[NE Coffee Chat] TCAG: "Enabling Data-Driven Decision Making for Offline Stores"

Presenting a Solution for Inefficient Offline Store Operations
with AI Consumer Agent Technology

How many customers visited our store today? Which products were they interested in? If there was a problem in the store, where did it occur? How should we improve it? These are questions that anyone operating an offline retail store would ask. However, answering these questions is not easy. The nature of offline stores makes it difficult to collect and analyze customer data one by one. Sungshin Cho, CEO of TCAG, believes that this is why offline stores are being operated inefficiently. To address this issue, TCAG has introduced a solution called 'THE---SIM,' which utilizes artificial intelligence (AI) consumer agents.

Sungshin Cho CEO of TCAG

On June 26, CEO Cho stated, "We plan to offer the most optimized solution for planning, operating, and managing offline retail stores." This solution starts with data collection in offline stores, which has been a challenge until now. Having data is essential to accurately understand what is happening in the store. Cho explained, "We collect data through IoT (Internet of Things) infrastructure such as Wi-Fi scanners and LiDAR sensors installed in the store." The IoT infrastructure sends and receives signals at 0.01-second intervals to determine the location information of people entering the store. This enables the collection of various data, such as the number of visitors, dwell time, movement paths, and churn rates. Cho said, "For example, by linking the location data of consumers who visited the store with information about which products are placed where, we can identify which products attracted the most attention from visitors."

While the initial step is to collect data in this way, Cho emphasized that the core strength of TCAG lies in analyzing this data and processing it into the format desired by clients. He said, "We will analyze customer behavior in detail and link it with existing membership data," adding, "Our technology enables clients to monitor what is happening in their stores and use AI models to actually improve business outcomes." The goal is to present key performance indicators (KPIs) based on data and help clients achieve them. To this end, TCAG plans to build a virtual store using digital twin technology and enable store operation simulations with AI. Cho stated, "Various decisions in offline store operations that were previously made based only on intuition can now be made quantitatively based on data," adding, "We are also developing AI consumer agents that use large amounts of high-quality data to mimic and reproduce consumer purchasing behavior."

Cho's experience is deeply reflected in the development of this solution. Having worked in the fashion retail sector, he experienced the entire process from apparel development to production and consumption, and recognized the inefficiencies in offline store operations during this time. This is why TCAG is initially targeting offline stores in the fashion and accessories sectors. The company is also considering the fact that there are many offline stores operated not only by large corporations but also by small business owners. Cho said, "Small merchants also have significant needs," adding, "We will offer a variety of license-based subscription models."

TCAG's solution is currently being piloted in several stores. The official version is scheduled to be released in the second half of this year. Cho said, "Until July or August, we plan to continue as many concurrent PoC (proof of concept) projects as possible in domestic stores," adding, "We expect to launch the official service in November." The company is also exploring global market entry at the same time. He said, "We plan to start global PoC projects mainly with popup stores in Tokyo and Osaka, Japan," adding, "We will conduct pilot projects in the Asian market through the second quarter of next year."