Automated Test Scenario Generation from Customer Experience Themes

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Solution Overview

Problem

Current methods for analyzing customer sentiments and extracting recurring themes from large datasets are largely manual and inefficient, failing to capture the complex and dynamic nature of customer journeys across various touchpoints.

Innovation Solution

The technology employs natural language processing, image processing, and computer vision techniques to extract themes, tests, and scenarios from unified customer data, allowing for automated trigger settings and simulated customer experience recreation, thereby leveraging insights from both positive and negative customer paths.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual analysis methods are used to capture customer journey information, then analysis depth can be maintained, but productivity and efficiency deteriorate due to the inability to process large datasets

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidcustomer sentiment capture completeness

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent replaces manual mechanical analysis with automated natural language processing systems. The system uses computational algorithms to analyze customer feedback, social media posts, and journey data, transforming the manual information processing task into an automated computational process that can handle large datasets while maintaining analytical depth

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates structured representations (copies) of unstructured customer feedback data through natural language processing. By transforming raw text into structured sentiment data, topics, and themes, the system enables efficient processing while preserving the essential information content of the original data

Inventive Principle:

Principle #26Copying

2Productivity

If automated processing is implemented to handle large customer datasets, then productivity improves, but measurement precision and analysis quality may deteriorate

Engineering Contradiction:

Improvetheme extraction speedVSAvoidsentiment analysis accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces natural language processing algorithms as intermediaries between raw customer data and analytical insights. These NLP components act as intelligent mediators that preserve semantic meaning and sentiment nuance while enabling automated processing, thus maintaining analysis precision at scale

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback mechanisms where processing results are continuously refined. By analyzing patterns in customer feedback and adjusting processing parameters based on identified themes and sentiments, the system improves measurement precision through iterative optimization while maintaining high productivity

Inventive Principle:

Principle #23Feedback

3Loss of information

If comprehensive customer journey tracking is implemented across all touchpoints, then information completeness improves, but device complexity and system requirements worsen

Engineering Contradiction:

Improvecustomer experience coverageVSAvoiddata processing system complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent implements a universal data processing platform that handles multiple data types (text, social media posts, journey data) through a single integrated system. This multi-functional approach reduces overall system complexity by consolidating processing capabilities rather than requiring separate systems for each data source

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system extracts and focuses on key thematic elements and sentiments from comprehensive customer journey data. By isolating and analyzing only the most relevant patterns and themes rather than processing every detail, the system maintains information completeness while reducing processing complexity

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11841892B2Generating test scenarios by detecting failure patterns and themes in customer experiences

Publication Date: 2023.12.12 DELL PROD LP

AI summary

The described technology is generally directed towards processing various customer input data to extract frequently recurring customer experience themes, including positive and negative sentiment regarding customer experiences. Natural language processing, image processing, speech recognition and/or computer vision techniques can be used on customer-related data to determine themes, tests and scenarios, as well as discover insights that can be used to improve customer experiences. The technology can be used to recreate a customer engagement, journey and overall experience by designing test scenarios around failure themes.