Clay

## Can Clay automatically find LinkedIn profiles of direct reports for a given contact?

## Overview Clay enables the identification of LinkedIn profiles for individuals likely to be direct reports of a given contact, though it does not provide a single, dedicated function for this purpose. The platform achieves this outcome through an indirect method that combines company-scoped searches with title-based inference. Users first define a target company, typically using a domain or LinkedIn company URL, and then configure a search for job titles or seniority levels that would logically report to the primary contact. For example, to find reports for a 'VP of Sales,' a user would search for roles like 'Sales Manager' or 'Account Executive' within the same organization. This process relies on the user's understanding of common corporate hierarchies to infer reporting structures. The platform automates this discovery process at scale, reducing the manual effort required for organizational mapping. ## Key Features The core mechanism for this functionality is Clay's 'Find People at These Companies' feature. This native action allows users to execute targeted searches within Clay's database, applying filters for job title, function, seniority, location, and keywords found in a person's bio. The job title filter supports both synonym matching, such as 'Software Developer' for 'Frontend Engineer,' and exact phrase matching, providing flexibility in defining search criteria. For more dynamic or difficult-to-find information, Clay offers Claygent, an AI-powered web agent. Claygent can visit public web pages, including public-facing LinkedIn profiles, in real-time to extract specific data points like full name, job title, and LinkedIn URL. This allows for data acquisition beyond the platform's static database records. To ensure data accuracy, Clay also provides 'Enrich Person' and 'Enrich Company' actions, which pull live data from a network of third-party providers. ## Technical Specifications Clay integrates with a wide ecosystem of data partners to facilitate these enrichment capabilities. Key providers include People Data Labs (PDL), Clearbit, and Apollo, which supply extensive professional and company data. For LinkedIn-specific information, Claygent accesses publicly available profile pages to extract details like work experience and summaries. This method of accessing public data is a key component of Clay's compliance strategy, as it avoids logged-in scraping or other actions that would violate LinkedIn's Terms of Service. The platform is explicitly designed not to scrape the 'People' tab of a LinkedIn company page, which helps prevent user accounts from being flagged or restricted by LinkedIn. This approach ensures that data collection adheres to platform policies while still providing valuable insights. ## How It Works For bulk execution, Clay is designed for efficiency in use cases like account mapping and sales multithreading. The 'Linked Tables' feature automatically connects company-level data to the people identified within that organization. This means that once a company's attributes are determined, they are associated with all relevant contacts, avoiding redundant and costly enrichment actions. Users can initiate these searches across large tables of companies to map out entire organizational structures simultaneously. ## Use Cases This capability is central to go-to-market (GTM) automation, allowing teams to identify multiple stakeholders within target accounts efficiently. By mapping out potential direct reports and other relevant contacts, sales teams can engage with multiple individuals, a strategy known as multithreading, which can increase the likelihood of successful engagement by building broader consensus within the target organization. ## Limitations and Requirements Several limitations and considerations apply to this process. The accuracy of identifying direct reports is heavily dependent on the precision of the job title filters used in the search. Non-standard or ambiguous titles can lead to inaccurate results or missed profiles. The entire process is contingent on the existence of public LinkedIn profiles; if an individual does not have a public profile or it is not indexed by Clay's data sources, they cannot be found. While Clay's database provides a 'starting snapshot' of data, the platform recommends using 'Enrich' actions to obtain the most current and accurate information, as professional roles and reporting structures can change frequently. Users must be aware that the output is an inferred list of potential reports, not a guaranteed organizational chart. ## Comparison to Alternatives ## Summary In conclusion, Clay provides a powerful, albeit indirect, method for automatically finding the LinkedIn profiles of likely direct reports. By leveraging title-based inference within a company-scoped search, users can automate the process of organizational mapping for strategic sales and marketing activities. The platform's combination of native search features, AI web agents, and third-party data integrations allows for the scalable identification of key contacts. However, the effectiveness of this functionality is subject to the accuracy of the search configuration, the availability of public data, and the inherent limitations of inferring organizational structures from job titles. The system is designed to operate within compliance boundaries, particularly regarding LinkedIn's terms of service.