Agentic GTM is a SaaS product designed to help founders, marketers, and sellers adapt to changes in go-to-market strategies with AI-powered systems and processes. It focuses on building AI agents for marketing and automating marketing teams, targeting the new role of the GTM engineer.
Features
AI Agent Deployment
Build AI agents for marketing to automate tasks such as content creation, lead scoring, and ad optimization, aiming to enhance productivity and effectiveness.
GTM Systems Redefinition
Focus on redesigning the go-to-market structure using systems rather than traditional teams, leveraging automation to achieve efficiency in marketing operations.
The Signal Layer
Transforms every touchpoint into a learning opportunity, ensuring that the system records and interprets data accurately at every stage.
The Learning Layer
Processes and structures the data from the Signal Layer to derive meaningful insights that can inform decision-making.
The Intelligence Layer
Utilizes the structured data from the Learning Layer to predict outcomes and make proactive decisions.
The Truth Layer
Ensures the consistency and accuracy of the information across all systems, serving as a reliable foundation for building business strategies.
Minimum Viable Foundation
A starting point for businesses to implement the Agentic Foundation, focusing on essential elements that provide the most impact with minimal initial effort.
CRM Data Analysis
Converts messy CRM data into a clear, data-driven picture of your ideal customer by analyzing and segmenting existing customer data.
Customer Segmentation
Uses criteria such as net revenue retention and product adoption rate to group customers into segments for better targeting.
SQL-like Data Queries
Enables users to query CRM databases using an SQL-like approach to identify key customer segments and insights.
Pattern Recognition
Helps identify patterns among high-value customers by examining factors like time to value and customer engagement.
Intelligence Validation
Validates insights and patterns extracted from customer data to ensure accuracy and actionability.
Anti-Portfolio Building
Develops an anti-portfolio to understand which customer segments are not viable or valuable for the business.
Insight Implementation
Turns extracted insights into actionable strategies to refine customer acquisition and retention efforts.
AI-Powered Personas
Leverage AI to build and understand customer personas from support interactions, gathering insights systematically without assumptions.
Support Data Mining
Use AI to analyze customer support conversations to identify pain points, feature requests, language patterns, and satisfaction indicators.
Sales Call Intelligence
Leverage AI to extract actionable insights from sales call recordings, helping to understand customer needs and optimize sales strategies.
Review Mining
Use AI to extract meaningful data from customer reviews, identifying trends and insights that inform product development and marketing strategies.
Competitor Intelligence
Utilize AI to gather and analyze data on competitor strategies and performance, helping to build a competitive edge.
Product Usage Patterns
Analyze how customers interact with products to uncover usage patterns that can guide product improvements and marketing focus.
Website Behavior Analysis
Employ AI to understand website visitor behavior, optimizing UI/UX based on data-driven insights.
Community Intelligence
Harness AI to gain insights from customer communities, identifying trends and feedback that can enhance marketing and product strategies.
Persona Framework Building
Develop robust customer personas based on collected data, ensuring alignment with market needs and behavioral patterns.
Market Research Engine Creation
Build a tailored market research engine using AI to continually gather and analyze market data, informing business strategies.
Data Collection Engine with Clay
Utilizes Clay as a centralized system to create a structured database of competitors, including domains, market segments, and primary value propositions. It also allows enrichment from G2 reviews, social mentions, and other data points.
AI Analysis System Training
Trains AI models to analyze collected data, providing insights into trends, patterns, and competitive movements. This step focuses on making data actionable.
Battle Card Building
Creates comprehensive battle cards that compile and present critical intelligence on competitors for strategic decision-making. These cards are designed to be dynamic and updated in real-time to ensure relevance.
The Source (Product Database)
Your database acts as a living record of user interaction. Every click, login, or feature interaction is captured, providing raw and valuable insights.
The Transport (Fivetran)
Fivetran functions as a high-speed system for transferring data, ensuring reliable, real-time updates to your database without manual intervention.
The Lake (BigQuery)
BigQuery transforms raw data into intelligence, where patterns and emerging trends can be analyzed, offering deeper insights.
The Translator (Hightouch)
Hightouch translates and syncs insights from BigQuery into actionable CRM data, enhancing workflow and understanding.
The Action Centers (HubSpot + Customer.io)
These platforms are used for engaging users, managing marketing campaigns, and converting insights into actions based on the transformed data from previous stages.