Artificial intelligence in agricultural marketing-what's the deal with “feeding ”AI models?.

Many oversimplifications have grown up around the term “artificial intelligence in agricultural marketing.” There is a lot of talk about „self-learning campaigns” or „automatic ad optimization,” 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 “fed with data” that will allow it to distinguish between a farmer from Mazovia and a student from Poznan.

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 „feeding AI models” (AI feeding), i.e. providing high-quality, up-to-date data that gives meaning and direction to the algorithm's work.

How do AI models work in agricultural marketing (in Google, Meta, AdTech)?

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:

• operate universally, they do not „understand” the specifics of industries (agriculture is niche for them);
• in the European Union, they are restricted by RODO, so they cannot profile after personal data;
• Their effectiveness depends on the quality of the signals we provide from outside.

In practice, the AI algorithm works like an engine, but stripped of fuel. That „fuel” is fresh, unique data on user behavior.

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.

Among the most important sources of such signals are:

• cookies - reporting on what content a user has viewed, what sites he or she has visited and how he or she has behaved;
  
• first-party data - including e-mail addresses, phone numbers or data from CRM systems;
  
• signals from their own portals - For example, what articles the farmer reads, what products he views, what content piques his interest.
  

The better the data is „fed” to the model, the more effectively the algorithm can identify the right audience.

What is feeding the AI model - how does artificial intelligence affect marketing?

“Feeding the model” (English. feeding model) is to provide algorithms with data that allows them to better „understand” their audience and their needs. In practice, this is not magic, but data-driven marketing and precise behavioral analysis.

In the context of using AI models in marketing, this primarily means providing signals such as:

• cookies - information about what the farmer reads on the Internet: industry articles, machine comparisons, fertilizers or crop protection products reviewed;
• first-party data - company's own sources: CRM, newsletters, customer databases, survey results or farm research;
• signals from industry portals - what topics the farmer is interested in, in what region he farms and what he does (growing crops, corn, vegetables, raising cattle or pigs).

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.

If you have your own portals or research systems, you can upload this data to the Google or Meta ecosystem, for example:

• By Customer Match (Google) or Advantage+ (Meta),
• or as remarketing signals in PMax campaigns.

As a result, the algorithm learns faster, targets more precisely and costs less.

Why are artificial intelligence models in agricultural marketing a competitive advantage?

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.

Entities with their own portals and user data achieve noticeably better advertising results. Organizations that are able to „feed” 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.

The result? Artificial intelligence in agricultural marketing works and brings benefits. The campaign becomes cheaper, faster and much more precise.

A brief comparison - two worlds of campaigning

The above summary illustrates well the fundamental difference between „no model fed” campaigns and those in which one provides one's own data and behavioral signals. The prevalence of fed models AI in agricultural marketing 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 „feed” the model, the more precise and effective the campaigns are.

Why is powering artificial intelligence models in agro marketing crucial? 

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 don't: local context and detailed knowledge of farmers' real-world behavior.

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 agricultural marketing  ceases to be based on intuition and conjecture, and becomes fully based on data and measurable behavioral signals.

Artificial intelligence in marketing is the engine, and data is the best fuel....

Feeding artificial intelligence models in agricultural marketing is all about 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 „blindly” - they cannot accurately identify target groups or optimize campaigns.

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 „feed” his AI models. Gain effective agricultural advertising Thanks to artificial intelligence in agriculture!

Read also: How to target agriculture ads well? 

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