Unbiasing Data Samples for Tax Return Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional tax return preparation systems are inflexible and fail to adapt to individual user needs, leading to irrelevant and confusing experiences, which results in low customer satisfaction and high abandonment rates, due to their generic design and inadequate testing techniques.
Innovation Solution
A software system that applies a user experience analytics model to dynamically segment users based on characteristics, providing personalized experiences by adapting and testing user experience options, and unbiasing data samples to improve preference identification and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional tax return preparation systems use fixed, pre-determined interview questions and generic user models, then the system structure is simple and easy to implement, but the user experience becomes irrelevant and confusing, leading to low customer satisfaction and high abandonment rates
Solution Approach 1:
The patent segments users into different groups based on their characteristics, preferences, and behaviors. By dividing the user base into segments, the system can tailor interview questions and user experience elements to each segment's specific needs, making the experience more relevant without requiring complete customization for every individual user.
Solution Approach 2:
The patent implements dynamic user experience analytics models that adapt and evolve based on user interactions and feedback. The system transitions from static, pre-determined questions to dynamic question sequences that adjust in real-time based on user responses, preferences, and engagement patterns, improving relevance while managing complexity through adaptive algorithms.
2Adaptability or versatility
If traditional systems present the same interview questions to all users, then the system is easy to implement and maintain, but it fails to meet individual user needs, resulting in frustrated users and abandoned processes
Solution Approach 1:
The patent performs preliminary actions by pre-segmenting users and pre-configuring personalized user experience models before the actual tax preparation process begins. User profiles and preferences are established in advance, allowing the system to quickly retrieve and apply appropriate interview question sequences without complex real-time decision-making during user interactions.
Solution Approach 2:
The patent implements feedback mechanisms where user responses, engagement patterns, and preferences are continuously collected and fed back into the analytics model. This feedback loop allows the system to learn from user interactions and progressively improve personalization, enabling adaptability while managing complexity through iterative learning rather than complex rule-based systems.
3Adaptability or versatility
If traditional tax return preparation systems use hard-coded user experience elements, then the system is stable and reliable, but it cannot evolve to meet changing user preferences or particular taxpayer needs
Solution Approach 1:
The patent changes key parameters of the user experience system from fixed, hard-coded values to dynamic, data-driven parameters. User experience elements such as interview question sequences, presentation formats, and navigation options are transformed into adjustable parameters that can be modified based on analytics model outputs, allowing the system to evolve while maintaining stability through controlled parameter adjustment rather than structural changes.
4Productivity
If traditional systems provide generic user experiences to all users, then implementation is straightforward, but processing time per user increases and bandwidth utilization is inefficient due to irrelevant content delivery
Solution Approach 1:
The patent applies local quality by delivering different user experience content to different user segments based on their specific needs and preferences. Instead of providing the same generic content to all users, the system tailors interview questions, explanations, and navigation options to match each segment's characteristics, reducing processing time by avoiding irrelevant content delivery and optimizing bandwidth utilization through targeted content distribution.
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 to some users while concurrently testing user responses to other user experience options, among a variety of 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. The method and system determines bias weights from characteristics of the analytics model and uses the bias weights to compensate for data biases when updating or generating analytics models.