Context-Aware Survey System for Personalized Customer Engagement
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Solution Overview
Problem
Current customer relationship management (CRM) systems fail to provide personalized surveys to customers based on unique factors such as customer journey, history, and preferences, leading to ineffective information collection and a suboptimal customer experience.
Innovation Solution
A system that uses customer data from previous interactions, cookies, and backend databases to identify customer intent and send targeted surveys based on customer journey, history, and preferences, employing machine learning and statistical models to determine the most appropriate survey for each customer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic surveys are offered to all customers, then survey implementation is simple and quick, but survey relevance and response rates deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing customer data (demographics, browsing behavior, purchase history, device information) before the survey is presented. This advance data gathering enables the system to personalize surveys without adding complexity at the moment of survey deployment, resolving the contradiction between implementation simplicity and survey personalization.
Solution Approach 2:
The patent introduces an intermediary component (survey system with machine learning models) that sits between the generic survey template and the customer. This intermediary processes customer data, determines personalization parameters, and dynamically customizes survey content, thereby enabling survey adaptation without requiring manual customization for each customer.
2Ease of manufacture
If static surveys are hard coded into Web pages, then survey deployment is straightforward, but survey flexibility and responsiveness to customer context deteriorate
Solution Approach 1:
The patent transforms static, hard-coded surveys into dynamic surveys that automatically adapt to customer context. The system uses machine learning models to determine which survey to present, what questions to ask, and how to tailor content based on real-time customer data such as browsing behavior, device type, and historical interactions, thereby enabling survey flexibility while maintaining automated deployment.
Solution Approach 2:
The system changes survey parameters (content, questions, timing, presentation format) based on customer data analysis. Machine learning models determine optimal survey parameters by processing customer demographics, browsing patterns, and historical survey responses, allowing the survey to dynamically adapt to each customer's context without requiring manual reconfiguration.
3Adaptability or versatility
If customer data is collected and processed to personalize surveys, then survey relevance improves, but system complexity increases
Solution Approach 1:
The patent segments the customer data processing system into distinct functional modules: data collection components (tracking browsing behavior, device information), data storage systems (customer profiles, historical survey responses), and machine learning models (customer intent prediction, survey recommendation engines). This segmentation allows each component to be developed, maintained, and scaled independently, managing overall system complexity while enabling comprehensive survey personalization.
Solution Approach 2:
The system implements self-service capabilities through automated machine learning models that independently analyze customer data and determine optimal survey personalization parameters without requiring manual intervention. The models automatically process customer information, predict intent, and configure survey content, thereby reducing operational complexity while maintaining high levels of personalization.
4Ease of operation
If surveys are sent based on multiple customer factors, then customer experience improves, but data processing requirements and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant customer data factors for each survey decision, rather than analyzing all available data. Machine learning models prioritize key indicators (such as current browsing context, recent purchase history, or device type) based on their predictive value, thereby improving customer experience through personalized surveys while reducing unnecessary computational overhead from processing less relevant data.
Data Source
AI summary
A context-aware computing system for delivering surveys to a customer. The choice of which survey to send to a customer may be tailored based on a click path (route), customer history, and customer interests. A customer browsing a Web page initiates the survey decision process. A control module selects a survey to send to a customer based on the criteria above and customer intent. Customer responses are then harvested from the Web-based survey.