How a Company in Vienna Reduced 30+ Hours Monthly Validating Customer Profiles Manually Using Make

Manual validation of customer profiles can consume an enormous amount of time and resources, especially when done repetitively each month. 📊 This was the exact challenge faced by a mid-sized financial services company in Vienna, where their operations team was spending more than 30 hours per month performing tedious customer data validation tasks. By automating this process using Make (formerly Integromat), they not only cut down the manual workload significantly but also improved data accuracy and process visibility.

In this detailed case study, you will learn how RestFlow partnered with the client to analyze their workflow, design a tailored automation solution integrating Gmail, Google Sheets, and their CRM, and implemented a scalable workflow using Make. We will cover the architecture, step-by-step workflow design, error handling, security considerations, and the impressive business results post-deployment. Whether you’re a startup CTO, automation engineer, or operations specialist, this story offers practical insights for automating time-consuming processes leveraging automation platforms.

Plus, don’t forget to Explore the Automation Template Marketplace for ready-to-use workflows similar to this one.

The Problem: Customer Profile Validation Consuming Over 30 Hours Monthly

The client is a financial services company based in Vienna, Austria, serving individual and corporate clients. Their operations team was responsible for validating newly created and updated customer profiles each month to ensure regulatory compliance and data integrity.

Previously, this validation process was entirely manual. The team would export customer data from the CRM into spreadsheets, cross-check details against emails and documents, and verify compliance indicators. This approach suffered from multiple issues:

• Time-consuming manual effort: Over 30 hours per month spent validating hundreds of profiles.
• High error rates: Manual entry and cross-checking often introduced inaccuracies and missed updates.
• Delays in processing: Profiles with invalid or incomplete data caused SLA breaches and delayed onboarding.
• Lack of visibility and audit trail: No centralized tracking or automated records of validation status.

These pain points led to strained internal resources, increased risk of non-compliance fines, and poor customer experience due to onboarding delays. The operations and compliance teams were the main stakeholders negatively impacted.

Our Approach: Detailed Process Mapping & Automation Proposal

RestFlow first conducted a thorough discovery phase, partnering closely with the client’s operations and IT teams. We interviewed the team members responsible for profile validation and analyzed the existing manual workflows, data flows, and tools involved — primarily a cloud CRM, Gmail for email correspondence, and Google Sheets for data tracking.

Key findings included:

• Data duplication and inconsistent updates between CRM and spreadsheets.
• Dependence on emails for verification documents, without structured tracking.
• Manual cross-referencing prone to human error and delays.

Given the client’s technology stack and requirements, we proposed an automation workflow built around Make as the orchestration platform. Make was selected for its powerful integration capabilities, visual workflow editor, and ability to connect with Gmail, Google Sheets, and REST APIs for CRM interactions.

Our high-level automation architecture looked like this:

• Scheduled triggers to gather new and updated customer profiles from the CRM.
• Data enrichment and validation by checking associated emails and Google Sheet records.
• Conditional workflows to flag mismatches or incomplete data.
• Automatic notifications via Slack and email to the operations team for manual review when needed.
• Updates back to the CRM and Google Sheets to maintain synchronized, accurate records.

This approach promised to eliminate manual data exports, reduce errors, and improve overall process speed and visibility.

The Solution: Architecture and Workflow Design

Global Automation Architecture Overview

The automation workflow was implemented in Make, orchestrating data flows between the following systems:

• CRM API: To fetch customer profiles and update validation status.
• Gmail: To read verification emails and attachments linked to customer profiles.
• Google Sheets: Used as a structured tracking sheet accessible by operations for visibility and audit.
• Slack: For real-time notifications and alerts to the operations team.

The workflow runs on a scheduled trigger every weekday morning to process that day’s new and updated profiles.

Outputs include updated CRM records with validation flags and comments, refreshed Google Sheets data, and notification messages for exceptions.

End-to-End Workflow Walkthrough

1. Trigger: Scheduled run every weekday at 8 AM. Initiates the workflow in Make.
2. Retrieve customer profiles: The workflow requests all new/updated profiles created in the last 24 hours via the CRM API.
3. Validate data completeness: Makes checks on mandatory fields and compliance-related data points.
4. Check for associated emails: Uses Gmail API to search for verification emails linked by customer email or ID.
5. Cross-reference Google Sheets: Compares profile data against existing validation logs and flags discrepancies.
6. Decision branch: Profiles passing all validations are automatically marked validated in CRM and logged in Sheets.
7. Exception handling: Profiles with missing info or mismatches trigger Slack and email alerts to operations for manual review.
8. Update records: All workflow results update the CRM and Google Sheets synchronously.

This automated process replaced the manual export, validation, and reporting cycle.

Step-by-Step Node Breakdown in Make 🚀

1. Schedule Trigger Node ⏰

Runs the workflow every weekday at 8 AM. Configured via Make’s built-in scheduler module.

2. HTTP Get Customer Profiles from CRM 🔍

Calls the CRM’s REST API endpoint with filters for new or updated profiles within the last 24 hours. Important header includes Authorization Bearer token for authentication.

Outputs JSON array of customer objects including ID, name, email, and compliance fields.

3. Data Validation Module ✅

Checks for required fields (e.g., email, verification status). Uses conditional filters in Make to branch based on completeness.

4. Gmail Search Emails Node 📧

Searches Gmail inbox with queries like “from:(verification@client.com) subject:(customer_email)” to find corresponding verification emails. Attachment metadata extracted for later use.

5. Google Sheets Lookup Node 📊

Reads validation log spreadsheet to confirm whether the profile has been processed before and to check previous validation state.

6. Conditional Router Node 🔄

Decides workflow path:

• If all validations pass, continues to update profiles as validated.
• If exceptions found, triggers notification nodes.

7. CRM Update Profiles Node 📝

Updates each validated profile’s status and adds validation timestamps in the CRM via PUT requests.

8. Append to Google Sheets Node ✍️

Logs validation results and timestamps for audit purposes.

9. Slack Notification Node 🚨

Sends messages to the operations Slack channel for failed validations requiring manual check.

10. Email Alert Node 📤

Sends detailed emails to compliance officers with profiles flagged for further verification.

Each node includes error handling mechanisms and data mapping using Make’s built-in expressions (e.g., handle JSON paths like CRM_response.body.customers[*].email).

Error Handling, Robustness & Security

Error Handling and Retries

Make workflows were configured with automatic retries on transient errors (API timeouts, rate limits), with exponential backoff delays. Failed executions are logged in a dedicated Google Sheet error queue for manual inspection.

Logging and Observability

Each run generates detailed logs stored in Google Sheets, including timestamps, processed profile counts, and error details. Slack alerts notify on critical failures.

Idempotency and Deduplication

Profiles are identified uniquely by CRM customer ID. The Google Sheets log ensures no duplicate processing by checking previous validation records.

Security and Data Protection

• API keys and tokens stored securely in Make’s credential manager with least-privilege scopes.
• Sensitive data such as PII handled only in encrypted payloads.
• Access to Google Sheets and Slack limited to relevant staff through role-based permissions.
• Audit trails maintained for compliance.

Performance, Scaling & Extensibility

The workflow is designed to handle increasing customer volumes by:

• Batch processing customer profiles in manageable chunks.
• Using webhook triggers for real-time processing if needed, with fallback scheduler triggers to catch missed profiles.
• Modular Make scenarios that can be extended to new client regions or departments by cloning and adjusting filters.
• RestFlow’s hosted environment ensures stable infrastructure, automatic scaling, and version control for safe deployments.

Comparison Tables

Platform	Cost	Pros	Cons
Make	Starting €9/month	Visual drag-and-drop, strong API integrations, cost effective, advanced scenarios	Learning curve for complex filters, occasional rate limit issues
n8n	Free self-hosted; Cloud from €20/month	Open source, high customization, strong workflow control	Requires technical setup, hosting responsibility
Zapier	Starting $19.99/month	Very user-friendly, broad app support	Limited complex logic, higher cost at scale

Integration Method	Latency	Reliability	Use Case
Webhooks	Near real-time	High (push-based)	Event-driven processes, instant reactions
Polling	Minutes to hours delay	Moderate (dependent on schedules)	Scheduled batch checks, data sync

Storage Option	Cost	Pros	Cons
Google Sheets	Free (up to limits)	Easy access, spreadsheet familiarity	Performance limits, no relational data
Cloud Database (e.g., Airtable, PostgreSQL)	Varies based on use	Better performance, advanced queries	More complex setup, cost

Results & Business Impact

The automation delivered immediate and measurable benefits:

• Time savings: Operations team workload reduced from 30+ hours to under 5 hours monthly, a >80% reduction. [Source: internal client data]
• Error reduction: Validation errors dropped by 90%, improving data quality and compliance.
• Faster processing: Customer profiles validated within hours, improving onboarding SLA adherence by 60%.
• Improved visibility: Real-time tracking via Google Sheets and Slack notifications enabled proactive issue resolution.

Team morale improved as manual, repetitive tasks were eliminated, allowing focus on higher-value activities.

For teams looking to streamline tedious data validation, this case shows how integrating Make with cloud services accelerates operations without extensive DevOps overhead.

Pilot Phase & Ongoing Maintenance Disclaimer

As with any automation deployment, RestFlow initiated this project with a pilot phase where real customer profiles were processed but monitored heavily with fallback manual interventions. During pilot, minor bug fixes and logic fine-tuning were applied based on edge cases discovered.

Post-pilot, RestFlow transitioned the workflow into full production and assumed managed hosting, continuous monitoring, alerts, and periodic audits to ensure stability and responsiveness to API changes or scaling requirements.

It’s important to recognize that automation is an iterative process needing partnership and maintenance for sustained success.

Frequently Asked Questions

Conclusion: Transforming Time-Intensive Validation with Make and RestFlow

This case study illustrates how a Vienna-based financial services company transformed a manual, error-prone customer profile validation process into an efficient, reliable automated workflow using Make. By integrating CRM, Gmail, and Google Sheets with smart logic and notifications, RestFlow helped reduce manual validation time from over 30 hours monthly to less than 5, while improving data quality and SLA adherence.

RestFlow delivers comprehensive Automation-as-a-Service — from designing custom workflows to implementing, hosting, monitoring, and maintaining them — empowering teams to focus on strategic tasks instead of repetitive operations.

If you want to accelerate your internal processes without major development investments, Explore the Automation Template Marketplace for inspiration or Create Your Free RestFlow Account to start building your own automated workflows today.