To prove automation ROI from a Keap and Make.com integration, track nine metrics across three categories: operational health (scenario success rate, execution duration, error frequency, operations consumed), recruiting outcomes (lead response time, stage conversion rate, time-to-hire), and financial returns (labor hours recovered, cost-per-hire reduction). Document baselines before launch or the numbers are meaningless.

Automation that runs but cannot be measured is expensive activity. If your Keap and Make.com integration does not produce numbers you can defend in a business review, you cannot justify continued investment, identify quietly broken workflows, or demonstrate the value you have actually delivered.

Asana research finds that knowledge workers spend roughly 60% of their time on work about work — status updates, manual handoffs, and data entry — rather than skilled work. Recruiting is one of the worst offenders. The metrics below give you the measurement layer that turns automation activity into a provable story.

Before diving in, two foundational posts will give you context: the OpsMap discovery process that surfaces which workflows to automate first, and the 7 questions to ask before you automate anything to avoid measuring the wrong things from the start. If you are still evaluating platforms, the Make vs. Zapier feature breakdown covers why Make.com is the platform this guide assumes throughout.

Why Baselines Come Before Every Metric on This List

Every metric below requires a pre-automation baseline or it produces nothing usable. Two weeks of documented pre-launch data is the minimum. Capture: time spent on each manual step, weekly error counts, current time-to-hire by pipeline stage, and cost-per-hire. Without these numbers, post-launch comparisons are guesses dressed up as data.

The fastest baseline setup: create four Keap custom fields before you build a single scenario — Last Automation Run (date/time), Last Scenario Name (text), Automation Error Flag (yes/no), and Stage Entered By Automation (text). These fields give you an audit trail on every contact record without opening Make.com.

Then activate a lightweight Make.com data-logging scenario that writes a daily snapshot row to Google Sheets. This gives you a time-series record from day one. The OpsMap audit guide walks through the full pre-automation documentation process if you need structure for this step.

Metric 1: Scenario Success Rate

Scenario success rate is completed executions divided by total executions, expressed as a percentage. A mature workflow targets 98% or higher. Anything below 95% in a workflow that has been live for more than 30 days signals a structural problem — an unreliable trigger, a fragile data mapping, or a third-party API that times out under load.

Make.com logs this at the execution level. Pull the number monthly, not weekly — short windows produce noise from one-off API disruptions that self-resolve. A downward trend over two consecutive months warrants investigation regardless of the absolute rate.

Track success rate per scenario, not as a combined average. A combined average lets a high-volume, reliable scenario mask a low-volume, broken one.

Metric 2: Average Execution Duration

Execution duration measures how long a scenario takes end-to-end from trigger to final module. This number trends upward as data volume grows and as additional modules are added to existing scenarios. Track it monthly rather than in real time.

A sudden spike in execution duration usually points to one of three causes: a new API rate limit slowing a module, a data payload that has grown larger than the scenario was designed to handle, or a loop that is iterating over more records than expected. Make.com’s execution detail view shows per-module duration, which isolates the bottleneck in under five minutes.

Baseline this number in the first week a scenario goes live. A 20% increase over 90 days is normal growth. A 20% increase in a single week is a signal.

Metric 3: Error Frequency by Module

Error frequency by module identifies which specific module or connection fails most often. Make.com logs this at the execution level, but the data is only useful if your scenarios have routed error handlers that write failures to a central location — either a Google Sheet error log or Keap notes on the affected contact record.

Every production scenario should have an error-handler route that writes: error flag value to the Automation Error Flag Keap field, error message text to a Keap note on the contact, and an internal task assigned to the responsible recruiter. This turns every failure into a visible, assignable work item rather than a silent gap in the candidate pipeline.

For a full walkthrough of error handler architecture, the routed error handling guide covers the setup in detail. The error handler case study shows what happens to investigation time once this system is in place.

Metric 4: Operations Consumed Per Month

Operations consumed is your Make.com billing unit. One operation equals one module execution. Track this against your plan limit monthly so you are never surprised by overage charges or a scenario that stops mid-execution because the monthly cap was hit.

The practical target is staying at or below 80% of your plan limit in any given month. This gives you headroom for volume spikes — hiring surges, campaign launches, or new scenario deployments — without hitting the ceiling mid-cycle.

Operations consumed also functions as a proxy for automation volume. A month where operations consumed drops 30% while hiring activity stays flat usually means a trigger broke and scenarios stopped firing. This is the early warning signal most teams miss because they only check Make.com when something is obviously wrong.

Metric 5: Lead Response Time

Lead response time measures the gap between a candidate’s first inquiry and the first automated or recruiter touchpoint. APQC data puts top-quartile firms at under one business day. With automation, the target is under two hours — the point at which candidate engagement rates drop significantly if no contact has been made.

Capture this in Keap using contact creation timestamps against the timestamp of the first outbound activity (email send, task creation, or tag application that triggers a sequence). The delta is your lead response time. Calculate it as a weekly median, not an average — outliers from weekends and holidays skew averages badly.

A sudden increase in median lead response time almost always means a trigger scenario has failed silently. This is why operations consumed (Metric 4) and lead response time should be reviewed together — they catch the same failure from different angles.

Metric 6: Stage Conversion Rate

Stage conversion rate is the percentage of candidates who advance from one pipeline stage to the next. Measure it per stage, not just overall. An overall conversion rate that looks healthy can hide a single stage where 60% of candidates stall — and that stall is exactly where automation should intervene.

Pull stage conversion data from Keap pipeline reports. Compare the post-automation rate to your documented baseline at each individual stage. The stages your automation touches should show improvement within 60 days of launch. Stages your automation does not touch should be roughly flat — if they degrade after launch, you have created a downstream bottleneck by accelerating earlier stages without improving later ones.

This metric connects directly to the ROI case. If your automation increases the application-to-screen conversion rate by 15 percentage points and your average placement fee is fixed, that improvement translates to a calculable number of additional placements per quarter.

Metric 7: Time-to-Hire by Pipeline Segment

Time-to-hire measures total days from application to offer. The useful version breaks that number out by pipeline segment rather than reporting a single end-to-end figure. This lets you see whether automation is actually compressing the stages it touches — or whether speed at one stage is being absorbed by delays at another.

Log this to Google Sheets using a Make.com scenario that captures a timestamp each time a Keap contact moves between stages. The resulting time-series gives you average days-in-stage before and after automation launch for every segment.

SHRM benchmarks average cost-per-hire at $4,129. Every day you compress from time-to-hire reduces the carrying cost of an open role. Quantify this with your actual placement fee or internal cost data — the benchmark gives you a floor for the ROI conversation.

Metric 8: Labor Hours Recovered Per Week

Labor hours recovered is the financial translation of every operational metric above. The formula is straightforward: document the time each manual step took before automation, measure how long (if any) the equivalent automated step takes now, and multiply the delta by the number of executions per week.

The resulting weekly hours figure is the input to your ROI calculation. Multiply it by your recruiter’s loaded hourly rate to get a weekly labor savings figure, then annualize it. Jeff, who ran a Las Vegas mortgage branch in 2007, tracked 10 minutes of daily manual work per person and found it equated to one full week of lost productivity per year per employee. That math applies directly to recruiting automation: small per-candidate time savings across high volume add up to recoverable weeks, not minutes.

For the financial case that demonstrates what this looks like at scale, the TalentEdge case study — $312K in annual savings and 207% ROI — is the clearest example of labor recovery translated to business results. The Make automation labor recovery case study shows the same calculation applied specifically to operations teams.

Do not estimate this number. Use actual time-audit data from your baseline period. Estimated labor savings that were not documented before launch are not credible in a business review.

Metric 9: Cost-Per-Hire Reduction

Cost-per-hire reduction is the most direct financial outcome metric in this list. It combines the labor savings from Metric 8 with the revenue impact of faster placements (Metric 7) and the higher conversion rates at each stage (Metric 6).

Calculate it as: (pre-automation cost-per-hire) minus (post-automation cost-per-hire), measured over a 90-day window. Use your actual internal cost data where available. Where you need an external benchmark, SHRM’s $4,129 average gives you a reference point — though for recruiting firms with placement fees, the relevant number is your internal cost to fill, not SHRM’s HR benchmark.

Cost-per-hire reduction is the number that converts an automation conversation from an IT discussion to a business case. It belongs in the executive summary of any ROI report, backed by the eight metrics above as supporting evidence.

Teams that want to accelerate this measurement cycle should consider the DIY vs. Make partner decision guide — having an experienced partner instrument measurement from day one compresses the time to a defensible ROI number significantly.

How to Build a Single ROI Report From All Nine Metrics

The nine metrics above are most powerful when they feed a single report rather than living in separate tools. The recommended architecture: a Google Sheets dashboard with one tab per metric category (operational, recruiting outcome, financial), populated daily by Make.com data-logging scenarios and weekly by manual pulls from Keap pipeline reports.

Structure the financial summary tab as a simple table: metric name, pre-automation baseline, current value, delta, and dollar translation. The dollar translation column is the one that matters in a leadership review. Every metric should have a corresponding dollar figure — even operational metrics like scenario success rate translate to a cost when failures create recruiter rework.

Review the full dashboard monthly. Flag any metric that moves more than 10% in either direction for investigation before the next review cycle. An improving metric is an expansion opportunity. A degrading metric is a broken workflow that is quietly costing you the ROI you already claimed.

For teams that want to go deeper on automation architecture before building the measurement layer, the 10 automations easy to build with Make and AI post covers the specific workflow types that generate the most measurable ROI in recruiting environments. The step-by-step Make scenario build guide shows how to construct the data-logging scenarios this reporting system depends on.

Additional Reading

• What Is OpsMap? The Discovery Step That Prevents Automation Mistakes
• 7 Questions to Ask Before You Automate Anything (The OpsMap Checklist)
• How to Run an OpsMap Audit Before Automating Anything
• How to Set Up Routed Error Handling in Make With AI Assistance
• How an AI-Built Error Handler Reduced Technician Research Time From 20 Minutes to a Glance
• How TalentEdge Saved $312K with HR Process Standardization
• How One Ops Team Recovered $103K in Annual Labor Hours With Make Automation
• DIY Automation vs. Hiring a Make Partner in 2026: When to Do Each
• 10 Automations That Are Finally Easy to Build With Make + AI — No Developer Needed
• How to Build a Make Scenario With Claude: A Step-by-Step Walkthrough
• Make.com vs. Zapier in 2026: Which Is Right for Your Operations?
• Make vs Zapier: A Straight Pricing and Feature Breakdown for 2026
• Recruiting Automation: Transforming Hidden Costs into Measurable ROI
• The $27K Overpayment: How One HRIS Data Entry Mistake Cost a Manufacturer a Year of Salary
• How Nick Cut 6 Manual Handoffs From Proposal Generation With One Make Workflow