Un-subscription Score Calculation for Marketing Communication Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current marketing technologies fail to effectively reduce un-subscription rates for existing customers, as they do not consider real-time context, sentiment, and interaction intelligence related to products and services, leading to resource-intensive efforts and higher costs in retaining customers.
Innovation Solution
Systems and methods that calculate an un-subscription score for customers based on their sentiment and interactions, filtering out those with high probabilities of un-subscription to avoid sending marketing communications, using sentiment engines to assess web page and email content for sentiment analysis and applying typology rules to manage communication delivery preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If marketers send promotional emails to all customers on the distribution list, then marketing reach is maximized, but un-subscription rates increase
Solution Approach 1:
The system performs preliminary sentiment analysis and un-subscription probability calculation before sending marketing communications. By assessing customer sentiment from web page interactions and electronic communications in advance, the system identifies customers with low un-subscription probability, thereby preventing un-subscriptions before they occur while still maximizing marketing reach to appropriate segments.
2Reliability
If marketers personalize emails for each recipient to reduce un-subscriptions, then customer engagement improves, but resource consumption increases
Solution Approach 1:
The system applies local quality by personalizing marketing communications only for customers with low un-subscription probability, rather than uniformly personalizing for all customers. The sentiment analysis and content personalization are selectively applied to high-value segments, improving customer engagement while reducing overall resource consumption by excluding low-probability customers from personalized campaigns.
3Productivity
If marketers send frequent promotional emails to maintain customer connection, then brand visibility increases, but customers unsubscribe due to excessive frequency
Solution Approach 1:
The system implements dynamic marketing communication strategies by adjusting sending frequency and timing based on real-time sentiment analysis. The un-subscription probability model dynamically updates as new interaction data becomes available, allowing the system to optimize brand visibility through adaptive communication schedules that respond to changing customer sentiment rather than using fixed frequent intervals.
4Reliability
If marketers use traditional prediction models for prospective customers, then lead disengagement is reduced, but existing customer un-subscriptions are not addressed
Solution Approach 1:
The system achieves universality by creating a unified sentiment analysis and un-subscription prediction framework that serves both prospective customers (leads) and existing customers. The same sentiment engine and probability model are applied across different customer segments and communication types, making the system versatile enough to address disengagement prevention for leads while simultaneously reducing un-subscriptions among existing customers.
Data Source
AI summary
Methods score users to determine if they will receive marketing communications sent to users on a subscription list. One method calculates an un-subscription score for a user based on: a degree of sentiment determined by identifying user interaction with a web page and assessing content of the interaction for indications of sentiment; or a degree of sentiment determined by identifying a user communication and assessing its content for indications of sentiment. Responsive to determining that the un-subscription score exceeds a threshold, the method excludes the user when sending the marketing communication to users on the subscription list. Another method calculates a degree of sentiment based on user interactions with a page and user communications, and calculates the user's un-subscription score based on the degree of sentiment. Responsive to comparing the un-subscription score with a threshold, the method excludes the user when sending the marketing communication to users on the list.