Your Recruitment & Talent Acquisition Data Template

This attribute records the name of each event in the recruitment process, such as 'Application Received', 'Interview Scheduled', or 'Offer Accepted'. It forms the sequence of events that make up the process map.

Analyzing the sequence and frequency of these activities is fundamental to process mining. It helps visualize the recruitment funnel, identify common process paths, detect deviations from the standard workflow, and pinpoint bottlenecks where the process stalls. For example, it allows tracking how many applications move from 'Application Reviewed' to 'Recruiter Screen Conducted'.

This attribute defines the steps in the recruitment process, allowing for the visualization of the process flow and the identification of bottlenecks and deviations.

This is often derived by mapping application stage changes, interview statuses, offer events, or other auditable actions within Greenhouse. It may require logic to translate system events into standardized activity names.

The activity timestamp marks the precise moment an event happened in the recruitment process. It is the temporal foundation for all performance and duration-based analysis, providing the chronological order of activities for each job application.

This timestamp is essential for calculating all time-related KPIs, such as Time to Hire, Interview Scheduling Cycle Time, and Interview Feedback Turnaround Time. By analyzing the time elapsed between different activities, organizations can measure efficiency, identify delays, and monitor adherence to service level agreements, directly supporting dashboards focused on performance and bottlenecks.

This timestamp is critical for ordering events, calculating cycle times, and analyzing the performance of the recruitment process.

Found in various objects within Greenhouse, such as 'applied_at' on the Application object, 'created_at' on Offer objects, or timestamps within activity feeds and audit logs.

The Job Application ID is the cornerstone of the recruitment process analysis, acting as the unique case identifier. It links all related activities, from the initial submission and screening to interviews, offers, and the final hiring decision. This allows for a complete, end-to-end view of each candidate's journey.

In process mining, this attribute is used to reconstruct the exact path each candidate follows through the hiring funnel. It enables the analysis of process variants, cycle times per application, and drop-off points, providing a clear picture of the overall recruitment lifecycle for every individual applicant.

This is the essential Case ID that connects all recruitment events for a single candidate, making it possible to analyze the entire hiring journey from start to finish.

This is typically the primary key for an application object. Consult the Greenhouse API documentation for the 'Applications' endpoint, often referred to as 'id' or 'application_id'.

This attribute records the date and time the data was last pulled from the source system. It is a metadata field that reflects the freshness of the data in the process mining model.

This information is essential for users to understand how current the analysis is. It helps manage expectations about data latency and is critical for validating that the data pipeline is running as scheduled. For example, if the 'Last Data Update' is several days old, users know the dashboards do not reflect the most recent recruitment activities.

Indicates the freshness of the data, helping users understand if the analysis reflects the most current state of the process.

This timestamp is generated and stamped onto the dataset during the data extraction, transformation, and loading (ETL) process.

This attribute specifies the origin of the recruitment data. For this process, the value would consistently be 'Greenhouse'.

While it may seem static, explicitly tracking the source system is crucial for data governance, troubleshooting, and in scenarios where data might be enriched from other systems, such as an HRIS. It ensures clarity on data provenance and helps maintain data integrity across the organization's data landscape.

Ensures clear data provenance, which is vital for data governance, validation, and managing data from multiple sources.

This is a static value that should be added during the data extraction and transformation process to label the origin of the data.

This attribute tracks the origin of a job application, for example, 'LinkedIn', 'Employee Referral', 'Company Website', or 'Indeed'. It provides insight into the effectiveness of different recruitment channels.

The 'Sourcing Channel Effectiveness' dashboard is built entirely around this attribute. By analyzing application volume, time-to-hire, and hire rates by source, organizations can optimize their recruitment marketing spend and effort. This data helps answer strategic questions about which channels deliver the highest quality candidates most efficiently, directly supporting the 'Sourcing Channel Conversion Rate' KPI.

Helps measure the effectiveness and ROI of different recruiting channels, enabling data-driven decisions on where to invest sourcing efforts.

Available on the Candidate object in Greenhouse, which is linked to the Application. The 'source' field provides this information.

This attribute indicates the disposition of an application, such as 'Hired', 'Rejected', or 'Active'. It represents the terminal state of a completed process or the current state of an ongoing one.

This is a critical dimension for outcome analysis. It allows for filtering cases to compare the process paths of hired candidates versus rejected ones, which can reveal characteristics of successful journeys. It is also used to calculate conversion rates in the 'Overall Recruitment Funnel' dashboard, showing what percentage of applications result in a hire.

Defines the outcome of the recruitment process, enabling analysis that compares successful (hired) versus unsuccessful (rejected) candidate journeys.

This information is on the Application object in Greenhouse, available via the 'status' field in the API.

This attribute specifies the organizational department, such as 'Engineering', 'Marketing', or 'Sales', associated with the job requisition. It allows for the aggregation and comparison of recruitment metrics across different parts of the business.

Segmenting the analysis by department is crucial for dashboards like 'Time To Hire Performance' and 'Offer Acceptance Rate Trends'. It helps identify if certain departments have longer hiring cycles, higher offer rejection rates, or different process compliance levels. These insights enable targeted interventions and process improvements tailored to the specific needs of each department.

Enables comparison of recruitment performance and process variations across different departments, revealing systemic issues or best practices.

Typically available as a standard or custom field on the Job object in Greenhouse. Can be found in the 'departments' section of a Job record via the API.

This attribute contains the official title of the job requisition, such as 'Senior Software Engineer' or 'Product Marketing Manager'. It provides essential context about the role being filled.

Analyzing recruitment processes by job title is fundamental for understanding role-specific challenges. For instance, the 'Time To Hire Performance' dashboard uses this attribute to show if senior or highly specialized roles take longer to fill. It also helps in analyzing offer acceptance rates for different positions, providing insights into compensation competitiveness or role attractiveness.

Allows for filtering and comparing recruitment metrics for specific roles, helping to understand how process performance varies by job complexity or type.

This is a primary field on the Job object in Greenhouse, often available as 'name' when querying the 'jobs' endpoint via the API.

This attribute tracks the state of a job offer, with values such as 'Created', 'Extended', 'Accepted', or 'Rejected'. It is a key indicator of the final stages of the recruitment process.

This attribute is essential for the 'Offer Acceptance Rate Trends' dashboard and the corresponding KPI. By tracking the progression from 'Offer Extended' to 'Offer Accepted' or 'Offer Rejected', companies can measure their ability to close candidates. Analyzing this by department or job title can reveal insights into compensation competitiveness, candidate experience, or other factors influencing a candidate's decision.

Tracks the outcome of job offers, which is crucial for calculating the Offer Acceptance Rate KPI and understanding how to improve it.

Available on the Offer object in Greenhouse, which is linked to an Application. The 'status' field provides this information.

This attribute identifies the recruiter assigned to a specific job application or requisition. This person is typically responsible for screening, coordinating interviews, and moving the candidate through the process.

Analyzing the process by recruiter is key to understanding individual performance and workload distribution. The 'Recruiter Workload & Efficiency' dashboard relies on this attribute to calculate metrics like throughput and cycle times per recruiter. It helps identify top performers, pinpoint individuals who may need additional support, and ensure a balanced workload across the talent acquisition team.

Attributes process activities to a specific recruiter, enabling analysis of individual performance, workload, and efficiency.

Available on the Job object in Greenhouse, typically as part of the 'hiring_team' section, where roles like 'Recruiter' are specified.

This attribute records the completion time for activities that span a period, such as an interview or a background check. While many activities are instantaneous, those with a measurable duration benefit from having both a start and end time.

Having an end time is crucial for accurately calculating the processing time or duration of specific activities. This helps differentiate between the waiting time between steps and the actual time spent on a task. For example, it allows for a more precise analysis of how long interviews actually take, versus how long it takes to schedule them.

Enables the precise calculation of activity processing times, which helps distinguish active work time from idle waiting time in the process.

This information might be available on objects like 'scheduled_interview' in Greenhouse, which often has both a 'start' and 'end' time. For other activities, it may need to be inferred from the timestamp of the subsequent activity.

The Candidate ID uniquely identifies an individual in the talent pool, whereas the Job Application ID is specific to one application for one job. A single candidate can have multiple job applications over time.

While the Job Application ID serves as the Case ID for this specific process view, having the Candidate ID allows for a different type of analysis. It can be used to track a candidate's journey across multiple job applications, identify frequent applicants, and analyze the overall relationship with the talent pool. It provides a more person-centric view of the recruitment data.

Allows for analysis across multiple applications from the same candidate, providing a broader view of candidate engagement over time.

This is the primary key for a candidate record in Greenhouse, available as 'id' on the Candidate object via the API.

This attribute identifies the manager of the team with the open position. The hiring manager is a key stakeholder in the process, often involved in reviewing candidates, conducting later-stage interviews, and making the final hiring decision.

Analyzing the process by hiring manager can reveal important patterns and bottlenecks. For example, the 'Interview Scheduling Bottlenecks' dashboard might show that delays are frequently associated with specific managers' availability. It helps in identifying needs for training or support to ensure managers are engaging with the hiring process efficiently.

Identifies a key stakeholder, allowing for analysis of process bottlenecks or efficiencies related to specific hiring managers.

Available on the Job object in Greenhouse, typically as part of the 'hiring_team' section, where roles like 'Hiring Manager' are specified.

This calculated metric measures the responsiveness of the interview panel. Delays in submitting feedback can significantly slow down the hiring process and negatively impact the candidate experience.

This attribute directly supports the 'Interview Feedback Loop Analysis' dashboard and the 'Interview Feedback Turnaround Time' KPI. It is calculated as the time difference between the 'Interview Completed' and 'Feedback Submitted' activities. Monitoring this metric helps identify bottlenecks caused by slow feedback and encourages quicker decision making.

Measures the efficiency of the post-interview feedback loop, a common source of delays in the hiring process.

This is a calculated field. It is derived by subtracting the timestamp of the 'Interview Completed' event from the timestamp of the 'Feedback Submitted' event.

This attribute specifies the particular stage within the overall interview process, such as 'Recruiter Screen', 'Technical Interview', or 'Final Round'. It provides more granularity than a generic 'Interview Completed' activity.

By analyzing metrics at each distinct interview stage, organizations can pinpoint more specific bottlenecks. For example, you can measure the 'Candidate Drop-off Rate by Stage' with high precision, identifying if candidates tend to withdraw after the technical interview versus the initial screen. This detail is crucial for making targeted improvements to the interview experience.

Offers a more detailed view of the interview process, allowing for analysis of cycle times and drop-off rates at each specific interview step.

This information is part of the interview scheduling data in Greenhouse. The 'interviews' object on a job application contains details about the interview stage.

This boolean attribute indicates whether an activity was executed by a user or by an automated system rule. Examples of automated activities could include sending auto-response emails or automatically rejecting candidates who fail a basic screening question.

Analyzing this attribute helps in understanding the level of automation in the recruitment process. It can be used to compare the efficiency and outcomes of automated versus manual steps, and to identify opportunities for further automation to improve speed and consistency.

Helps distinguish between manual and automated activities, enabling analysis of automation's impact on process efficiency and outcomes.

This information is not a standard field and typically needs to be derived. It can be inferred from the user associated with an activity (e.g., a 'System' user) or from specific event types known to be automated.

This boolean attribute is the result of a conformance check, which compares the actual sequence of activities for a job application against a predefined ideal process model. It flags cases that have deviated, such as skipping required steps or performing activities out of order.

This is a core attribute for the 'Hiring Compliance Deviation' dashboard and supports the 'Process Conformance Rate' and 'Compliance Violation Count' KPIs. By filtering for non-compliant cases, organizations can investigate why deviations occur, whether due to inadequate training, system limitations, or necessary exceptions. This helps in standardizing workflows and mitigating compliance risks.

Identifies process deviations, which is critical for measuring process conformance, ensuring compliance, and standardizing hiring workflows.

This is a calculated field generated by the process mining software. It compares the event log data against a defined target model or set of business rules.

This attribute is the unique ID for the job itself, distinct from the application ID. Multiple applications will be linked to the same Job ID.

Using the Job ID allows for aggregation of data at the requisition level. For example, one can analyze the total number of applications received for a specific job, or the average time to hire for all roles of a similar type. It provides a way to group and analyze recruitment efforts centered around a single open position.

Allows for aggregating and analyzing all candidate data related to a single job opening, providing a requisition-centric view.

This is the primary key for a job record in Greenhouse, available as 'id' on the Job object via the API.

This attribute captures the specific reason why a candidate was not moved forward in the process. Examples include 'Not a culture fit', 'Salary expectations too high', or 'More qualified candidates'.

Analyzing rejection reasons provides invaluable feedback for the recruitment process. It can highlight issues such as misaligned job descriptions, non-competitive compensation, or recurring skill gaps in the talent pool. This information is particularly useful for improving sourcing strategies and enhancing the candidate experience by understanding common points of failure in the hiring funnel.

Provides qualitative insight into why candidates are dropped from the funnel, helping to refine job descriptions, sourcing, and screening criteria.

Available on the Application object when it is rejected. The API provides a 'rejection_reason' object with details.

This attribute captures the final recommendation made by an interviewer on a structured interview scorecard, typically values like 'Strong Yes', 'Yes', 'No', or 'Strong No'.

This data is vital for assessing the quality and consistency of the interview process. It helps correlate interview feedback with actual hiring outcomes, answering questions like 'Do candidates with stronger recommendations get hired more often?'. It is also the basis for the 'Scorecard Completion Rate' KPI, which measures the adoption of structured hiring practices within the organization.

Connects structured interview feedback to process outcomes and helps measure the adoption of data-driven hiring practices.

Found within the Scorecard object associated with a completed interview in Greenhouse. The API provides this information via the 'scorecards' endpoint.

This calculated metric measures the end-to-end duration of a successful hiring process. It is one of the most critical KPIs for evaluating the overall efficiency of the talent acquisition function.

This attribute directly supports the 'Time To Hire Performance' dashboard and the 'Average Time to Hire' KPI. It is calculated as the time difference between the 'Application Received' and 'Candidate Hired' activities for a given job application. Analyzing this metric helps organizations set benchmarks and identify systemic delays in the hiring process.

This is a key performance indicator that measures the overall efficiency of the recruitment funnel for successful hires.

This is a calculated field. It is derived by subtracting the timestamp of the 'Application Received' event from the timestamp of the 'Candidate Hired' event.