Case Studies Uncovering Factors Contributing to Inconsistent Conversion

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	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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	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.
Impact #2	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.

Impact #1

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.

Impact #2

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.

Benefit #1	Conversion rates during previously underperforming times increased to match other operational hours.

Benefit #1

Conversion rates during previously underperforming times increased to match other operational hours.

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Overview

Uncovering Factors Contributing to Inconsistent Conversion

Operational Impact

Quantitative Benefit