How to use Google Sheets for data aggregation guide

1. Traditional, manual ways to aggregate data

Story setup: Imagine you are a performance marketing lead. Every Monday, you open five tabs: Google Ads, Meta Ads, LinkedIn, Stripe, and your CRM. By noon you still do not have a clean view of last week’s ROAS. This is the classic manual aggregation trap.

Below are concrete manual workflows in Google Sheets you might already be doing.

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1) Export CSVs from every tool and merge in Sheets

• Step 1: From each platform (for example Google Ads, Meta Ads), export a CSV of last week’s performance.
• Step 2: In Google Sheets, go to File > Import > Upload and import each CSV into its own sheet tab.
• Step 3: Standardize column names manually (for example rename Campaign, Ad set, Ad group to a single Campaign column).
• Step 4: Build a master sheet and use formulas like ={Sheet1!A:F;Sheet2!A:F;Sheet3!A:F} to stack data.
• Step 5: Use SUMIFS or QUERY to aggregate:
  • Example: =SUMIFS(Master!H:H, Master!A:A, "Last week", Master!C:C, "Google Ads").
• Docs: see Google’s guide on importing data and supported file types: https://support.google.com/docs/answer/6055139

Pros: Full control; works in any tool stack. Cons: Time consuming, error prone, impossible to scale.

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2) Copy-paste tables from dashboards into Sheets

• Step 1: Filter the dashboard in each tool to the date range you want.
• Step 2: Select the table, copy, and paste it into a fresh sheet tab.
• Step 3: Repeat for every source.
• Step 4: Use VLOOKUP or INDEX/MATCH to join additional attributes (for example join CRM pipeline stage onto ad spend by campaign).
• Docs: joining data with lookup functions in Sheets: https://support.google.com/docs/answer/3093318

Pros: Quick for ad hoc questions. Cons: No refresh, lots of rework, easy to misalign columns.

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3) Use pivot tables directly in Sheets Once you have all data in one master sheet:

• Step 1: Select your data range.
• Step 2: Click Insert > Pivot table.
• Step 3: In the Pivot editor, add Campaign to Rows and Cost, Conversions, Revenue to Values.
• Step 4: Add Source (Google Ads, Meta, etc.) to Columns to compare channels.
• Docs: official pivot table help: https://support.google.com/docs/answer/1272898

Pros: Fast aggregation and drilldown. Cons: Still limited by how often you manually refresh data.

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4) Use Sheets functions to pull from other Sheets If different teams maintain their own spreadsheets:

• Step 1: Ask for view access to each source sheet.
• Step 2: In your master sheet, use IMPORTRANGE to pull data:
  • =IMPORTRANGE("sheet_url","TabName!A:Z")
• Step 3: Aggregate imported ranges using QUERY or SUMIFS.
• Docs: IMPORTRANGE: https://support.google.com/docs/answer/3093340 and QUERY: https://support.google.com/docs/answer/3093343

Pros: Always-on links to other Sheets. Cons: Still not connected to the original SaaS tools; schema changes can break things.

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2. No-code automation methods

Manual work collapses once you add no-code automation on top of Google Sheets. Think of this as installing pipes between your tools and Sheets, so you are not dragging buckets of data every week.

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1) Use no-code ETL tools to feed a warehouse, then Sheets Platforms inspired by Integrate.io, Solvexia, or Databricks-style ETL let you:

• Connect 100+ sources (ad platforms, CRMs, payment processors) with prebuilt connectors.
• Define transformations (rename columns, map currencies, filter dates) in a visual editor.
• Load aggregated tables into a data warehouse.

From there you can:

• Use the official BigQuery connector to link Google Sheets to a curated table of metrics.
• Docs: connect BigQuery to Sheets: https://support.google.com/docs/answer/9137168

Pros: Central, governed data; scalable; strong security. Cons: Needs some data engineering help, plus warehouse costs.

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2) Use no-code workflow tools to push data into Sheets Automation tools like Zapier, Make, or n8n can:

• Trigger when a new row appears in your CRM or a new sale appears in Stripe.
• Transform data (for example compute net revenue, normalize country codes).
• Append rows into a Google Sheet in real time.

Typical setup for a sales leader:

• Trigger: “New Closed Won deal in CRM”.
• Actions: calculate ACV, tag channel, send to a Deals sheet.
• Aggregation: use pivot tables in Sheets to summarize revenue by channel, rep, or industry.
• Docs: Google Sheets API usage overview for automations: https://developers.google.com/sheets/api/guides/concepts

Pros: Great for near real-time operational dashboards. Cons: Zaps or scenarios can proliferate and become hard to manage at large scale.

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3) Spreadsheet add-ons for live connectors Add-ons like Coefficient or Supermetrics embed data aggregation directly into the Sheets UI:

• You choose a source (for example Google Ads) from a sidebar.
• Configure metrics, dimensions, and filters.
• Schedule automatic refreshes (for example every hour).

From there, you layer Sheets’ own tools:

• QUERY for flexible SQL-like aggregation.
• Pivot tables and charts for reporting.
• Docs: overview of using functions in Sheets: https://support.google.com/docs/answer/3093990

Pros: Designed for business users; minimal engineering. Cons: Each add-on is another vendor to manage and pay for.

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3. AI agents for aggregation at scale (with Simular)

No-code tools remove a lot of friction, but they still depend on APIs and fixed connectors. In reality, your data is not only in well-behaved APIs; it is in CSV attachments, niche SaaS tools, custom admin panels, and internal dashboards.

This is where a desktop-level AI computer agent like Simular Pro changes the game. Instead of just moving data between APIs, Simular acts like a tireless analyst operating your entire computer.

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Method 1: Agent as a cross-platform data collection specialist Imagine you want a daily sheet of campaign performance across:

• Google Ads, Meta Ads, LinkedIn Ads.
• Your subscription billing tool.
• A custom internal dashboard.

With Simular Pro you can:

• Describe the workflow in natural language: open each platform, apply filters, export reports, clean columns, paste into the Master Performance sheet, refresh pivot tables.
• Let the agent navigate UIs, handle logins, download files, and upload cleaned data into Google Sheets.

Pros:

• Works even when no API or connector exists.
• Handles thousands to millions of UI steps with production-grade reliability.
• Every action is transparent and editable, so operations leads can audit the process.

Cons:

• Requires an initial investment to design and test the workflow.
• Best value when you have recurring, complex tasks rather than tiny one-offs.

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Method 2: Agent for web research and enrichment into Sheets For agencies and growth teams:

• Give Simular a list of URLs or brand names in Google Sheets.
• Ask it to visit each site, collect pricing, tech stack, or contact info, and write the results back into new columns.
• Use Sheets’ pivot tables to aggregate competitors by category, average prices, or feature coverage.

Pros:

• Turns messy web data into structured rows without manual copying.
• Perfect for lead enrichment, market maps, or competitive analysis.

Cons:

• Web layout changes can require updating the agent’s instructions.

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Method 3: Agent as the last-mile orchestrator on top of existing tools You might already have a data warehouse and BI tool. Simular Pro can:

• Log into your BI dashboards.
• Export a weekly summary.
• Paste key metrics into an executive Google Sheet and distribute it via email or Slack.

Here, the AI agent is a glue layer between tools that were never designed to talk to each other, giving leadership a single, narrative-friendly view without adding more APIs.

Across all these methods, the pattern is the same: let Google Sheets be the familiar, shared canvas for your numbers, and let an AI computer agent handle the drudgery of collecting, cleaning, and refreshing the data behind the scenes.