Unstructured Social Data Clustering for Next-Best-Action Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current solutions fail to effectively capture and analyze unstructured customer data to determine next-best-actions, as they lack the ability to automatically recognize sentiment and shopping patterns at scale, especially for companies with millions of customers.
Innovation Solution
A data clustering and user modeling tool that receives unstructured social data to assign feature vectors based on sentiment, personality, and emotional state, groups similar users into clusters, and inputs these attributes into a predictive model to determine tailored commercial offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If companies manually read every e-mail and social media post to capture customer information, then the accuracy of customer understanding is improved, but the time and labor required become prohibitively large at scale
Solution Approach 1:
The patent replaces manual human analysis of unstructured data with an automated computational system. The system uses natural language processing, sentiment analysis algorithms, and machine learning models to automatically extract insights from emails, social media posts, and other unstructured customer data, eliminating the need for manual reading while maintaining analytical accuracy.
Solution Approach 2:
The system enables self-service analysis where the data processing system automatically performs sentiment extraction, customer segmentation, and action recommendation without requiring human intervention. The automated pipeline processes data independently, generating insights and recommendations that can be directly acted upon by business teams.
2Ease of operation
If companies use existing data aggregation solutions to monitor brand awareness, then the ease of operation is improved, but the ability to recognize next best actions deteriorates
Solution Approach 1:
The system performs preliminary analysis by automatically extracting sentiment, personality traits, and emotional state indicators from unstructured data before generating final recommendations. This preliminary processing prepares the data in advance, enabling the system to quickly identify next-best-actions without requiring manual analysis at the time of decision-making.
Solution Approach 2:
The system incorporates feedback loops where the extracted insights and generated recommendations are fed back into the data processing system for continuous refinement. This feedback mechanism allows the system to learn from previous analyses and improve its ability to recognize next-best-actions over time, while maintaining ease of operation through automated iterative processing.
3Loss of information
If companies attempt to analyze all unstructured social data to capture customer patterns, then the completeness of customer insights is improved, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent extracts only the most relevant information from unstructured data by identifying and isolating key sentiment indicators, personality traits, and emotional state markers. The system uses natural language processing to extract meaningful patterns while filtering out irrelevant information, thereby maintaining insight completeness while reducing processing complexity through selective data extraction rather than comprehensive analysis of all data.
Solution Approach 2:
The system segments the data processing task into distinct modular components: data collection, sentiment analysis, personality trait extraction, emotional state identification, and recommendation generation. Each segment handles specific aspects of data processing independently, making the overall complex system more manageable and scalable while maintaining complete customer insight capture through coordinated operation of segments.
Data Source
AI summary
Embodiments herein provide data clustering and user modeling for next-best-action decisions. Specifically, a modeling tool is configured to: receive indicators within unstructured social data from a plurality of users; analyze the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each feature vector corresponding to one or more personality characteristics of each of the plurality of users; and analyze the feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors. The modeling tool is further configured to: group the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identify attributes of the cluster; and input the attributes of the cluster into a predictive model to determine an offer corresponding to the cluster.