Wait Time Estimation for Retail Pickup
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Customers experience long wait times when picking up purchased goods at store locations due to uncertainty about the availability of associates and the number of customers waiting, leading to inefficient service experiences.
Innovation Solution
A system and method that determine an expected wait time for customers by calculating the number of customers and associates available, using machine learning algorithms to predict wait times based on historical data and real-time information, and transmitting this information to customers via mobile devices or messages, allowing for better resource allocation and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If customers wait in person at the store for associate assistance, then they can receive their purchased goods, but they experience long wait times and uncertainty about service availability
Solution Approach 1:
The system continuously monitors the number of customers waiting and associates available, then provides real-time feedback to customers through the mobile application. This feedback loop allows customers to see current wait times and queue positions, eliminating uncertainty and allowing them to make informed decisions about when to arrive at the store.
Solution Approach 2:
The system calculates and communicates expected wait times to customers before they arrive at the store. Customers can check the application ahead of time to see projected wait times, allowing them to plan their arrival accordingly. This preliminary information empowers customers to choose optimal arrival times rather than experiencing long waits.
2Productivity
If the retailer uses traditional customer service pickup methods, then customers can collect their orders, but the process is inefficient and creates long queues
Solution Approach 1:
The system performs preliminary calculations of wait times based on current queue length and associate availability before customers arrive. This allows the retailer to proactively manage customer expectations and distribute arrivals more evenly throughout the day, preventing queue buildup and improving overall throughput.
Solution Approach 2:
Customers use the mobile application to self-check in and receive real-time wait time information without requiring associate intervention. The system automatically processes check-ins and updates queue status, reducing the manual workload on associates while improving customer service efficiency.
Data Source
AI summary
In some examples, a system may include a first computing device communicatively coupled to a second computing device. Additionally, the first computing device is configured to obtain, from the second computing device, check-in data indicating an arrival of the user of the second computing device at a first location, and in response to obtaining the check-in data, determine current wait times. Moreover, the first computing device is configured to determine a first number of customers waiting for service, determine a first number of associates available to assist the first number of customers, and determine an expected wait time for the user operating the second computing device based at least on the current wait times. In some examples, the first number of customers waiting for service, and the first number of associates available. Further, the first computing device is configured to transmit the expected wait time to the second computing device.