Personalized User Experience System for Tax Preparation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional tax return preparation systems are inflexible and generic, leading to irrelevant and confusing user experiences, which results in low customer satisfaction and high abandonment rates, as they fail to adapt to individual user needs due to static analytics and inadequate testing techniques.
Innovation Solution
A software system that provides personalized user experiences by applying user characteristics data to a user experience analytics model, dynamically allocating user experience options to test and confirm their effectiveness, allowing for real-time adaptation based on user feedback, thereby improving user satisfaction and conversion rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional tax return preparation systems use static, hard-coded interview questions and generic user models, then the system structure is simple and easy to maintain, but the user experience becomes irrelevant and confusing, leading to high abandonment rates
Solution Approach 1:
The patent implements dynamic user experience by replacing static, hard-coded interview questions with a flexible system that adapts questions and presentation based on user characteristics. The system dynamically generates personalized interview sequences using analytics models that process user data in real-time, allowing the user experience to evolve during the interaction rather than following a predetermined path.
Solution Approach 2:
The system changes key parameters of the user experience including question selection, question ordering, and presentation format based on user characteristics. By varying these parameters dynamically according to user profiles and analytics model predictions, the system delivers personalized experiences without requiring complete system redesign for each user type.
2Ease of operation
If traditional systems present fixed interview sequences to all users, then the system is easy to implement and maintain, but it fails to meet specific user needs, resulting in low customer satisfaction
Solution Approach 1:
The system performs preliminary actions by analyzing user characteristics and predicting optimal interview paths before the actual interview begins. The analytics model pre-processes user data to determine which questions are most relevant and in what order, allowing the system to skip unnecessary questions and directly present the most valuable interview sequence for each user.
Solution Approach 2:
The patent segments the user base into different groups based on characteristics such as tax complexity, user expertise, and preferences. Each segment receives a customized interview sequence tailored to their specific needs, rather than a one-size-fits-all approach. This segmentation allows the system to reduce interview duration for simple cases while providing comprehensive coverage for complex situations.
3Measurement precision
If traditional tax preparation systems use generic user models, then the system development is straightforward, but the questions presented are irrelevant and confusing to real users
Solution Approach 1:
The system implements feedback loops where user responses and interactions are continuously analyzed to refine the analytics models and improve future personalization. User characteristics data and interview outcomes feed back into the system to update user profiles and adjust question sequences, creating a learning system that becomes more accurate over time while managing complexity through iterative improvement.
Solution Approach 2:
The patent introduces an intermediary analytics model layer that bridges the gap between raw user characteristics data and the interview question selection. This intermediary component processes and interprets user data, translating complex characteristics into actionable insights for question personalization, thereby managing the complexity of matching user needs with appropriate questions.
4Adaptability or versatility
If traditional systems require full system redeployment to modify interview processes, then the system structure is simple and stable, but it cannot adapt to changing user needs or incorporate new analytics insights
Solution Approach 1:
The system achieves dynamic adaptability by separating the analytics model from the core system code. Interview sequences and user characteristics are stored as configurable data rather than hard-coded elements, allowing modifications to be made through data updates rather than system redeployment. This dynamic structure enables continuous improvement of user personalization without disrupting the stable core system.
Solution Approach 2:
The patent prepares the system for future modifications by establishing a flexible architecture upfront with configurable user characteristics and analytics models. This preliminary design decision allows the system to adapt to changing user needs and incorporate new analytics insights through simple configuration changes rather than requiring complex redevelopment, balancing stability with adaptability.
Data Source
AI summary
A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.