Conversational Index for Targeted Marketing Campaigns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional marketing systems fail to accurately target consumers who will interact with marketing campaigns, leading to marketing fatigue and reduced campaign effectiveness due to their inability to accurately determine consumer interest and limit communications to less interested individuals.
Innovation Solution
A marketing system that accumulates and analyzes data from past campaigns to generate consumer-centric conversational indices, indicating user interest levels by fitting a parametric distribution to interaction data such as time to click or purchase, allowing for strategic segmentation and targeted marketing communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If marketers increase the amount of marketing emails sent to users, then the coverage and reach of marketing campaigns is improved, but the effectiveness of marketing campaigns deteriorates due to consumer fatigue
Solution Approach 1:
The patent segments the consumer base into distinct groups based on their interaction patterns with marketing communications. By analyzing historical data on opens, clicks, and conversions, the system identifies segments with different engagement levels and preferences. This segmentation enables marketers to tailor communication frequency and content to each segment, maintaining effectiveness while increasing overall reach.
Solution Approach 2:
The system dynamically adjusts marketing parameters such as email frequency, timing, and content based on consumer behavior patterns. By changing these parameters according to individual consumer responses and segment characteristics, the system optimizes campaign effectiveness while managing communication volume to prevent fatigue.
2Reliability
If marketers target primarily those consumers most likely to purchase, then the effectiveness of marketing communications is improved, but the ability to identify which consumers will interact deteriorates due to lack of accurate prediction methods
Solution Approach 1:
The system implements continuous feedback loops by tracking consumer interactions with marketing communications and using this data to refine prediction models. Historical interaction data feeds into machine learning algorithms that improve their accuracy over time, enabling more precise identification of consumers likely to respond positively to future campaigns.
Solution Approach 2:
The system performs preliminary analysis of consumer behavior patterns before launching marketing campaigns. By pre-segmenting audiences and predicting response likelihood based on historical data, marketers can identify high-probability targets in advance, improving campaign effectiveness from the outset rather than relying on post-campaign analysis.
3Loss of information
If conventional marketing systems use consumer-performance metrics to determine interaction, then some marketing insights for groups of consumers are provided, but the ability to accurately determine which individual consumers will interact deteriorates
Solution Approach 1:
The patent transitions from two-dimensional group-level metrics to three-dimensional individual consumer profiling by incorporating multiple data dimensions including interaction timing, frequency, content preferences, and behavioral patterns. This multi-dimensional approach enables accurate individual prediction while preserving valuable aggregate insights through hierarchical analysis.
Data Source
AI summary
Methods and systems for providing targeted marketing include using consumer-centric indices to identify users who are most conversant with marketing communications. In particular, one or more embodiments generate a model that indicates a probability of user interactions based on dynamic data. The dynamic data indicates a time to action for each user interaction with a marketing communication within an observation window. The model fits the dynamic data to a distribution and determines the parameters of the distribution. Using the parameters of the distribution, one or more embodiments calculate interest scores for users who have received marketing communications. One or more embodiments select a set of users as a target audience based on the interest scores and provide marketing communications to target audience.