An analysis of the familiarity between users in a group requires large amounts of information. We could determine the degree of familiarity by using personal information gleaned from a social networking service. For a realtime service, we usually use video data. Unfortunately, this data is closely related to a user's privacy, so the user may feel uncomfortable about its use. Therefore, in this study, we set out to devise a real-time familiarity analysis model using a minimal amount of information and a Bluetooth low-energy beacon. Unlike the traditional approach, the devices receiving the beacon signal are placed on a desk, wall, or ceiling and the user carries a beacon. The beacon transmits only its ID and a received signal strength indication (RSSI) signal. Using the device for receiving the beacon signal, a user's location can be monitored so that the server can analyze the intragroup and calculate the degree of familiarity between users. This study addressed those situations arising in a party-like event, in a school, in a company, etc. to attempt to analyze the degree of familiarity by determining a person's location at specific times. This technology could also be applied to exhibitions, parks, and amusement parks to determine the most popular exhibits, spots, and facilities in real time.