How to Track AI Citations for Your Brand (Monthly Tracker Template)

What AI citation tracking actually measures

AI citation tracking measures three things simultaneously: whether your brand appears in AI-synthesized answers at all (cited rate), whether you are the lead recommendation when cited (primary recommendation rate), and how the AI describes you when it cites you (citation accuracy). All three matter and each one points to different fixes.

Citation tracking is not the same as AI traffic measurement. There is no equivalent of Search Console for AI engines yet. You are tracking visibility, not traffic, until OpenAI and Anthropic ship analytics for cited domains.

Why monthly is the right cadence

Daily tracking is noise; quarterly tracking misses model changes. Monthly captures the right signal:

• Live-retrieval engines (Perplexity, AI Overviews) update fast enough that monthly catches structural-fix wins inside 4 to 6 weeks.
• Training-data engines (ChatGPT, Claude, Gemini standalone) update on model release cycles, which usually fall every 4 to 12 weeks. Monthly catches model-cycle changes.
• Monthly cadence is sustainable. Daily tracking exhausts whoever does it; quarterly tracking misses the tactical decisions that need to be made between quarters.

How to choose your 15-20 buyer-intent prompts

Prompt selection is the highest-leverage decision in the tracker. Wrong prompts produce noise; right prompts produce decisions. Source prompts from four places:

• Search Console queries. Pull the top 50 queries you already get impressions for and reformat them as buyer questions.
• Sales call recordings. Note the exact phrasing prospects use when they ask about your category.
• Competitor FAQ pages. Their buyer-question coverage is a useful prompt seed.
• People Also Ask in Google. Direct buyer-question phrasing for your seed terms.

Mix four prompt categories: comparative ("best AI receptionist for a dental clinic"), informational ("how does an AI receptionist work"), local ("AI receptionist provider in Berlin"), and brand-specific ("is [your brand] reliable" once you have brand presence). The mix shows you visibility across the buyer journey.

Which engines to track and how

Run each prompt in a fresh browser session, logged out where possible, on the free tier of each engine. This produces results that match what your buyers will see.

The spreadsheet template

Use this column structure (Google Sheets or Excel works fine):

• Prompt - the buyer question text
• Category - comparative, informational, local, brand
• Date run - YYYY-MM-DD
• Engine - Perplexity, ChatGPT-search-on, ChatGPT-search-off, AI Overviews, Gemini, Claude
• Cited - yes / no
• Position in citation list - 1, 2, 3 (or blank if not cited)
• Primary recommendation - yes / no (only fill if cited)
• URL cited - the specific page the engine linked to
• Verbatim text - the exact sentence the engine returned about your brand
• Top competitor 1 cited - yes / no
• Top competitor 2 cited - yes / no
• Top competitor 3 cited - yes / no
• Notes - anything unusual (model-change behavior, error states, ad placements)

One row per prompt-engine combination. Twenty prompts times six engines is 120 rows per month. The whole exercise takes 90 minutes by hand.

Five metrics that matter

Roll the row data into five top-line metrics:

• Cited rate per engine. Percentage of prompts where your brand is cited. Track per engine and overall.
• Primary recommendation rate. Percentage of cited prompts where you are the lead recommendation, not one of several alternatives.
• Citation share vs top three competitors. Your cited rate divided by total citation slots taken across you and the top three competitors. This normalizes for category competition.
• Citation accuracy. Percentage of citations where the AI describes you correctly. Wrong descriptions point to a content problem.
• Month-over-month delta. Change in cited rate per engine compared to last month. The deltas tell you what fixes worked.

Common pitfalls in citation tracking

• Running prompts logged in to a paid account. Personalization skews results. Use logged-out or fresh-browser sessions.
• Only tracking comparative prompts. You miss the informational and local visibility that compounds over time.
• Tracking too many prompts. 15 to 20 is the sweet spot. 50 prompts produces unmaintainable monthly work.
• Switching engines or prompts between months. Keep the prompt set and engine list constant for at least six months to see trends.
• Skipping verbatim text. Without verbatim quotes you lose the qualitative signal that points to content fixes.
• Treating one bad month as a trend. Citation rates fluctuate; require three consecutive months of decline before acting on it.

Tools to automate the tracker

Manual tracking works fine for 15 to 20 prompts. If you scale to 50+ prompts or 10+ engines, automated tools become useful:

• Otterly.AI - tracks brand mentions in ChatGPT, Perplexity, Gemini.
• Profound - dedicated AI search visibility platform.
• Peec AI - AI citation monitoring across multiple engines.
• Brandwatch / Mention / Brand24 - traditional brand monitoring tools that track unlinked mentions feeding AI training data.

Most early-stage GEO programs do not need these. Manual tracking gives you the qualitative depth that automated tools strip out.

How to act on the tracker output