Analytics With AI
Analytics data tells a story, but extracting insights requires analysis skills that take time to develop. AI assistants can accelerate this process — helping you spot patterns, interpret trends, and generate recommendations from your data. The key is knowing how to present your data and ask the right questions.
Funnel Analysis With AI
Conversion funnels show where users drop off. AI can help identify problems and suggest solutions:
"Here's our analytics data for the signup funnel:
- Landing page: 10,000 visits
- Signup form: 2,000 views (20% of landing)
- Form submitted: 500 (25% of form views)
- Email verified: 300 (60% of submissions)
- Completed profile: 150 (50% of verified)
Analyze this funnel and suggest improvements."
AI might notice that the landing-to-form conversion is low and suggest A/B testing different call-to-action placements. It might flag the email verification drop-off and recommend simplifying the verification process or adding reminder emails.
Feature Usage Analysis
Understanding which features matter helps prioritize development:
"Our app has these feature usage stats from last month:
- Dashboard: 95% of users, daily
- Reports: 45% of users, weekly
- Export: 12% of users, monthly
- Advanced filters: 3% of users, rarely
Which features should we prioritize improving?
Which might we consider removing?"
AI can help interpret these patterns — high-frequency features deserve polish, rarely-used features might need better discoverability or removal.
Cohort and Retention Analysis
Retention data reveals how well you keep users engaged:
"Here's user retention data by weekly cohort:
Week 0: 100% (by definition)
Week 1: 45%
Week 2: 28%
Week 3: 25%
Week 4: 24%
What patterns do you see?
What might explain the drop at week 2?"
AI might suggest the week 1-2 drop indicates users aren't finding ongoing value, recommending onboarding improvements or engagement features. The stabilization at week 3+ suggests users who make it that far tend to stick around.
Generating Reports
AI excels at transforming raw data into readable summaries:
"Here's this week's analytics summary:
[paste key metrics]
Write a brief report for stakeholders highlighting:
- Key wins
- Areas of concern
- Recommended actions"
This saves time on routine reporting while ensuring important insights don't get buried in spreadsheets.
Best Practices
Provide context — Tell AI about your business, goals, and what you've already tried. "We're a B2B SaaS product targeting small businesses" changes how AI interprets your data.
Ask specific questions — "What should we do?" is vague. "Why might users abandon at step 3?" is actionable.
Verify suggestions — AI provides hypotheses, not certainties. Test recommendations before committing resources.
Iterate on analysis — Follow up with "What else might explain this?" or "What data would help confirm that hypothesis?"
AI doesn't replace analytics expertise, but it makes that expertise more accessible. Use it to accelerate your learning and catch insights you might otherwise miss.