AI in advertising.

What artificial intelligence in marketing is

Artificial intelligence in marketing almost always means machine learning algorithms that find patterns in large volumes of data and make predictions based on them. There is no thinking involved: the system sees that people with a certain set of attributes buy more often and suggests showing them ads first.

This is important to understand from the outset, because what to expect depends on understanding it correctly. An algorithm will not come up with your positioning or explain why customers are leaving for a competitor. It does three things well: it processes volumes a human could not handle, it notices non-obvious connections and it repeats the same action a million times without tiring.

At the same time, some familiar tools have long run on the same algorithms, just without the big name. Automated strategies in ad accounts, search suggestions, product recommendations in an online store, spam filtering — all of this is machine learning you are most likely already using.

Where algorithms genuinely help

Data analysis. The system processes order history, on-site behaviour and ad account data and shows which segments bring revenue and which only consume budget. A human can do this too, but on a sample of several tens of thousands of events they would spend weeks and still miss some of the connections.

Behaviour prediction. Based on past data, the algorithm estimates the probability of a purchase, churn or return. The practical benefit is simple: you know who is worth calling today and who just needs an email.

Personalisation. Selecting products, collections and offers for a specific person. The bigger the range, the more noticeable the effect: a catalogue of ten items does not need a recommendation engine; a catalogue of ten thousand cannot do without one.

Advertising optimisation. Automated strategies distribute budget between platforms and audiences faster than a specialist does by hand. The human's job shifts from managing bids to setting goals and controlling traffic quality.

Customer support. Chatbots handle routine questions around the clock and take the monotonous load off staff. We wrote in detail about their capabilities and limits in a separate article.

Sentiment analysis. The algorithm goes through reviews and brand mentions and labels them as positive, negative or neutral. This lets you notice a spike in negativity in the first hours rather than a week later.

Content creation. Text drafts, headline variants, transcripts, product descriptions. A big separate subject, covered in our article on neural networks and content.

What algorithms do not do

They do not replace an understanding of the market. A model is trained on the past and is good at predicting the continuation of what has already been happening. A law changes, a major competitor leaves, the season shifts — the forecast stops working, and a human has to notice it.

They do not fix a bad product or a bad offer. Precise targeting will simply show an unsuitable offer to more people faster.

They are not responsible for the result. The decision always remains yours, and you will also be the one explaining it to the client or the boss. Saying “the algorithm decided so” does not work in that conversation.

Data quality decides everything

The main limitation is not in the algorithms but in what they are fed. A model learns from your data, and if the data is incomplete or contradictory, the result will be exactly the same, only with an air of scientific precision.

Before any implementation it is worth checking a few things. Do you have a single customer database, or is it scattered across spreadsheets and messengers. Is the lead source filled in, and is it filled in the same way by all staff. Are advertising campaigns linked to real deals, or does analytics end at form submission. Is purchase history stored in a form suitable for analysis.

If the answer to most of these questions is no, you need to start not with artificial intelligence but with end-to-end analytics and order in your records. More on this in our article on the role of analytics in a campaign.

How to adopt it without wasting money

Start with the task, not the technology. Define exactly what you want to change: reduce cost per lead, raise repeat sales, take load off support, speed up content production. Each of these goals suits different tools, and some of them are already built into the services you use.

The second step is to calculate what the task costs now. If an employee spends two hours a week on it, an expensive platform will never pay off. If support cannot cope with the flow and customers are leaving, it will pay off quickly.

The third is to try it on a limited area. One segment, one campaign, one product category. A small test gives a clear answer in a couple of weeks and does not put all of your marketing at risk.

The fourth is to allow time for training people. A tool that one person figured out and then went on holiday sits idle. Agree in advance who is responsible for the settings and who checks them.

What to think about in advance

Working with personal data is regulated by law, and handing a customer database to a third-party service is a legal decision, not a technical one. Check where the data is physically stored and what the contract with the supplier says.

A separate question is the boundary of what is acceptable in personalisation. An offer that seems to know too much about a person's private life provokes not gratitude but anxiety. We examined the ethical side of working with big data here.

And lastly: any texts and images produced with the help of algorithms remain your responsibility. Facts, figures and wording need to be checked before publication just as you would check a junior employee's work.

If you want automated strategies in advertising to genuinely work for revenue, you should start with correctly configured campaigns and goals. That is a separate advertising setup service, and without it the algorithms optimise for the wrong thing.