How a Company in Rome Saved 30+ Hours Monthly by Automating Client Reports with Make

In today’s fast-paced business environment, efficiency is king. 🕒 A company based in Rome was spending over 30 hours every month manually preparing client reports, causing delays and increasing error rates. This case study explores how a company in Rome solved a problem where a company spent more than 30 hours per month preparing client reports manually using Make, transforming a tedious process into a streamlined, automated workflow.

We will dive deep into the client’s background, their manual reporting challenges, and how RestFlow introduced an automation architecture using Make to solve their pain points. You’ll learn the detailed technical workflow and best practices to implement similar solutions integrating popular services like Gmail, Google Sheets, and Slack.

The Problem: Manual Client Report Preparation in a Busy Operations Department

The client is a mid-sized digital marketing agency based in Rome, Italy, specializing in delivering tailored campaign insights and client performance reports. The operations team was primarily responsible for aggregating data from various sources (CRM, Google Analytics, billing platforms) to compile comprehensive monthly client reports.

Before automation, this process was:

• Entirely manual, involving copying data from CRM exports, Google Sheets, and email threads.
• Took more than 30 hours per month just for report preparation.
• Prone to human error, with occasional missing data or incorrect formatting.
• Caused delays in report delivery, affecting client satisfaction and internal SLAs.
• Lacked transparency and audit trails, making it difficult for managers to monitor progress.

These inefficiencies impacted the finance and account management teams as well, with invoices and follow-up actions delayed due to late reporting.

In terms of scale, the company managed reports for 50+ clients monthly, with increasing volume expected as they onboarded new accounts. It was clear that manual processes could not keep pace without risk to quality and timely delivery.

Our Approach: Discovery, Mapping, and Selecting Make for Automation

RestFlow’s automation architects began by conducting discovery workshops to map the entire report preparation workflow with key stakeholders in operations, sales, and IT departments. We identified critical systems that generated or stored client data:

• HubSpot CRM for client and sales data.
• Google Analytics for campaign performance metrics.
• Google Sheets where intermediate data was aggregated.
• Gmail for communication and final report dispatch.
• Slack for team notifications.

Given the multiple distinct systems and the need for flexible, visually understandable workflow orchestration, we recommended Make (formerly Integromat) for automation due to its:

• Robust multi-step scenario builder with extensive API connectors.
• Visual map that helps both technical and operational teams understand the flow.
• Ability to schedule and trigger workflows with conditional logic.
• Cost-effective pricing aligned with the client’s volume and expected scaling needs.

The high-level architecture aimed to automate data extraction, transformation, and report generation with end-to-end monitoring and alerts to reduce manual work and improve accuracy.

For readers interested in accelerating their automation journey, Explore the Automation Template Marketplace for ready-to-use workflow templates integrating Make and popular tools.

The Solution: Architecture & Workflow

Global Architecture Overview

The automated client report generation workflow employed the following architecture:

• Trigger: A scheduled Make scenario runs monthly, aligned with client reporting deadlines.
• Orchestration Tool: Make manages the workflow, integrating various APIs and services.
• External Services: HubSpot CRM, Google Analytics API, Google Sheets, Gmail, Slack.
• Outputs: Auto-generated reports sent by email to clients, notification messages to Slack channels, summary dashboard updates in Google Sheets.

End-to-End Workflow Walkthrough

1. Scheduler Trigger: The workflow starts on a monthly schedule, e.g., first weekday of each month.
2. HubSpot Client Data Extraction: It fetches updated client details and campaign assignments via API.
3. Analytics Data Retrieval: The scenario pulls the last month’s performance metrics from Google Analytics using authorized API calls.
4. Data Validation & Transformation: Data sets from HubSpot and Analytics are merged and validated (checking for missing fields or discrepancies).
5. Populate Google Sheets: The consolidated data is written into a Google Sheet template used for report formatting.
6. Report Generation: Google Sheets generates PDF reports via an integrated Google Drive export module.
7. Dispatch via Gmail: Customized emails with attached reports are sent to clients automatically.
8. Slack Notification: The Ops team receives a Slack message confirming report dispatch for visibility.
9. Logging & Error Handling: All steps log status entries; failures trigger alert notifications with retry attempts.

Step-by-Step Node Breakdown 🚀

1. Scheduler Node

Role: Initiates the automation on a set monthly schedule.
Configured to trigger on the 1st Monday at 8:00 AM Rome time.
Key Fields: Interval type set to monthly; timezone set to Europe/Rome.

2. HubSpot Search Contacts Node

Role: Retrieves a filtered list of active clients with active campaigns.
Input: API Key stored securely in Make credentials.
Fields: Filter by lifecycle stage = ‘customer’; fields: email, contact_id, associated deals.
Output: JSON array of contacts for next steps.

3. Google Analytics Data Fetch Node

Role: Pulls key metrics (sessions, conversions, bounce rate) for each client campaign.
Input: Client-specific GA view IDs mapped dynamically.
Fields: Date range set from first to last day of previous month.
Output: Metrics JSON, formatted for data merge.

4. Data Aggregator and Validator

Role: Combines HubSpot and Analytics data.
Logic: Checks for missing matches and logs warnings.
Filters: Skips clients with incomplete data; flags for manual review.
Output: Clean dataset sent to Google Sheets.

5. Google Sheets Append Rows

Role: Writes client data to the monthly report spreadsheet.
Key Configs: Spreadsheet ID and worksheet name configured.
Mappings: Cell ranges mapped to JSON fields like client name, metrics, month.

6. Google Drive Export PDF Node

Role: Converts the populated Google Sheet report into a PDF.
Config: Target folder path set; PDF export options customized.
Output: URL to the generated PDF file.

7. Gmail Send Email Node 📧

Role: Sends personalized emails to clients with attached reports.
Input: Recipient email from HubSpot data.
Fields: Subject line uses month and client name; body includes dynamic placeholders.
Attachments: PDF report file from previous node.

8. Slack Notification Node 🔔

Role: Notifies ops team about completed dispatch.
Message: “Monthly reports for 50+ clients sent successfully on [date].”
Channel: #operations-alerts configured.
Additional: Alerts on errors sent to #dev-alerts.

9. Error Handling & Logging Module

Role: Catches and logs errors at every stage with retries.
Retries: 3 per step with exponential backoff.
Logging: Writes errors and statuses to a Google Sheet log for auditing.

Error Handling, Robustness & Security

Error Handling and Retries

Each automation node is wrapped in error handling subflows allowing up to three retry attempts with increasing delays (exponential backoff). If errors persist, alerts are posted to Slack and logged in a Google Sheet error register for manual intervention.

Logging and Observability

The system maintains detailed logs of every step’s outcome, enabling traceability. Run histories on Make present execution metrics and full JSON payloads to facilitate debugging.

Alerting to Slack and Email

Operational alerts are sent real-time to Slack channels to keep teams informed. Critical errors trigger email notifications to Ops managers with detailed error reports.

Idempotency and Deduplication

To avoid duplicate reports, the workflow stores a unique monthly report key per client in a dedicated CRM custom field. This ensures any re-runs do not generate redundant emails.

Security & Data Protection

API keys are securely stored as encrypted credentials within Make. Least-privilege scopes are applied for each integration to minimize risk. Personally Identifiable Information (PII) is handled carefully with no logs stored outside secure encrypted environments. Access controls on shared Google Sheets and Slack channels enforce team-based permissions.

Performance, Scaling & Extensibility

The workflow scales efficiently as client numbers grow by processing clients in batches of 10 per scenario iteration, minimizing API throttling. By using scheduled triggers and webhooks where possible, unnecessary polling is avoided, improving throughput.

The modular scenario design allows new data sources to be added with minimal disruption. For example, integrating an ERP for billing or a new CRM tool can be done using separate Make modules chained in sequence.

RestFlow’s managed hosting environment ensures workflows continue to run reliably even as volumes increase, with monitoring to optimize performance proactively.

Automation Tool	Cost	Pros	Cons
n8n	Open-source (self-hosted) & cloud plans from $20/month	Highly customizable, modular, supports complex workflows	Requires more setup; hosting and maintenance overhead if self-hosted
Make	Starts free; paid plans from $9/month	Visual, easy integration with many APIs; good scheduling; robust error handling	Pricing scales with operation count; limited offline or self-hosted options
Zapier	Free tier limited; paid plans from $20/month	User-friendly, large app ecosystem, good for simple tasks	Less flexible for complex workflows; higher cost at scale

Method	Latency	Resource Usage	Pros	Cons
Webhooks	Near real-time	Efficient, event-driven	Immediate response, lower API use	Requires endpoint exposure; complexity in security
Polling	Delayed (interval-based)	Higher due to repeated checks	Simpler to implement; no public endpoint needed	Wasteful API calls; delays in data capture

Storage Option	Cost	Pros	Cons
Google Sheets	Free with limits; pay for Google Workspace	Easy to share/edit; integrates well with Make; low setup	Not ideal for large datasets; limited relational capabilities
Database (e.g., Airtable, MySQL)	Varies; can be higher	Better data integrity, scalability, relational data	More complex setup; requires maintenance

Results & Business Impact

Following the successful deployment of the Make automation scenario, the client realized significant improvements:

• Time Savings: Over 30 hours per month freed from manual report compilation, equating to approximately 8 hours saved per week.
  [Source: to be added]
• Error Reduction: Manual errors in reports dropped by more than 90%, improving client trust.
• Faster SLAs: Reports were consistently delivered within 2 business days of month-end versus an average 7-day delay before automation.
• Improved Visibility: The operations team accessed detailed logs and Slack alerts, enabling proactive issue resolution.
• Scalability: The automation comfortably handled a 20% client volume increase without additional staff.

The daily life of the operations team shifted from repetitive manual tasks to value-added work like analysis and strategy, dramatically increasing job satisfaction and operational efficiency.

Pilot Phase & Ongoing Maintenance Disclaimer

RestFlow emphasizes that every automation rollout begins with a controlled pilot phase wherein the workflow is tested with real but closely monitored data, allowing us to identify and fix bugs or edge cases early.

During pilot, small adjustments are routine. Once the workflow is stable, RestFlow takes full ownership of ongoing hosting, monitoring, and maintenance to ensure continuous smooth operation and quick adaptations to process or tool changes.

This partnership ensures that automation remains a reliable business asset rather than a set-it-and-forget-it experiment.

FAQs About How a Company in Rome Solved a Problem Spending 30+ Hours per Month Preparing Client Reports Manually Using Make

Conclusion: Transforming Manual Reporting Through Make Automation with RestFlow

This Real-Life case study illustrates how a company in Rome solved a problem where a company spent more than 30 hours per month preparing client reports manually using Make, achieving remarkable efficiency gains and error reduction.

By mapping the existing processes meticulously and selecting Make for its powerful integrations, the solution automated the end-to-end reporting cycle, freeing significant staff time and elevating data quality.

RestFlow doesn’t just deliver automation scenarios — we provide Automation-as-a-Service including design, implementation, hosting, monitoring, and ongoing maintenance to ensure lasting success and scalability.

If your organization faces similar time-consuming manual reporting challenges, consider leveraging proven automation workflows to increase operational agility.

Take the next step: Explore the Automation Template Marketplace or Create Your Free RestFlow Account today to unlock the power of automation.