About The Client
The client is a highly successful convenience store chain in Korea, and is currently the fastest-growing chain in the country. As of 2022, the company had a total of over 6,000 stores nationwide. In 2021, they made its first foray into the overseas market by opening its first store in Malaysia.
This expansion represents a major milestone for the company and marks the beginning of a new chapter in its growth and development.
Key Challenges:
Inability to gather and analyze basic customer traffic
Lack of data about customer profile for developing future marketing activations
Non-standardized security system and procedures across the store network
Implemented Solution
The client had several specific needs that needed to be addressed in order to optimize the unmanned store operating system and improve the overall customer experience. These needs included the ability to gather and analyze basic customer traffic statistics, develop targeted marketing strategies based on in-store customer analysis, and implement a robust security and safety solution to protect both customers and employees.
To meet these needs, the solution provided included offline traffic digitalization to track and analyze customer traffic patterns, visiting customer analysis to gather data on customer behavior and preferences, and a real-time detection and alert system to identify and respond to any security or safety-related abnormal activities. By implementing these solutions, the client was able to improve the efficiency and effectiveness of their unmanned store operating system and provide a safer, more enjoyable shopping experience for their customers.
Key Results
The results of the project were extensive and covered a range of areas, including gender and age analysis, product preference analysis, and store operation evaluation. By analyzing customer data, the team was able to restructure the product selection based on the gender and age group of visiting customers, and identified popular and unpopular product lines by analyzing the duration rate of customer visits to different product zones. In addition, the team was able to maximize the efficiency of the hybrid store operating system by identifying store peak and off-peak hours and optimizing staffing accordingly.
To ensure the security and safety of customers and employees, the team also developed behavior identification algorithms that could detect and alert staff to any in-store abnormal activities in real-time. A total of five behavior identification algorithms were developed and implemented, and the system was able to accurately record and respond to both correct and false alerts. In the event of a security or safety issue during unattended hours, the system was able to rapidly deploy field personnel to address the issue. Overall, the results of the project were highly positive and contributed to a safer and more efficient store environment.
