{"id":5277,"date":"2025-12-12T16:01:39","date_gmt":"2025-12-12T15:01:39","guid":{"rendered":"https:\/\/adagri.com\/?p=5277"},"modified":"2026-01-19T11:25:56","modified_gmt":"2026-01-19T10:25:56","slug":"artificial-intelligence-in-agricultural-marketing-whats-the-deal-with-feedingmodels-ai","status":"publish","type":"post","link":"https:\/\/adagri.com\/en\/blog\/sztuczna-inteligencja-w-marketingu-rolniczym-o-co-chodzi-z-karmieniemmodeli-ai\/","title":{"rendered":"Artificial intelligence in agricultural marketing - what is involved in \u201cfeeding \u201dAI models?"},"content":{"rendered":"<p>Many oversimplifications have grown up around the term \u201cartificial intelligence in agricultural marketing.\u201d There is a lot of talk about \u201eself-learning campaigns\u201d or \u201eautomatic ad optimization,\u201d as if algorithms alone know who to target and how to sell products. Meanwhile, artificial intelligence in agricultural marketing does not operate in a vacuum. In order for a model to predict the behavior of an audience, it must first be \u201cfed with data\u201d that will allow it to distinguish between a farmer from Mazovia and a student from Poznan.<\/p>\n<p>Of course, this is not just about the artificial intelligence of marketing, but also about the use of AI models in other industries. Correctly differentiating the audience is what is at the heart of the concept of \u201efeeding AI models\u201d (AI feeding), i.e. providing high-quality, timely data that gives meaning and direction to the algorithm's work.<\/p>\n<h2><strong>How do AI models work in agricultural marketing (in Google, Meta, AdTech)?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Google and Meta are today's de facto global advertising infrastructure. Their algorithms analyze billions of interactions - clicks, time spent on a page, location, reactions to posts. All to predict who will respond to an ad and in what context. Unfortunately, even the most advanced AI models have their limitations:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">operate universally, they do not \u201eunderstand\u201d the specifics of industries (agriculture is niche for them);<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">in the European Union, they are restricted by RODO, so they cannot profile after personal data;<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Their effectiveness depends on the quality of the signals we provide from outside.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In practice, the AI algorithm works like an engine, but stripped of fuel. That \u201efuel\u201d is fresh, unique data on user behavior.<\/span><\/p>\n<p>The AI model used in Google Ads or Meta Ads analyzes patterns in the data to predict who will click an ad, make a purchase or otherwise respond to a message. However, the algorithm does not create the target audience on its own. It needs reliable signals about users to do so.<\/p>\n<p><span style=\"font-weight: 400;\">Among the most important sources of such signals are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>cookies<\/b><span style=\"font-weight: 400;\"> - reporting on what content a user has viewed, what sites he or she has visited and how he or she has behaved;<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>first-party data<\/b><span style=\"font-weight: 400;\"> - including e-mail addresses, phone numbers or data from CRM systems;<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>signals from their own portals<\/b><span style=\"font-weight: 400;\"> - For example, what articles the farmer reads, what products he views, what content piques his interest.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The better the data is \u201efed\u201d to the model, the more effectively the algorithm can identify the right audience.<\/span><\/p>\n<h2><strong>What is feeding the AI model - how does artificial intelligence affect marketing?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">\u201cFeeding the model\u201d (English. <\/span><i><span style=\"font-weight: 400;\">feeding model<\/span><\/i><span style=\"font-weight: 400;\">) is to provide algorithms with data that allows them to better \u201eunderstand\u201d their audience and their needs. In practice, this is not magic, but data-driven marketing and precise behavioral analysis.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the context of using<\/span><span style=\"font-weight: 400;\"> AI models in marketing<\/span><span style=\"font-weight: 400;\"> This primarily means providing signals such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>cookies<\/b><span style=\"font-weight: 400;\"> - information about what the farmer reads on the Internet: industry articles, machine comparisons, fertilizers or crop protection products reviewed;<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>first-party data<\/b><span style=\"font-weight: 400;\"> - company's own sources: CRM, newsletters, customer databases, survey results or farm research;<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>signals from industry portals<\/b><span style=\"font-weight: 400;\"> - what topics the farmer is interested in, in what region he farms and what he does (growing crops, corn, vegetables, raising cattle or pigs).<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Thanks to these signals, the algorithm stops treating all Internet users the same and begins to precisely target the farmer with a specific production profile and specific needs. The result? Campaigns become more effective, faster and better adapted to the realities of Polish agriculture.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you have your own portals or research systems, you can upload this data to the Google or Meta ecosystem, for example:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">By Customer Match (Google) or Advantage+ (Meta),<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">or as remarketing signals in PMax campaigns.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">As a result, the algorithm learns faster, targets more precisely and costs less.<\/span><\/p>\n<h2><strong>Why are artificial intelligence models in agricultural marketing a competitive advantage?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Google and Meta have powerful AI models at their disposal, but on their own they can't accurately determine who is farming - especially in Poland, where user data is anonymous and highly aggregated. Therefore, the competitive advantage is gained by those companies that have their own behavioral data (e.g., cookies) and can effectively integrate it into their campaigns.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Entities with their own portals and user data achieve noticeably better advertising results. Organizations that are able to \u201efeed\u201d Google's or Meta's models with valuable signals enable the algorithms to realistically understand that the target audience is not a general Internet user, but a specific farmer - for example, from Lublin, growing corn and looking for fertilizers.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The result? <\/span><span style=\"font-weight: 400;\">Artificial intelligence in agricultural marketing<\/span><span style=\"font-weight: 400;\"> works and brings benefits. The campaign becomes cheaper, faster and much more precise.<\/span><\/p>\n<h2><strong>A brief comparison - two worlds of campaigning<\/strong><\/h2>\n<table>\n<thead>\n<tr>\n<th><b>Script<\/b><\/th>\n<th><b>What's going on?<\/b><\/th>\n<th><b>Result<\/b><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">Without feeding the model<\/span><\/td>\n<td><span style=\"font-weight: 400;\">You trust the Google\/Meta algorithm - it works \u201eafter general patterns.\u201d.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Advertising reaches a wide audience, high cost to reach, low conversion.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">With the feeding of the model<\/span><\/td>\n<td><span style=\"font-weight: 400;\">You provide your own data and behavioral signals.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">AI learns the right audience segment, the ad goes exactly to the farmer-decider.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">The above summary illustrates well the fundamental difference between \u201eno model fed\u201d campaigns and those that provide their own data and behavioral signals. The prevalence of fed <\/span><span style=\"font-weight: 400;\">models <a href=\"https:\/\/adagri.com\/en\/blog\/ai-in-agro-marketing-facts-myths-and-practice-in-poland\/\">AI in agricultural marketing <\/a><\/span><span style=\"font-weight: 400;\">is not only due to the power of the algorithm itself, but to the quality and relevancy of the data we provide it with. The better we \u201efeed\u201d the model, the more precise and effective the campaigns are.<\/span><\/p>\n<h2><strong>Why is powering artificial intelligence models in agro marketing crucial?\u00a0<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">In Polish agriculture, data is scattered - it is found in industry portals, CRM systems, market research results or agricultural applications. They are valuable on their own, but their potential is only revealed when they are integrated into one cohesive ecosystem. Such data aggregation provides what global advertising platforms do not: <\/span>local context and detailed knowledge of farmers' real-world behavior.<\/p>\n<p>Therefore, agencies with their own media - industry portals, newsletters or research databases - gain a distinct advantage. They can feed Google and Meta algorithms with signals from real users, which allows AI to not only analyze patterns, but to precisely target the right segment of farmers. As a result <a href=\"https:\/\/adagri.com\/en\/\">agricultural marketing<\/a>\u00a0 ceases to be based on intuition and conjecture, and becomes fully based on data and measurable behavioral signals.<\/p>\n<h2><strong>Artificial intelligence in marketing is the engine, and data is the best fuel....<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Feeding the models <\/span><span style=\"font-weight: 400;\">Artificial intelligence in agricultural marketing<\/span><span style=\"font-weight: 400;\"> is primarily <\/span>strategy, not the technology itself. It is important to consciously invest in data that allows artificial intelligence to truly understand the audience and its needs. Without the right signals, even the most advanced artificial intelligence models in agricultural marketing operate \u201eblindly\u201d - they cannot accurately identify target groups or optimize campaigns.<\/p>\n<p>However, when we provide the algorithms with precise data, artificial intelligence in agro marketing begins to work like a turbocharged engine: faster, more accurate and more cost-effective. In practice, this means that in the race for a farmer's attention, the advantage goes not to the one with the biggest budget, but to the one who can best \u201efeed\u201d his AI models. Gain effective <a href=\"https:\/\/adagri.com\/en\/offer\/google-ads\/\" target=\"_blank\" rel=\"noopener\">agricultural advertising<\/a>\u00a0Thanks to artificial intelligence in agriculture!<\/p>\n<p>Read also: <a href=\"https:\/\/adagri.com\/en\/blog\/how-to-target-farmers-well-in-the-search-network-what-is-agricultural-ad-targeting-and-which-words-really-work\/\" target=\"_blank\" rel=\"noopener\"><span data-sheets-root=\"1\">How to target agriculture ads well?\u00a0<\/span><\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Wok\u00f3\u0142 poj\u0119cia \u201csztuczna inteligencja w marketingu rolniczym\u201d naros\u0142o wiele uproszcze\u0144. Wiele m\u00f3wi si\u0119 o \u201esamoucz\u0105cych si\u0119 kampaniach\u201d czy \u201eautomatycznej optymalizacji reklam\u201d, jakby algorytmy same wiedzia\u0142y, kogo targetowa\u0107 i jak sprzedawa\u0107 produkty. Tymczasem, sztuczna inteligencja w marketingu rolniczym nie dzia\u0142a w pr\u00f3\u017cni. \u017beby model m\u00f3g\u0142 przewidzie\u0107 zachowanie odbiorcy, musi zosta\u0107 wcze\u015bniej \u201cnakarmiony danymi\u201d, kt\u00f3re pozwol\u0105 mu [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":5278,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[82,55,86,85],"class_list":["post-5277","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-porady-marketingowe","tag-ai-w-agro-marketingu","tag-ai-w-marketingu-rolniczym","tag-modele-ai","tag-sztuczna-inteligencja-w-marketingu-rolniczym"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Sztuczna inteligencja w marketingu rolniczym \u2014 o co chodzi z \u201ckarmieniem\u201dmodeli AI? - Adagri<\/title>\n<meta name=\"description\" content=\"Sztuczna inteligencja w marketingu rolniczym wymaga danych. 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Uko\u0144czy\u0142 Wydzia\u0142 Elektroniki i Telekomunikacji Politechniki Wroc\u0142awskiej o specjalno\u015bci Sztuczna Inteligencja oraz Uniwersytet Przyrodniczy we Wroc\u0142awiu. Od 2013 roku absolwent MBA na Franklin University. Pasjonat fotografii rolniczej i nowych technologii w rolnictwie. Za\u0142o\u017cyciel serwisu Agrofoto.pl. W 2016 roku stworzy\u0142 pierwszy w Polsce algorytm analizuj\u0105cy zachowania u\u017cytkownik\u00f3w rolniczych portali internetowych, kt\u00f3ry inteligentnie buduje kategorie behawioralno-demograficzne u\u017cywane w kampaniach mikro targetowanych oraz badaniach. 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