Customer Analytic Record for Adaptive Marketing
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Solution Overview
Problem
Current marketing processes are inefficient and costly, relying on generalized models that degrade over time, leading to sub-optimal customer targeting and poor marketing returns due to the difficulty in integrating and analyzing customer data from disparate sources, and the lack of a closed-loop process for refining marketing campaigns based on real-time customer interactions.
Innovation Solution
A closed-loop system for adaptive marketing that uses a Customer Analytic Record (CAR) to extract, transform, and format customer data for predictive modeling, allowing for continuous refinement of customer segments and offer targeting through a multi-dimensional segmentation approach, enabling the creation of adaptive response models based on real-time campaign results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If generalized marketing response models are used, then campaign lists can be produced quickly, but the models degrade over time and produce sub-optimal results
Solution Approach 1:
The patent implements a closed-loop system where campaign results are fed back into the predictive models through the CAR. The system automatically captures campaign performance data, updates the customer analytic records with new insights, and re-trains the predictive models to improve accuracy over time without requiring manual model rebuilding
Solution Approach 2:
The CAR automatically extracts, transforms, and loads data from multiple sources without manual intervention. The system self-updates customer records with new campaign results and automatically re-trains models, eliminating the need for manual data integration and model maintenance while maintaining current data accuracy
2Loss of information
If data is gathered from multiple disparate databases, then a more complete customer view can be obtained, but the integration process is laborious and time-consuming
Solution Approach 1:
The CAR serves as a universal data integration platform that automatically connects to multiple disparate databases using standardized extract procedures. It performs extraction, transformation, and loading of data from various sources including internal databases, third-party vendors, and external data providers into a unified customer analytic record structure
Solution Approach 2:
The CAR acts as an intermediary layer between disparate data sources and analytical models. It standardizes data formats, resolves inconsistencies, and provides a common interface for accessing customer data from multiple sources without requiring custom integration code for each data source
3Measurement precision
If sophisticated predictive models are developed, then customer targeting accuracy improves, but the development cost and complexity increase
Solution Approach 1:
The system automatically re-trains predictive models using updated data from the CAR without manual intervention. The automated model maintenance and re-training processes eliminate the need for sophisticated manual model development while maintaining high prediction accuracy through continuous learning from campaign results
Solution Approach 2:
The CAR pre-processes and prepares customer data in advance by automatically extracting, transforming, and loading data from multiple sources into standardized analytic records. This preliminary data preparation simplifies subsequent model development and enables faster, less complex model building while maintaining data quality
4Loss of information
If third-party data is purchased and integrated, then customer insights are enhanced, but the integration requires sophisticated programming skills
Solution Approach 1:
The CAR provides a universal interface for integrating third-party data with internal customer data. It handles data extraction, transformation, and loading from third-party vendors using standardized procedures, eliminating the need for custom programming and making data integration accessible to non-technical users
Data Source
AI summary
A system and method for adaptive marketing using insight driven customer interaction. The invention uses a closed-loop process for developing insight that may be used to refine further customer interactions. Results of a first customer interaction such as a marketing campaign are stored in a database. The results may be used to retrain predictive models and gain new insights regarding how customers are responding to marketing campaigns. The insights may be used to refine the offers delivered to customers or to extend additional offers in an effort to increase the likelihood that customers will redeem the offers. After each marketing campaign, the results are stored in the database. New and/or modified offers are created based on insights provided by the results of past campaigns. This process may be repeated such that subsequent campaigns are based on insights generated by the predictive models. The insight enables businesses to better target customers with better offers. These offers can be delivered through ensuing marketing campaigns or, through any form of interaction that the business has with the targeted customers.