Relationship-Determining Modules for Granular Customer Profiles
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Solution Overview
Problem
Existing customer profiling systems fail to accurately reflect the complex similarities between customers, often resulting in outdated and inaccurate profiles that do not account for qualitative and subjective information, and struggle to combine disparate data across different business metrics or departments, limiting their ability to tailor communications effectively and predict future behavior.
Innovation Solution
A characteristic-based system that uses relationship-determining modules to identify and visualize relationships between individuals, metrics, and sub-metrics, allowing for the creation of detailed, predictive profiles that incorporate both objective and subjective data without revealing sensitive information, and can be used across various business departments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional customer profiling systems use only historical, static, and quantitative information, then the system is simple to implement, but the customer profiles become outdated and inaccurate
Solution Approach 1:
The system merges multiple data sources including historical quantitative data with real-time qualitative data from social media, website analytics, and customer interactions. This combination allows the system to maintain profile accuracy by continuously updating profiles with both objective metrics and subjective behavioral indicators, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The system transitions from static historical profiles to dynamic real-time profiles that continuously update as new data becomes available. The profiling system adapts to changing customer behavior and preferences by incorporating real-time data streams, ensuring profiles remain current and accurate without requiring complete system redesign.
2Loss of information
If customer surveys or focus groups are used to collect qualitative information, then subjective customer data can be obtained, but the process becomes expensive and time-consuming
Solution Approach 1:
The system enables customers to voluntarily provide qualitative information through social media posts, website interactions, and mobile app engagements without requiring dedicated survey sessions. Customers naturally generate this data through their regular digital activities, allowing the system to capture rich qualitative information continuously without imposing time burdens or incurring survey costs.
Solution Approach 2:
The system replaces traditional mechanical survey methods with automated digital tracking and analysis of customer behavior across multiple platforms. Instead of manually conducting focus groups or surveys, the system automatically collects and analyzes qualitative data from social media, website analytics, and customer communications, dramatically reducing both time and resource requirements.
3Adaptability or versatility
If customer information is collected for a single business metric, then the data collection process is focused and simple, but the information cannot be used across different departments or metrics
Solution Approach 1:
The system creates a universal customer profile framework that can serve multiple business departments and metrics simultaneously. The same core profile structure and data collection mechanisms support marketing, sales, customer service, and risk management functions, allowing information to be reused across different business objectives without requiring separate collection systems for each department.
Solution Approach 2:
The system segments customer information into modular components that can be independently accessed and utilized by different departments. The profile is divided into distinct data elements and categories that can be selectively applied to various business metrics and functions, enabling flexible data reuse while maintaining organized, manageable data structures that reduce integration complexity.
Data Source
AI summary
Systems, apparatus, and methods for correlating two items of interest, based on a plurality of data items and characteristics. The data items may include objective and quantitative data, as well as subjective and qualitative data. In one implementation, the relationship of an individual to a metric is determined. The system, apparatus, and methods may store characteristics describing individuals generally, along with metrics relevant to an organization; receive a plurality of data items; extract information associated with the individual from the data items; determine a number of relationships between the data items, individuals, metric, and characteristics; and use the relationships to determine an overall relationship between the individual and the metric, based on the data and characteristics. In addition, related groups of characteristics may be identified. Similarly, the relationships between any individual, metric, sub-metric, group of characteristics, data item, data source, characteristic, or groups thereof may also be determined.