What Is Share of Recommendation?

Share of Recommendation (SoR) is the share of AI answers, across a fixed set of buying questions, in which an answer engine names your brand as an option. It is a share of a defined answer set, not a ranking position and not a traffic figure.

The formula

SoR = your brand's recommendations divided by all brand recommendations in the same answer set, times 100.

The denominator is the part people get wrong. Divide by the number of answers instead of the number of brand mentions and you measure something else: how often you show up at all, a figure that rises whenever engines start naming more brands per answer. Dividing by total brand mentions keeps the competitive set in frame, so your SoR falls when a rival is named more often even though your own count never moved.

Both quantities are legitimate. Only one of them is Share of Recommendation, and a report that does not state its denominator is not reporting a measurement.

What has to be frozen

SoR only means something against a fixed instrument. Our audit fixes it at 30 to 40 prompts, four models, three iterations each: the same prompts, in the same wording, every time the test runs.

The iterations are not padding. Answer generation is non-deterministic, so the same model asked the same question three times can return three different brand lists. One run is a sample. Three iterations turn that sample into a rate you can compare next quarter.

The prompt set is frozen for the same reason. Add prompts between runs and a movement in SoR tells you the test changed, not that your visibility did.

What SoR is not

• Not Citation Share. Citation Share counts how often your pages are used as a source. A brand can be cited in an answer that goes on to recommend someone else.
• Not Top3. Top3 records whether you were among the first three brands named. SoR counts presence, Top3 counts prominence.
• Not sentiment. Being named in "avoid X because" still counts toward SoR, which is why sentiment is recorded per answer as its own field.
• Not a ranking. A generated answer has no result list to hold a position in. A brand is either named or absent.

The counting rules

These rules decide the number more than the prompt wording does, so they belong on paper before the first run rather than in an argument after it.

• A brand named repeatedly inside one answer counts once for that answer. Otherwise long answers inflate whoever they discuss at length.
• Product names and parent-company names resolve to one entity, and the mapping is declared in advance.
• Every session starts clean: no memory, no prior turns, no personalisation, same locale and interface language.
• Co-mentions are logged next to the count, because who you were listed with is a separate finding from how often you appeared.

Reading the number

SoR is relative by construction. It carries no meaning without the competitor set, the model list and the date attached to it, and those are part of the metric rather than notes around it.

A low number and a zero mean different things. A low number means the engines already have a confident recommendation set and you are not in it, so the work is displacement. A zero across every model usually means there is no settled set for that question yet, and the work is to become the answer the engines settle on.

Movement is the signal worth acting on. The level tells you where you stand today. The same instrument run again in a quarter tells you whether anything you built changed how you are described.

Where it sits in the method

SoR is the headline output of F1, the audit: a baseline for your brand and for the named competitors, reported per model. It is also the number F5 re-runs on a schedule against the same prompt set, so that before and after are the same question asked the same way.

The phases in between change the inputs SoR reads. F2 decides whether an engine can identify you at all, F3 decides whether your pages give it something it can lift, and F4 decides whether other sources confirm what you say about yourself.