Consumer Sentiment Scoring for Cross-Channel Creative Selection
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Solution Overview
Problem
Existing digital marketing technologies lack a unified system to predict consumer behavior and personalize content across various marketing channels to enhance conversion probabilities.
Innovation Solution
A system and method for predicting consumer sentiment and behavior using a score-driven approach, incorporating static and dynamic consumer features, to optimize content delivery and enhance conversion probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate technologies are used for different digital marketing areas, then each area can be optimized independently, but there is no unified system to predict consumer behavior across all areas
Solution Approach 1:
The system employs a unified consumer behavior prediction model that serves multiple digital marketing functions including search marketing, display marketing, online advertisement, lead generation, voucher distribution, and content personalization. This multi-functional approach allows a single system to predict consumer behavior across diverse marketing areas rather than requiring separate specialized systems for each function.
Solution Approach 2:
The system segments consumers into distinct groups based on predicted behavior probabilities and assigns different creative elements or content strategies to each segment. This segmentation enables targeted marketing while maintaining a unified prediction framework, resolving the complexity by organizing data and processing into manageable consumer segments that can be systematically managed across all marketing channels.
2Productivity
If a unified system is implemented to predict consumer behavior across all digital marketing areas, then intelligent decisions can be made to increase conversion, but the system complexity increases
Solution Approach 1:
The system transforms complex consumer behavior prediction into actionable parameters by calculating probability scores for different consumer segments and mapping them to specific creative elements. This parameter transformation simplifies the decision-making process for marketers while maintaining high predictive accuracy, enabling automated intelligent decisions that improve conversion without requiring overly complex manual intervention.
Solution Approach 2:
The system performs self-optimization by automatically selecting the best creative elements and content strategies based on predicted consumer behavior probabilities. This self-service capability reduces the need for manual marketing strategy adjustments and system reconfiguration, thereby improving productivity through automated intelligent decisions while keeping the operational complexity manageable.
3Reliability
If consumer data is collected and analyzed in real-time for personalized content delivery, then conversion probability increases, but data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary consumer segmentation and probability calculation before content delivery occurs. By pre-calculating consumer behavior probabilities and preparing creative element mappings in advance, the system reduces real-time processing requirements while maintaining high accuracy in conversion prediction. This preliminary action allows personalized content delivery without overwhelming data processing demands during the actual marketing interaction.
Data Source
AI summary
The subject technology predicts consumer sentiment based on demographics and other static features of the consumer as well as dynamic features generated based on engagement of the consumer with previously presented targeted content. The sentiment predictions are used to recommend and generate new targeted content that is published to the consumer.