{"id":81513,"date":"2025-11-25T09:45:14","date_gmt":"2025-11-25T08:45:14","guid":{"rendered":"https:\/\/www.humanlevel.com\/uncategorized\/searcher-personas-una-alternativa-al-zero-search-volume"},"modified":"2025-11-25T09:45:14","modified_gmt":"2025-11-25T08:45:14","slug":"searcher-personas-an-alternative-to-zero-search-volume","status":"publish","type":"post","link":"https:\/\/www.humanlevel.com\/en\/blog\/seo\/searcher-personas-an-alternative-to-zero-search-volume","title":{"rendered":"Searcher personas: an alternative to zero search volume"},"content":{"rendered":"

The concept of the buyer persona<\/em> has always been present in the work methodology we use at Human Level. In my presentation Customer Journey Analysis and Search Scenarios<\/a><\/em>, which I presented at The Inbounder back in 2016, I already anticipated that the purchasing decision process had stopped being linear.<\/p>\n

The use of AI platforms as online search tools has led us to revisit this concept, which now takes on enormous importance. In my recent presentation Personas: New Findability Opportunities<\/em> for the Sistrix Digital Marketing Meetup in Madrid<\/a>, I presented a reflection that completely inverts the process and has led us to a methodological shift: instead of starting from a keyword<\/em> analysis, we should begin by understanding our searcher personas<\/em>\u2014that is, the customers who are searching for us. And artificial intelligence becomes the perfect ally to achieve this. Let’s see how we have put this new perspective into practice at Human Level.<\/p>\n

The Problem: Search Has Fragmented<\/h2>\n

Traditionally, we worked under the hypothesis that SEO was, to some extent, predictable. We selected ranking goals based on keywords with measurable search volume<\/em>, we knew what positions we occupied on Google, and we optimized with the aim of improving them. The process was linear: keyword research \u2192 grouping or clustering by search intent \u2192 technical and semantic optimization \u2192 strategic creation of new content.<\/p>\n

But that scenario has changed drastically. As we see in Figure 1, search habits<\/a> have fragmented across multiple platforms: traditional search engines, aggregators and marketplaces, social networks, AI chatbots\u2026 each with its own dynamics. And most importantly: while only Google shares search potential data<\/a> for various queries, the prompts<\/em> used in AI platforms are conversational and extremely specific (zero search volume<\/em>). And their results are highly personalized.<\/p>\n

\"Search
Fig. 1. Search scenarios have fragmented. Each user profile decides, based on their characteristics and needs, which channels to use to gather information for their purchasing decisions.<\/figcaption><\/figure>\n

Let’s think of a concrete example. On Google, someone interested in smart lighting would search for something like \u201csmart lighting system<\/em>.\u201d Now, that same person might ask ChatGPT: “What is the best smart lighting system for a home theater room that doesn’t interfere with video games and works with Alexa?<\/em>” We have moved from generic searches to an “ultra-long tail<\/strong>” of queries that is impossible to measure with traditional tools.<\/p>\n

SEO Challenges for Ranking on AI Platforms<\/h2>\n

The growing use of AI chatbots as online search tools opens up new challenges for SEO:<\/p>\n