Adaptive Communication Selection for Higher Recipient Engagement
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Solution Overview
Problem
Existing communication systems struggle to effectively increase user interaction with recipients, as many recipients ignore calls to action due to the high volume of received messages.
Innovation Solution
A method involving a selection algorithm that uses stored parameters to select communications, observes recipient actions, determines successful outcomes, and updates parameters based on observed actions, utilizing reinforcement learning methods like epsilon-greedy, Thompson sampling, and contextual Thompson sampling to optimize message selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If communications are sent to recipients, then information is delivered, but user interaction decreases due to high volume of messages
Solution Approach 1:
The system applies local quality by personalizing communications to individual recipients based on their specific characteristics, behaviors, and preferences. Each recipient receives tailored communications rather than generic mass messaging, making the content more relevant and likely to elicit interaction despite the high volume of messages being sent overall
Solution Approach 2:
The system implements dynamics through continuous optimization of communication selection based on observed recipient actions. The selection algorithm dynamically adjusts communication choices based on real-time feedback about what resonates with recipients, allowing the system to adapt to changing user preferences and behaviors over time
2Device complexity
If all recipients receive the same communication, then delivery is simplified, but engagement increases due to relevance
Solution Approach 1:
The system applies segmentation by dividing the recipient population into distinct segments based on their characteristics, behaviors, and response patterns. The communication selection algorithm then segments the available communications into different options tailored to each recipient segment, enabling personalized delivery without requiring complete customization for each individual
Solution Approach 2:
The system implements self-service through automated selection algorithms that independently determine which communications to send to which recipients based on observed data and predefined criteria. The system serves itself by automatically optimizing communication matching without requiring manual intervention, balancing complexity management with personalized engagement
3Productivity
If communications are optimized for each recipient, then engagement increases, but system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-defining selection algorithms and criteria that guide communication selection before actual delivery occurs. The system prepares optimization rules and observation frameworks in advance, enabling complex personalization to be executed systematically without requiring real-time complex computations for each communication decision
Data Source
AI summary
A method for sending a communication to a recipient device comprises selecting an initial communication from a subset of communications based on a selection algorithm, wherein the selection algorithm uses at least one stored parameter; sending the selected communication to the recipient device; observing any actions taken by the recipient in response to the communication; determining whether the communication resulted in a successful outcome; and updating the at least one stored parameter based on the observed action. A successful outcome is determined based on comparing the actions taken by the recipient in response to the communication with one or more desired actions.