Building a Privacy-First Google Ads Analyzer (No Server, No Data Upload)

We built a Google Ads CSV analyzer that finds wasted spend and suggests optimizations. The twist? Everything runs in the browser. No server uploads. No data leaves your machine.

Here's how we built it and why we made those choices.

The Problem

Small business owners often waste 20-40% of their Google Ads budget on irrelevant search terms and underperforming keywords. Enterprise tools cost $100-500/month to identify these issues.

We wanted something free, but privacy was a concern — businesses are reluctant to upload their ad spend data to unknown servers.

The Solution: Client-Side Processing

Instead of uploading CSVs to a server, we process everything in the browser using the FileReader API.

Reading CSV Files

 ⁠javascript
const handleFileUpload = (file: File) => {
const reader = new FileReader();

reader.onload = (e) => {
const text = e.target?.result as string;
const rows = parseCSV(text);
analyzeData(rows);
};

reader.readAsText(file);
};

⁠ ### Parsing CSV Data

Google Ads exports aren't clean — they have summary rows, currency symbols, and percentage signs. We strip all of that:

 ⁠javascript
const parseCSV = (text: string): Record[] => {
const lines = text.split('\n').filter(line => line.trim());
const headers = lines[0].split(',').map(h => h.trim().toLowerCase());

return lines.slice(1)
.filter(line => !line.startsWith('Total'))
.map(line => {
const values = line.split(',');
const row: Record = {};
headers.forEach((header, i) => {
row[header] = values[i]?.replace(/[$%",]/g, '').trim() || '';
});
return row;
});
};

⁠ ### Analysis Logic

We check for three main issues:

1. Wasted Spend (Zero Conversions)
 ⁠javascript
const wastedKeywords = keywords.filter(kw =>
parseFloat(kw.conversions) === 0 &&
parseFloat(kw.cost) > 10
);

⁠ 2. Low Quality Score
 ⁠javascript
const lowQualityKeywords = keywords.filter(kw =>
parseInt(kw.quality_score) < 5
);

⁠ 3. Irrelevant Search Terms
 ⁠javascript
const irrelevantTerms = searchTerms.filter(term =>
parseFloat(term.ctr) < 0.01 &&
parseInt(term.impressions) > 100
);

Why Client-Side?

1. Privacy — Ad spend data is sensitive. Businesses don't want it on someone else's server.
2. Speed — No upload/download latency. Analysis is instant.
3. Cost — No server infrastructure to maintain.
4. Trust — Users can verify via DevTools that no network requests are made.

The Trade-offs

• No AI analysis — Can't use LLMs without sending data somewhere
• Browser memory limits — Very large CSV files could crash the tab
• No historical tracking — Each analysis is one-time

For our use case, these trade-offs were worth it.

Try It

Live tool: siteauditr.io/ads-audit

Upload your Google Ads CSVs (campaigns, keywords, search terms) and get instant insights. Open DevTools Network tab if you want to verify — zero external requests.

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Would love feedback on what else would be useful to add. Thinking about bid adjustment recommendations or ad copy analysis.

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