LLM-Personalized Conversation Content for Outbound Contact Centers
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Solution Overview
Problem
Outbound marketing campaigns often rely on generic scripts and lack personalized content, leading to disengaged customers and low conversion rates due to insufficient customer data analytics and agent training.
Innovation Solution
A computerized system and method that utilizes a Large Language Model (LLM) AI engine to generate personalized content for agents during outbound marketing campaigns, incorporating customer and campaign details to improve engagement and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic scripts are used in outbound marketing campaigns, then device complexity is reduced and ease of operation is improved, but customer engagement and conversion rates deteriorate
Solution Approach 1:
The system performs preliminary actions by gathering customer data, analyzing preferences, and generating personalized content before the marketing interaction occurs. This advance preparation enables agents to deliver tailored messages without increasing operational complexity during the actual campaign execution.
Solution Approach 2:
An AI-powered content generation system acts as an intermediary between customer data and marketing messages. This intermediary automatically creates personalized content based on customer profiles, bridging the gap between data analytics and practical application without requiring complex human intervention.
2Productivity
If personalized content is generated using AI engine, then customer engagement is improved, but device complexity and processing time increase
Solution Approach 1:
The system segments the personalization process into distinct modular components: data collection module, AI content generation module, and delivery module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining personalized content generation capabilities.
Solution Approach 2:
The system dynamically adjusts parameters such as content length, tone, and formatting based on customer profiles and campaign objectives. By changing these parameters rather than creating entirely new content structures, the system achieves personalization with reduced computational complexity.
3Adaptability or versatility
If real-time decision-making is required during interactions, then adaptability is improved, but response time and processing speed worsen
Solution Approach 1:
The system performs preliminary analysis of customer data and pre-generates personalized content options before interactions begin. This advance preparation enables agents to access ready-to-use personalized messages in real-time without requiring complex processing during the actual customer interaction.
Solution Approach 2:
The system provides dynamically adjustable content templates that agents can modify in real-time based on conversation flow. This dynamic approach allows adaptability during interactions while maintaining a structured framework that prevents excessive processing requirements.
Data Source
AI summary
A computerized-method for providing personalized-content for an interaction of an agent with a customer during an outbound conversational-marketing-campaign of a tenant operated via a cloud-based contact-center-platform. The computerized-method includes: (i) receiving details of the customer and details of the outbound conversational-marketing-campaign of the tenant; (ii) creating a prompt-text based on the received details of the customer and the details of the outbound conversational-marketing-campaign; (iii) generating the personalized content for the interaction with the customer in text-format by executing an LLM AI engine with a trained model with the created prompt-text; (iv) storing a text-file with the generated personalized content in text-format in a contents-database; (v) initiating the interaction of the agent by dialing to the customer; (vi) retrieving the text-file for the interaction from the contents-database; and (vii) sending the text-file to a computerized-device of the agent to be presented via a display-unit that is associated to the computerized-device.