Scrunch recommends pursuing both brand citations and placements in cited sources. It’s worth investing in original content when competitors are the cited source, as they likely won’t offer placement opportunities, and when cited sources are substance-light or mismatched to the prompt. Meanwhile, getting mentioned in trusted, frequently-cited sources will improve AI search performance and establish credibility.

For example, imagine a Scrunch user at a cybersecurity company is tracking the prompt, "What's the best endpoint security software for small businesses?"
In the Citations tab, they find three types of sources appearing frequently in AI responses:
Scenario 1: Competitor pages (invest in original content)
A competitor's product page appears in 65% of responses. Since the competitor is unlikely to offer a placement opportunity, the user instead focuses on creating a stronger original page—a detailed comparison guide with structured data, clear use cases, and other optimizations—designed to displace it.
Scenario 2: Substance-light cited sources (invest in original content)
A blog post titled "Best Endpoint Security Tools" appears in 40% of responses but contains only a brief paragraph per tool with no real depth. The user creates a more authoritative alternative—with benchmarks, expert quotes, and a detailed feature breakdown—to give AI platforms a better source to cite.
Scenario 3: Trusted third-party domains (pursue placement)
A well-known cybersecurity review site appears consistently across dozens of the user's tracked prompts. Rather than competing with it, the user contacts the site to request a product listing or contributed article, earning a mention in a source AI platforms already trust and cite frequently.
Patterns that typically indicate a low-hanging fruit citation opportunity include:
Brand citations:
Third-party placements:
Scrunch recommends tracking brand presence, citations, referral traffic, AI agent traffic, and share of voice versus competitors as key performance indicators.
Scrunch recommends monitoring AI search trend data like brand mentions and citations consistently over 2-3 week periods to identify real trends versus one-off changes.
Scrunch recommends estimating how many prompts to track for AI search using the following approach: X [# of topic clusters] x Y [12-15 questions related to each topic cluster] = Z [# of AI search prompts to track]. The primary goal is to get a representative sampling of data across all customer journey stages via a mix of branded and non-branded prompts.