How a Company in London Saved 12+ Hours Weekly Generating Performance Dashboards Using Make

Generating performance dashboards manually was consuming over 12 hours per week for a London-based SaaS startup’s operations team, causing delays and frequent errors. 🚀 In this case study, we explore how automation using Make completely transformed their reporting process, freeing up their valuable time and improving accuracy.

Starting from the client’s initial challenges, through our detailed automation design and implementation, to the tangible business results achieved, you will gain practical insights into building scalable automation workflows. We will delve into tool integration, workflow architecture, step-by-step node breakdowns, error handling, scalability, and security considerations.

Whether you are a startup CTO, an automation engineer, or an operations specialist looking to solve similar challenges, this comprehensive guide will provide clear, hands-on directions and comparisons to empower your automation journey.

The Problem: Manual Dashboard Generation Wasting Time and Accuracy

The client is a rapidly growing SaaS startup headquartered in London, UK, operating in the technology sector. Their operations department was responsible for preparing weekly performance dashboards summarizing sales, marketing, and customer success metrics drawn from multiple data sources.

Before automation, this reporting process was entirely manual. The team aggregated data from Google Sheets, HubSpot CRM, and Slack channel statistics, copying and pasting data, running queries, and employing manual calculations.

This process consumed over 12 hours per week, roughly 48+ hours monthly, equating to more than one full working day. In addition, manual data entry caused a 15% error rate on average across reports, leading to incorrect insights and delayed decisions. Stakeholders often received dashboards late, impacting SLA adherence and team productivity.

The lack of real-time visibility and repetitive human work caused frustration among the operations staff, hampering the company’s agility in an increasingly competitive market.

Our Approach: Mapping the Process and Proposing an Automated Solution

RestFlow approached the project with an initial discovery phase to observe and map the complete dashboard generation process. We conducted workshops with the operations team to identify pain points, data sources, and key decision nodes.

We identified three main systems to integrate:

• Google Sheets – used to maintain interim data and logs.
• HubSpot CRM – source of sales and customer metrics.
• Slack – channel usage statistics and team notifications.

Given the complexity and needs for flexible data transformation, scheduling, and multi-step workflows, we selected Make (formerly Integromat) as our core orchestration tool. Make provided robust API integrations and a visual designer perfectly suited for building modular flows.

We proposed an overarching architecture involving automated extracts, real-time data consolidation, calculations, and multi-channel report generation (Slack notifications, Google Sheets dashboards, and emailed PDFs). RestFlow planned to deliver end-to-end Automation-as-a-Service, handling design, implementation, hosting, monitoring, and maintenance to guarantee continuity.

For readers interested in jump-starting their projects, explore the Automation Template Marketplace to find pre-built workflows adaptable to various use cases.

The Solution: Architecture & Workflow Overview

The automated dashboard generation system was orchestrated around Make as the central hub. Key architectural components included:

• Trigger: Scheduled triggers within Make to run every Monday at 8:00 AM, ensuring fresh weekly dashboards.
• Data Retrieval: API calls to HubSpot to fetch sales and customer data; Google Sheets API operations to pull interim data; Slack API to collect channel statistics.
• Data Processing: Built-in Make modules processed JSON responses, performed calculations (e.g., conversion rates, average resolution times), and merged datasets.
• Decision Logic: Conditional modules to handle missing data, inconsistent values, and alert thresholds.
• Output: Populated Google Sheets dashboard tabs automatically, generated PDF reports using third-party PDF modules, and sent notifications to relevant Slack channels and email distribution lists.

This architecture ensured that data was gathered and processed seamlessly each week without manual intervention.

Below is the end-to-end workflow in detail:

1. Scheduler Trigger: Make’s built-in scheduler initiates the workflow every Monday.
2. HubSpot Data Module: API module queries HubSpot endpoints for deal stages, sales pipeline metrics, and customer tickets.
3. Google Sheets Fetch: Retrieves additional KPIs stored manually or calculated outside CRM systems.
4. Slack Stats Collection: Fetches channel activity summaries via the Slack API.
5. Data Transformation: Data is merged, cleaned, and calculations performed using Make’s data manipulation features.
6. Conditional Branching: For example, if sales conversion drops below threshold, triggers alert notifications.
7. Report Generation: Data is exported to a prepared Google Sheets template; PDF reports are created based on spreadsheet content.
8. Notifications: Reports are emailed to stakeholders; summaries posted on designated Slack channels.

Step-by-Step Node Breakdown of the Make Workflow 🚀

1. Scheduler Trigger Node

Role: Initiates the workflow on a fixed schedule.
Key fields: Schedule set to cron expression for Monday 8:00 AM UTC.
Input: None.
Output: Trigger signal to downstream modules.

2. HubSpot CRM API Modules

Role: Extract sales and customer metrics via HubSpot’s REST API.
Configuration: OAuth 2.0 authentication credentials; end points for deals and tickets.
Data: JSON arrays containing deals filtered by status, ticket resolution times.
Key expressions: Filters applied using HubSpot query parameters (e.g., ‘dealstage=closedwon’).
Output: Raw CRM data passed to data transformation node.

3. Google Sheets Read Module

Role: Pulls supplementary KPIs stored manually.
Connection: OAuth 2.0 with appropriate scopes.
Settings: Reads specific ranges using A1 notation for KPI tables.
Output: Array of rows with timestamps, values.

4. Slack API Module

Role: Retrieve channel message counts and active users.
Configuration: Use Slack Bot token with least privileges.
Filtering: Only channels relevant to sales and operations.
Output: JSON with message counts and user participation metrics.

5. Data Aggregation and Transformation Node

Role: Merge HubSpot, Sheets, and Slack data.
Logic: Use Make functions like map() and filter() to cleanse and combine.
Transformations: Calculate metrics such as sales conversion %, average response time.
Output: Structured JSON object to feed reports.

6. Conditional Alert Module ⚠️

Role: Evaluate metrics thresholds.
Conditions: E.g., if sales conversion < 20%, trigger alert.
Outputs: Branching to notification or normal report path.

7. Google Sheets Write Module

Role: Write updated metrics and charts back to a dashboard spreadsheet.
Details: Overwrite specific ranges to keep dashboard current.
Fields: Maps JSON data to cell arrays.

8. PDF Report Generator Module

Role: Generate PDF snapshots of Google Sheets report using third-party integration.
Outputs: Binary PDF file attached to email.

9. Email Notification Module

Role: Sends the generated PDF report to a mailing list.
Fields: Subject lines with dates; CC list; email body with summary.
Error Handling: Retry on SMTP failures.

10. Slack Notification Module 🎯

Role: Post summary message with report link to Slack.
Messages use markdown blocks for formatting.
Channels: #operations, #sales-leaders.

Error Handling, Robustness & Security

Error Handling & Retries
Make was configured to retry API requests up to three times with exponential backoff to handle transient network errors.

Logging & Observability
Each module’s execution logs and errors are recorded in a dedicated Google Sheets log file. Critical failures trigger real-time Slack alerts to the ops team.

Idempotency & Dedupe
The workflow includes unique identifiers for transactions based on timestamps and query parameters to prevent duplicate data processing.

Security Measures
API credentials are stored securely within Make’s credential vault and RestFlow’s managed environment, using least-privilege OAuth scopes for HubSpot and Slack.
PII is masked in logs, and access control limits who can modify workflows.

RestFlow manages routine audits to verify credentials and monitor suspicious activities.

Performance, Scaling & Extensibility

Scalability
The workflow’s modular design supports scaling from weekly to daily or hourly reports by adjusting scheduler triggers.

Webhooks vs Polling
For real-time triggers (e.g., new deals), future enhancement could add webhook listeners instead of polling. Currently, scheduled pulls fit the reporting cadence.

Batching & Parallelism
Make supports iterators and concurrent calls allowing processing of large data volumes in parallel to reduce runtime.

Extensibility
The architecture easily incorporates new data sources (e.g., Salesforce), additional reports, or region-specific dashboards by cloning and modifying workflow branches.

RestFlow’s managed hosting ensures stable operation under increased workloads with automatic scaling.

Comparison Tables

Option	Cost	Pros	Cons
n8n	Free self-hosted; paid SaaS from $20/month	Open source; highly customizable; supports self-hosting	Requires some setup effort; fewer prebuilt modules than others
Make (Integromat)	Free up to 1,000 ops/month; paid from $9/month	Visual designer; comprehensive API & app modules; good error handling	Pricing can increase rapidly with volume; learning curve on complex workflows
Zapier	Free limited; paid from $19.99/month	Large ecosystem; simple interface; good for 1-2 step automations	Limited multi-step workflow complexity; less flexibility for data transformation

Integration Approach	Latency	Complexity	Reliability
Webhook Trigger	Near real-time	Medium to high	High with proper retries
Polling Scheduled Jobs	Minutes to hours	Low	Medium, dependent on intervals

Storage Option	Cost	Pros	Cons
Google Sheets	Free up to quotas	Easy access; no setup; good for light data	Slow with large datasets; concurrency limits; manual access risks
Database (e.g., PostgreSQL)	Hosting cost applicable	High performance; concurrent usage; complex queries	Requires DB admin; more complex maintenance

Depending on your exact use case, choosing the right tool and architecture will significantly impact execution speed, cost, and maintainability.

To experiment with these tools and templates, create your free RestFlow account and start building your first automation workflows today.

Results & Business Impact

The automation solution delivered by RestFlow resulted in significant improvements:

• Time Savings: Over 12 hours per week, equivalent to 48+ hours monthly, were reclaimed from manual reporting tasks.
• Error Reduction: Data errors dropped from 15% to under 2% due to elimination of manual entry.
• Faster SLA Compliance: Weekly dashboards were consistently delivered before Monday noon, improving stakeholder satisfaction.
• Operational Visibility: Real-time Slack notifications empowered faster decision making.
• Cost Efficiency: Reduced manual labor allowed the client to redeploy staff to higher-value tasks.

Operations staff reported less frustration and more time for strategic analytics, creating a positive cultural shift.

[Source: RestFlow Client Data]

Pilot Phase & Maintenance Disclaimer

As with any automation rollout, the workflow underwent a pilot phase in which it ran alongside manual processes handling controlled data sets. This phase allowed detection and correction of edge cases, data anomalies, and user feedback.

Post-pilot, RestFlow assumed responsibility for continuous hosting, monitoring, updates, and auditing of the automation ecosystem. This ensures that integrations remain functional and secure despite API changes, volume growth, or process evolution.

Automation success depends on iterative refinement and long-term partnership, which RestFlow is committed to delivering for all clients.

Conclusion

This case study clearly demonstrates how a London SaaS startup tackled a tedious, error-prone manual process of generating performance dashboards consuming over 12 hours weekly by implementing an automated workflow using Make.

Through detailed analysis, design, and integration of key tools such as HubSpot, Google Sheets, and Slack, the company achieved remarkable time savings, accuracy improvements, and operational agility.

RestFlow’s Automation-as-a-Service approach ensured a smooth deployment with ongoing hosting, monitoring, and maintenance, underscoring the benefits of partnering with experts for scalable automation.

If you are looking to transform repetitive reporting tasks or similar business processes with automation, explore the Automation Template Marketplace for inspiration or create your free RestFlow account today and start building your intelligent workflows.