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Case Studies > Uncovering Factors Contributing to Inconsistent Conversion

Uncovering Factors Contributing to Inconsistent Conversion

Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Remote Monitoring & Control Systems
Applicable Industries
  • Retail
Applicable Functions
  • Business Operation
  • Sales & Marketing
Services
  • Data Science Services
  • System Integration
The Challenge
Consistent traffic, but also consistent lulls in conversion. In-store analytics showed consistent traffic at an international airport location, but revealed consistently low conversion at specific times and days of every week. The retailer was interested in using in-store analytics to better understand what factors were contributing to the consistently under-performing periods of time.
About The Customer
A century-old luxury brand who sells products exclusively through an international network of authorized dealers, jewelers, and some 400 company-owned boutiques, this brand has become world-renowned as it shares its stories of preservation and opulence.
The Solution
RetailNext deployed traffic sensors at the front entrance of the retail location to measure traffic, and integrated the Point-of-Sale (POS) system to accurately calculate conversion. However, nothing in the correlated data pointed to an obvious cause-and-effect relationship. Then, RetailNext examined arriving and departing flights within the same time frame as the lower-than-normal conversion periods, and the deeper analysis revealed that the periods with lower conversion coincided with flights to and from Brazil. The retailer realized the store lacked a Portuguese-speaking sales associate at those critical times.
Operational Impact
  • Armed with these new insights, the retailer adjusted its staffing schedule and added at least one Portuguese-speaking sales associate during the low conversion periods.
  • As a result of this adjustment, conversion for the previously underperforming times and days of the week rose to a level consistent with the other hours of store operations.
Quantitative Benefit
  • Conversion rates during previously underperforming times increased to match other operational hours.

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