Creator webinar: Actor SEO

Summary: Actor SEO

Fabian’s core recommendation is to treat SEO as a practical channel for bringing prospective users directly to an Actor page—and potentially increasing usage and revenue. AI search changes how people discover products, but he argues that strong conventional SEO remains a foundation for visibility in tools such as ChatGPT and Perplexity.

SEO playbook

• Start with user intent, not the technical implementation. People often search for the data or business outcome they need, rather than for a “web scraper.” Translate what the Actor does into use cases and search terms. For example, a Google Maps scraper might address lead generation or finding restaurants in a particular city.
• Research keywords before editing the README.
  • For a large portfolio, paid tools such as Ahrefs can suggest related keywords and search volumes.
  • For a budget-conscious approach, use Apify’s Google Search Scraper and Google Keyword Data Extractor. Brainstorm relevant terms first, then check search volume and competition.
  • Consider terms related to APIs, MCP, data extraction, monitoring, and tracking, as well as use cases specific to the website being scraped.
  • Prioritize keywords with meaningful search volume and lower competition where possible.
• Use search results to expand keyword ideas. Search-result scrapers can surface related topics. Fabian also demonstrated using ChatGPT’s “fan-out” queries—subqueries generated from a user’s question—and related questions from Perplexity as prompts for README FAQ content.
• Treat the README as both marketing and product documentation. Balance persuasive copy with information users need to evaluate and run the Actor:
  • Open with a clear hook and bullet-point use cases, incorporating relevant target keywords naturally.
  • Include technical details such as inputs and outputs, ideally with examples. Fabian noted that structured examples can also be easier for AI systems to interpret.
  • Add FAQs covering related use cases, integrations, alternatives, and questions such as why to use the Actor instead of a website’s API.
  • Embed a short video showing setup or a sample run. A simple Loom recording may be sufficient; the video can also target the same keywords as the README.
• Use task pages for specific use cases. Create a task for each relevant use case and optimize its title and description for the corresponding keyword. Fabian recommended roughly five tasks for a typical Actor, but said the right number depends on the use cases. Tasks have a hard limit of 50 per Actor.
• Build a relevant internal-link cluster. Link to related Actors—even if you do not own them—to help clarify the topic and connect complementary use cases. Links between your own relevant Actors may also help users discover more of your products. For a portfolio of similar Actors, prioritize the most relevant and higher-search-volume products rather than linking everything indiscriminately.
• Add external distribution and links selectively. Fabian said Apify’s strong domain authority can reduce the need for extensive backlink work, though links can still help. He suggested Peerlist for launching micro-products and Medium or LinkedIn for tutorials and comparison articles. For LinkedIn, he recommended long-form articles over short posts based on an Authority AI report he cited.
• Consider AI-discovery channels. Developers can publish relevant skills in community GitHub repositories and create AI templates that call their Actors. Fabian also suggested targeting Brave Search as a possible route to Claude visibility, while emphasizing that this was still speculative.

Apify Store search and product execution

• Fabian described Apify Store search as a personalized recommender, not simply a keyword-ranking system. He said that Actor quality and signals of recurring use matter more than keyword optimization alone.
• Encourage repeat usage by documenting suitable integrations and scheduling patterns. If an Actor supports ongoing monitoring, explain an appropriate run frequency and how to schedule it.
• Use the Actor quality score to identify missing quality elements. Fabian specifically called out output schemas as an important area to review.
• Design Actors for agent use as well as human use. Clear input, output, and dataset schemas help agents understand how to use an Actor through MCP.
• Prioritize a smaller number of high-quality Actors over many low-quality ones.

Metrics, limits, and timelines mentioned

• Apify’s homepage received nearly 600 organic Google clicks over the previous three months, according to Fabian; he said Actor pages received substantially more organic clicks.
• Apify had more than 40,000 Actor pages at the time of the webinar.
• Fabian cited an Apify domain rank of 80 as a reason creators may be able to gain search visibility without extensive backlink building.
• Google was described as displaying 10 results per search, rather than the previous option to display 100; Fabian linked this change to shifts in which sites appear in AI-generated citations.
• Actor tasks have a 50-task maximum. Around five relevant tasks per Actor was offered as a typical starting point.
• At the time of the Q&A, personalized Store recommendations were live in the console. Marek said MCP was not yet personalized and that broader rollout across surfaces was expected within the next month or two.
• Other Store updates mentioned as upcoming included more granular categories and changes to the quality score. The transcript gives no firm launch dates.

Example

Fabian used a TrustMRR Actor, which scrapes a startup database, to illustrate keyword research and README planning. He considered terms around the database name, startup acquisition and scouting, and related platforms such as Y Combinator, Product Hunt, and Indie Hackers. He then used relevant Actor links, a keyword-targeted task page, and use-case content to connect the product to what users might search for.

Presenters and sources

Presenters and participants: Ella (Senior Developer Community Manager, Apify), Fabian (Senior Product Marketing Manager, Apify; main presenter), and Marek (Apify Store representative). Natasha and Yiannis/Yannis were also acknowledged as supporting the session.

Sources and tools cited: Semrush (SEO industry graph); Authority AI (report on LinkedIn content in AI-search citations); Ahrefs; Apify’s Google Search Scraper and Google Keyword Data Extractor; and Google, ChatGPT, Perplexity, and Brave Search as discovery platforms discussed.