Mining Security Data for Operational Efficiencies
Explore how the merging of security and business data has revolutionized a retailer’s operations.
One area of the market that is rapidly evolving around the concept of data intelligence and demanding a solution is the retail vertical. This comes from many different types of applications and needs to be converted into solutions that look at the security data, business data and, at times, behavioral analysis of the scene.
Let’s explore an example of how the merging of security and business data has revolutionized a retailer’s operations.
A large clothing apparel retailer, whose target market is in the teens to thirty-something category, was looking for a solution to provide the business with a better way to track store efficiency and effectiveness. The initiative was funded by store operations in an effort to identify low-performing stores and train managers on how to run the stores with a higher close rate and profitability.
The solution was to deploy technology that consisted of a combination of video analytics that were tied to the in-store point-of-sale (POS) system to allow the retailer to actively mine the data, dashboard key performance indicators and validate key outliers via the video information. This was achieved by using smart cameras at the store entrance to count people and traffic coming into the store. The people counting data allowed the retailer to dashboard the total store deployment to see the total traffic per store and peak hours of traffic, with the ability to drill all the way down to the store level, and, as a result, staff locations more effectively.
In addition, this allowed the retailer to classify buying units by group, as well as a height threshold set within the smart camera. This data was then paired up with POS transactions by day for each store across the United States. This allowed the retailer to stack rank stores by their conversion rate of sales and see which stores were the highest performers vs. lowest performers on converting this store traffic into sales. The retailer was able to see a return on investment within the first year of deployment through in-store operational efficiency via training, staff management and scheduling during peak- and low-traffic times.
After exhibiting success with store traffic and conversion rates, the project grew to incorporate other store metrics in the areas that affected the customer experience. Measures were put in place to determine both service times once the customer was being rung up at the register, as well as POS exceptions that could be flagged and reviewed by store loss prevention. Additional smart cameras were set over the register area to monitor customer traffic at the register. The retailer incorporated lanes within the store operations, so that queue traffic leading up to the register could be monitored to determine how effectively store management staffed the registers.
This would not have been achievable by data alone and required a solution that could mine and mesh data together based on specific exception criteria. An interesting aspect about this solution was that initially it had nothing to do with security or theft. It was entirely set in motion to proactively identify areas of performance loss in order to better train or replace resources to increase profitability.
The application can be deployed in a number of retail and service applications, including quick-service restaurants, convenience stores, automotive service specialists, grocery stores and more.
Rich Mellott is Director of Technology & Channel Strategy for Stanley Security.
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