Physical Product Interaction Tracking for Personalized Webpages
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Solution Overview
Problem
Consumers face overwhelming information overload when researching products online, and existing web search engines lack the ability to accurately identify potential purchasers and provide relevant product information, leading to inefficient marketing strategies by electronic commerce providers.
Innovation Solution
A system and method that analyzes product interaction data within a venue using machine logic to configure personalized webpages based on user interactions, such as touching, reading, or questioning products, and sends tailored product information to users based on their interaction profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If web search engines are used to channel keywords to advertisement networks, then potential purchasers can be directed to e-commerce websites, but the probability of resulting in purchases is not high and user profiles remain shallow
Solution Approach 1:
The system performs preliminary actions by tracking user interactions with products in physical stores (picking up, examining, comparing) before the user makes a purchase decision. This advance tracking creates detailed interaction profiles that predict purchase intent, allowing the system to prepare targeted digital advertisements that follow the user online, thereby increasing conversion probability while maintaining rich user profile information.
Solution Approach 2:
The system implements feedback loops by continuously monitoring user interactions with products in-store and using this data to refine and update user profiles in real-time. The interaction profiles are fed back into the advertisement targeting system to improve future ad delivery accuracy, creating a closed-loop system that enhances both profile depth and purchase conversion through iterative learning.
2Loss of information
If consumers research products across multiple online resources, then comprehensive product information can be gathered, but information overload occurs making it difficult to make sense of the data
Solution Approach 1:
The system extracts only the most relevant product information based on the user's specific interaction patterns in-store. Instead of presenting all available information from multiple sources, the system filters and extracts precisely the information the user needs based on what products they examined and compared, thereby maintaining information completeness while eliminating overload.
Solution Approach 2:
The system applies local quality by customizing the information presentation according to the specific user's interaction profile. Different users receive different information configurations based on their unique product examination patterns, ensuring each user gets the most relevant information in the most useful format for their specific research needs.
3Loss of information
If traditional web search engines track user location and browsing history, then basic user profiling is achieved, but the profiles lack sufficient detail for effective purchaser segmentation
Solution Approach 1:
The system introduces an intermediary layer - the in-store interaction tracking infrastructure - that bridges the gap between simple web browsing data and comprehensive user profiling. This intermediary captures rich interaction data (product handling, comparison behavior, time spent examining) that serves as a mediator between basic location tracking and detailed purchaser segmentation, enhancing profile detail without requiring direct implementation of complex tracking algorithms across all touchpoints.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining product interaction data, the product interaction data being in dependence on a user's interaction with an item within a venue; examining, by machine logic, data of the product interaction data, wherein the examining includes examining of data of the product interaction data to determine that the user has interacted with the item; configuring, by machine logic, a webpage that specifies product information, wherein the configuring is based on the examining data of the product interaction data, and wherein the configuring is performed in response to a communication received from the user; and sending the configured webpage to the user.