Review platform strategy
Source: Goodie, The Most Cited B2B SaaS Domains in AI Search. 5.7 million citation links analysed across B2B SaaS prompts.
Why assistants lean so heavily on review marketplaces
A buyer asking an assistant for software recommendations is asking a comparison question, and review marketplaces are the only sources on the open web that hold structured, comparable, third-party data across an entire category. Consistent fields, ratings, category taxonomies and volume of independent opinion make them unusually easy to retrieve from and unusually safe to quote.
They also solve an attribution problem for the model. Quoting a vendor's own site to recommend that vendor is circular, and assistants behave as though they know it. A review platform lets the model attribute an evaluative claim to something that is not the seller.
What follows is that a thin, out-of-date or miscategorised listing does more damage than no listing at all. It is retrieved either way, and what it says becomes what the assistant says about you.
What the programme actually involves
Category placement and taxonomy
Being in the wrong category, or in too few of them, removes you from the comparison sets assistants retrieve. This is the fastest fix available on these platforms and the most commonly neglected.
Profile completeness and currency
Feature lists, integrations, pricing structure, screenshots. Assistants quote these fields directly, so a stale profile becomes a stale answer about your product.
A sustainable review-generation programme
Reviews at a steady cadence from real customers, prompted at the right moments in the lifecycle. Volume matters, but recency and distribution across segments matter more for retrieval.
Comparison and alternatives pages on the platforms
Head-to-head comparison pages on review sites are heavily retrieved for exactly the queries with buying intent, and most vendors never think about how they are presented on them.
Response and correction
Answering reviews, correcting factual errors, keeping the record accurate. Quietly one of the highest-leverage activities, because it changes what gets quoted.
What we will not do, and why
This is the part of AI search where buying the outcome is cheapest, most tempting and most damaging. The exposure sits with your brand, not with your advisor, so we would rather be explicit before you buy than after.
We do not buy, incentivise beyond platform rules, or fabricate reviews
It violates every platform's terms, it is detectable, and the downside when detected exceeds any plausible upside. If someone offers you review volume as a deliverable, that is what they are selling.
We cannot make a bad product review well
This programme surfaces and amplifies what customers genuinely think. Where the underlying sentiment is the problem, we will say so rather than sell you a review programme.
Review platform strategy questions
Citations come from more than one place
Reddit and community presence
Reddit is the most-cited domain in the analysed set, ahead of G2. Assistants, and ChatGPT in particular, lean on user-generated discussion for software recommendations, because it is where people say what a product is actually like to use.
Learn moreAnalyst and publisher placement
Five of the ten most-cited domains for B2B SaaS prompts are publishers or analysts: PCMag, TechRadar, Gartner, Forbes and TechCrunch. These are earned placements with long lead times and unusually long half-lives.
Learn moreYouTube and video
YouTube is in the ten most-cited domains for B2B SaaS prompts, and it is the one most SaaS marketing teams treat as a brand channel rather than a citation source.
Learn more