How to Find the First Quartile in Excel and Google Sheets at Scale

You probably do not care about quartiles for their own sake. You care because Q1 tells you which campaigns, leads, or products sit in the bottom 25% – the ones dragging the average down or quietly wasting budget. Let’s walk through practical ways to get Q1 in Excel and Google Sheets, then scale the workflow with no‑code tools and AI agents.

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1. Manual methods in Excel and Google Sheets

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1.1 Excel QUARTILE / QUARTILE.INC / QUARTILE.EXC

Excel offers several functions to get the first quartile (Q1):

• QUARTILE(array, quart)
• QUARTILE.INC(array, quart)
• QUARTILE.EXC(array, quart)

According to Microsoft: https://support.microsoft.com/en-us/office/quartile-function-93cf8f62-60cd-4fdb-8a92-8451041e1a2a

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Steps (classic QUARTILE):

1. Put your numeric data in a single column, for example A2:A101.
2. Click an empty cell where you want Q1, say B2.
3. Enter:
   • =QUARTILE(A2:A101,1)
4. Press Enter. Excel returns the first quartile (25th percentile).

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Steps (recommended QUARTILE.INC / QUARTILE.EXC):

1. Use the same range A2:A101.
2. In B2, type:
   • =QUARTILE.INC(A2:A101,1) to include endpoints, or
   • =QUARTILE.EXC(A2:A101,1) to exclude endpoints.
3. Hit Enter. This gives a more statistically precise Q1 for many use cases.

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Docs:

• QUARTILE.INC: https://support.microsoft.com/en-us/office/quartile-inc-function-1bbacc80-5075-42f1-aed6-47d735c4819d
• QUARTILE.EXC: https://support.microsoft.com/en-us/office/quartile-exc-function-5a355b7a-840b-4a01-b0f1-f538c2864cad

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Pros: Built-in, fast, easy to audit.
Cons: You must set up ranges and formulas each time for new datasets.

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1.2 Google Sheets QUARTILE

Google Sheets mirrors Excel’s syntax, so your team can switch tools without relearning everything.

Google’s stats function reference: https://support.google.com/docs/answer/3093991

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Steps:

1. Paste your data in column A, starting at A2.
2. Choose an output cell, for example B2.
3. Enter:
   • =QUARTILE(A2:A101,1)
4. Press Enter. Sheets returns the first quartile.

You can also use QUARTILE.INC and QUARTILE.EXC in recent versions of Sheets the same way you do in Excel.

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Pros: Familiar Excel-like syntax, easy sharing and collaboration.
Cons: Still manual; you must repeat for each new sheet or client.

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1.3 Excel Data Analysis ToolPak (descriptive stats)

If you want quartiles alongside mean, median, and standard deviation, Excel’s Analysis ToolPak can generate a full descriptive summary.

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Enable ToolPak (once):

1. Go to File → Options → Add-ins.
2. At the bottom, choose Excel Add-ins and click Go.
3. Check Analysis ToolPak → OK.

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Run descriptive statistics:

1. Go to Data → Data Analysis → Descriptive Statistics.
2. Set Input Range to your data (e.g., A2:A101).
3. Choose an Output Range, say C2.
4. Tick Summary statistics → OK.

Excel outputs a stats table, including quartiles depending on version. You can cross-check Q1 from there.

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Pros: One click for a bundle of stats; good for analytics teams.
Cons: Less flexible if you only need Q1; not reactive to new rows unless you rerun the analysis.

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1.4 Manual percentile logic (for understanding)

Sometimes you want to sanity-check the formula or explain it to stakeholders.

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Conceptual steps:

1. Sort your data ascending.
2. Count the number of observations, n.
3. Compute position p = 0.25 × (n + 1).
4. If p is an integer, Q1 is the value at position p.
5. If not, interpolate between the two surrounding values.

You would rarely do this by hand in production, but it builds intuition for what Q1 represents.

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Pros: Builds trust and understanding.
Cons: Way too slow and error-prone for real work.

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2. No-code methods and automation helpers

Now imagine you run weekly performance reviews across dozens of campaigns. Doing everything above by hand is a tax on your attention. No-code tools can help.

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2.1 Template-driven Sheets and Excel tables

Instead of re-creating formulas each time, create a reusable template.

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In Excel:

1. Store raw data in a formatted Excel Table (Ctrl+T) named tblData.
2. Add formulas referencing the table column, for example:
   • =QUARTILE.INC(tblData[Revenue],1)
3. Save the workbook as a template.
4. For each new campaign or client, duplicate the file and paste data into tblData. The Q1 output updates instantly.

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In Google Sheets:

1. Use a dedicated Raw Data tab for imports.
2. On a Metrics tab, place formulas like:
   • =QUARTILE(Raw!B2:B,1) (using an open-ended range so new rows are included).
3. Save as a template and duplicate the file whenever you onboard a new client.

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Pros: Low effort, consistent structure, minimal reconfiguration.
Cons: Still requires humans to paste or import data.

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2.2 No-code automation with Zapier, Make, or similar

You can automatically push data into Sheets or Excel and let existing formulas compute Q1.

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Example: send CRM deals to Google Sheets and watch Q1 update:

1. In Google Sheets, set up a template as above with a Q1 formula on a summary tab.
2. In Zapier:
   • Trigger: “New deal” in your CRM (e.g., HubSpot, Pipedrive).
   • Action: “Create Spreadsheet Row” in Google Sheets, targeting Raw Data.
3. Each new row instantly affects the QUARTILE formula, so Q1 is always live.

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Example: Slack alert when Q1 drops below a threshold:

1. In Sheets, calculate Q1 into a single cell, say Summary!B2.
2. In Zapier or Make:
   • Trigger: Schedule every morning.
   • Action 1: “Lookup Spreadsheet Row” or “Get cell value” for Summary!B2.
   • Action 2: If Q1 < target, send a Slack or email alert with the value.

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Pros: No code, no manual copy-paste, near real-time Q1 visibility.
Cons: External tools add cost and moving parts; logic still scattered between automations and spreadsheets.

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2.3 Macros and Apps Script (light low-code)

If your team is comfortable recording actions, you can wrap repetitive steps in a macro.

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Excel macro idea:

1. Start recording a macro.
2. Import or clean data.
3. Insert QUARTILE.INC or QUARTILE.EXC formulas, format output, and create a chart.
4. Stop recording.
5. Next time, run the macro on a new dataset with one click.

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Google Sheets + Apps Script:

1. Open Extensions → Apps Script.
2. Write a small script to read a column, compute Q1 with the QUARTILE function, and write results to a summary cell.
3. Attach the script to a custom menu item or time-based trigger.

Pros: Reusable and faster; hides complexity from non-technical teammates.
Cons: Requires someone to maintain scripts; still bounded to the spreadsheet.

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3. Scaling Q1 calculation with AI agents (Simular)

Manual and no-code options are fine until you are juggling 50 files, 10 clients, and constant data refresh. This is where an AI computer use agent like Sai becomes your operations specialist.

Sai can operate across your desktop, browser, and cloud tools the way a human assistant would: opening files, typing formulas, reading results, and pasting them into other systems, but with production-grade reliability.

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3.1 Agent to standardize Q1 across many Excel and Sheets files

Workflow idea:

1. You drop raw exports (CSV, XLSX) from your CRM or ad platforms into a folder.
2. Sai watches that folder.
3. When a new file appears, the agent:
   • Opens it in Excel or Google Sheets.
   • Locates the metric column you care about (e.g., Revenue or CPC).
   • Inserts the correct Q1 formula (QUARTILE.INC or QUARTILE.EXC in Excel, QUARTILE in Sheets).
   • Stores the Q1 value in a dedicated cell or summary sheet.
   • Copies all Q1 values into a master reporting file or Google Sheet.

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Pros:

• Handles dozens of files in one run.
• Transparent execution: every click and keystroke is inspectable in Simular.
• No custom code; you describe the workflow once.

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Cons:

• Requires initial setup of a robust “golden path” workflow.
• Best suited for teams with recurring, structured reporting.

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3.2 Agent-driven reporting pipeline for sales and marketing

Imagine a weekly pipeline review across markets:

1. Sai logs into your ad platforms and CRM in a secure, repeatable way.
2. It exports performance data, saves it, and loads each file into Excel or Google Sheets.
3. The agent runs Q1 calculations per channel, country, or segment using QUARTILE.INC.
4. It pastes the quartiles into a clean dashboard file, updates charts, and even publishes a PDF or sends it via email or Slack.

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Pros:

• End-to-end, human-like automation across browser, desktop, and cloud.
• Q1 is always calculated the same way, reducing analytic drift.
• You and your team stay focused on decisions instead of assembling numbers.

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Cons:

• More complex flows benefit from a bit of upfront design and testing.

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3.3 Agents as QA and governance for analytics

Simular’s neuro-symbolic approach gives you transparent, deterministic steps. You can:

• Ask the agent to recompute Q1 in multiple ways (QUARTILE vs QUARTILE.INC) and flag discrepancies.
• Have it validate that all client reports actually contain a first quartile metric before sending.
• Use webhooks from your existing pipelines to trigger the agent only when new data is ready.

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Pros:

• Automated QA on top of your spreadsheets.
• Fits neatly into existing pipelines via webhooks.

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Cons:

• Requires you to think about governance rules (which is a good problem to have).

By moving from manual clicks to no-code automation and finally to AI agents that behave like tireless spreadsheet specialists, you turn the humble first quartile into a living signal that updates itself across Google Sheets and Excel, at the scale your business actually operates.