Forty hours a week. That is a full-time employee’s worth of work — and it was being spent on tasks that a well-built automation stack could handle in seconds.
This is the exact story of how we eliminated that manual work for one of our clients — a SaaS operations team of eight people who were drowning in repetitive admin, data entry, lead routing, and reporting — using Make.com as the automation engine. We will walk through every scenario we built, the trigger logic behind each one, the tools connected, and the measurable time saved from each workflow.
If your team is spending hours every week on tasks that follow a predictable pattern, this case study will show you exactly what is possible — and how to think about building it.
The Situation: Where the 40 Hours Were Going
When we first met this client, they had a clear problem and no system to solve it. They were a growing SaaS company with eight people on the operations team. Revenue was growing. The team was not. And every new client or lead that came through added more manual work to a stack that was already at capacity.
We mapped every repetitive task their team performed in a single week. The list looked like this:
Lead intake and CRM entry — Leads came in from three sources: a website contact form, a Typeform qualification survey, and inbound emails. Someone on the team manually copied each lead’s information into their CRM (GoHighLevel), assigned it to the right team member, and sent a confirmation email. This happened dozens of times per week and took an average of eight minutes per lead.
Client onboarding documentation — Every new client required a welcome pack: a custom onboarding document, login credentials, access to their project management board, and a kickoff call invitation. A team member spent 45 minutes per new client assembling and sending this manually.
Weekly reporting — Every Monday morning, someone pulled data from three platforms — their payment processor, their CRM, and their project management tool — copied it into a Google Sheet, formatted it, and emailed a summary to the management team. This took three hours every single week.
Invoice creation and follow-up — When a new client signed up or a subscription renewed, someone manually created an invoice in their accounting software, sent it to the client, and followed up if payment was not received within five days. This consumed four to six hours per week.
Support ticket routing — Support requests came in via email and a web form. Someone read each one, decided which team member should handle it, created a task in their project management tool, and notified the team member via Slack. Ninety tickets per week. Two to three minutes each.
Social media and content scheduling — The marketing person spent four hours every Monday manually scheduling the week’s content across LinkedIn, Twitter, and Facebook from a content calendar in Google Sheets.
Total: approximately 40 hours of manual work per week — spread across the team but representing one full-time equivalent in labour. None of it required human judgment. All of it followed a predictable, repeatable pattern. All of it was automatable.
Why We Chose Make.com
Make.com (formerly Integromat) is a visual no-code automation platform that connects over 3,000 apps and allows you to build multi-step workflows with conditional logic, data transformation, error handling, and branching — on a visual canvas where you can see exactly how data flows through each step.
We chose Make.com for this client over alternatives like Zapier for three reasons:
Visual scenario builder — Unlike Zapier’s linear list format, Make shows your entire workflow as a visual map. For complex, multi-branch scenarios, this makes building and debugging dramatically faster. You can see every data path at a glance.
Data transformation capabilities — Make allows you to manipulate data between steps — format dates, parse text, run calculations, filter arrays — without writing code. This was essential for the reporting and invoice scenarios we needed to build.
Cost at volume — Make’s pricing is based on operations (individual steps within scenarios) rather than tasks (entire workflow runs). For high-volume scenarios like support ticket routing — where each ticket triggers multiple steps — Make is significantly more cost-effective than Zapier at scale.
According to Make’s own data, the platform processed 5.6 billion automation scenarios in 2024, representing 26% growth year over year — driven by businesses moving from simple app-to-app connections toward sophisticated, multi-step workflows that replace entire manual processes.
The Automation Stack: Every Scenario We Built
Scenario 1: Unified Lead Intake and CRM Entry
Problem: Leads arriving from three different sources — website form, Typeform, and inbound email — were being manually entered into GoHighLevel one by one.
The Build: We created a single Make.com scenario with three triggers — one for each lead source — that all fed into a single pipeline of actions:
Trigger fires (form submission, Typeform completion, or email parsed by Make’s email parser) → Data normalised into a standard format → Contact created in GoHighLevel CRM automatically → Lead assigned to the correct team member based on source and service type → Confirmation email sent to the lead automatically → Slack notification sent to the assigned team member with lead details → Lead added to the correct nurture sequence in GoHighLevel.
Time saved: 8 minutes per lead × approximately 60 leads per week = 8 hours per week eliminated entirely.
Additional benefit: Response time dropped from an average of four hours (when someone got around to manually entering the lead) to under two minutes. Lead-to-call conversion rate increased by 31% in the first month.
Scenario 2: Automated Client Onboarding Pack
Problem: Every new client required 45 minutes of manual assembly and sending — welcome document, login credentials, project board access, and kickoff call invitation.
The Build: Triggered when a deal is moved to “Closed Won” in the GoHighLevel pipeline:
Deal marked as Closed Won in GHL → Make.com triggers → Client record pulled from CRM → Welcome document generated using a Google Docs template with client name, service package, and start date automatically populated → Document converted to PDF → Notion page created for the client’s project workspace with their details pre-filled → Kickoff call invitation generated and sent via GoHighLevel calendar → Welcome email sent with PDF attachment and Notion workspace link → Slack notification sent to the account manager assigned to the client.
Time saved: 45 minutes per new client × 12 new clients per month = 9 hours per month. Approximately 2.25 hours per week eliminated.
Additional benefit: Onboarding consistency improved dramatically. Every client received the same quality of onboarding regardless of who on the team was handling it or how busy they were.
Scenario 3: Automated Weekly Management Report
Problem: Three hours every Monday morning pulling data from three platforms and compiling a management summary report.
The Build: A scheduled Make.com scenario that runs automatically every Sunday at 11pm so the report is ready in inboxes by Monday morning:
Schedule trigger fires → Payment processor API called to pull previous week’s revenue, new subscribers, churned subscribers, and MRR → GoHighLevel API called to pull leads received, calls booked, calls completed, and pipeline value → Project management tool (Notion) API called to pull tasks completed, tasks overdue, and active client count → All data pulled into a Google Sheet that formats automatically using pre-built formulas → Charts generated via QuickChart API → Report compiled into a Google Doc from a pre-built template with dynamic data insertion → PDF exported → Email sent to management team with PDF attached and key metrics summarised in the email body → If any metric falls below a defined threshold (MRR drop, churn spike, overdue tasks above a set number), an urgent Slack alert fires to the CEO separately.
Time saved: 3 hours per week × 52 weeks = 156 hours per year. 3 hours per week eliminated entirely.
Additional benefit: The report now includes more data and better visualisation than the manual version ever did — because Make can pull from all three platforms simultaneously in seconds, while a human doing it manually would cut corners on which metrics to include.
Scenario 4: Invoice Creation and Payment Follow-Up
Problem: Four to six hours per week manually creating invoices, sending them, and chasing late payments.
The Build: Two linked scenarios — one for invoice creation, one for payment follow-up:
Invoice creation scenario: New client signed (GoHighLevel trigger) or subscription renewal date reached (scheduled trigger) → Client billing details pulled from CRM → Invoice created in accounting software (Xero) with correct line items, amounts, and due date → Invoice PDF generated → Email sent to client with invoice attached → Invoice record linked back to the client’s CRM contact in GoHighLevel.
Payment follow-up scenario: Scheduled trigger runs daily → Checks Xero for invoices overdue by 3 days, 7 days, and 14 days → At 3 days overdue: automated email reminder sent → At 7 days overdue: SMS sent via GoHighLevel + email reminder → At 14 days overdue: Slack alert sent to account manager with client details and outstanding amount for personal follow-up.
Time saved: 5 hours per week average = 5 hours per week eliminated.
Additional benefit: Average days-to-payment dropped from 18 days to 9 days. Late payment rate reduced by 60% in the first quarter after implementation.
Scenario 5: Support Ticket Routing and Task Creation
Problem: 90 support tickets per week being manually read, categorised, assigned, and notified — consuming three to four hours of team time.
The Build: Triggered by new support form submission or support email received:
Support ticket received → Make.com parses the ticket content → AI module (OpenAI integration) classifies the ticket by category (billing, technical, account, feature request) and urgency (high, medium, low) based on the text → Task created in project management tool and assigned to the correct team member based on category → Slack notification sent to the assigned team member with ticket summary, category, and urgency → Automatic acknowledgment email sent to the client with a ticket reference number and expected response time → If urgency is marked High by the AI, a separate Slack alert fires to the team lead immediately.
Time saved: 2.5 minutes per ticket × 90 tickets per week = 225 minutes = 3.75 hours per week eliminated.
Additional benefit: Ticket response acknowledgment time dropped from hours to under 60 seconds. Client satisfaction scores increased measurably in the quarter following implementation.
Scenario 6: Social Media Content Scheduling
Problem: Four hours every Monday manually scheduling the week’s content from a Google Sheet content calendar.
The Build: A scheduled Make.com scenario that runs every Sunday evening:
Schedule trigger fires → Google Sheet content calendar read → Posts for the coming week extracted and formatted per platform (character limits, hashtag rules, image format requirements differ by platform) → Posts scheduled in Buffer for LinkedIn, Twitter, and Facebook at the designated times from the content calendar → Slack notification sent to the marketing person confirming how many posts were scheduled and flagging any rows in the sheet with missing content or formatting issues that need human attention before the week begins.
Time saved: 4 hours per week = 4 hours per week eliminated.
Additional benefit: Posting consistency improved — the automated system never forgot to schedule a post, never ran late because someone was in a meeting, and never skipped a platform because it seemed less important that week.
Total Results After 90 Days
Here is what the numbers looked like 90 days after the full automation stack went live:
Manual hours eliminated per week: 40.75 hours (across all six scenarios)
Lead response time: Reduced from average 4 hours to under 2 minutes
Lead-to-call conversion rate: Increased 31%
Days-to-payment: Reduced from 18 days to 9 days
Late payment rate: Reduced by 60%
Support acknowledgment time: Reduced from hours to under 60 seconds
Onboarding consistency: 100% of new clients received the same onboarding experience regardless of team bandwidth
Team capacity: The equivalent of one full-time hire freed up — reallocated to client-facing strategy work instead of admin
The client did not hire to replace the freed capacity. They redeployed it. The same eight-person team now handles 40% more clients than they did before the automation stack was built — without working additional hours.
What Most Businesses Get Wrong About Automation
The most common mistake we see when businesses try to automate is starting with the most complex process first. They want to automate their entire operation in week one and end up with a fragile system nobody trusts — because it was built before anyone fully understood how the process worked.
The right approach is the opposite. Start with the process you understand best and that has the clearest, most measurable output. Build it. Test it thoroughly. Measure the time saved. Then move to the next one.
According to research cited by Make.com, businesses that implement automation systematically across multiple processes see average ROI of 150–200% over six months. The ROI compounds with each scenario added — because the platform setup cost is already paid, and every additional automation built on top of it costs only the time to build it.
The other mistake is trying to remove all human oversight. The best automation stacks keep humans in the loop at the right decision points — flagging exceptions, escalating edge cases, and sending alerts when something falls outside the expected range. Fully removing human oversight to maximise automation speed is how quality problems compound silently until they become crises.
In every scenario we built for this client, the automation handles the predictable, repetitive work — and escalates anything that requires judgment to a human immediately.
How to Identify What to Automate in Your Business
Run this exercise with your team before building anything. For one week, every time someone on your team does a task, ask three questions:
Does this task follow a predictable pattern? If the steps are the same every time and the inputs are consistent, it is automatable.
Does this task require human judgment? If the answer is no — if a set of rules could make every decision — automation can handle it entirely. If yes, automation can handle the predictable parts and escalate the judgment calls.
How often does this task happen? Automation ROI compounds with frequency. A task that happens once a year is not worth automating. A task that happens 90 times a week — like support ticket routing — generates immediate, measurable returns.
Map your highest-frequency, lowest-judgment tasks first. Build the automation for those. Measure the time saved. Then move down the list.
Tools Used in This Automation Stack
Make.com — Automation engine connecting all tools and running all scenarios.
GoHighLevel — CRM, email and SMS automation, pipeline management, and calendar booking.
Google Workspace (Docs, Sheets, Drive) — Document generation, data storage, and report templates.
Xero — Invoice creation, payment tracking, and accounting.
OpenAI — AI classification of support tickets by category and urgency.
Notion — Client project workspace creation and task management.
Slack — Internal notifications, escalation alerts, and team communication.
Buffer — Social media scheduling across LinkedIn, Twitter, and Facebook.
QuickChart — Automated chart generation for weekly management reports.
Typeform — Lead qualification forms feeding directly into the lead intake scenario.
Final Thought
Forty hours of manual work per week is not unusual for a growing operations team. It is the natural result of a business that scaled its client base faster than it scaled its systems. The work was always there — it was just being done by humans who could have been doing something more valuable.
Make.com did not replace anyone on this team. It gave every person on the team back a significant portion of their week — and let them spend it on work that actually requires their expertise.
If you are looking at your team’s weekly task list and recognising the same patterns we found here — predictable, repetitive, high-frequency tasks that consume hours every week — the question is not whether automation will work for your business. It is which process to start with.
At DoubleUpFunnels, we build Make.com automation stacks for agencies and service businesses — from lead intake and CRM entry to reporting, invoicing, onboarding, and support routing. If you want to see what an automation audit of your business looks like, book a free strategy call and we will map out your highest-ROI automation opportunities in 30 minutes.
Ready to Eliminate Your Team’s Manual Work?
Book a free 30-minute automation audit with DoubleUpFunnels. We will identify your highest-frequency manual tasks, map the automation logic behind each one, and show you exactly what your Make.com stack should look like — before you build a single scenario.