Which sources back pricing, feature, and integration recommendations?

The source mix behind an AI recommendation changes with the buyer's need. In Parse's data through August 2026, social and editorial domains supplied 17.5% of source links attached to pricing-and-contract recommendations, versus 11.8% for general needs. That is a 48% relative difference.

The two sides of that comparison are not the same size. The pricing figure rests on 744 source links across 69 pricing prompts; the general figure rests on 197,258 links across 4,839 prompts, a base roughly 265 times larger. The pricing share is the less settled of the two, and is best read as a direction to investigate rather than a precise constant. Feature and workflow recommendations sat between them, while integration recommendations used the smallest social-and-editorial share at 9.0%.

Pricing recommendations use more social and editorial evidence

The clearest difference is pricing. Of the 744 source links attached to pricing-and-contract needs, 130 point to domains classified as social or editorial. That is 17.5%.

General needs contain 23,287 social or editorial links among 197,258 total links, or 11.8%. Pricing's share is 5.7 percentage points higher and 48% higher in relative terms.

The table does not say social or editorial coverage causes a recommendation. It says these source types appear more often in the evidence attached to pricing picks than in the general baseline. That makes them a stronger place to investigate when a brand loses commercial questions.

Why price needs third-party interpretation

Official pricing pages can state numbers and contract terms. They are still poor at answering every comparative question a buyer asks. A pricing decision often needs interpretation: total cost, value at a given scale, hidden fees, contract flexibility, or whether the cheaper option gives up an important capability.

Editorial comparisons and social discussions fill that interpretive layer. They can put two offers in the same frame, explain a tradeoff, or describe what happened after purchase. AI systems can use that evidence to justify why a brand fits a budget rather than simply quote a price.

This helps explain why source work on pricing cannot stop at the official page. The price-prompt benchmark by category shows that explicit cost questions concentrate in a small set of markets. Where they do concentrate, the source plan should include accurate owned pricing plus independent comparison and experience evidence.

The combination matters. Third-party coverage without a current official price can amplify an outdated claim. An official page without independent context can leave the engine unable to compare value. The useful source stack gives the model both fact and interpretation.

Feature and workflow questions sit in the middle

Feature and workflow recommendations have similar social-and-editorial shares: 13.6% and 13.4%. Those questions often need a blend of product fact and practical use.

A feature requirement can be verified through documentation or a product page, then interpreted through a review, comparison, or user discussion. A workflow question asks whether the product works inside a real process. Social and editorial sources can supply implementation detail, but official documentation and vendor pages still carry much of the evidence.

This is why a source strategy based only on domain authority is incomplete. The best source for a feature specification may be documentation. The best source for day-to-day usability may be a review or community discussion. The best source for a changing price may be the official pricing page, with editorial evidence explaining tradeoffs.

Parse's earlier study of AI citation sources by industry shows that finance, retail, health, software, and other industries have different source recipes. The current study adds a second axis: even inside one industry, the recipe can change when the buyer moves from a general question to price, workflow, feature, or integration.

If you want to see which sources shape AI answers about your brand, run a free brand check — it takes a minute.

Integration recommendations use the smallest social and editorial share

Integration requirements have a 9.0% social-plus-editorial share, the lowest of the five reported need types. The sample includes 929 source links across 108 prompts and 114 brands.

That result is directionally consistent with the job. Compatibility, API behavior, supported connectors, authentication methods, and configuration steps are facts that should be verified in official product documentation. Reviews can describe whether an integration works well, but they are a weaker source for whether a current connector exists or what it supports.

The campaign implication is not "ignore editorial for integrations." It is "lead with the source that can prove the claim." For integration questions, that often means a maintained documentation page, connector directory, changelog, or partner listing. Then add third-party proof where implementation quality or reliability matters.

The existing product documentation citation playbook covers how to make those pages extractable. If an engine cannot retrieve a clear compatibility statement, a strong social reputation will not repair the missing fact.

Build a source map by buyer need

A useful source audit should cross two dimensions:

1. The buyer need: general, feature, workflow, price, integration, trust, audience, or another decision type.
2. The source role: official fact, independent comparison, user experience, institutional authority, or reference context.

Create one row per important prompt and record which domains support the brands the engine recommends. Then classify the gap:

• Missing fact: the brand lacks a current official page or documented claim.
• Missing comparison: credible third parties compare competitors but omit the brand.
• Missing experience: social or review evidence discusses competitors but not the brand.
• Conflicting evidence: official and third-party sources disagree.
• Stale evidence: cited pages describe an old price, feature, integration, or name.

This is more actionable than a generic top-domain list. The AI citation gap analysis explains how to turn missing sources into a backlog. Buyer-need tagging tells you which gap affects a commercial question and which is merely visible.

How to use Parse for the sequence

Start with /rankings to identify the buyer questions that matter in the category. Use /brands to see which brands the engines put forward on those questions. Then open /sources to inspect the domains behind the answer.

For a pricing loss, check official price accuracy and then the independent comparisons or discussions that frame value. For a feature loss, check whether the feature is stated clearly and verified outside the brand's site. For an integration loss, check documentation and partner directories first. For a workflow loss, look for implementation evidence from credible users and practitioners.

The sequence keeps source work tied to a recommendation outcome. A placement on a popular domain is not automatically useful. It is useful when that domain supplies evidence for a buyer question the campaign needs to win.

Why a universal source list stays saturated

The competitor audit found extensive content on Reddit, top cited domains, source gaps, earned media, and broad citation strategy from Profound, Peec, Scrunch, Semrush, and Ahrefs. Parse also has mature source coverage. Another list of "sites AI cites" would overlap a crowded market.

The open question is allocation: which source class matters for which buyer need? The 17.5% pricing share is useful because it narrows an otherwise generic tactic. Social and editorial evidence is not a universal answer. It has a larger role on pricing than on general or integration questions in this data.

The same principle applies to directories. Parse found there is no universal directory tax. Source leverage is local to a category and a question. Campaign plans should be built at that intersection.

How we measured this

We queried PromptEvidenceLanguageAnalysis, PromptEvidenceSourceProvenance, and CitationDomain through Cosmo. The transaction used SET default_transaction_read_only = on, repeatable read, and a 60-second statement timeout. The frozen snapshot was taken at August 29, 2026, 16:20 UTC.

We selected validated language-evidence records executed on August 18, 2026, the latest complete reviewed evidence day identified in the 30-day preflight. We joined each language analysis to its source-provenance rows, then deduplicated to one link per buyer need, prompt, recommended brand, and citation domain. Citation domains were grouped using the existing sourceType taxonomy.

The primary query and an independent COUNT(DISTINCT ROW(...)) reconstruction returned identical domain-link and social-plus-editorial counts for all five need types. The provenance layer was being backfilled during research, so all published figures come from the named repeatable-read snapshot rather than a later ad hoc count.

The source taxonomy is heuristic and noisy at the margins. social and editorial are broad classes. The unit is a repeated evidence link, not a unique user session, and the panel is not a causal experiment. Smaller types, especially pricing and integration, should be read as a directional planning signal with the reported denominators attached.