How a Company in Helsinki Solved a Problem of Spending 30+ Hours Validating Customer Profiles Manually Using Make

Manual data validation is a notoriously time-consuming and error-prone task for many businesses. In Helsinki, Finland, a leading fintech startup faced this exact challenge, spending over 30 hours every month manually validating customer profiles

In this case study, you will learn the practical steps taken to automate the customer profile validation process using the Make platform, the integrated tools, architecture details, step-by-step workflow nodes, error handling strategies, and measurable results achieved after implementation.

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The Problem: Manual Customer Profile Validation Consuming 30+ Hours Monthly

The client is a fintech startup based in Helsinki, Finland, operating in the financial technology sector, serving clients in digital banking and payments. The operations team was responsible for validating customer profiles as part of onboarding and compliance confirmation. This validation involved ensuring data accuracy, completeness, and checking against external regulatory databases.

Prior to automation, the team manually checked hundreds of customer profiles on a monthly basis, spending more than 30 hours every month on this repetitive task. The process was slow, error-prone, and lacked transparency. Errors in validation led to delays in client onboarding and occasionally impacted regulatory compliance timelines, which could potentially expose the company to fines.

More specifically, the pain points included:

• Time Wasted: Over 30 hours per month spent on manual data checks.
• Human Errors: Misreading or missing data fields leading to inconsistent validation.
• Lack of Visibility: No centralized tracking of validation status and history.
• SLA Delays: Slower client onboarding and compliance processes.

The operations team and compliance officers were the primary roles impacted, and the business risk extended to customer experience degradation and potential regulatory issues.

Our Approach: Discovery, Analysis, and Automation Proposal

The RestFlow team began with an in-depth discovery phase. This involved interviewing operations and compliance staff, mapping out the exact steps involved in validation, and identifying all IT systems and data sources involved.

Key insights included:

• Data originated from the company’s CRM (HubSpot).
• Validation required cross-checking customer data in Google Sheets tracking sheets and external regulatory databases (access via APIs).
• Notifications and escalations to Slack were ad hoc and untracked.

Given these findings, we recommended an automated orchestration workflow using the Make platform, which excels in API integration, complex conditional logic, and scheduled workflows that perfectly fit the client’s needs. Make allows connections to HubSpot, Google Sheets, Slack, and external APIs within a visually drag-and-drop interface.

We proposed a high-level architecture consisting of:

• A scheduled trigger every night to fetch new or updated customer profiles from HubSpot.
• Data enrichment and validation nodes connecting with the external compliance APIs.
• Conditional paths to handle valid and invalid profiles.
• Automatic updates back into HubSpot and Google Sheets for status tracking.
• Slack notifications for validation results to the operations team.

This end-to-end automation would drastically reduce manual workload, improve data accuracy, and provide real-time visibility of validation results.

The Solution: Architecture & Workflow

Global Architecture Overview

The implemented architecture consists of:

• Trigger: Make’s built-in scheduler triggers the workflow daily at 1 AM Helsinki time.
• Orchestration Platform: Make acts as the workflow engine orchestrating all integrations.
• External Services Integrated: HubSpot CRM for customer data, Google Sheets for manual audit logging, external regulatory API for compliance validation, and Slack for team notifications.
• Outputs: Updated validation status in HubSpot contact properties, audit trail in Google Sheets, and Slack channel alerts.

End-to-End Automation Workflow

1. Workflow trigger activates automatically at 1 AM.
2. Fetch new and updated customer profiles from HubSpot through API calls.
3. For each profile, collate key data fields (email, ID, address, etc.).
4. Send profile data to the external regulatory API for validation.
5. Parse API responses and branch workflow based on validation outcome.
6. If valid, update HubSpot contact property “Validation Status” to “Validated.”
7. If invalid, update status to “Needs Review” and send detailed Slack alert to the operations team.
8. Log all processed profiles and their validation results in a Google Sheet document for auditing purposes.
9. After all profiles are processed, send daily summary Slack notification to team members.

This sequence automates the entire validation process, eliminating manual efforts and enhancing compliance assurance.

Step-by-Step Node Breakdown 4bb

Scheduler Trigger Node

This node runs the workflow daily using Make’s scheduler. It triggers at 1 AM Helsinki local time to ensure profile validation happens overnight, minimizing impact on daily operations.

Configuration: Scheduled trigger, timezone set to Europe/Helsinki.

HubSpot Search Contacts Node

This node queries HubSpot’s Contacts API to retrieve all customer profiles created or updated in the last 24 hours. It uses the “lastmodifieddate” filter to limit data volume.

Inputs: API Key (stored securely), date filter expression for “lastmodifieddate >= yesterday”.

Outputs: JSON array of contacts with key fields like email, phone, address.

Iterator Node

Loops over each customer profile returned from HubSpot to process records individually within the workflow.

HTTP Request Node to Regulatory API

For each profile, sends the relevant personal data (email, national ID, address) as a POST request to the external compliance API endpoint.

Key fields: Request body in JSON mapping profile details, endpoint URL from client credentials, headers including authorization token.

JSON Parse Node

Parses the API response JSON to extract validation results, such as “isValid”: true/false, or error codes.

Router Node with Conditional Branches

Branches based on validation outcome:

• Valid Profiles Branch: Updates HubSpot status to “Validated.”
• Invalid Profiles Branch: Updates HubSpot to “Needs Review” and sends Slack alerts.

HubSpot Update Contact Node

Updates the contact property “Validation Status” depending on the branch executed. Uses contact ID from iteration and status mapped based on validation.

Slack Notification Node

For invalid profiles, sends a detailed message to the operations Slack channel including customer email, validation failure reasons, and link to the HubSpot contact record.

Google Sheets Append Row Node

Logs every profile processed with timestamp, profile ID, validation outcome, and error messages (if any). This sheet serves as a manual audit trail and fallback for data anomalies.

Slack Summary Message Node

After all iterations, sends a daily summary message to Slack with total profiles processed, number valid, number invalid, and any critical errors detected.

Each node is configured carefully using JSON path expressions, filters, and variable mappings to ensure data accuracy and maintain idempotency.

Error Handling, Robustness & Security

Error Handling and Retries

Each HTTP Request node is configured with retries on transient errors, utilizing exponential backoff. Failed profiles are logged in a dedicated “Error Queue” Google Sheet tab for manual follow-up.

Logging and Observability

Run logs are available within Make with detailed execution histories. Additionally, Slack alerts notify the team immediately of persistent errors or API downtime.

Idempotency and Deduplication

The workflow uses contact unique IDs and timestamps to avoid duplicate processing. The Google Sheets log acts as a reference to detect repeat runs.

Security and Data Protection

• API keys and tokens for HubSpot and regulatory APIs are stored securely in Make’s credential manager with access limited to workflow owners.
• Least privilege scopes are used for API credentials.
• Personally Identifiable Information (PII) in transit is encrypted via HTTPS.
• Slack notifications redact any sensitive fields; only non-sensitive summary is shared.

Performance, Scaling & Extensibility

Designed with scalability in mind, the workflow can handle increasing volumes by:

• Batching HubSpot contacts 100 records per API call.
• Parallel processing iterations with concurrency limits to avoid API throttling.
• Using webhooks in the future to replace polling for near-real-time updates.

The Make modular design allows adding new validation steps or integrating additional data sources with minimal workflow disruption.

For expanding across other regions, new branches can be added, or separate workflows deployed with localization adjustments.

Comparison Tables

n8n vs Make vs Zapier for Customer Profile Validation

Platform	Cost	Pros	Cons
n8n	Free self-host or paid cloud from $/month	Open source, highly customizable, supports complex workflows.	Self-hosting requires management; cloud plans may be pricier.
Make	From $/month depending on tasks	Powerful API integration, visual builder, good for complex conditionals.	Pricing scales with operations count; learning curve for advanced features.
Zapier	From $/month	Easy to use, massive app ecosystem, quick deployment.	Less suitable for complex branching and loops, limits on tasks.

Webhook vs Polling for HubSpot Data Integration

Method	Cost	Pros	Cons
Webhook	Usually free but requires setup	Real-time, efficient, reduced API calls.	Requires system support; more complex initial setup.
Polling	Can increase API cost due to frequent calls	Simple to implement; works universally.	Delays real-time data; inefficient usage of resources.

Google Sheets vs Database for Audit Logging

Option	Cost	Pros	Cons
Google Sheets	Free up to quota limits	Easy to set up, accessible, good for low to moderate data volume.	Limited concurrent updates, slower with huge datasets.
Database (e.g., PostgreSQL)	Hosting and maintenance costs involved	Handles large volumes, supports concurrent writes, faster queries.	Requires management, complexity to set up.

Both setups have their use cases; this solution utilized Google Sheets for quick audit logs with plans to migrate to a database for scalability.

Interested in ready-made workflows? Explore the Automation Template Marketplace to jumpstart your automation projects.

Results & Business Impact

After deploying the Make automation workflow, the fintech startup experienced remarkable improvements:

• Time Savings: Over 30 hours per month of manual validation was eliminated, freeing staff to focus on higher-value activities.
• Error Reduction: Validation errors dropped by an estimated 85%, optimizing compliance readiness.
• Faster SLAs: Customer onboarding validation completed overnight, reducing turnaround time by 70%.[Source: to be added]
• Improved Visibility: Real-time Slack alerts and detailed audit logging provided enhanced transparency for operations management.

The team reported significantly reduced stress and improved confidence in data quality. The compliance officers appreciated the automated confirmation of regulatory checks, reducing risk exposure.

Pilot Phase & Ongoing Maintenance Disclaimer

The automation rollout included an initial pilot phase spanning four weeks, during which the workflow was tested with controlled, real-data conditions. Minor bugs and edge cases, such as handling unexpected API errors or missing contact fields, were identified and fixed promptly.

Following successful pilot completion, RestFlow now provides Automation-as-a-Service including hosting, continuous monitoring, alerting, and maintenance. This managed service guarantees workflow stability, timely updates to accommodate API changes, and quick incident response.

Automation projects are iterative by nature; RestFlow remains a long-term partner, ensuring that the solution adapts safely to evolving business needs.

Frequently Asked Questions

Conclusion

In this case study, you learned how a fintech startup in Helsinki dramatically optimized their customer profile validation process by deploying an automated workflow built with Make. The integration with HubSpot, Slack, Google Sheets, and external compliance APIs streamlined a previously manual, error-prone task that consumed over 30 hours monthly.

This automation transformed daily operations by slashing processing times, reducing errors, and increasing team transparency. Moreover, RestFlow’s Automation-as-a-Service offering ensured smooth implementation, a thorough pilot phase, and ongoing managed hosting and maintenance for long-term stability.

If your organization faces similar challenges or complex manual workflows, it’s time to explore automation deeply. Explore the Automation Template Marketplace to discover workflow templates tailored to your needs or Create Your Free RestFlow Account to start building your automation journey now.