The concept of ‘AI fanning’ as outlined in the latest insights revolves around transforming an initial query into a multitude of subqueries, scraping hundreds of top-ranked pages, and consolidating this data to deliver in-depth, research-oriented answers.
For AI product managers, this methodology is a vital framework for driving high-intent traffic and achieving conversion rates between 10–40%—a significant uptick compared to typical conversion figures. The first step is to understand and implement effective prompt expansion.
By generating around 100 targeted subqueries from one core prompt, product teams can increase the search engine’s surface area, resulting in higher visibility on platforms like Google. Integrating AI SEO tools such as Prompt Watch or an AI SEO tracker enables tracking of source URLs and monitoring the reach of the organic content.
Furthermore, securing listicle mentions or paid placements through affiliate strategies—often costing around $500 per link—can provide an additional boost, ensuring that your product or feature is discovered by users who are in the final stages of their decision-making process.
This approach needs to be paired with deep analytics that measure not only the traffic but also the quality of conversions. Product managers should coordinate closely with technical teams, data analysts, and marketing to establish A/B tests, refine query spreads, and optimize the amplification of content. Strategic alignment between engineering innovation and marketing tactics is crucial.
Ultimately, the takeaway for PMs is to conceptualize the AI search framework as an ecosystem that bridges product discovery and conversion. This innovative, data-driven strategy provides actionable insights to enhance user engagement and drive significant conversion improvements.