How to Track PPC KPIs with Google Sheets and Excel

If you run paid search for a business or agency, you already know the pattern: end of month, twenty tabs open, three spreadsheets half-broken, and a client asking, "So, did this actually work?" Let’s turn that slog into a system you can eventually hand off to an AI computer agent.

Below are three tiers of maturity: manual, no-code automation, and fully agentic. Start where you are and climb.

1. Manual workflows in Google Sheets and Excel

1.1 Export data from Google Ads

• In Google Ads, go to Campaigns or Ad groups.
• Click the Download icon and choose .csv.
• Include columns for: Impressions, Clicks, Cost, Conversions, Conversion value, and any extra metrics.

1.2 Build a KPI sheet in Google Sheets

1. Upload the CSV to Google Drive and open with Google Sheets.
2. Create a new tab called KPI_Dashboard.
3. Reference raw data with formulas:
   • CTR: =Clicks/Impressions
   • CPC: =Cost/Clicks
   • Conversion Rate: =Conversions/Clicks
   • ROAS: =ConversionValue/Cost
4. Use conditional formatting to highlight problem rows (e.g., CTR below 3%, ROAS below 2x).
5. Add charts (Insert → Chart) for CTR trend, CPC trend, and Conversion Rate.

Useful docs:

• Google Sheets formulas overview: https://support.google.com/docs/answer/3098241
• Charts in Sheets: https://support.google.com/docs/answer/63824

1.3 Do the same in Excel for deeper analysis

1. Open the same CSV in Excel.
2. Turn the range into a Table (Ctrl+T) so formulas auto-fill.
3. Insert a PivotTable to summarize by Campaign, Device, or Network.
4. Add calculated columns in the table:
   • =[@Clicks]/[@Impressions] for CTR
   • =[@Cost]/[@Clicks] for CPC
5. Use Slicers to filter by date or campaign when presenting to stakeholders.

Useful docs:

• PivotTables in Excel: https://support.microsoft.com/en-us/office/create-a-pivottable-to-analyze-worksheet-data-a9a84538-bfe9-40a9-a8e9-f99134456576
• Excel formulas quick start: https://support.microsoft.com/en-us/office/overview-of-formulas-in-excel-ecfdc708-9162-49e8-b993-c311f47ca173

Pros (manual)

• Maximum control and transparency.
• Great for understanding the math behind each KPI.

Cons (manual)

• Time-consuming and error-prone.
• Doesn’t scale across many accounts or daily reporting.

2. No-code automation with Sheets and Excel

Once you’re confident in your KPI definitions, automate the plumbing.

2.1 Connect Google Ads to Google Sheets directly

1. In Google Sheets, install Google Ads add-on or use Connected Sheets if you’re on Google Workspace.
2. Set up a query to pull metrics (Impressions, Clicks, Cost, Conversions, Conversion Value) daily.
3. Point your existing KPI formulas to this live data tab.
4. Schedule refreshes so your dashboards update automatically.

Docs:

• Connect external data to Sheets: https://support.google.com/docs/answer/3093301

2.2 Use Apps Script for daily KPI snapshots

1. Open your Sheets KPI file.
2. Go to Extensions → Apps Script.
3. Write a small script to copy today’s metrics into a History tab each night.
4. Set a time-driven trigger (e.g., every day at 2am).

Docs:

• Apps Script overview: https://developers.google.com/apps-script/guides/sheets

2.3 Automate data refresh in Excel with Power Query

1. In Excel, go to Data → Get Data to connect to a CSV, database, or API output.
2. Use Power Query to clean and shape the data once (rename columns, filter dates, etc.).
3. Load into a data model and build your PivotTables and KPI measures on top.
4. Click Refresh All to update everything in one go or set up scheduled refresh via Power BI / connected services.

Docs:

• Get & Transform (Power Query) in Excel: https://support.microsoft.com/en-us/office/get-started-with-power-query-7104fbee-9e62-4cb9-a02e-5bfb1a6c536a

Pros (no-code)

• Major time savings; no more copy-paste.
• Still human-readable and easy to adjust.

Cons (no-code)

• You’re still the operator: reviewing anomalies, writing summaries, and nudging stakeholders.
• Complex setups can break when account structures change.

3. Scaling with AI computer agents

Now we add the final layer: an AI computer agent that behaves like a tireless junior analyst who never sleeps and never mistypes a formula.

3.1 Agent as data runner and checker

• The agent logs into Google Ads in your browser, downloads reports, or uses your existing data connections.
• It opens Google Sheets and Excel, pastes or refreshes data, and verifies that key KPIs (CTR, CPC, Conversion Rate, ROAS, CPA) are calculated correctly.
• It flags anomalies: spikes in CPC, sudden drops in conversions, or ROAS below your thresholds.

Pros

• Eliminates repetitive daily actions across tools.
• Reduces risk of human error in KPI calculations.

Cons

• Requires a clear, documented workflow and access permissions.

3.2 Agent as narrative KPI storyteller

• Once data is refreshed, the agent reads your KPI dashboard.
• It drafts a human-style summary: "Search campaigns grew ROAS from 2.3x to 3.1x week-over-week, driven by Brand and Competitor groups; Non-Brand CPC rose 18%."
• It pastes this into a Google Doc, slides, or an email draft so your team just reviews and sends.

Pros

• Turns raw numbers into ready-to-use client updates.
• Frees you to focus on strategy and experimentation.

Cons

• You still need to review for nuance and context.

3.3 Agent as cross-account KPI monitor at scale

• For agencies running dozens of accounts, the AI computer agent iterates through logins, refreshes all Sheets and Excel workbooks, and updates a master "Portfolio KPIs" file.
• It applies rules you define: e.g., "Mark any campaign with ROAS < 1.5x and spend > $500 in red, and compile them in an 'Action Needed' tab."

Pros

• Scales KPI governance across clients without adding headcount.
• Gives leaders a single, current view of portfolio health.

Cons

• Needs careful onboarding and testing so the automations match your agency’s playbook.

Start with solid manual KPIs, graduate to no-code automation, then invite an AI computer agent to run the whole circuit for you. The spreadsheets stay; the late nights don’t.