Multi AI Agent Market Segmentation

The Challenge

Marketing segmentation is a cornerstone of data-driven strategies, but creating accurate segments is fraught with complexity. Traditional segmentation methods struggle with inconsistent, misspelled, and incomplete data in contact lists, leading to flawed customer insights. Moreover, marketing teams have to fill out complex forms to capture nuanced customer behavior. This issue is amplified when dealing with large datasets where manual corrections are impractical.

Key Challenges:

• Data Inconsistency: Complex forms, incorrect contact details, and inconsistent data entries undermine segmentation accuracy.
• Behavioral Complexity: Capturing subtle customer behaviors to create dynamic, personalized segments.

The Strategic Approach

To address these challenges, I developed a multi-AI agent customer segmentation system that leverages advanced technologies, including Large Language Models (LLMs), prompt engineering, and a vector database. The core strategy was to create a system where agents filled out complex marketing forms based on user queries. The parsing agent recognized both fuzzy terms (e.g., title, location, industry) and non-fuzzy terms (e.g., user opened or clicked on 5 emails in the last 3 months, user filled out a form in the last month) and routed those terms to specialized agents (fuzzy agent, non fuzzy agent) that further parsed and processed the terms. The outputs of the fuzzy agent and non fuzzy agent were passed to an aggregation agent that combined the outputs of the two agents into Json that is used by the user interface.

Technical Implementation

LLMs & Prompt Engineering: Crafted prompts to guide LLMs in identifying anomalies, extracting key customer attributes, and making sense of imperfect data.

Vector Database Integration: Used a vector database to return related data and variations along with misspellings (Healthcare, Health care, hospital, medical devices, health carf).

Multi-AI Agent Coordination: Designed a system where multiple AI agents collaborated, specializing in tasks like query parsing, fuzzy term interpretation and expansion, behavioral analysis, and form completion.

Collaborative Development

Worked closely with the product team to implement their design.

Collaborated with the Segmentation Engine development team to provide them with data in a format that they could use to generate the user interface.

Worked with the Core Team to deploy the prototype to the development environment.