Mastering HR Efficiency: 10 Critical Data Points HR Teams Must Filter in Make.com
In today’s fast-paced business landscape, Human Resources teams are no longer just administrators; they are strategic partners crucial for organizational success. The sheer volume of data HR processes generate—from recruitment and onboarding to performance management and offboarding—can be overwhelming. This is where automation platforms like Make.com (formerly Integromat) become indispensable. Make.com empowers HR professionals to connect disparate systems, automate workflows, and, most importantly, filter incoming data to extract actionable insights. Without proper data filtering, even the most sophisticated automation can lead to “garbage in, garbage out,” resulting in flawed decisions, wasted resources, and a poor employee experience. For HR teams looking to optimize their operations, enhance strategic decision-making, and truly unlock the potential of their talent pool, understanding which data points to prioritize and effectively filter within Make.com is not just beneficial—it’s absolutely critical. This article delves into ten essential data points that HR teams must meticulously filter to achieve peak efficiency, improve talent acquisition, streamline employee lifecycle management, and foster a data-driven culture. By implementing these filtering strategies, you can transform raw data into a strategic asset, ensuring your HR initiatives are always grounded in clear, precise information.
1. Candidate Source Tracking for Recruitment ROI
Understanding where your best candidates originate is fundamental to optimizing recruitment spend and strategy. Without proper filtering, you might see applications from various job boards, social media platforms, direct applications, and referrals, but lack the granular insight to determine which sources yield not just the most applicants, but the most *qualified* and *hired* candidates. In Make.com, this involves filtering data points like ‘Applicant Source,’ ‘Referral Code,’ or ‘Campaign ID’ from your Applicant Tracking System (ATS) or initial application forms. For instance, if you’re using a tool like Greenhouse or Workday Recruiting, data flows from these systems into Make.com. A critical filter here would be to group sources (e.g., ‘LinkedIn’, ‘Indeed’, ‘Company Website’, ‘Employee Referral’) and then correlate this with subsequent stages in the hiring funnel. You might set up a Make.com scenario that captures new applications, filters them by source, and then updates a dashboard in Google Sheets or a BI tool, tracking how many candidates from each source move to interview, offer, and ultimately, hire. Advanced filtering might include a ‘source quality score’ based on the ratio of interviewed candidates to applicants from that source. This ensures HR budget is allocated to channels that consistently deliver high-caliber talent, maximizing your return on investment in recruitment marketing.
2. Application Status Progression for Bottleneck Identification
Monitoring the progression of candidates through your hiring pipeline is crucial for identifying bottlenecks and ensuring a smooth, efficient process. HR teams often track dozens or even hundreds of candidates concurrently, and without automated filtering, manually identifying where candidates get stuck is a monumental task. Make.com scenarios can be configured to pull ‘Application Status’ data (e.g., ‘Applied,’ ‘Screening,’ ‘Interview Scheduled,’ ‘Offer Extended,’ ‘Hired,’ ‘Rejected’) from your ATS. The key filtering here lies in not just capturing the status, but also timestamps associated with status changes. For example, a filter might identify all applications that have been in the ‘Screening’ stage for more than X days, or those that moved from ‘Interview Scheduled’ directly to ‘Rejected’ without an ‘Interview Completed’ status. By filtering for these specific status transitions and durations, HR can proactively identify delays in recruiter response times, scheduling challenges, or stages where candidates frequently drop off. This data allows for targeted interventions, such as re-evaluating interview processes, providing additional recruiter training, or automating follow-ups, ultimately reducing time-to-hire and improving the candidate experience.
3. Time-to-Hire Metrics for Process Optimization
Time-to-hire is a critical metric that impacts candidate experience, operational cost, and even the competitiveness of your offers. Manually calculating this for every role is inefficient and prone to error. Make.com allows HR teams to automate the capture and filtering of ‘Application Date’ and ‘Offer Acceptance Date’ (or ‘Start Date’). The filtering isn’t just about collecting these dates; it’s about calculating the duration between them and segmenting this data. For instance, you could filter by department, job level, or even specific hiring manager to identify where time-to-hire is exceptionally long or short. A Make.com scenario might pull new hire data, calculate the delta between the application submission date and the candidate’s start date, and then push this metric to a reporting dashboard. Further filtering could flag any time-to-hire metric exceeding a predefined threshold (e.g., 45 days for an entry-level role, 90 days for an executive role), triggering an alert to the HR business partner. This proactive insight enables HR to pinpoint process inefficiencies, such as slow feedback loops, lengthy approval cycles, or protracted interview stages, allowing for precise adjustments to streamline recruitment workflows and ensure you’re securing top talent before competitors do.
4. Offer Acceptance Rates for Compensation Benchmarking
The offer acceptance rate is a strong indicator of your compensation competitiveness, brand appeal, and the effectiveness of your recruitment pitch. Simply tracking the number of offers extended versus accepted isn’t enough; valuable insights come from filtering this data by various dimensions. In Make.com, you can filter ‘Offer Status’ (e.g., ‘Extended,’ ‘Accepted,’ ‘Rejected’) alongside other critical data points like ‘Compensation Package Details’ (base salary, bonus, equity), ‘Candidate Experience Level,’ ‘Department,’ or even ‘Reason for Rejection’ (if collected). For example, a Make.com scenario could monitor offer status changes and, upon rejection, check if a ‘Reason for Rejection’ field was populated. Filters could then identify patterns, such as a high rejection rate for offers within a specific salary band for a particular role, or rejections predominantly due to competing offers. By aggregating and filtering this data, HR can gain a nuanced understanding of their market position, highlight areas where compensation might be falling short, or where the value proposition needs strengthening. This empowers HR and leadership to make data-driven adjustments to compensation strategies, benefits packages, and recruitment messaging, ultimately improving your ability to attract and retain top talent.
5. Onboarding Completion Data for Early Employee Success
A smooth and comprehensive onboarding process is crucial for new hire retention and productivity. Tracking onboarding completion isn’t just about checking boxes; it’s about ensuring every new hire receives essential information and feels integrated. Make.com can be used to filter ‘Onboarding Task Completion’ statuses from your HRIS or onboarding platform. This involves tracking specific tasks like ‘HR Policy Acknowledgment,’ ‘IT Setup Complete,’ ‘Benefits Enrollment,’ ‘Manager Meet & Greet,’ and ‘Mandatory Training Modules.’ Filtering should focus on identifying delays or non-completion of critical tasks within predefined timelines. For instance, a Make.com scenario could trigger an alert if ‘Benefits Enrollment’ isn’t completed within the first five days, or if ‘Mandatory Compliance Training’ isn’t finished within 30 days. Furthermore, filtering can segment this data by department or manager to identify teams or leaders who might need additional support in guiding new hires. By proactively addressing these gaps, HR can ensure new employees are fully equipped and engaged from day one, significantly reducing early turnover and accelerating their path to productivity, contributing directly to a positive employee experience and organizational stability.
6. Employee Performance Data (Initial 90-Day & Annual)
Performance data is vital for identifying high performers, addressing underperformance, and guiding professional development. Rather than just collecting raw scores, HR teams must filter performance data for trends and correlations. Within Make.com, this involves integrating with performance management systems and filtering key metrics like ‘Performance Rating,’ ‘Goal Attainment Percentage,’ ‘Feedback Received (positive/constructive),’ and ‘Development Plan Progress.’ For initial 90-day reviews, filtering should focus on early indicators of success or areas needing immediate attention, such as a new hire consistently missing deadlines or exceeding expectations on core tasks. For annual reviews, filters can identify employees consistently rated ‘Exceeds Expectations’ (potential for promotion), or those consistently ‘Needs Improvement’ (requiring coaching or a performance improvement plan). You might filter performance ratings against ‘Training Program Participation’ to assess the efficacy of learning initiatives, or against ‘Department’ to identify high-performing teams. This level of filtering allows HR to move beyond individual performance reviews to identify systemic issues, validate training effectiveness, and strategically allocate resources for talent development, ensuring a high-performing workforce that aligns with business objectives.
7. Training & Development Participation and Effectiveness
Investing in employee training and development is critical for skill enhancement and career growth, but merely offering courses isn’t enough; HR needs to know if they’re effective and utilized. Make.com can filter data from Learning Management Systems (LMS) or training platforms, focusing on ‘Course Enrollment,’ ‘Completion Rates,’ ‘Quiz Scores,’ and ‘Post-Training Feedback.’ The filtering process should go beyond simple completion to assess impact. For example, you could filter for specific skill-based training completion and then correlate it with subsequent performance reviews or project success rates. A Make.com scenario might pull completion data for a ‘Leadership Skills’ course, then cross-reference it with ‘Manager Effectiveness Scores’ collected six months later. Further, you can filter participation by ‘Department,’ ‘Job Role,’ or ‘Tenure’ to identify gaps in skill development across the organization or to ensure equitable access to learning opportunities. By meticulously filtering this data, HR can evaluate the ROI of training programs, identify popular and impactful courses, discontinue ineffective ones, and tailor future learning initiatives to directly address skill gaps and strategic business needs, fostering a culture of continuous learning and growth.
8. Employee Turnover Intent Signals for Retention Strategies
Proactive retention strategies rely on identifying employees at risk of leaving before they do. While directly filtering “intent to leave” is impossible, Make.com can help by filtering for indirect signals from various HR data points. This involves combining and analyzing seemingly disparate data. Consider filtering for ‘Employee Engagement Survey Scores’ (specifically low scores in ‘Satisfaction’ or ‘Growth Opportunities’), ‘Frequency of HR Helpdesk Tickets’ (e.g., numerous inquiries about benefits portability or retirement accounts), ‘Lack of Participation in Optional Company Events,’ or even ‘Unusual Login Patterns’ in HR systems (e.g., accessing benefit documents not typically reviewed, like 401k withdrawal forms). While these are not definitive, filters can highlight unusual patterns that warrant further investigation. A Make.com scenario could flag employees with consistently low engagement scores *and* increased access to specific HR portal sections. This allows HR business partners to initiate timely, empathetic conversations, offer targeted support, or address underlying issues before an employee begins looking for external opportunities. Filtering these subtle signals can transform HR from a reactive to a proactive retention force, significantly reducing attrition costs and preserving institutional knowledge.
9. Payroll & Compensation Discrepancies
Accuracy in payroll and compensation is paramount for employee satisfaction and regulatory compliance. Even minor discrepancies can lead to significant trust issues. Make.com can be a powerful tool for filtering and identifying potential errors by integrating with payroll systems, time-tracking tools, and HRIS. Key data points to filter include ‘Hours Worked’ vs. ‘Hours Paid,’ ‘Overtime Approvals’ vs. ‘Overtime Paid,’ ‘Bonus Payouts’ vs. ‘Bonus Accruals,’ and ‘Benefit Deductions’ vs. ‘Enrollment Records.’ A robust Make.com scenario might compare daily time clock entries with scheduled shifts, flagging any discrepancies that exceed a set variance (e.g., more than 15 minutes difference). Another filter could compare ‘Approved Expense Reimbursements’ with ‘Paid Reimbursements’ on a bi-weekly basis. Filtering for ‘Changes in Salary/Wage’ against ‘Approval Records’ ensures that all compensation adjustments are properly authorized. By setting up these automated filters, HR and payroll teams can catch errors before they propagate, prevent overpayments or underpayments, and ensure that employees are compensated accurately and on time, bolstering trust and preventing costly compliance issues and employee grievances.
10. HR Service Request Resolution Times
The efficiency of HR service delivery directly impacts employee satisfaction and productivity. When employees have questions or issues, they expect timely and accurate responses. Make.com can be invaluable for filtering data from HR ticketing systems or internal communication platforms to measure and improve response times. Key data points include ‘Ticket Submission Timestamp,’ ‘Assignment Timestamp,’ ‘Resolution Timestamp,’ ‘Category of Request’ (e.g., benefits, payroll, IT support), and ‘Requester Department.’ Filtering should focus on identifying bottlenecks and areas of high demand. For instance, you could filter for requests in a specific category (e.g., ‘Benefits Inquiry’) that have remained ‘Open’ for more than a defined service level agreement (SLA) period (e.g., 24 hours). Another filter could identify which HR team members consistently have the longest resolution times, or which departments submit the highest volume of urgent requests. By analyzing these filtered insights, HR leadership can reallocate resources, provide additional training to support staff, refine FAQ resources, or even automate responses for common queries. This proactive approach ensures that HR remains a responsive and effective resource, enhancing the overall employee experience and freeing up the team to focus on more strategic initiatives.
Implementing a data-driven approach to HR operations is no longer optional; it’s a strategic imperative. By meticulously filtering critical data points within Make.com, HR teams can transform raw information into actionable intelligence. This not only streamlines daily operations and boosts efficiency but also empowers HR to make informed decisions that directly impact talent acquisition, employee retention, performance management, and overall organizational success. From optimizing recruitment spend by understanding candidate sources to proactively addressing employee concerns through service request analysis, the power of filtered data is immense. Embrace these strategies, and watch as your HR function evolves into a more precise, proactive, and powerfully strategic partner for your entire organization.
If you would like to read more, we recommend this article: The Automated Recruiter’s Edge: Clean Data Workflows with Make Filtering & Mapping
