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Case Studies > Improving Store Operations by Better Understanding Traffic

Improving Store Operations by Better Understanding Traffic

Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Remote Monitoring & Control Systems
  • Sensors - Camera / Video Systems
Applicable Industries
  • Apparel
  • Retail
Applicable Functions
  • Business Operation
  • Sales & Marketing
Use Cases
  • Retail Store Automation
Services
  • Data Science Services
  • System Integration
The Challenge
The brand was restricted in its efforts to optimize store operations and the shopper experience. Over 60 percent of its stores lacked any type of traffic counting solutions, even in the most fundamental form. What pre-existing traffic counting solutions the brand did have were inaccurate and unreliable. The solutions were standardized within regions, much less globally. The retailer wanted to improve its ability to calculate basic managerial metrics like conversion.
About The Customer
One of the world’s largest apparel companies and a global leader in jeans, this iconic brand operates over 2,600 store locations and sells its products in over 100 countries around the world.
The Solution
First, RetailNext piloted advanced analytics in a newly relocated store. This delivered an accurate front door traffic counting solution to empower the calculation of conversion and other operational key performance indicators (KPIs). Secondly, RetailNext deployed sensors to include dwell zones and fitting room analytics, providing rich data on the effectiveness of product displays and the efficiencies of service areas at a very low incremental capital expenditure. RetailNext worked with the brand to establish baseline metrics on: Pass-by traffic, Pass-by capture rate, Store traffic, Average store traffic per day, Conversion, Average Transaction Value (ATV), Exposure (display traffic/store traffic), Dwell, Engagement (# of dwell/display).
Operational Impact
  • With the new metrics, the brand was able to identify clear opportunities to optimize store performance and shopper engagement.
  • Staff fitting rooms to align with traffic demand based on day of the week and hour of the day.
  • Focus on improving conversion on the three lowest-converting days (Sunday, Thursday, Saturday) from 7.2% to 8%, potentially resulting in a 5% increase in weekly sales.
Quantitative Benefit
  • Focus on improving conversion on the three lowest-converting days (Sunday, Thursday, Saturday) from 7.2% to 8%, potentially resulting in a 5% increase in weekly sales.
  • Target an improvement in conversion during the three lowest hours of the day (1-2 p.m. and 6-8 p.m. during lunch and dinner), where a 0.3 point increase could lead to a 4.4% increase in sales during those hours.

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