Queuing up: predictive wait times in retail
Challenge
Physical retail stores have to deal with the issue of long queues, which can have detrimental effects on sales. It can be a challenge for a physical retail store to redistribute resources, such as waiting desks and changing rooms staff, to accommodate rushes of customers that may occur in certain periods of the day.
It is difficult to know, ahead of time, where and when to place resources such as waiting desks and changing room staff without some reliable information that guides the manager. This includes:
The number of visitors coming into the store within a certain period of time
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The length of time a visitor is likely to take to complete their purchase
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The demographic details of the visitor (gender and age) in compliance with personal information protection laws
Foot Traffic in Shopping Malls: Identifying Hot Spots
Solution
Queues occur as a result of measuring the number of visitors that enter into a store at a given time. Queue prediction involves making predictions of these values based on statistical data analysis and using the information to reposition resources or make changes to a store’s layout to make processing customers faster and more efficient.
There are a number of reasons why retail stores need queue prediction information. These include:
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Having access to extensive KPIs that provide the necessary insights
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Having alerts for repositioning sales-oriented resources
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Avoiding the problem of abandoned queues
Exceptional service with quick responses and immediate support. Implementation was rapid and effective, and remote maintenance is a big advantage in comparison to other systems.
Benefits
Retail stores can use queue prediction to improve most aspects of running a retail store. The right statistical data analysis to predict queues can:
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Improve customer loyalty by reducing wait time frustrations
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Improve workforce productivity through the use of customer prediction
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Reduce operational costs since queue prediction eliminates the need for over-staffing
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Increase sales through reduced wait times and fewer abandoned carts
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Additional retail use cases
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Measure Customer Engagement
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Improving Store Layout
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View Direction
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Customer Counting
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Optimizing Product Positioning / Assortment
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Real-Time Waiting Analytics
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Preventing Queue Formation
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Expanding Sales with Demographic Data
Understanding gender-specific shopping trends helps retailers optimize resources.
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Customer Centric Staff Allocation
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Staff Exclusion and Sales Performance
Real-time KPIs help retailers optimize performance across all locations
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Use-Based Cleaning
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Group Shopping Analytics
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Window Shopping Analytics
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