AI Data Analytics Platform Linking CRM and POS for Brand Loyalty Metrics
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Solution Overview
Problem
Existing data analytic tools lack the sophistication to provide coherent explanations of customer behaviors and fail to measure brand loyalty effectively, unable to advise on improving customer loyalty and brand sustainability.
Innovation Solution
A data analytic platform and techniques that integrate customer relationship management (CRM) and point-of-sale (POS) data using AI tools to quantify brand equity through customer activation cycles, calculating metrics like Customer Activation Score (CAS) and adjusting marketing strategies to enhance customer loyalty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If prior art CRM packages are used to identify correlations between discrete data sets, then data organization is improved, but insight into customer behavior motivations is lost
Solution Approach 1:
The patent introduces brand equity categories as an intermediary framework that mediates between discrete CRM data points and customer behavior insights. These categories (prospects, casuals, loyalists, cheerleaders) serve as a conceptual bridge that translates raw data into meaningful behavioral motivations without requiring overly complex analytical systems.
Solution Approach 2:
The patent transforms customer data by applying parameter changes through brand equity metrics and customer activation scores. By changing the parameters from simple transactional data to branded behavioral categories, the system recovers information about customer motivations while maintaining manageable system complexity through standardized measurement frameworks.
2Measurement precision
If traditional data analytic tools are used to collect and analyze customer data, then data collection capability is improved, but ability to measure brand loyalty is lost
Solution Approach 1:
The patent segments the customer base into distinct brand equity categories (prospects, casuals, loyalists, cheerleaders) based on their relationship with the brand. This segmentation enables precise measurement of brand loyalty by categorizing customers according to their engagement level and emotional connection, rather than treating all customers uniformly.
Solution Approach 2:
The patent implements feedback mechanisms through customer activation scores and brand equity metrics that continuously measure and report on brand loyalty levels. This feedback loop enables businesses to track loyalty changes over time and adjust strategies accordingly, improving both measurement precision and the efficiency of loyalty improvement initiatives.
3Loss of information
If existing analytic tools are used to evaluate business performance, then statistical analysis is improved, but coherent explanation of customer behaviors is lost
Solution Approach 1:
The patent creates a universal brand equity framework that serves multiple analytical functions simultaneously. The same brand equity categories and customer activation scores used for measuring loyalty also provide coherent explanations for customer behaviors, eliminating the need for separate analytical systems and reducing overall platform complexity despite the enhanced explanatory capability.
Data Source
AI summary
The present application discloses a novel data analytic method and an Artificial Intelligence (AI) platform for integrating, analyzing and managing data collected from diverse sources such as channel performance, marketing, and sales. The disclosed data analytic method and platform are developed based on a brand ecosystem model and are designed to provide tools and techniques for analyzing real-time and holistic data quantifying customer loyalty to and customer relationship with a particular product and brand.