Using AI for Data Analysis: A Non-Technical Founder's Guide

I can’t code. I definitely can’t write SQL queries.

But I use AI to analyze data every week. It’s changed how I run the business.

Here’s exactly how.

The Old Way (Painful)

Before AI, getting data analysis meant:

  1. Asking our developer to write a query
  2. Waiting 2-3 days
  3. Getting results that weren’t quite what I needed
  4. Repeating steps 1-3

By the time I had answers, the question was stale.

The New Way (Fast)

Now:

  1. Export data to CSV
  2. Upload to Claude or ChatGPT
  3. Ask questions in plain English
  4. Get answers in seconds

Same insights. Ten minutes instead of three days.

What You Need

A way to export data

Most tools let you export CSV files:

  • Stripe: Payment and customer data
  • Google Analytics: Traffic and behavior
  • Your CRM: Lead and deal data
  • Your database: Whatever your dev can export

Ask your developer for monthly data dumps if you can’t export yourself.

Claude Pro or ChatGPT Plus ($20/month)

Both can read CSV files and analyze them.

Claude is better at nuanced analysis. ChatGPT is better with visualizations (Code Interpreter).

I use both depending on the task.

Real Examples

Customer analysis

Uploaded: 12 months of customer data (signup date, plan, spend, churn status)

Asked: “What’s the profile of customers who churn vs those who stay?”

Got: Breakdown showing churned customers were mostly monthly plans, signed up from paid ads, and used fewer than 3 features in first week.

Action: Added onboarding prompts. Churn dropped 15%.

Marketing spend

Uploaded: Ad platform exports (spend, clicks, conversions by campaign)

Asked: “Which campaigns have the best ROI? Which should I cut?”

Got: Ranked list with cost per acquisition. Found two campaigns eating 40% of budget with worst results.

Action: Cut bad campaigns. Same results at 60% of spend.

Revenue patterns

Uploaded: Stripe payments export

Asked: “What’s our revenue trend? Any seasonal patterns? Which plans are growing fastest?”

Got: Month-over-month breakdown, seasonality chart, growth rates by plan tier.

Action: Found our annual plan growing fastest. Adjusted pricing page to push annual harder.

The Prompting Approach

Bad prompt: “Analyze this data.”

Better prompt: “I’m trying to understand why customer churn increased last month. Can you analyze this customer data and identify patterns that distinguish churned customers from retained customers? Look at signup source, plan type, feature usage, and time-to-first-value.”

Give context. State the goal. Suggest what to look for.

Limitations To Know

File size limits

Claude and ChatGPT have context limits. For large datasets:

  • Export only relevant columns
  • Filter to relevant time periods
  • Sample if necessary (ask AI to work with a sample)

Accuracy requires verification

AI can misread columns. Misinterpret dates. Make arithmetic errors.

Always verify surprising findings. Cross-check key numbers manually.

Privacy matters

Don’t upload sensitive customer data to AI tools unless you’re comfortable with their data policies.

Anonymize when possible. Remove names, emails, personal identifiers.

My Weekly Analysis Routine

Monday morning (30 minutes)

  1. Export weekly metrics from key tools
  2. Upload to Claude
  3. Ask: “Compare this week to last week. What changed? What should I investigate?”
  4. Follow up on anything interesting

Monthly deep dive (2 hours)

  1. Gather all monthly data exports
  2. Run through standard questions:
    • Customer acquisition trends
    • Churn analysis
    • Revenue breakdown
    • Marketing effectiveness
  3. Document findings
  4. Create action items

Ad-hoc questions

Whenever I wonder something about the business, I check if I can answer it with data.

Usually takes 10 minutes. Better than guessing.

Getting Help

If you need more sophisticated analysis, work with AI consultants Sydney. They can build dashboards that update automatically and run analysis on schedule.

For most founders, manual uploads and questions are enough.

The Bottom Line

You don’t need technical skills to use data.

You need:

  • Exports of your data
  • AI tools that can read them
  • Curiosity about your business

The insights are already in your data. AI just helps you find them faster.

Stop making decisions on gut. Start making decisions on data. It’s easier than ever.