A brand appearing in an AI answer is worth knowing about. It doesn't tell you which recommendation caused a purchase or which publisher should receive commission. Rakuten's collaboration with Similarweb makes that distinction useful for affiliate managers: ask what the new data can help you decide before treating it as another revenue report.
Give the report one job to do
Consider a fictional luggage brand reviewing AI answers about carry-on bags for short business trips. Its team wants to know whether those answers mention its bags and whether the cited pages describe the dimensions accurately. A useful first task is to identify factual gaps in product information. Increasing a publisher's commission merely because one answer cites its page would answer a different question without the evidence to support it.
For this illustrative review, record the question, AI service, check date, brand mention and cited URL. If an answer says a bag has a laptop pocket that the bag doesn't have, verify the real specification and locate the source of the error. That gives the content team a concrete issue to fix. It is not a measured campaign result or a promise that changing the page will change the next AI answer.
What the announcement actually promises
Rakuten International's March 25, 2026 announcement described plans to integrate Similarweb data into Rakuten Advertising's analytics and reporting environment. It said the capabilities would initially reach selected clients, with broader availability and additional reporting details to follow. That supports an integration announcement, not a claim that every advertiser already has the report or needs no additional commercial agreement.
Ask your account team to demonstrate the current product, explain account eligibility and identify any costs or contract requirements. The original article's blanket claims about included access and competitors having no equivalent capability were not established by the announcement. A product briefing is more useful than assuming your account contains a feature you haven't seen.
Read the metric before using the score
Request definitions for any visibility metric: which AI services, questions, countries, languages and dates it covers; what counts as a brand mention or citation; and whether it represents sampled answers, observed referral traffic or something else. Don't combine those different measurements under one convenient label.
In the fictional luggage review, three mentions in ten manually checked answers means exactly that: three mentions in that small set on that date. It doesn't mean a 30% share of all AI recommendations, and it isn't an estimate of buyers. If a paid report uses a larger or different sample, read its methodology before comparing the two. Keep the question set stable enough that a later comparison has meaning.
Turn an observation into a sensible action
Suppose the review finds that a frequently cited comparison page uses last year's bag dimensions. The next action is to check the page and the manufacturer's current specification, then work through the appropriate correction process. Separately, assess that publisher using your normal audience, content-quality and approved-sales evidence. Citation visibility can inform a conversation without becoming an automatic payment rule.
If you want to relate visibility to sales, involve the measurement owner. A rise in mentions alongside affiliate sales is a reason to investigate timing, promotions and other changes. It does not isolate the effect of the AI answer, the cited article or the affiliate relationship.
The next step
Bring one real business question to the product demonstration, such as finding inaccurate product information in cited pages. Ask the provider to show how its report answers that question and what remains unknown. You will leave with a usable decision about the data instead of a score that everyone admires and nobody knows how to use.