{"id":5047,"date":"2026-01-19T09:59:37","date_gmt":"2026-01-19T09:59:37","guid":{"rendered":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/"},"modified":"2026-08-24T10:24:34","modified_gmt":"2026-08-24T08:24:34","slug":"google-analytics-advisor-what-is-how-it-works","status":"publish","type":"post","link":"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/","title":{"rendered":"Google Analytics Advisor: what it is, how it works, and what to expect from Google's new AI"},"content":{"rendered":"<p class=\"wp-block-paragraph\">In December 2025, Google announced the launch of Google Analytics Advisor. Although this functionality <strong>is in Beta phase<\/strong> and is only available in the properties configured in English, we have been testing it to see what we think is worthwhile and what is not.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First of all, let's start with the definition. The official sources themselves say that <strong>it is a conversational AI assistant powered by the Gemini models<\/strong>. Users can turn to this wizard within the interface whenever they have questions or need help. How do I access it? It is very simple:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As can be seen in the image above, a new icon appears in the upper right margin of the properties, located between the Google product selector and the help center. Another option is to go directly to the magnifying glass, where the first suggestion offered is to ask Analytics Advisor.&nbsp;<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_88 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contents of the entry<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Contents\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #a09353;color:#a09353\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #a09353;color:#a09353\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#Algunas_de_sus_funcionalidades_clave\" >Some of its key functionalities<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#1_Interaccion_en_lenguaje_natural\" >1. Natural language interaction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#2_Visualizaciones_y_resumenes_instantaneos\" >Instant displays and summaries<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#3_Analisis_diagnostico_y_estrategico\" >3. Diagnostic and strategic analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#4_Razonamiento_transparente\" >4. Transparent reasoning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#5_Asistencia_tecnica_integrada\" >5. Integrated technical assistance<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#Casos_practicos_de_Google_Analytics_Advisor\" >Google Analytics Advisor Case Studies<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#%C2%ABData_Fetching%C2%BB_obtencion_de_datos_multidimensionales\" >\u00abData Fetching: multidimensional data collection<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#%C2%BFEs_capaz_de_crear_segmentos\" >Are you able to create segments?&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#%C2%BFPuede_realizar_comparativas_que_crucen_metricas_y_dimensiones_en_una_misma_tabla_para_una_fecha_concreta\" >Can you make comparisons that cross metrics and dimensions in the same table, for a specific date?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#Identificacion_de_%C2%ABkey_drivers%C2%BB_o_factores_clave\" >Identification of \u00abkey drivers\u00bb (or key factors)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#Visualizacion_de_tendencias_sobre_la_marcha\" >On-the-fly trend visualization<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#Limitaciones_actuales_y_aspectos_a_tener_en_cuenta\" >Current limitations and aspects to be taken into account<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#%C2%BFPuede_usar_grupos_de_canales_personalizados\" >Can you use custom channel groups?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-que-es-como-funciona\/#%C2%BFPuede_usar_metricas_calculadas\" >Can you use calculated metrics?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Algunas_de_sus_funcionalidades_clave\"><\/span>Some of its key functionalities<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Interaccion_en_lenguaje_natural\"><\/span>1. Natural language interaction<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of having to click through different reports and multiple submenus to find a specific piece of data, users can type in questions such as \u201cWhy did my mobile traffic drop on Sunday?\u201d or \u201cWhat were my top traffic sources last month?\u201d This is <strong>especially useful if you do not know in which report you can find this information or if you are simply looking to save some time.&nbsp;<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_Visualizaciones_y_resumenes_instantaneos\"><\/span>Instant displays and summaries<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This is one of the main advantages, since <strong>almost instantly generates certain graphs and trend lines.<\/strong>because, if we had to generate them manually, it might take us some time. (Note: We\u2019ll provide more information on this at the end of the post.)\u00a0<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_Analisis_diagnostico_y_estrategico\"><\/span>3. Diagnostic and strategic analysis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The tool <strong>simplifies the process of identifying the root cause of the anomalies<\/strong>. This means you can read all the information available in the reports in a second and thus identify possible causes for fluctuations in the metrics.\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">According to Google's own sources, once the causes have been identified, the assistant itself will <strong>can suggest growth strategies based on users' behavior<\/strong>. Example: recommend ways to re-engage users who left the site without ever seeing a product page.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Razonamiento_transparente\"><\/span>4. Transparent reasoning<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This seems to us to be the top of the line because <strong>provides a layer of transparency by showing your intermediate reasoning steps<\/strong>, This allows users to understand the logic behind an answer and verify its accuracy.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is particularly interesting because <strong>we will be able to see how he thinks and how he accesses information <\/strong>to provide those answers. It will also allow us to see if you have encountered certain obstacles, which we will see below through examples.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Asistencia_tecnica_integrada\"><\/span>5. Integrated technical assistance<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">This consultant also acts as an on-demand guide. It can provide <strong>step-by-step instructions for certain theoretical questions without the user having to leave the control panel <\/strong>to search for external support documents.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Casos_practicos_de_Google_Analytics_Advisor\"><\/span>Google Analytics Advisor Case Studies<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%ABData_Fetching%C2%BB_obtencion_de_datos_multidimensionales\"><\/span>\u00abData Fetching: multidimensional data collection<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Saves you from navigating through the report menu, adding secondary dimensions and applying manual filters<\/strong>. Doing this by hand involves going to Procurement, filtering by country, adding city secondary dimension and then adding a device category filter. Advisor does this in a few seconds.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example prompt: <\/strong><em>\u00abTell me the 5 cities in Spain with the highest conversion rate from mobile devices in the last 30 days\u00bb.<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"360\" height=\"757\" src=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-6.png\" alt=\"image\" class=\"wp-image-4748\" srcset=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-6.png 360w, https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-6-143x300.png 143w\" sizes=\"(max-width: 360px) 100vw, 360px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%BFEs_capaz_de_crear_segmentos\"><\/span>Are you able to create segments?&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes, and it does it very fast, being <strong>one of the most interesting uses<\/strong> we have tested so far.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example Query:<\/strong> \u201c<em>Show me the number of users who have viewed a product page, but have not completed a purchase in the last 7 days.\u201d<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">TIP: if you want to ensure a correct methodology, you can ask the agent directly how he has made this calculation. In this case, he tells us the following: <em>I calculated this number by creating a temporary user segment. This segment included users who triggered a view_item event (indicating that they viewed a product page), but did not trigger a purchase event in the last 7 days (January 7-13, 2026).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"441\" height=\"791\" src=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-8.png\" alt=\"image\" class=\"wp-image-4746\" style=\"width:353px;height:auto\" srcset=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-8.png 441w, https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-8-167x300.png 167w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%BFPuede_realizar_comparativas_que_crucen_metricas_y_dimensiones_en_una_misma_tabla_para_una_fecha_concreta\"><\/span>Can you make comparisons that cross metrics and dimensions in the same table, for a specific date?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Although it does manage to pull out the table comparing the conversion rate of the different cities, you have to pay close attention to the issue of dates.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example prompt: <\/strong><em>Compare the conversion rate based on sessions in Burgos, Valladolid and Palencia during the last month. Show me the results in a single table.<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"337\" height=\"792\" src=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-2.png\" alt=\"image\" class=\"wp-image-4752\" style=\"width:296px;height:auto\" srcset=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-2.png 337w, https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-2-128x300.png 128w\" sizes=\"(max-width: 337px) 100vw, 337px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Attention! <strong>It is vital to always check the date ranges in the generated tables.<\/strong>. As can be seen in the example, the Advisor mixed different periods for Valladolid versus Burgos and Palencia. An analyst who does not detect this could draw totally erroneous strategic conclusions based on non-comparable data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Identificacion_de_%C2%ABkey_drivers%C2%BB_o_factores_clave\"><\/span>Identification of \u00abkey drivers\u00bb (or key factors)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We could say that it is one of the most advanced AI functions. It nimbly analyzes the movements of a metric and the possible causes behind it. The Advisor <strong>automatically analyzes which dimensions<\/strong> (pages, cities, browsers) c<strong>ontributed further to this decline.<\/strong> We would need more time to find the exact culprit; the AI does it by instant statistical rule-out.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example prompt:<\/strong> <em>\u00abWhy did sessions from Organic Search drop yesterday?\u00bb<\/em>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"360\" height=\"757\" src=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-7.png\" alt=\"image\" class=\"wp-image-4747\" style=\"width:331px;height:auto\" srcset=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-7.png 360w, https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/image-7-143x300.png 143w\" sizes=\"(max-width: 360px) 100vw, 360px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Continuing with the example from the previous section (conversion rate by city), let's see what level of detail it provides.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example prompt: <\/strong><em>Why is the conversion rate higher in Burgos?<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The analysis is quick and the data obtained are very interesting to understand why in this city the conversion rate is higher than in other cities that we have marked as a reference.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In order to be able to make a more accurate analysis, it would be interesting to use the metric <strong>\u201csession conversion rate\u201d <\/strong>only based on the purchase, and not on the other events that are marked as key. To do this, we will continue to consult the following:&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example prompt: <\/strong><em>I want the same analysis, but filtering only by event \u201cpurchase\u201d.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is where it starts to fall short, and we can't continue with the analysis. Since it's unable to calculate the conversion rate based solely on purchases, the insights it provides are related to transaction volume.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That is why it is so interesting again that <strong>let's go into the \u201cshow thinking\u201d section to understand what barriers and obstacles have been encountered in the process.<\/strong>&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>(Show thinking from this prompt: \u201cAlthough I can't filter the conversion rate analysis by a single event, I can perform a similar analysis by looking at the total number of purchase events for each channel and device.\u201d)<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Visualizacion_de_tendencias_sobre_la_marcha\"><\/span>On-the-fly trend visualization<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Often, creating a trend chart requires going to the Explorations module. Now this chart can be generated right within the chat, which is ideal for quick \u00abscreenshots.\u00bb But how well does this feature actually work? Let\u2019s take a look:\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example prompt: <\/strong>\u201cShow me a daily graph of active users over the last 14 days compared to the previous 14 days.\u201d.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here we can see how the agent fails to return graphs with comparisons. However, he himself admits being unable to do so and proposes to return the graph with a basic timeline, which actually corresponds to the following query: \u201cShow me a daily graph of active users during the last 14 days\u201d.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"850\" src=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/01\/ejemplo-grafico-ga4.gif\" alt=\"\" class=\"wp-image-10200\" style=\"width:580px;height:auto\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Limitaciones_actuales_y_aspectos_a_tener_en_cuenta\"><\/span>Current limitations and aspects to be taken into account<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%BFPuede_usar_grupos_de_canales_personalizados\"><\/span>Can you use custom channel groups?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Although a priori it tells you it does, and manages to identify them correctly within the property, <strong>when cross-referencing metrics with this dimension, errors occur on a recurring basis<\/strong> and finally cannot be used to generate valid reports. The Advisor recognizes the name of the custom channel groups and seems to understand their existence, but fails to integrate them into tables or comparisons along with metrics such as sessions, conversions or revenue.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This <strong>limits its usefulness in more advanced acquisition analysis<\/strong>, where such groupings are often key. As it stands, any analysis that relies on custom channel groups still requires recourse to standard reports or Scans, as the wizard does not provide reliable results at this point.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%C2%BFPuede_usar_metricas_calculadas\"><\/span>Can you use calculated metrics?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Similar to the previous case, a priori it manages to identify the calculated metrics that are created, they know the exact number of metrics of the property, but when asking for a report with them, it usually asks you to provide the formula that has been entered, so that it is<strong> The lack of reading of the metric as such is perceived.&nbsp;<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At the end of the day, <strong>Google Analytics Advisor can save you a lot of time on quick queries and help you see patterns without getting lost in menus.<\/strong> However, don't rely 100% on everything you get back: check the comparisons, the channel groups and the calculated metrics before making important decisions. It is a useful tool to complement your work, but the analyst's experience and eye remain irreplaceable.<\/p>","protected":false},"excerpt":{"rendered":"<p>In December 2025, Google announced the launch of Google Analytics Advisor. Although this feature is currently in beta and is only available for properties configured in English, we\u2019ve been testing it to see what we think is worthwhile and what isn\u2019t. First of all, let\u2019s start with the definition. Official sources describe it as a conversational AI assistant powered by Gemini models. Users can access this assistant within the interface whenever they have questions or need help. How do you access it? It\u2019s very simple: As shown in the image above, a new icon appears in the upper-right corner of properties, located between the Google product selector and the Help Center. Another option is to go directly to the magnifying glass, where the first suggestion offered is to ask Analytics Advisor.\u00a0 Some of its key features 1. Natural Language Interaction Instead of having to click through different reports and multiple submenus to find a specific piece of data, users can type questions such as \u201cWhy did my mobile traffic drop on Sunday?\u201d or \u201cWhat were my top traffic sources last month?\u201d This is especially useful if you don\u2019t know which report contains that information or if you\u2019re simply looking to save some time.\u00a0 2. Instant Visualizations and Summaries This is one of the main advantages, as it generates certain charts and trend lines almost instantly\u2014something that would take time to create manually. (Note: We\u2019ll provide more information on this at the end of the post.)\u00a0 3. Diagnostic and Strategic Analysis The tool simplifies the process of identifying the root cause of anomalies. This means it can analyze all the information available across the reports in a second, thereby attributing possible causes to fluctuations in metrics.\u00a0 According to Google\u2019s own sources, once the causes are identified, the assistant itself can suggest growth strategies based on user behavior. For example, it might recommend ways to re-engage users who left the website without viewing a product page. 4. Transparent Reasoning We find this feature particularly impressive because it provides a layer of transparency by showing its intermediate reasoning steps, allowing users to understand the logic behind a response and verify its accuracy.  It\u2019s especially interesting because we can see how it thinks and how it accesses information to provide those answers. It will also let us see if it has encountered certain obstacles, which we\u2019ll explore later through examples.  5. Integrated technical support This advisor also acts as an on-demand guide. It can provide step-by-step instructions for certain theoretical questions without the user having to leave the dashboard to search for external support documents.  Google Analytics Advisor \u00abData Fetching\u00bb Case Studies: Retrieving Multidimensional Data It saves you from having to navigate the reports menu, add secondary dimensions, and apply manual filters. Doing this manually involves going to Acquisition, filtering by country, adding a secondary dimension for city, and then adding a device category filter. The Advisor does this in just a few seconds. Example prompt: \u00abTell me the 5 cities in Spain with the highest conversion rate from mobile devices over the last 30 days.\u00bb Can it create segments?  Yes, and it does so very quickly\u2014one of the most interesting uses we\u2019ve seen so far.\u00a0 Example query: \u201cShow me the number of users who have viewed a product page but haven\u2019t completed a purchase in the last 7 days.\u201d TIP: If you want to ensure the methodology is correct, you can ask the agent directly how they performed that calculation. In this case, they tell us the following: \u00abI calculated this number by creating a temporary user segment.\u00bb This segment included users who triggered a `view_item` event (indicating they viewed a product page) but did not trigger a purchase event in the last 7 days (January 7\u201313, 2026). Can you create comparisons that cross-reference metrics and dimensions in a single table for a specific date? Although the system does manage to generate the table comparing conversion rates across different cities, you need to pay close attention to the dates. Prompt example: Compare the conversion rate based on sessions from Burgos, Valladolid, and Palencia over the last month. Show me the results in a single table. Warning! It\u2019s vital to always review the date ranges in the generated tables. As seen in the example, the Advisor mixed different time periods for Valladolid compared to Burgos and Palencia. An analyst who fails to notice this could draw completely erroneous strategic conclusions based on data that isn\u2019t comparable. Identifying \u00abkey drivers\u00bb (or key factors) This is arguably one of the most advanced AI features. It quickly analyzes changes in a metric and the possible causes behind them. The Advisor automatically analyzes which dimensions (pages, cities, browsers) contributed most to that decline. It would take us longer to pinpoint the exact cause; AI does this through instant statistical elimination. Example prompt: \u201cWhy did sessions from Organic Search drop yesterday?\u201d Continuing with the example from the previous section (conversion rate by city), let\u2019s see what level of detail it provides.\u00a0 Example prompt: Why is the conversion rate higher in Burgos? The analysis is quick, and the data obtained is very helpful for understanding why the conversion rate in this city is higher than in other cities we\u2019ve designated as benchmarks.\u00a0 To perform a more accurate analysis, it would be helpful to use the \u201csession conversion rate\u201d metric based solely on purchases, rather than on the other events marked as key. To do this, let\u2019s continue with the following query:  Example prompt: I want<\/p>","protected":false},"author":12,"featured_media":6314,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-5047","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analitica-web"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Google Analytics Advisor: qu\u00e9 es, c\u00f3mo funciona y qu\u00e9 puedes esperar de la nueva IA de Google - Geotelecom<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-what-is-how-it-works\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google Analytics Advisor: qu\u00e9 es, c\u00f3mo funciona y qu\u00e9 puedes esperar de la nueva IA de Google - Geotelecom\" \/>\n<meta property=\"og:description\" content=\"En diciembre de 2025, Google anunci\u00f3 el lanzamiento de Google Analytics Advisor. Aunque esta funcionalidad se encuentra en fase Beta y solo est\u00e1 disponible en las propiedades configuradas en ingl\u00e9s, hemos estado prob\u00e1ndola para ver lo que a nuestro parecer merece la pena y lo que no. Primero de todo, empecemos por la definici\u00f3n. Las propias fuentes oficiales dicen que se trata de un asistente de IA conversacional impulsado por los modelos Gemini. Los usuarios pueden acudir a este asistente dentro de la interfaz siempre que tengan preguntas o que necesiten ayuda. \u00bfC\u00f3mo se accede? Muy sencillo: Como puede verse en la imagen anterior, aparece en el margen superior derecho de las propiedades un icono nuevo, situado entre el selector de productos Google y el centro de ayuda. Otra opci\u00f3n es acceder directamente a la lupa, donde la primera sugerencia ofrecida es la de preguntar a Analytics Advisor.&nbsp; Algunas de sus funcionalidades clave 1. Interacci\u00f3n en lenguaje natural En lugar de tener que hacer clic en diferentes informes y m\u00faltiples submen\u00fas para encontrar un dato espec\u00edfico, los usuarios pueden escribir preguntas como \u201c\u00bfPor qu\u00e9 baj\u00f3 mi tr\u00e1fico m\u00f3vil el domingo?\u201d o \u201c\u00bfCu\u00e1les fueron mis principales fuentes de tr\u00e1fico el mes pasado?\u201d. Esto es especialmente \u00fatil si desconoces en qu\u00e9 informe puedes encontrar esa informaci\u00f3n o si simplemente lo que buscas es ahorrar algo de tiempo.&nbsp; 2. Visualizaciones y res\u00famenes instant\u00e1neos Esta es una de las principales ventajas, ya que genera casi al instante ciertos gr\u00e1ficos y l\u00edneas de tendencia que, de tener que generarlos manualmente, podr\u00edan llevarnos cierto tiempo. (Ojo, al final del post daremos algo m\u00e1s de informaci\u00f3n a este respecto.)\u00a0 3. An\u00e1lisis diagn\u00f3stico y estrat\u00e9gico La herramienta simplifica el proceso de identificaci\u00f3n de la causa ra\u00edz de las anomal\u00edas. Esto quiere decir que puede leer toda la informaci\u00f3n disponible a lo largo de los informes en un segundo, y de este modo atribuir causas posibles a fluctuaciones en las m\u00e9tricas.\u00a0 Seg\u00fan las fuentes del propio Google, una vez identificadas las causas, el propio asistente puede sugerir estrategias de crecimiento basadas en el comportamiento de los usuarios. Ejemplo: recomendar formas de volver a atraer a los usuarios que abandonaron la web sin llegar a ver una p\u00e1gina de producto. 4. Razonamiento transparente Esto nos parece de lo m\u00e1s top porque proporciona una capa de transparencia al mostrar sus pasos de razonamiento intermedios, lo que permite a los usuarios comprender la l\u00f3gica detr\u00e1s de una respuesta y verificar su precisi\u00f3n.&nbsp; Es especialmente interesante, ya que podremos ver c\u00f3mo piensa y c\u00f3mo accede a la informaci\u00f3n para brindar esas respuestas. Tambi\u00e9n nos permitir\u00e1 ver si se ha topado con ciertos obst\u00e1culos, lo que veremos m\u00e1s adelante a trav\u00e9s de ejemplos.&nbsp; 5. Asistencia t\u00e9cnica integrada Este asesor tambi\u00e9n act\u00faa como una gu\u00eda bajo demanda. Puede proporcionar instrucciones paso a paso para ciertas dudas te\u00f3ricas sin que el usuario tenga que salir del panel de control para buscar documentos de asistencia externos.&nbsp; Casos pr\u00e1cticos de Google Analytics Advisor \u00abData Fetching\u00bb: obtenci\u00f3n de datos multidimensionales Te ahorra navegar por el men\u00fa de informes, a\u00f1adir dimensiones secundarias y aplicar filtros manuales. Hacer esto a mano implica ir a Adquisici\u00f3n, filtrar por pa\u00eds, a\u00f1adir dimensi\u00f3n secundaria de ciudad y luego a\u00f1adir un filtro de categor\u00eda de dispositivo. El Advisor lo hace en pocos segundos. Ejemplo prompt: \u00abDime las 5 ciudades de Espa\u00f1a con mayor tasa de conversi\u00f3n desde dispositivos m\u00f3viles en los \u00faltimos 30 d\u00edas\u00bb. \u00bfEs capaz de crear segmentos?&nbsp; S\u00ed, y lo hace muy r\u00e1pido, siendo uno de los usos m\u00e1s interesantes que hemos comprobado hasta el momento.&nbsp; Ejemplo Query: \u201cMu\u00e9strame el n\u00famero de usuarios que han visto una p\u00e1gina de producto, pero no han completado una compra en los \u00faltimos 7 d\u00edas.\u201d TIP: si queremos asegurarnos una metodolog\u00eda correcta, se puede preguntar directamente al agente c\u00f3mo ha realizado ese c\u00e1lculo. En este caso, nos dice lo siguiente: Calcul\u00e9 este n\u00famero creando un segmento de usuarios temporal. Este segmento inclu\u00eda a los usuarios que activaron un evento view_item (lo que indica que vieron una p\u00e1gina de producto), pero no activaron un evento de compra en los \u00faltimos 7 d\u00edas (del 7 al 13 de enero de 2026). \u00bfPuede realizar comparativas que crucen m\u00e9tricas y dimensiones en una misma tabla, para una fecha concreta? Aunque s\u00ed que consigue sacar la tabla comparando la tasa de conversi\u00f3n de las diferentes ciudades, hay que prestar mucha atenci\u00f3n al tema de las fechas. Ejemplo prompt: Compara la tasa de conversi\u00f3n con base en sesiones de Burgos, Valladolid y Palencia durante el \u00faltimo mes. Mu\u00e9strame los resultados en una sola tabla. \u00a1Atenci\u00f3n! Es vital revisar siempre los intervalos de fechas en las tablas generadas. Como se ve en el ejemplo, el Advisor mezcl\u00f3 periodos distintos para Valladolid frente a Burgos y Palencia. Un analista que no detecte esto podr\u00eda extraer conclusiones estrat\u00e9gicas totalmente err\u00f3neas bas\u00e1ndose en datos no comparables. Identificaci\u00f3n de \u00abkey drivers\u00bb (o factores clave) Podr\u00edamos decir que es una de las funciones de IA m\u00e1s avanzadas. De manera \u00e1gil analiza los movimientos de una m\u00e9trica y las posibles causas que hay detr\u00e1s. El Advisor analiza autom\u00e1ticamente qu\u00e9 dimensiones (p\u00e1ginas, ciudades, navegadores) contribuyeron m\u00e1s a esa ca\u00edda. Nosotros necesitar\u00edamos m\u00e1s tiempo para encontrar el culpable exacto; la IA lo hace por descarte estad\u00edstico instant\u00e1neo. Ejemplo prompt: \u00abWhy did sessions from Organic Search drop yesterday?\u00bb. Continuando con el ejemplo del apartado anterior (tasa de conversi\u00f3n por ciudades) vamos a ver qu\u00e9 nivel de detalle nos consigue brindar.&nbsp; Ejemplo prompt: \u00bfPor qu\u00e9 es m\u00e1s alta la tasa de conversi\u00f3n en Burgos? El an\u00e1lisis es r\u00e1pido y los datos obtenidos son muy interesantes para entender por qu\u00e9 en esta ciudad la tasa de conversi\u00f3n es superior a la de otras ciudades que le hemos marcado como referencia.&nbsp; Para poder hacer un an\u00e1lisis m\u00e1s certero, ser\u00eda interesante que utilizase la m\u00e9trica \u201csession conversion rate\u201d \u00fanicamente basada en la compra, y no en el resto de eventos que est\u00e9n marcados como clave. Para ello, vamos a seguir consultando lo siguiente:&nbsp; Ejemplo prompt: Quiero\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-what-is-how-it-works\/\" \/>\n<meta property=\"og:site_name\" content=\"Geotelecom\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-19T09:59:37+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-24T08:24:34+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/03\/cabecera-15.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"400\" \/>\n\t<meta property=\"og:image:height\" content=\"400\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Cristina Ortu\u00f1o\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Cristina Ortu\u00f1o\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Google Analytics Advisor: what it is, how it works and what you can expect from Google's new AI - Geotelecom","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-what-is-how-it-works\/","og_locale":"en_US","og_type":"article","og_title":"Google Analytics Advisor: qu\u00e9 es, c\u00f3mo funciona y qu\u00e9 puedes esperar de la nueva IA de Google - Geotelecom","og_description":"En diciembre de 2025, Google anunci\u00f3 el lanzamiento de Google Analytics Advisor. Aunque esta funcionalidad se encuentra en fase Beta y solo est\u00e1 disponible en las propiedades configuradas en ingl\u00e9s, hemos estado prob\u00e1ndola para ver lo que a nuestro parecer merece la pena y lo que no. Primero de todo, empecemos por la definici\u00f3n. Las propias fuentes oficiales dicen que se trata de un asistente de IA conversacional impulsado por los modelos Gemini. Los usuarios pueden acudir a este asistente dentro de la interfaz siempre que tengan preguntas o que necesiten ayuda. \u00bfC\u00f3mo se accede? Muy sencillo: Como puede verse en la imagen anterior, aparece en el margen superior derecho de las propiedades un icono nuevo, situado entre el selector de productos Google y el centro de ayuda. Otra opci\u00f3n es acceder directamente a la lupa, donde la primera sugerencia ofrecida es la de preguntar a Analytics Advisor.&nbsp; Algunas de sus funcionalidades clave 1. Interacci\u00f3n en lenguaje natural En lugar de tener que hacer clic en diferentes informes y m\u00faltiples submen\u00fas para encontrar un dato espec\u00edfico, los usuarios pueden escribir preguntas como \u201c\u00bfPor qu\u00e9 baj\u00f3 mi tr\u00e1fico m\u00f3vil el domingo?\u201d o \u201c\u00bfCu\u00e1les fueron mis principales fuentes de tr\u00e1fico el mes pasado?\u201d. Esto es especialmente \u00fatil si desconoces en qu\u00e9 informe puedes encontrar esa informaci\u00f3n o si simplemente lo que buscas es ahorrar algo de tiempo.&nbsp; 2. Visualizaciones y res\u00famenes instant\u00e1neos Esta es una de las principales ventajas, ya que genera casi al instante ciertos gr\u00e1ficos y l\u00edneas de tendencia que, de tener que generarlos manualmente, podr\u00edan llevarnos cierto tiempo. (Ojo, al final del post daremos algo m\u00e1s de informaci\u00f3n a este respecto.)\u00a0 3. An\u00e1lisis diagn\u00f3stico y estrat\u00e9gico La herramienta simplifica el proceso de identificaci\u00f3n de la causa ra\u00edz de las anomal\u00edas. Esto quiere decir que puede leer toda la informaci\u00f3n disponible a lo largo de los informes en un segundo, y de este modo atribuir causas posibles a fluctuaciones en las m\u00e9tricas.\u00a0 Seg\u00fan las fuentes del propio Google, una vez identificadas las causas, el propio asistente puede sugerir estrategias de crecimiento basadas en el comportamiento de los usuarios. Ejemplo: recomendar formas de volver a atraer a los usuarios que abandonaron la web sin llegar a ver una p\u00e1gina de producto. 4. Razonamiento transparente Esto nos parece de lo m\u00e1s top porque proporciona una capa de transparencia al mostrar sus pasos de razonamiento intermedios, lo que permite a los usuarios comprender la l\u00f3gica detr\u00e1s de una respuesta y verificar su precisi\u00f3n.&nbsp; Es especialmente interesante, ya que podremos ver c\u00f3mo piensa y c\u00f3mo accede a la informaci\u00f3n para brindar esas respuestas. Tambi\u00e9n nos permitir\u00e1 ver si se ha topado con ciertos obst\u00e1culos, lo que veremos m\u00e1s adelante a trav\u00e9s de ejemplos.&nbsp; 5. Asistencia t\u00e9cnica integrada Este asesor tambi\u00e9n act\u00faa como una gu\u00eda bajo demanda. Puede proporcionar instrucciones paso a paso para ciertas dudas te\u00f3ricas sin que el usuario tenga que salir del panel de control para buscar documentos de asistencia externos.&nbsp; Casos pr\u00e1cticos de Google Analytics Advisor \u00abData Fetching\u00bb: obtenci\u00f3n de datos multidimensionales Te ahorra navegar por el men\u00fa de informes, a\u00f1adir dimensiones secundarias y aplicar filtros manuales. Hacer esto a mano implica ir a Adquisici\u00f3n, filtrar por pa\u00eds, a\u00f1adir dimensi\u00f3n secundaria de ciudad y luego a\u00f1adir un filtro de categor\u00eda de dispositivo. El Advisor lo hace en pocos segundos. Ejemplo prompt: \u00abDime las 5 ciudades de Espa\u00f1a con mayor tasa de conversi\u00f3n desde dispositivos m\u00f3viles en los \u00faltimos 30 d\u00edas\u00bb. \u00bfEs capaz de crear segmentos?&nbsp; S\u00ed, y lo hace muy r\u00e1pido, siendo uno de los usos m\u00e1s interesantes que hemos comprobado hasta el momento.&nbsp; Ejemplo Query: \u201cMu\u00e9strame el n\u00famero de usuarios que han visto una p\u00e1gina de producto, pero no han completado una compra en los \u00faltimos 7 d\u00edas.\u201d TIP: si queremos asegurarnos una metodolog\u00eda correcta, se puede preguntar directamente al agente c\u00f3mo ha realizado ese c\u00e1lculo. En este caso, nos dice lo siguiente: Calcul\u00e9 este n\u00famero creando un segmento de usuarios temporal. Este segmento inclu\u00eda a los usuarios que activaron un evento view_item (lo que indica que vieron una p\u00e1gina de producto), pero no activaron un evento de compra en los \u00faltimos 7 d\u00edas (del 7 al 13 de enero de 2026). \u00bfPuede realizar comparativas que crucen m\u00e9tricas y dimensiones en una misma tabla, para una fecha concreta? Aunque s\u00ed que consigue sacar la tabla comparando la tasa de conversi\u00f3n de las diferentes ciudades, hay que prestar mucha atenci\u00f3n al tema de las fechas. Ejemplo prompt: Compara la tasa de conversi\u00f3n con base en sesiones de Burgos, Valladolid y Palencia durante el \u00faltimo mes. Mu\u00e9strame los resultados en una sola tabla. \u00a1Atenci\u00f3n! Es vital revisar siempre los intervalos de fechas en las tablas generadas. Como se ve en el ejemplo, el Advisor mezcl\u00f3 periodos distintos para Valladolid frente a Burgos y Palencia. Un analista que no detecte esto podr\u00eda extraer conclusiones estrat\u00e9gicas totalmente err\u00f3neas bas\u00e1ndose en datos no comparables. Identificaci\u00f3n de \u00abkey drivers\u00bb (o factores clave) Podr\u00edamos decir que es una de las funciones de IA m\u00e1s avanzadas. De manera \u00e1gil analiza los movimientos de una m\u00e9trica y las posibles causas que hay detr\u00e1s. El Advisor analiza autom\u00e1ticamente qu\u00e9 dimensiones (p\u00e1ginas, ciudades, navegadores) contribuyeron m\u00e1s a esa ca\u00edda. Nosotros necesitar\u00edamos m\u00e1s tiempo para encontrar el culpable exacto; la IA lo hace por descarte estad\u00edstico instant\u00e1neo. Ejemplo prompt: \u00abWhy did sessions from Organic Search drop yesterday?\u00bb. Continuando con el ejemplo del apartado anterior (tasa de conversi\u00f3n por ciudades) vamos a ver qu\u00e9 nivel de detalle nos consigue brindar.&nbsp; Ejemplo prompt: \u00bfPor qu\u00e9 es m\u00e1s alta la tasa de conversi\u00f3n en Burgos? El an\u00e1lisis es r\u00e1pido y los datos obtenidos son muy interesantes para entender por qu\u00e9 en esta ciudad la tasa de conversi\u00f3n es superior a la de otras ciudades que le hemos marcado como referencia.&nbsp; Para poder hacer un an\u00e1lisis m\u00e1s certero, ser\u00eda interesante que utilizase la m\u00e9trica \u201csession conversion rate\u201d \u00fanicamente basada en la compra, y no en el resto de eventos que est\u00e9n marcados como clave. Para ello, vamos a seguir consultando lo siguiente:&nbsp; Ejemplo prompt: Quiero","og_url":"https:\/\/www.geotelecom.es\/en\/blog\/google-analytics-advisor-what-is-how-it-works\/","og_site_name":"Geotelecom","article_published_time":"2026-01-19T09:59:37+00:00","article_modified_time":"2026-08-24T08:24:34+00:00","og_image":[{"width":400,"height":400,"url":"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/03\/cabecera-15.jpg","type":"image\/jpeg"}],"author":"Cristina Ortu\u00f1o","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Cristina Ortu\u00f1o","Est. reading time":"9 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/#article","isPartOf":{"@id":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/"},"author":{"name":"Cristina Ortu\u00f1o","@id":"https:\/\/www.geotelecom.es\/#\/schema\/person\/0f5420297c9cc090b3bef428efde6791"},"headline":"Google Analytics Advisor: qu\u00e9 es, c\u00f3mo funciona y qu\u00e9 puedes esperar de la nueva IA de Google","datePublished":"2026-01-19T09:59:37+00:00","dateModified":"2026-08-24T08:24:34+00:00","mainEntityOfPage":{"@id":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/"},"wordCount":1680,"commentCount":0,"image":{"@id":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/#primaryimage"},"thumbnailUrl":"https:\/\/www.geotelecom.es\/wp-content\/uploads\/2026\/03\/cabecera-15.jpg","articleSection":["Anal\u00edtica Web"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/","url":"https:\/\/www.geotelecom.es\/blog\/google-analytics-advisor-que-es-como-funciona\/","name":"Google Analytics Advisor: what it is, how it works and what you can expect from Google's new AI - 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