Multi-Channel Content Recommender for Cross-Channel Effectiveness Scoring
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Solution Overview
Problem
Existing multi-channel customer communication platforms struggle with inconsistent and ineffective content distribution across channels due to siloed design environments and lack of data-driven solutions to measure content effectiveness, making it difficult to deliver personalized and timely content at scale.
Innovation Solution
A data-driven system that processes interaction events across channels to build a training dataset for a multi-channel recommender model, using algorithms like collaborative filtering to score content effectiveness and provide recommendations for consistent content distribution across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If content designers work in siloed design environments for each channel, then each channel can have specialized content creation, but content distribution becomes inconsistent and ineffective across channels
Solution Approach 1:
The patent merges previously siloed channel-specific design environments into a unified multi-channel platform that shares common content repositories, design tools, and distribution mechanisms. This allows content designers to create content once and have it automatically adapted and distributed across multiple channels consistently, while still maintaining channel-specific customization capabilities.
Solution Approach 2:
The platform implements universal content management capabilities that serve multiple channels simultaneously. A single content item can be configured for distribution across email, mobile, web, and social media channels with unified management, while the system automatically adapts the content format and delivery timing for each specific channel.
2Ease of operation
If traditional metadata-based search is used to find content, then content can be located in the repository, but effectiveness in invoking customer response cannot be determined
Solution Approach 1:
The system implements feedback loops that track customer interactions with content across all channels. Effectiveness metrics such as click-through rates, engagement time, and conversion data are collected and fed back into the content repository, enriching metadata with performance information that guides future content selection and optimization.
Solution Approach 2:
The patent replaces traditional mechanical metadata-based search with an intelligent recommendation system that uses machine learning algorithms to analyze content effectiveness patterns. The system automatically recommends content based on predicted customer response rather than relying solely on manual metadata queries, substituting automated intelligence for manual search processes.
3Manufacturing precision
If content is designed for specific channels separately, then channel-specific optimization is achieved, but scaling content distribution across multiple channels becomes exceedingly difficult
Solution Approach 1:
The platform segments content into modular components that can be independently configured for different channels. Content is divided into reusable elements that can be assembled and adapted for specific channel requirements while maintaining centralized management, enabling both channel-specific optimization and scalable distribution.
Solution Approach 2:
The system implements dynamic content adaptation that automatically adjusts content format, timing, and delivery based on channel characteristics and real-time performance data. Content distribution is dynamically optimized for each channel while maintaining overall campaign consistency, allowing the system to scale across multiple channels without manual intervention for each channel configuration.
4Ease of operation
If manual content selection is used by content designers, then content can be chosen based on experience, but data-driven effectiveness scoring is not available
Solution Approach 1:
The system implements self-service content recommendation capabilities where the platform automatically scores and ranks content based on effectiveness metrics without requiring manual analysis. Content designers can query the system for recommended content with automatically calculated effectiveness scores, allowing them to make data-driven selections efficiently without manual measurement processes.
Data Source
AI summary
Interaction events collected across disparate customer communication channels of an enterprise are processed to generate an encoded unique content item identifier for each content item referenced in an interaction event such that the content item is resolvable to a location in a content repository. A training data set is built using the interaction events thus processed and a multi-channel content recommendation model is trained using the training data set. The multi-channel content recommendation model thus trained stores data points representing intersections of customers and content items that the enterprise has been tracking, with each data point having an effectiveness score for an associated content item. The multi-channel content recommendation model thus trained can be queried by content designers of the disparate customer communication channels through a recommender application for content recommendations based on the effectiveness of the content, agnostic to the disparate customer communication channels.