Case Studies > Global Surfing Brand Powers High Performance With RetailNext

Global Surfing Brand Powers High Performance With RetailNext

Company Size

1,000+

Region

America

Country

United States

Product

RetailNext
Aurora IoT Sensor

Tech Stack

IoT Sensors
AI
APIs
HD Video Recording

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Customer Satisfaction
Productivity Improvements

Technology Category

Analytics & Modeling - Predictive Analytics
Functional Applications - Remote Monitoring & Control Systems

Applicable Industries

Retail

Applicable Functions

Business Operation
Sales & Marketing

Use Cases

Predictive Maintenance
Retail Store Automation

Services

Data Science Services
System Integration

About The Customer

Born from water, Hurley was founded in Huntington Beach in 1999 on the principle of empowering and fueling the voice of the next generation. Over the years, this unique surfing brand has partnered with the world’s best musicians, surfers, skateboarders, and more, growing into a global youth culture brand with roots sunk deep in beach lifestyle. Today, the Southern Carolina-based surf company has 38 stores in North America - predominantly in Hawaii, California, Texas, and Florida - and they’re working to expand their fleet as a new, exciting chapter for the company.

The Challenge

The larger than life brand had previously installed another retail analytics solution which failed to provide a layered and contextual understanding of the in-store experience. This resulted in inaccurate traffic counts, conversion rates, and wasted labor hours due to incorrect store traffic forecasts. Additionally, there was a lack of support post-deployment and a lack of comprehensive data about shopper journeys in stores.

The Solution

Following the recommendation of other retailers, Hurley selected RetailNext to replace its existing traffic solution. RetailNext guarantees industry-leading accuracy of traffic data with its all-in-one IoT sensor, Aurora, which detects people ten times each second to ensure maximum tracking accuracy. RetailNext customers can view in-store data in real-time, with data available within seconds on the user interface and APIs, no matter where the stores are located. Additionally, every sensor is manually audited for accuracy post-install through video recording and comparing it to the solution results. HD video recording is available for validation within 30 days. The RetailNext platform also leverages AI to provide predicted traffic trends and automatic recommendations, making the data actionable for users.

Operational Impact

Hurley was able to establish accurate baseline traffic metrics for all its stores, helping to identify and forecast peak traffic periods. This allowed store managers to plan daily tasks for staff during off-peak hours and reserve the full staffing complement for power hours.
The data revealed opportunities to adjust store hours at some locations, leading to extended hours that helped achieve traffic gains and capture sales late. Other locations with declining traffic adjusted labor hours accordingly, realizing significant cost savings.
By integrating workforce management data, store managers received recommendations on optimizing staff schedules by comparing against traffic data. Hurley was able to add staff when needed and remove staff when traffic was low yet conversion remained stable.
By integrating POS and traffic data, Hurley store managers and district managers were empowered with access to real-time performance metrics. The RetailNext platform recommended actions to increase results, allowing Hurley’s leadership team to accurately identify top performers for reward and recognition and make sound business decisions to improve underperforming areas.
Hurley was able to understand how its customers behaved in-store, revealing how different demographic segments navigated their stores, engaged, and converted differently. These insights were leveraged to improve merchandising decisions and marketing campaigns.

Quantitative Benefit

Extended store hours helped achieve traffic gains and capture sales late.
Significant cost savings were realized by adjusting labor hours in locations with declining traffic.