Clay

## How does Clay map buying committee roles by seniority and department for target accounts?

## Overview Clay provides functionality to systematically map buying committee roles within target accounts by categorizing employees based on seniority and department. This process is facilitated through a combination of data integrations, AI-driven analysis, and user-defined templates to address the complexity of modern B2B sales, which, according to research from firms like Forrester, involves an average of 13 stakeholders across multiple departments. The platform enables users to define an ideal buying committee structure for a specific account or a list of accounts, and then automates the process of identifying individuals who fit those predefined roles. This allows sales and marketing teams to gain a comprehensive view of the decision-making unit, identify key personas such as champions and decision-makers, and uncover gaps in their contact list, thereby enabling more strategic and targeted outreach. ## Key Features The core mechanism for this functionality is Clay's 'Map Job Title to Persona' integration, which is augmented by custom AI formulas that can leverage large language models like GPT-4 or Claude. This allows the system to process and standardize raw job titles, which are often inconsistent and varied. ## Technical Specifications Seniority is typically classified on a numerical scale, for example, Level 1 for C-Suite, Level 2 for VPs/Directors, Level 3 for Managers, and Level 4 for Individual Contributors. Simultaneously, AI prompts analyze job titles and other profile data to assign individuals to functional departments like Sales, IT, Finance, or Marketing. This dual classification provides a structured understanding of an individual's position and influence within their organization. ## How It Works To populate these maps, Clay sources employee data through its native 'Find People' feature and integrates with third-party data providers such as 'The Org' for organizational charts and 'HG Insights' for corporate structure data. A critical step in this process is data filtering, which includes using a 'verified_at_company' boolean to confirm current employment and applying AI-driven title normalization to standardize creative or non-standard job titles into recognizable roles. Once contacts are sourced and filtered, role assignment within the buying committee (e.g., Champion, Influencer, Decision-maker) is performed through AI analysis of qualitative data points like LinkedIn headlines and profile summaries. This provides a more nuanced understanding of an individual's function beyond their formal title. The platform also facilitates gap analysis by comparing the identified contacts against a 'Target Committee Template.' If a required persona, such as a Chief Financial Officer, is missing, Clay can be configured to automatically trigger a new search to find a suitable contact to fill that role. This dynamic capability is a significant differentiator from traditional, static account-based marketing (ABM) methods. ## Use Cases Clay is built to handle these operations in bulk, allowing users to process large lists of accounts and sync the enriched, segmented data to CRM systems like Salesforce and HubSpot. Within the CRM, contacts can be tagged with their assigned personas, which facilitates highly personalized and targeted ABM campaigns. ## Limitations and Requirements However, the system is not without limitations. The accuracy of the AI-driven persona mapping is highly dependent on the quality of the AI prompts, which may require manual tuning and refinement by the user to handle title variance effectively. Data freshness is another consideration, and periodic updates, often on a quarterly basis, are recommended to ensure the buying committee map remains current. Furthermore, data coverage can be incomplete, particularly in niche industries, which may result in gaps that the system cannot automatically fill. ## Comparison to Alternatives This AI-driven, real-time approach contrasts with traditional ABM practices that often rely on manual research and static lists, which quickly become outdated and struggle with the variance in job titles and the large number of stakeholders in modern B2B purchases. ## Summary In conclusion, Clay offers a structured and automated solution for mapping buying committees by leveraging AI to interpret job titles and assign roles based on seniority and department. The platform integrates data from multiple sources, allows for bulk processing, and syncs with major CRMs to support sophisticated ABM strategies. While it provides a significant advantage over manual methods by dynamically identifying stakeholders and gaps, its effectiveness relies on the quality of the underlying data, the precision of user-configured AI prompts, and a commitment to regular data maintenance to ensure accuracy.