[Case Study] Retail Store Visitor Analysis Solution

Retail Store Visitor Analysis Solution

TecAce, a provider of retail store solutions, recognized new market demands. We wanted to know which customers were enjoying the various experiences offered by the devices displayed in retail stores. If we could identify which experiences were preferred by certain demographic groups and how they were being used, it would greatly aid the development of future experience apps. Additionally, we could provide targeted content to customers, achieving more effective advertising.

Challenge

Initially, we considered using the camera on the devices to identify visitors' gender, age, and ethnicity using AI algorithms, comparing this data to app activity analysis data. However, this method had several issues. First, there was a risk of invading visitors' privacy using the camera. Second, the device-based recognition solution had relatively low accuracy compared to cloud-based AI solutions. Lastly, there were limitations to analyzing the entire store situation since tracking was only done on the device.

Solution

Therefore, we researched a video-based retail store visitor analysis solution that does not violate privacy. The solution we researched uses video but does not identify population statistics by recognizing individuals' faces. Instead, it uses AI algorithms based on appearance, gait, and other information to identify population statistics only. Furthermore, we matched the tracked data from the video solution with app activity analysis data in real-time and made the data available on an integrated dashboard. Although there was a time lag between the visitor tracking data extracted from the video solution and app activity analysis data, we developed a data matching algorithm to implement it as accurately as possible on the dashboard.

Result

The implemented solution became useful for various purposes. Firstly, we understood retail store visitor traffic and how many people were interested in each device. This was implemented through Funnel analysis, and we could analyze visitors who entered the store, those who entered the device section, those who interacted with the device, and those who interacted with the device in turn. Such analysis results can be used in developing the next experience app and device placement.

Furthermore, this solution can help identify the most popular experiences in the store and develop experiences that customers of certain demographics prefer. Based on this, we established tailored marketing strategies and could achieve more effective advertising to customers.

In addition, store employees understood customers' behavior patterns through the solution and were able to provide improved service. This could increase customer satisfaction, store visit rates, and sales growth in the long term.

In conclusion, the retail store visitor analysis solution developed by TecAce is an important tool that respects customer privacy while effectively analyzing store visitor data and aiding store operations. This allows retail stores to provide better experiences and establish successful business strategies.