AEO and GEO for food and beverage brands: getting cited in AI recipe and product answers
How food and beverage brands can position recipe and product content to be cited when someone asks AI for a recipe idea or product recommendation.
4 min read
Adapting traditional share-of-voice measurement — a well-established concept in marketing — to AI answer visibility provides a practical, comparative framework for understanding competitive position, even given the current limitations in precise, comprehensive measurement tools.
The basic framework
For a fixed, defined set of priority queries relevant to your business, test each query across major AI platforms and record which sources get cited. Your 'share' is the proportion of total citations across all competitors that belong to you, tracked consistently over the same query set over time.
- Define a fixed, representative set of priority queries relevant to your business and competitors.
- Test consistently across the same AI platforms each measurement period.
- Record citations for yourself and identifiable competitors for each query.
- Calculate your share as your citations divided by total citations observed across everyone tracked.
Why this comparative framing is more useful than an absolute number
Given the current measurement limitations, an absolute 'AI visibility score' is hard to validate meaningfully. A comparative share-of-answers metric, tracked consistently against the same competitors over the same queries, provides a more genuinely useful, defensible trend line even without perfect precision.
Perfect measurement is not currently available for this metric — but a consistent, comparative measurement, tracked the same way every time, is genuinely useful even while imperfect.
Reporting this to stakeholders
Present this as a directional, comparative trend — 'our share of AI answer citations for our priority query set grew from X% to Y% this quarter' — while being transparent about the manual, sampling-based nature of the underlying measurement.
Frequently asked questions
How many queries should be included in this tracking framework?
Enough to be genuinely representative of your priority topics — typically 10-20 carefully chosen queries provides a reasonable, manageable sample for most businesses to track consistently.
Should this framework include every AI platform, or a select few?
Focus on the platforms most relevant to your specific audience's actual usage patterns, rather than attempting comprehensive coverage across every platform that exists.
Can this framework be automated rather than manually tested?
Partially, as dedicated tools mature, per our LLM citation tracking tools guide — currently, manual testing remains a reliable, if labour-intensive, fallback.
If you want help building a share-of-AI-answers measurement framework for your business, talk to our team.
The takeaway
Calculate your share of AI answers exactly like traditional share-of-voice — citations you received divided by total citations across all competitors observed, tracked consistently over the same fixed query set each month.
Written by Fibvo — Bengaluru and Erode, India — working with clients in other time zones. Questions, or think we have it wrong? Tell us.
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