Location-Aware Display Content Selection for Personalized Service
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Solution Overview
Problem
Display devices in establishments such as stores and banks often present static information, failing to account for the diverse needs and identities of individual customers, leading to inefficiencies in customer engagement and service.
Innovation Solution
A system that utilizes a communication device's location and activity data to tailor display content, employing machine learning models to predict customer reactions, allowing for personalized greetings and targeted messaging based on customer segments and real-time interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If display devices present static information to all customers, then device complexity is reduced and ease of operation is improved, but customer engagement and service personalization deteriorate
Solution Approach 1:
The system segments customers into different groups based on their characteristics, behaviors, and preferences stored in the database. This segmentation enables the display device to present customized content to different customer segments, improving adaptability without requiring complex real-time analysis for each individual customer.
Solution Approach 2:
Customer data, preferences, and segmentation criteria are collected and prepared in advance in the database before the customer actually interacts with the display device. This preliminary action allows the system to quickly retrieve and display personalized content without complex real-time processing, thus improving customer engagement while maintaining manageable system complexity.
2Adaptability or versatility
If display devices use personalized content based on customer data, then customer engagement is improved, but loss of information privacy increases
Solution Approach 1:
The system extracts only the necessary customer information needed for personalization from the complete customer profile in the database. By selectively extracting and using only relevant data points for display content personalization, the system reduces privacy intrusion while maintaining effective personalization capability.
3Productivity
If display devices remain static and simple, then device complexity is minimized, but productivity in customer service deteriorates
Solution Approach 1:
The display device automatically retrieves customer information, determines appropriate segmentation, and selects personalized content without requiring manual intervention from staff. This self-service capability significantly improves customer service productivity by enabling instant personalization, while the automated nature of the process prevents excessive complexity from manual configuration requirements.
Data Source
AI summary
A method may include receiving, at an application server, a set of device characteristics of a mobile device including: a current location data of the mobile device; a mobile device identifier of the mobile device; and an indication of current user activity being performed on the mobile device; accessing a segmentation group identifier based on the mobile device identifier; determining that the mobile device is within a threshold range of a display device based on the current location data; and based on the determining: generating an input feature data set based on the segmentation group identifier and the indication of current user activity; executing a machine learning model using the input feature data set as input to the machine learning model; automatically selecting a content identifier from a set of content identifiers based on an output of the machine learning model; and transmitting the content identifier to the display device.