Integrated Communication System for Multi-Channel Strategy Optimization
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Solution Overview
Problem
Businesses face challenges in effectively communicating with customers across multiple devices and channels due to the complexity of managing various tools and data sources, which can lead to overwhelming customers and failing to engage them appropriately.
Innovation Solution
The development of an integrated communication system (ICS) that utilizes machine learning (ML) models for send-time optimization, frequency optimization, channel optimization, and engagement scoring, enabling personalized communication strategies based on user data and engagement patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If marketers use multiple tools and spreadsheets to manage customer data across different channels, then they can attempt to reconcile data about customers, but the system complexity increases and effectiveness decreases
Solution Approach 1:
The patent combines multiple separate tools, spreadsheets, and data sources into a single unified customer data platform. This integration allows marketers to manage customer data across email, mobile, web, and social media channels through one cohesive system rather than multiple disconnected tools, directly resolving the contradiction between multi-channel adaptability and system complexity
Solution Approach 2:
The unified platform performs multiple functions including data collection, analysis, segmentation, and communication orchestration across various channels. This multi-functional system replaces the need for separate specialized tools for each channel while maintaining the ability to adapt to different communication needs
2Productivity
If companies increase communication frequency to maintain customer relationships, then engagement opportunities increase, but customer satisfaction decreases due to overwhelming messages
Solution Approach 1:
The system dynamically adjusts communication frequency based on individual customer preferences, engagement history, and real-time behavior patterns. Rather than using a static high-frequency approach, the platform continuously adapts the timing and frequency of messages for each customer segment, enabling high productivity without causing customer overwhelm
Solution Approach 2:
The platform incorporates feedback loops that monitor customer responses and engagement metrics to automatically adjust communication strategies. When customers show signs of being overwhelmed or disengaging, the system reduces frequency or pauses communications, while increasing engagement triggers more frequent targeted messages, resolving the contradiction between productivity and customer satisfaction
3Loss of information
If marketers process increasing amounts of customer data for decision making, then communication effectiveness should improve, but the difficulty of detecting and measuring patterns increases
Solution Approach 1:
The patent introduces an intermediary layer of artificial intelligence and machine learning algorithms that process raw customer data and transform it into actionable insights. This intermediary system handles the complexity of analyzing multiple data types (transactional, behavioral, demographic) and presents simplified, ready-to-use customer profiles and predictions to marketers, maintaining complete information processing while reducing analytical difficulty
Solution Approach 2:
The system replaces manual data analysis and pattern recognition with automated AI-driven analytics. Machine learning models automatically detect patterns in customer behavior, predict future actions, and generate segmentation strategies without requiring manual intervention, thereby processing complete datasets while eliminating the complexity of human analysis
4Ease of operation
If marketers manually create communication strategies for each customer segment, then customization improves, but time consumption and resource requirements increase
Solution Approach 1:
The platform enables self-service communication strategy creation through automated customer segmentation and personalization. The system automatically analyzes customer data, creates segments based on behavior and preferences, and generates tailored communication strategies without requiring manual marketer intervention for each segment, achieving high personalization while eliminating time consumption
Solution Approach 2:
The system performs preliminary analysis and segmentation of customer data before communication campaigns are launched. By pre-processing data, creating customer profiles, and preparing personalized content in advance, the platform eliminates the need for time-consuming manual strategy development at the moment of campaign execution
Data Source
AI summary
An example method of message routing includes: receiving, by one or more processors, a request to send a message to a specified user of a plurality of users of a communication services platform; providing a user profile of the specified user to a communication channel selection model, wherein the user profile characterizes actions of one or more predefined action types that were performed by the specified user in response to receiving previous communications; identifying, based on the output of the communication channel selection model, a preferred communication channel for communicating with the specified user; determining, based on the preferred communication channel, a communication strategy for the specified user; and causing, pursuant to the communication strategy, a message to be sent to the specified user.