-store customer traffic.
  • Generate a real-time instore heatmap.
  • Pluggable to the existing CCTV infrastructure.
  • Count the number of people enting the store.
  • Count people visiting the defined sections.
  • Track the customer journey within the store
  • Deduct the average customer journey
  • The dashboard in the web as well as mobile interfaces

THE SOLUTION

We built a computer vision-based in-store heatmap analytics solution for our client. The solution collects in-store customers’ traffic data from CCTV cameras installed in the store and analyzes it to create a visualization of the “hot” and “cold” areas of the store. The hot zones represent the areas that are more visited by customers and cold zones represent less-visited or ignored areas.

The solution is capable to count the number of customers entering the store, track the split-up of incoming customers between different sections, track the split-up of customers between different zones, etc. The solution displays the heatmap data, traffic flow data, customer traffic funnels neatly in an intuitive and interactive dashboard that can be accessed by the store admins and managers.

BUSINESS BENEFITS

The solution enabled our customers to access insights generated from in-store data analytics and make informed decisions on their product placement strategies. The solution is deployed as a pilot run in 2 supermarkets as an AB testing and evaluated the results.

  • Insights on customer traffic flow
  • Enhanced queue management
  • Efficient facilities management
  • Dynamic staffing based on customer traffic flow
  • Insights on customer traffic funnels, traffic segmentation

SOLUTION DEPLOYMENT

The solution was deployed on top of the existing CCTV infrastructure of the store. For one store we used the store’s own server and for the second store, we used a dedicated server for AB testing to compare the result/expense metic. The customer analyzed the performance of both stores for  3 months and saw substantial improvements in sales. The results are as follows;

RESULTS

  • Based on the data generated from the solution, multiple AB testing is done on the product placements, and noticed an increase in overall sales by 18% in 3 months.
  • Noticed an increase in average commodity per customer value from 24 to 31.
  • Reduced peak time queue waits time from 4 minutes to 40 seconds.
  • The efficient use of facilities resulted in a 10% reduction in facilities procurement expenses.

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Highlights

In-store Heatmap analytics

Customer traffic funnel segmentation

Customer counting

Customer flow analytics

Customer re-identification