Filtering Candidate Duplicates in Make: A Time-Saving Trick for Talent Acquisition

In the fast-paced world of talent acquisition, efficiency is not just a buzzword; it’s a strategic imperative. Recruiters grapple daily with vast amounts of data, sifting through applications, resumes, and candidate profiles. One of the most insidious time sinks, often overlooked until it becomes a significant bottleneck, is dealing with duplicate candidate entries. Whether it’s a candidate applying through multiple channels, an internal referral already in the system, or just a data entry error, duplicates inflate databases, skew metrics, and lead to wasted effort in outreach and screening.

While various Applicant Tracking Systems (ATS) offer some level of duplicate detection, they often fall short when integrating data from disparate sources, or when dealing with slightly varied entries. This is where automation platforms like Make (formerly Integromat) become indispensable. Make empowers talent acquisition teams to build intricate, intelligent workflows that not only automate routine tasks but also introduce layers of data hygiene, such as effectively filtering out candidate duplicates before they even enter your primary system or trigger redundant actions.

The Hidden Cost of Duplicate Candidates

Before diving into the solution, it’s crucial to understand the tangible costs associated with duplicate candidate records. Firstly, there’s the administrative burden: recruiters waste valuable time reviewing the same profile multiple times, updating redundant records, or chasing down candidates who have already been contacted. This isn’t just inefficient; it’s demoralizing. Secondly, duplicate data pollutes your talent pool. Your analytics, which guide strategic decisions about sourcing channels and talent pipeline health, become unreliable. If your system shows 10,000 candidates when 20% are duplicates, your conversion rates and time-to-hire metrics are inherently flawed. Finally, and perhaps most critically, a candidate receiving multiple outreach attempts for the same role, or even different roles, from different recruiters within the same organization creates a disjointed and unprofessional candidate experience. In a competitive talent market, this can deter top talent.

Leveraging Make for Proactive Duplicate Management

The core philosophy behind using Make for duplicate filtering is proactive prevention rather than reactive cleanup. Instead of waiting for duplicates to accumulate and then running periodic purges, we build a workflow that checks for existing entries *before* new data is processed or pushed to your ATS. This approach ensures that your primary data source remains clean and accurate from the moment data enters your ecosystem.

The beauty of Make lies in its modularity and powerful filtering capabilities. A typical scenario might involve candidates applying via a web form, being imported from a job board, or referred through an internal system. Each of these data streams can be configured as a ‘scenario’ in Make. Before the candidate data reaches your ATS, it passes through a series of modules designed to identify and handle potential duplicates. The most common unique identifier for a candidate is their email address, though a combination of first name, last name, and phone number can also be used for a more robust check, accounting for typos or multiple email addresses.

Crafting the Make Scenario for Duplicate Filtering

Let’s consider a practical application. Imagine a workflow where new candidate applications from a Typeform submission are meant to be added to your ATS. The Make scenario would look something like this:

1. Webhook/Source Module: Capturing New Data

The first module in your Make scenario would be the trigger – for instance, a ‘Webhooks > Custom webhook’ module if you’re pulling from a form, or a ‘Google Sheets > Watch new rows’ if you’re importing a list, or even an API connection to a job board. This module listens for and captures new candidate data as it comes in.

2. Search Module: The Duplicate Detector

Immediately after capturing the new data, you would introduce a ‘Search’ module connected to your ATS or a dedicated database where your candidate records reside. This could be an ‘ATS Name > Search candidates’ module, or if your ATS doesn’t have a direct search module in Make, you might use a ‘Google Sheets > Search rows’ module if you’re maintaining a master list there, or even a ‘Data Store > Search records’ module within Make itself for temporary storage. The key here is to configure this module to search for the unique identifier of the incoming candidate – typically their email address. For example, if the incoming data contains `{{email}}`, you’d configure the search to look for that email in your existing records.

3. Filter Module: The Gatekeeper

This is where the magic happens. After your search module attempts to find a match, you’ll add a ‘Filter’ module. The filter’s condition is simple yet powerful: “Continue only if the previous search module returned zero bundles (i.e., no existing candidate found).” If the search module found a match (meaning the candidate already exists), the filter will stop the scenario at this point, preventing the duplicate from proceeding. If no match is found, the filter allows the data to pass through.

4. Create/Update Module: Processing Unique Candidates

Finally, if the data successfully passes the filter (meaning it’s a unique candidate), it proceeds to the next module, which would typically be an ‘ATS Name > Create candidate’ or ‘ATS Name > Add record’ module. This module takes the clean, unique candidate data and pushes it into your primary system, ensuring that every record added is a genuinely new and distinct entry.

This proactive filtering mechanism saves immense time and resources. Instead of dedicating hours to manual cleanup or dealing with the fallout of duplicated outreach, your team can focus on what truly matters: engaging with unique, qualified talent. By embedding these intelligent checks within your automated workflows, 4Spot Consulting helps talent acquisition teams achieve a level of data integrity that translates directly into improved efficiency, more accurate reporting, and a superior candidate experience. It’s not just about automating tasks; it’s about building smarter, cleaner, and more strategic talent operations.

If you would like to read more, we recommend this article: The Automated Recruiter’s Edge: Clean Data Workflows with Make Filtering & Mapping