Unified Feature Vector for Customer Channel Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current communication channel determination systems fail to maximize customer engagement and response rates due to channel siloing and lack of awareness of optimal channels, leading to inefficient contact strategies and customer dissatisfaction.
Innovation Solution
A computer system that captures historical interaction data across multiple channels, generates feature vectors, and uses trained classification models to predict optimal communication channels for each user based on engagement probability, integrating features from various channels into a common set for personalized contact optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If communication channels are siloed with separate exclusion criteria for each channel, then channel-specific contact rules can be applied, but customers are inadvertently excluded from optimal channels and engagement rates decrease
Solution Approach 1:
The patent merges multiple channel-specific exclusion criteria into a unified set of features that are evaluated across all channels simultaneously. Instead of having separate decision logic for each channel, the system creates a common feature vector that captures customer characteristics relevant to all channels, then uses a single classification model to determine optimal channels. This integration eliminates inadvertent exclusions while maintaining channel-specific nuances through the learned feature weights.
Solution Approach 2:
The patent implements a universal classification model that serves all communication channels through a single system. The model takes a common feature vector as input and outputs engagement probability scores for multiple channels simultaneously. This multi-functional approach allows the system to determine optimal channels without requiring separate specialized models for each channel type, thereby improving engagement while reducing overall system complexity.
2Productivity
If customers are contacted via channels they do not prefer, then contact campaigns can be executed, but customers unsubscribe in greater numbers from future promotional campaigns
Solution Approach 1:
The patent incorporates feedback from historical interaction data into the feature vector that feeds the classification model. The model learns from past customer responses and engagement patterns to predict which channels are most likely to succeed for each customer. This feedback loop continuously refines the engagement probability predictions, ensuring that customers are contacted through their preferred channels and reducing unsubscribe rates while maintaining campaign effectiveness.
Solution Approach 2:
The patent dynamically adjusts contact channel selections based on predicted engagement probabilities and customer-specific parameters. Instead of using fixed channel assignment rules, the system changes the selected channel parameters based on real-time predictions from the classification model. This allows the system to adapt to individual customer preferences and optimize both campaign effectiveness and customer retention by selecting channels that maximize engagement probability.
3Reliability
If high cost phone representatives are used, then personalized contact can be achieved, but contact bandwidth must be managed efficiently to reduce costs
Solution Approach 1:
The patent implements self-service channel determination where the automated classification model independently evaluates each customer and selects the optimal contact channel without requiring manual intervention from phone representatives. The system uses pre-trained models and historical data to make autonomous decisions about channel selection, freeing up representative bandwidth for high-value situations while maintaining personalized contact quality through data-driven customization.
Solution Approach 2:
The patent replaces the mechanical manual decision-making process with an automated machine learning-based classification system. Instead of relying on human representatives to determine optimal channels, the system uses computational models that process customer data and predict engagement probabilities automatically. This substitution maintains personalized contact quality through sophisticated algorithms while dramatically increasing contact bandwidth efficiency by eliminating manual intervention requirements.
Data Source
AI summary
Methods and apparatuses are described for automated optimization and personalization of customer-specific communication channels using feature classification. A server captures historical interaction data comprising a channel type, a user identifier, an interaction date, and a user response value. The server generates a channel feature vector for each combination of channel type, user identifier, and interaction date. The server identifies features from the channel feature vectors for each different channel type and aggregates the features into a common feature vector. The server executes a trained classification model on the common feature vectors to select user identifiers for each different channel type that have an engagement probability value at or above a corresponding threshold. The server determines, for each different channel type, a distance value between the engagement probability value and the corresponding threshold and communicates with a remote computing device via a channel that is associated with an optimal distance value.