Real-Time Location Tracking System for Proximity Marketing and Foot-Traffic Analysis

Retail Optimize is an innovative Swedish company specializing in creating advanced solutions that enable businesses to monitor in-store visitor movements, gain deeper customer insights, enhance sales, bolster brand strength, and, most importantly, offer a more personalized customer experience.

Customer

Retail Optimize

Location

Industry

Retail

Company’s Request

Retail Optimize's collaboration with Sirin Software centered on establishing a city-wide network of access points that served a dual purpose: providing free internet and enabling customer movement tracking for marketing purposes. A key feature of this project was the development of a solution that can create 'temperature maps' in large shopping centers. These maps aimed to visually represent foot traffic, aiding in identifying high-traffic areas and thus informing store placement and rental strategies following consumer movement patterns.

The project had three principal requirements: cost-effectiveness and affordability for large-scale deployment, ease of installation to reduce disruption in busy retail spaces, and low maintenance to guarantee consistent and reliable data collection. The ultimate objective was to combine connectivity with insightful data analytics, transforming how retail areas manage and optimize their marketing and spatial strategies in commercial environments.

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Technology Set

OpenWRT OS

Wi-Fi Probe Requests Sniffing

Bluetooth BLE Beacons

C and C++

Python

Node.js

MySQL

Redis

Wireshark

JIRA and Confluence

802.11 Wi-Fi Standards (b/g/n)

Bluetooth 4.0 and BLE Protocols

Solution

At Sirin Software, we engineered a middleware layer on top of the OpenWRT platform, an open-source, Linux-based system. Our solution was focused on capturing Wi-Fi probe requests near the access points. These probe requests – signals sent by devices seeking Wi-Fi connections, were key in tracking customer movements.

Our team developed a specialized blacklisting algorithm to filter out static devices like routers and laptops, ensuring that only relevant mobile device data was captured. This step was key in maintaining the accuracy of the foot traffic analysis.

On the server side, we established a centralized hub for data aggregation. This hub was responsible for processing and recording the movement data, forming a part of our marketing analysis toolkit.

Devices in use

OPEN-MESH OM2P ROUTER

OPEN-MESH OM5P ROUTER

CHIP MICROCOMPUTER

The Open-Mesh OM2P Router was our budget-friendly choice, equipped with an optional outdoor enclosure and Power over Ethernet (PoE) capability. It was adept at sniffing on the 2.4GHz Wi-Fi band, which was widely used for its balance of range and bandwidth.

We also employed the Open-Mesh OM5P Router. This high-end device, also featuring an optional outdoor enclosure and PoE power, was selected for its reliability and performance in varied locations.

Additionally, our engineers utilized the CHIP Microcomputer, an experimental for that time, a cost-effective device designed for sniffing both Wi-Fi and BLE (Bluetooth Low Energy) probe requests. This was especially valuable in areas where traditional Wi-Fi tracking might be limited.

In crafting this solution, our team leveraged our technical know-how to integrate these technologies into a cohesive system. The combination of our custom-developed middleware, careful selection of hardware, and robust server architecture allowed us to effectively capture and analyze movement data for comprehensive retail analytics.

Value Delivered

Detailed Customer Behavior Analysis. Our Movement Reproducing Algorithm, implemented on the server, enables Retail Optimize to track and analyze customer movements accurately. This capability is important for understanding customer behavior, aiding in-store layout optimization, and targeting marketing strategies.
Efficient Data Management. The Cloud Management Dashboard provides Retail Optimize with an easy-to-use interface for monitoring and managing data. This tool allows for streamlined decision-making based on up-to-date customer traffic analysis.
Precise Data Collection. With Automatic Blacklisting, our system effectively excludes static devices like laptops and access points. This ensures that Retail Optimize receives accurate data on active customer movements.
Respect for Customer Privacy. Our filtration of randomly generated MAC Addresses complies with modern privacy concerns. This feature allows Retail Optimize to gather foot traffic data while respecting the privacy of their customers.
Enhanced Tracking Consistency. The hysteresis-based filtering method we’ve implemented resolves the issue of devices fluctuating between nearby access points.
Extended Tracking Range. By employing the BLE and Wi-Fi Address Matching Algorithm, we ensure Retail Optimize can continue tracking customer movements even when Wi-Fi is disabled on devices. This broadens the tracking capabilities, offering a more comprehensive understanding of customer behaviors.