Personalizing Ride Experience Based on Contextual Usage Data
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Solution Overview
Problem
Current advertising technologies struggle to provide personalized and contextually relevant content to users during on-demand transportation services, failing to fully leverage user demographics and interests to enhance the ride experience and monetize user engagement effectively.
Innovation Solution
A transport facilitation system that collects and analyzes ride history data to determine user demographics and interests, providing personalized content and route preferences, and offers targeted advertising, which can be interacted with to discount ride fares, using machine learning to cluster users and tailor services such as vehicle selection and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional advertising methods are used in transportation services, then advertising content can be displayed to users, but the content is not personalized or contextually relevant to user demographics and interests
Solution Approach 1:
The system performs preliminary data collection during ride bookings and historical analysis to determine user demographics and interests before advertising content is displayed. This advance preparation enables personalized advertising without adding complexity during the actual ad delivery process
Solution Approach 2:
The patent introduces an intermediary advertising system that acts as a bridge between the transportation service and users. This intermediary layer handles the complex tasks of data analysis and personalization, keeping the core transportation system simple while enabling sophisticated advertising capabilities
2Ease of operation
If user data is collected and analyzed to provide personalized content, then user satisfaction improves, but additional processing time and computational resources are required
Solution Approach 1:
User demographics and interests are determined in advance during ride booking and historical analysis, so that when the ride occurs, personalized content can be immediately delivered without real-time processing delays. This eliminates time loss during the actual service delivery
Solution Approach 2:
The system automatically collects and analyzes user data without requiring manual intervention. The machine learning models autonomously process historical ride data to generate user profiles, eliminating the need for time-consuming manual data processing while maintaining high user satisfaction
3Productivity
If targeted advertising is implemented to monetize user engagement, then additional revenue streams are generated, but the system becomes more complex in tracking and analyzing user interactions
Solution Approach 1:
The patent combines advertising delivery with the existing ride booking and historical data analysis infrastructure. By merging advertising functionality with already-present user data collection systems, the patent generates revenue without requiring a completely separate complex tracking system
Solution Approach 2:
The system uses a universal data collection mechanism that serves multiple purposes: it collects information for both service improvement and advertising personalization. This multi-functional approach generates advertising revenue without duplicating data collection efforts, thereby avoiding additional system complexity
Data Source
AI summary
A transport system can manage an on-demand transportation service to connect available vehicles with users, and can compile ride history data for each user. The ride history data can indicate the contextual usage of the on-demand transportation service by the user. Based on the ride history data, the transport system can determine demographic and personal interest information of the respective user. The transport system may then personalize one or more ride characteristics of any ride requested by the user based on the demographic and personal interest information determined from the ride history of the user.