QSR Case Study

INTRODUCTION

One of the world’s largest quick service restaurant chain which pioneered the fast food model, recently noticed that its customers are having negative experiences based on customer feedback and online discussions. It was realised that these experiences could be due to things like crowds, queues, bad customer service and even other non-restaurant related issues.

With this as a starting point, need for other Video Analytics products for processes which were happening manually was realised and a discussion was had on how store performance can be improved. The following sections give a brief about which services were used and how exactly the results were obtained from the services

EXPRESSIONS AND DEMOGRAPHICS

By using Agrex.ai Face Analytics service, they got rich information about the kinds of visitors they were getting in their restaurants broken down by Age for example, kids, young adults etc and gender. The main goal of the project however was to measure customer satisfaction. Hence reports contain a breakdown of expressions noticed in different areas, namely, Exit Gate, Billing counter, Food collection counter. This gives the managers an immediate tool with which to measure any negative reaction and any positive emotion on a macro level.

KITCHEN HYGIENE COMPLIANCE

In a large food operation, hygiene is always a top priority in order to maintain the high standard of food safety. With frequent enforcement, the restaurant cannot afford even occasional slip-ups. Hence a solution was implemented which monitored :
1) If the cooks were wearing proper gear (Headgear, gloves) according to veg areas and non-veg areas.
2) If the counters and floor were being wiped according to regulation
3) If the employees were washing hands properly
4) If the employees are busy with other activities such as browsing phones and thereby doing their duties with tardyness

RESULTS

According to the information gathered about the customers the store, and the analysis done, the following things were identified :
1) Customer dissatisfaction was due to general reasons such as crowding, large queue etc. but also due to reasons related to which employee was on the counter. In this was specific issues can be identified which might differ per store and can be improved by A/B testing.
2) Kitchen compliance was improved drastically and a monitoring team was
shrunk down, thereby drastically reducing the cost relating to that. Now the monitoring of compliance happens 24×7 with real-time alerts. Whereas earlier it happened twice a day at random intervals.
The brand is now rolling out these technologies at more of their stores to improve key performance metrics