Skip to content

Latest commit

 

History

History
44 lines (31 loc) · 2.41 KB

use-case-queue-time.md

File metadata and controls

44 lines (31 loc) · 2.41 KB
title titleSuffix description author manager ms.service ms.topic ms.date ms.collection ms.author
Overview: Measure retail queue wait time with Spatial Analysis
Azure AI services
Spatial Analysis can generate real-time information about how long users are waiting in a queue in a retail store. A store manager can use this information to manage open checkout stations and employee shifts.
PatrickFarley
nitinme
azure-ai-vision
conceptual
02/21/2025
ce-skilling-fresh-tier2, ce-skilling-ai-copilot
pafarley

Measure queue wait time with Spatial Analysis

[!INCLUDE spatial-analysis-deprecation]

Spatial Analysis can generate real-time information about how long users are waiting in a queue in a retail store. The service monitors the length of time customers spend standing inside a checkout zone you specify. A store manager can use this information to manage open checkout stations and employee shifts.

:::image type="content" source="media/use-cases/queue-wait-time.png" alt-text="Photo of people in line at a store.":::

Key features

You can deploy an end-to-end solution to track customers' queue wait time.

  • Customize areas to monitor: With Spatial Analysis, you can customize the space you want to monitor.
  • Count individual entrances: You can monitor how long people spend within these spaces.
  • Customize models to differentiate between shoppers and employees: with the customization options offered, you can train and deploy models to differentiate shoppers and employees so that you only generate data about customer behavior.

Benefits for your business

By managing queue wait time and making informed decisions, you can help your business in the following ways:

  • Improve customer experience: customer needs are met effectively and quickly by equipping store managers and employees with actionable alerts triggered by long checkout lines.
  • Reduce unnecessary operation costs: optimize staffing needs by monitoring checkout lines and closing vacant checkout stations.
  • Make long-term data-driven decisions: predict ideal future store operations by analyzing the traffic at different times of the day and on different days.

Next step

Get started using Spatial Analysis in a container.

[!div class="nextstepaction"] Install and run the Spatial Analysis container