Automated Persona Generation from Small Text Components
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Solution Overview
Problem
Existing customer segmentation systems face challenges in accurately identifying and generating actionable insights from limited data, particularly when dealing with small text components, leading to inaccurate classification and laborious analysis, which hinders the creation of effective customer personas.
Innovation Solution
A system and method for automatic persona generation that processes data profiles through a series of phases, including pre-defined persona creation, data processing, clustering, and ranking, using techniques like k-means cluster analysis and neural sequence models to identify and characterize personas based on limited text indicators, enabling personalized customer engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cluster analysis is used to discover groups of similar customers based on small variations, then classification accuracy improves, but large datasets are required which increases data requirements and processing complexity
Solution Approach 1:
The patent segments the customer data into structured profiles with predefined attributes and hierarchical clustering into personas at multiple levels (persona, super-persona, meta-persona). This segmentation allows accurate classification with smaller, more focused datasets by breaking down complex customer characteristics into manageable segments that can be analyzed independently and then reassembled into comprehensive personas.
2Measurement precision
If traditional cluster analysis is applied to create customer archetypes, then group identification improves, but laborious manual analysis is required which reduces productivity
Solution Approach 1:
The system implements automated persona generation where the computational model independently performs clustering, pattern recognition, and persona creation without requiring manual analysis. The system self-serves by automatically processing customer data through machine learning algorithms that identify segments and generate actionable personas, eliminating the need for laborious manual deconstruction and analysis while maintaining high accuracy.
3Loss of information
If detailed cluster analysis is performed to understand customer segments, then insight quality improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces hierarchical dimensions to organize persona complexity, creating multiple levels (persona, super-persona, meta-persona) that structure the analysis results. This dimensional organization allows detailed insights to be captured and preserved while managing system complexity through a clear hierarchical framework that separates different levels of abstraction and makes the complex analysis results more manageable and interpretable.
Data Source
AI summary
Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results. What is further required are the mean to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the user profiles.