AI Visibility Metrics: A 20-Minute Weekly Routine

Track your product's presence across ChatGPT, Perplexity, and AI Overviews with nothing but a spreadsheet

Learn how to monitor GEO signals and measure AEO presence across major AI search platforms using a free, repeatable weekly system. Build a tracking sheet with scored prompts, baseline readings, and week-over-week trend data — no enterprise dashboard required.

TL;DR

You don't need expensive tools to track AI visibility - A spreadsheet, 10 category prompts, and 20 minutes per week across ChatGPT, Perplexity, and Google AI Overviews gives you a reliable baseline and trend data.
Start tracking the week you launch - Your first score will likely be 0%, and that's your baseline. Waiting means missing weeks of trend data you can't recover.
Track mention rate, position, sentiment, and competitive share of voice - These simplified versions of the five core AI visibility metrics tell you whether you have an existence, authority, or perception problem.
Use weekly aggregates, not daily snapshots - LLM responses vary significantly day to day. Weekly averages across all prompts and platforms reveal actual trends.
Every tracking session should produce one action item - Use the decision framework (0% = publish more, low position = build authority, negative sentiment = fix perception) to turn data into specific weekly tasks.

What You'll Build: A 20-Minute Weekly AI Visibility Routine

By the end of this tutorial, you'll have a repeatable system to monitor GEO signals and track your AI visibility metrics across ChatGPT, Perplexity, and Google AI Overviews. No enterprise dashboard. No $200/month subscription. Just a spreadsheet, a timer, and a weekly habit that tells you whether AI search engines are surfacing your product when people ask the questions your app answers.

Your success criteria are concrete: you'll have a tracking sheet with 10 high-value prompts, baseline readings across three AI platforms, and a scoring system that shows you week-over-week whether your AEO presence measurement is climbing, flat, or dropping. The whole routine takes about 20 minutes once you've set it up.

Prerequisites and Setup Checklist

Before you start, make sure you have the following ready. This setup takes roughly 30 minutes the first time, then the weekly routine runs in 20.

Free accounts on three AI platforms:ChatGPT (free tier works), Perplexity AI (free tier), and access to Google Search with AI Overviews enabled
A spreadsheet tool: Google Sheets, Notion table, or Airtable (all free)
Your product's core category defined: You need to articulate what your app does in one sentence (e.g., "task management for freelancers" or "invoice automation for solo consultants")
20 minutes of uninterrupted time each week, same day preferred
A browser with incognito/private mode to avoid personalization bias in results

Potential blocker: If your product launched less than 48 hours ago, AI models almost certainly haven't indexed any content about it yet. That's fine. You'll establish a zero baseline and track from there.

Why Manual Tracking Before Automated Tools

Enterprise GEO platforms like Otterly, Peec, and Profound cost $100 to $500+ per month. They're built for marketing teams managing dozens of product lines. If you're a solo founder pre-$1k MRR, that spend doesn't make sense yet.

More importantly, manual tracking teaches you something tools can't: how AI models actually talk about your category. You'll notice the phrasing they use, which competitors get cited, and what types of sources earn mentions. That pattern recognition is worth more than any dashboard at this stage.

As Peec.ai's research team notes, "We should stop using traffic as a reliable KPI, and start using a blend of Branding and Performance KPIs: AI visibility, sentiment, purchases, and revenue." This tutorial gives you the branding and visibility layer without the enterprise price tag.

Step 1: Define Your 10 High-Value Category Prompts

Open your spreadsheet and create a tab called "Prompt Bank." You're going to write 10 prompts that a potential user of your product might type into an AI assistant. These aren't random. They map to your buyer's journey.

Write 3-4 discovery prompts: These are broad category questions. Examples: "What are the best tools for [your category]?" or "How do I [solve the problem your app solves]?" Write them exactly as a non-technical person would ask.

Write 3-4 comparison prompts: These pit solutions against each other. Examples: "[Competitor A] vs [Competitor B] for [use case]" or "What's the cheapest [category] tool for solo founders?"

Write 2-3 solution prompts: These are high-intent. Examples: "Best [category] tool under $20/month" or "[Category] tool with [specific feature] for small teams."

Expected result: A column of 10 prompts in your spreadsheet. Checkpoint: Read each prompt aloud. If it sounds like something you'd actually type into ChatGPT, keep it. If it sounds like a keyword, rewrite it in natural language.

Common failure: Writing prompts that are too specific to your brand name. If nobody knows your brand yet, nobody is prompting for it. Focus on category and problem language instead.

Step 2: Build Your Tracking Spreadsheet

Create a new tab called "Weekly Tracking." Set up columns in this exact order:

| Date | Prompt | Platform | Mentioned? (Y/N) | Position (1st/2nd/3rd/Not Listed) | Sentiment (+/0/-) | Competitors Mentioned | Source Cited | Notes |

"Mentioned?" is binary: did the AI response include your product name or a direct reference to your product? Yes or No.

"Position" tracks where in the response your product appears. First recommendation, second, third, or not listed at all. According to AIrops, the five core AI visibility metrics that matter most are citation share, competitive share of voice, mention rate, sentiment score, and drift. Your spreadsheet captures simplified versions of all five.

"Sentiment" is your quick read: did the AI say something positive (+), neutral (0), or negative (-) about your product? If you weren't mentioned, leave it blank.

Expected result: An empty but structured sheet ready for data entry. Each week will add 30 rows (10 prompts × 3 platforms).

Step 3: Run Your First Baseline Test on ChatGPT

Open an incognito browser window and go to ChatGPT. Use the free tier. Do not use any custom GPTs or system instructions that might bias responses.

Type your first prompt exactly as written in your Prompt Bank. Do not add context like "I'm a founder" or "I'm looking for something cheap" unless that's part of your original prompt. Read the full response. Record your data in the spreadsheet.

Repeat for all 10 prompts. This takes about 5-7 minutes.

Checkpoint: After all 10 prompts, count how many times your product was mentioned. For most newly launched apps, the answer will be zero. That's your baseline, not a failure. You now have a number to beat.

Common failure: Running prompts in a logged-in session where ChatGPT has memory of previous conversations about your product. Always use incognito or a fresh session. Also note that GetFancy.ai recommends 60-100 prompt repetitions per topic for statistical accuracy, but for a solo founder's weekly check, 10 prompts give you a directional signal that's good enough to act on.

Step 4: Repeat on Perplexity AI

Open Perplexity AI in another incognito tab. Run the same 10 prompts. Perplexity is especially useful because it shows its sources, so you can see exactly which URLs the AI pulled from.

Pay close attention to the "Source Cited" column. When Perplexity mentions a competitor, note the URL it links to. Is it a blog post? A comparison article on a third-party site? A Product Hunt page? A Reddit thread? This tells you where you need to show up to get cited.

Record all 10 results. This takes another 5-7 minutes.

Expected result: 10 more rows in your spreadsheet. You'll likely notice that Perplexity and ChatGPT give different answers to the same prompt. That's normal. AI visibility metrics quantify brand presence inside AI responses specifically, and each platform draws from different source pools.

Step 5: Check Google AI Overviews

Open Google Search in incognito. Type each of your 10 prompts. Not all prompts will trigger an AI Overview, and that's expected. When one does appear, record whether your product is mentioned in the AI-generated summary at the top of the page.

Also note the traditional organic results below. If your product appears in organic results but not in the AI Overview, that gap is actionable intelligence. It means Google knows about you but its AI layer isn't pulling you into generated answers yet.

This step takes 5-7 minutes. You now have approximately 30 data points for the week.

Step 6: Calculate Your Weekly AI Visibility Score

Create a new tab called "Scores." Here's the formula for your weekly AI visibility score:

AI Visibility Score = (Number of mentions across all platforms / Total prompts tested across all platforms) × 100

For your first week, if you tested 10 prompts across 3 platforms (30 total checks) and got mentioned 2 times, your score is 6.7%. Write it down. This is your baseline.

Also calculate per-platform scores. You might be at 0% on ChatGPT, 10% on Perplexity, and 0% on Google AI Overviews. That tells you Perplexity is pulling from sources where you already have some presence.

AI visibility is defined as the quantitative measure of how often and how prominently a brand appears in responses generated by AI assistants and generative search engines. Your weekly score is your personal version of this metric, tailored to the prompts that matter for your product.

Step 7: Track Competitive Share of Voice

Go back to your data and count how many times each competitor was mentioned across all 30 checks. Create a simple leaderboard:

| Competitor | Mentions | Share of Voice |
|-----------------|----------|----------------|
| Competitor A | 18 | 60% |
| Competitor B | 9 | 30% |
| Your Product | 2 | 6.7% |
| Competitor C | 1 | 3.3% |

This competitive share of voice tells you who owns the AI conversation in your category. It also tells you who to study. What content does Competitor A have that earns them 60% mention rate? Check the sources Perplexity cited for them.

If you're using heycatch for your daily growth plans, this competitive data feeds directly into the kind of competitor research the platform surfaces. It can help you prioritize which content gaps to close first based on what's actually driving visibility in your category.

Step 8: Set Up Your Weekly Cadence

Pick a day and time. Tuesday morning works well because it gives you the rest of the week to act on findings. Block 20 minutes on your calendar. Make it recurring.

Each week, run the same 10 prompts across the same 3 platforms. Record the same data points. Then compare your scores to the previous week.

Day-to-day LLM position results can vary significantly, making weekly averages the required method for identifying actual trends. Don't panic if you drop from 6.7% to 3.3% one week. Look at the 4-week trend instead.

Every 4 weeks, review and rotate 2-3 prompts. Replace prompts that consistently generate irrelevant results with new ones that better match how your target users actually search. Your prompt bank should evolve as you learn the language your audience uses.

Step 9: Connect Visibility Changes to Content Actions

Your tracking routine isn't useful unless it drives action. Here's the decision framework:

If your mention rate is 0% after 4 weeks: You have a content existence problem. AI models can't cite what doesn't exist. Publish comparison posts, get listed on aggregator sites, and contribute to relevant community discussions where AI models scrape data.
If you're mentioned but in 3rd+ position: You have an authority problem. Your content exists but isn't authoritative enough. Focus on building content that ties to measurable outcomes and earning third-party mentions on sites AI models trust.
If sentiment is negative: You have a perception problem. Check what specific claims the AI is making and address them in your content, documentation, or product.
If one platform shows you but others don't: Study the sources that platform uses and replicate your presence on sources the other platforms favor.

This decision tree turns passive tracking into an active growth lever. Each week, you should leave your 20-minute session with one specific action item for the coming week.

Configuration and Customization

Adjusting Your Prompt Count

Initial manual tracking should focus on 10-20 high-priority prompts across 2-3 primary platforms. If 10 prompts across 3 platforms feels like too much, start with 5 prompts across 2 platforms (ChatGPT and Perplexity). You can always expand later.

Platform Priorities

If your audience skews technical, add Claude to your rotation. If your audience searches primarily on mobile, prioritize Google AI Overviews. Match your platform selection to where your users actually ask questions.

Safe Defaults vs. Must-Change Settings

Safe default: 10 prompts, 3 platforms, weekly cadence
Must change: Your actual prompts. The example prompts in this tutorial are templates. Replace them with language specific to your product category. If you're building a time-tracking app for freelancers, "best project management tool" is too broad. "Best time tracker for freelance designers" is closer to what your users actually type.

Verification and Testing

After your second week of tracking, verify your system is working by checking three things:

Consistency test: Run 3 of your prompts twice on the same platform in the same session. Results should be similar (not identical, but the same products should appear). If results are wildly different each time, your prompts may be too vague.
Relevance test: Are the AI responses actually answering a question your target user would care about? If the responses discuss enterprise solutions when you're targeting solo founders, your prompts need refinement.
Actionability test: After two weeks of data, can you identify at least one specific action to take? If not, your tracking columns might be missing context. Add a "Source Type" column (blog, Reddit, Product Hunt, docs) to understand where winning competitors get their citations.

Common Errors and Fixes for AI Visibility Metrics Tracking

"The AI gives completely different answers each time I ask"

Cause: LLMs are non-deterministic by design. Fix: Don't compare individual responses. Compare weekly aggregates. If your mention rate was 10% last week and 13% this week across all prompts, that's a real signal. One prompt flipping from yes to no is noise.

"I'm mentioned on Perplexity but never on ChatGPT"

Cause: Perplexity searches the live web. ChatGPT relies more heavily on training data and plugins. Fix: Focus on getting mentioned on high-authority sites that are likely included in ChatGPT's training data (well-established blogs, documentation sites, Wikipedia-adjacent sources).

"My competitor gets mentioned even though their product is worse"

Cause: AI models don't evaluate product quality. They reflect what's written about products across the web. Fix: Publish more citable content. Comparison posts, listicle placements, and choosing the right marketing channel to build topical authority all increase your citation surface area.

"I've been tracking for a month and nothing has changed"

Cause: Tracking alone doesn't create visibility. You need to pair it with content creation and distribution. Fix: Review Step 9's decision framework. Identify whether you have an existence problem, authority problem, or perception problem, and act on it.

"Google AI Overviews never appear for my prompts"

Cause: AI Overviews don't trigger for all queries. Highly specific or niche queries may not generate them. Fix: Test slightly broader versions of your prompts. If "best invoice tool for solo yoga instructors" doesn't trigger an Overview, try "best invoice tool for freelancers."

Next Steps and Extensions

Once you've run this routine for 4-6 weeks, you'll have enough data to make strategic decisions about your content and positioning. Here's where to go next:

Add structured data and schema markup to your website to make your content more machine-readable. This improves your chances of being cited by AI models that parse structured sources.
Build a "citation source" hit list from the URLs Perplexity cites for your competitors. Pitch guest posts, get listed, or create content specifically for those platforms.
Graduate to automated tracking once you hit $1k MRR and can justify $50-100/month on a dedicated tool. Your manual data gives you the baseline to evaluate whether any tool is actually telling you something new.

If you're running a daily growth loop alongside this weekly AI visibility check, you'll have both real-time user signals and longer-term discoverability trends feeding your decisions. That combination is how solo founders compete with funded teams without matching their spend.

Frequently Asked Questions

What is AI Search Visibility and why does it matter for new apps?

AI search visibility measures how often and how prominently your product appears in AI-generated answers from tools like ChatGPT, Perplexity, and Google AI Overviews. It matters because a growing number of potential users discover software by asking AI assistants rather than browsing traditional search results. If your product isn't surfaced in those responses, you're invisible to that audience segment regardless of how good your SEO is.

When should I start measuring my AI visibility?

Start the week you launch. Your first readings will almost certainly be zero, and that's the point. You need a baseline to measure progress against. Waiting until you "have more content" means you're flying blind during the critical early weeks when every growth lever matters. The 20-minute weekly routine described here costs nothing but time.

Which AI platforms should I monitor for visibility?

Start with three: ChatGPT (largest user base), Perplexity AI (shows its sources, giving you actionable intelligence), and Google AI Overviews (captures the traditional search audience). If your audience is technical, consider adding Claude. The key is consistency. Pick your platforms and test the same prompts on them every week.

How can I optimize my content for better AI citations?

Focus on content citability. Write clear, factual comparison posts. Get your product listed on aggregator sites and directories that AI models frequently reference. Use structured data and schema markup on your website. Contribute substantive answers in community forums like Reddit and Stack Overflow. AI models favor sources that are authoritative, frequently referenced by other sites, and clearly structured.

Do I need paid tools to track AI visibility metrics?

Not at the early stage. Enterprise tools like Otterly, Peec, and Profound are designed for teams managing large portfolios of keywords and products. A solo founder pre-$1k MRR gets more value from the manual routine in this tutorial because it builds intuition about how AI models discuss your category. Consider paid tools once your revenue justifies the expense and your manual tracking has established clear baselines.

How long before I see changes in my AI visibility score?

Expect 4-8 weeks of consistent content work before you see meaningful movement. AI models update their training data and web indexes on different schedules. Perplexity (which searches live) may reflect changes within days. ChatGPT may take longer depending on when its training data is refreshed. Track weekly averages over a 4-week rolling window to identify real trends rather than reacting to single-week fluctuations.