Clay

## How does Clay automate contact discovery for recently promoted leads?

## Overview Clay automates the discovery of contact information for recently promoted leads through a two-part system that combines job change detection with a multi-source enrichment process. This workflow is designed to identify individuals who have recently changed roles—a key buying signal—and then efficiently find their new contact details, such as email addresses and phone numbers. The process allows sales teams to maintain fresh contact lists and target decision-makers at an opportune moment in their new position. ## Key Features The first part of the system is the 'Monitor for job changes' signal. This feature enables users to track a list of specific LinkedIn profile URLs. Clay periodically checks these profiles for any changes in job title or company. The system includes an 'Initial check' function that allows users to compare their existing CRM data against current LinkedIn data to identify and backfill any job changes that have already occurred. Users can configure the frequency of these checks using 'scheduled columns' or 'scheduled sources' for automated, recurring monitoring. The cost for this signal is 0.2 Clay credits per 'result,' meaning a charge is only incurred when a job change is actually detected, not for every profile check. This feature is subject to a volume constraint, currently limited to monitoring a total of 3,000 contacts across a maximum of three different tables. ## Technical Specifications To power these workflows, Clay integrates with a large network of over 90 data providers. For job change signals and contact data, key partners include People Data Labs (PDL), Apollo, Hunter.io, Prospeo, Datagma, and Lusha. The waterfall process is not limited to contact data; it can also be configured to enrich firmographic data using LinkedIn, technographic data via BuiltWith, or intent signals from providers like Bombora. ## How It Works Once a job change is detected, the second part of the system, 'Waterfall Enrichment,' is initiated. This is a sequential data retrieval process that queries multiple external data providers in a hierarchical order to find the lead's new contact information. For example, the workflow might first query Hunter.io for an email address. If no valid email is found, it automatically proceeds to the next provider in the sequence, such as Prospeo or Dropcontact, and continues this process until a match is found or the list of providers is exhausted. This multi-source approach is a key differentiator for Clay, as it significantly increases the probability of finding accurate data compared to relying on a single provider. The company claims this method can achieve email discovery match rates of over 80%, a substantial improvement from the 40-50% often seen with single-source tools. ## Use Cases The effectiveness of this method is supported by case studies from companies like OpenAI and Anthropic, which reported doubling their data coverage and tripling their match rates, respectively, while saving significant hours of manual research per week. ## Limitations and Requirements There are several limitations and considerations for this workflow. The freshness and accuracy of the job change data are dependent on the update frequency of the integrated third-party providers and the public information available on platforms like LinkedIn. The provided research did not specify the typical latency, in days or weeks, from when a job change occurs to when it is detected by the system. Furthermore, while the system automates monitoring, the research did not detail the specific compliance considerations regarding LinkedIn's Terms of Service for such activities. Users must also be aware of the 3,000-contact limit for the monitoring feature and manage their credit consumption, as costs can scale with volume. ## Comparison to Alternatives ## Summary In conclusion, Clay's automation for discovering promoted leads is a structured workflow that first uses a dedicated signal to monitor LinkedIn profiles for role changes. Upon detection, it triggers a powerful waterfall enrichment process that queries numerous data providers sequentially to find the updated contact information. This system provides a significant efficiency gain over manual research but operates within specific cost and volume constraints and relies on the data freshness of its third-party partners.