Marketing is no longer impersonal.
In the past, we communicated with a mass audience, where everyone was just one of many. Now this has changed: it's not about a broad audience, but about each individual person.
We can no longer speak simultaneously to everyone and no one: the key to success is targeting a specific individual. For example, instead of a standard email advertisement, you can offer the buyer a discount on the product they were looking for. This is personalized content—it speaks directly, reflecting a person's needs and desires. In contrast, non-personalized content sounds like general information, not addressing the interests of a specific user and is often ineffective.
But it’s not as simple as it seems. Previously, it was enough to divide the audience into several large groups by interest, but today this is not enough. For example, if you sell outdoor products and group all anglers into one category, within this group, there will be those who love winter fishing, fans of fishing in mountain rivers, and those who hunt for large fish only. This general classification does not allow for taking into account the nuances and needs of each one.
Instead of generalized segments, we are now striving to create individualized offers. This means that every user is offered content that matches their specific interests and needs.
And here comes artificial intelligence. AI allows processing huge amounts of data, identifying subtle details of consumer interests and behaviors.
The Importance of Personalized Content
Personalized content acts as a magnet for user engagement.
When a user sees content created specifically for them, they are more willing to engage with it. It could be anything from personalized offers, product recommendations that truly interest them, or unique promotions based on their previous purchases.
Engagement leads to the next, much more important point—conversions. The user arrives, sees what they are looking for, and buys it.
Imagine you enter an online store. Instead of encountering hundreds of irrelevant products, you see offers that exactly match your interests and needs. It's not just convenient; it reduces the stress associated with finding the right product. Less time is spent, and the result is achieved faster.
When a client doesn’t have to spend time searching, they make purchase decisions quicker. A simple and clear offer lowers the purchase barrier and makes the process much more enjoyable. This is directly reflected in your metrics: the number of completed deals and the average check increase.
Personalized content not only simplifies the search process, but also significantly increases the level of satisfaction. When a customer sees that they are offered exactly what they need, it creates a feeling of being valued and understood. In turn, this builds loyalty and strengthens trust.
In a highly competitive environment, this decision becomes an integral part of successful marketing.
Examples of Personalized Content
Consider Yandex.Music. "My Wave" knows exactly what a user likes to listen to and picks tracks based on this. If a person regularly listens to certain genres or artists, the algorithms analyze these preferences and suggest something new that might please them.
Another example is the online cinema Kinopoisk. The recommendation system works on the same principle here. It analyzes viewing history and, based on this, offers films and series that may interest the user. Moreover, Kinopoisk also creates selections based on interests, be it comedies, dramas, or science fiction.
Let's not ignore marketplaces. They use personalized recommendations to offer products that might interest the buyer. They collect data on products that were viewed but not purchased and then suggest relevant selections.
Personalized Data: What to Consider
When it comes to personalization, it's important to remember that success lies not just in using all available data but in the correct combination and interpretation of this information.
The first factor is demographic data. This is the foundation for understanding who your client is: their age, gender, income level, and other characteristics. These data give a general idea of the target audience and help create segments for more precise targeting.
The next element is behavioral data. This is information about how a user interacts with a product or service: what they view, what actions they take, how much time they spend on certain pages.
Interaction history is another important aspect. It's like tracking the development of a relationship with a customer: what they bought before, what services they used. These data allow for offering relevant updates or improvements. For example, if a client bought a particular phone model, you can offer accessories compatible with that device.
And, of course, geolocation data. Where exactly is the client, and what regional characteristics could influence their preferences? This is useful for providing local offers and promotions.
Ignoring Personal Data
Ignoring personalized data is not just a missed opportunity but a real threat to business. When content does not take into account a user's interests and behavior, its relevance plummets sharply. This means that potential customers see only general offers that do not correspond to their requests. As a result, engagement decreases, and users begin to perceive the brand as less valuable.
But the consequences can be even more serious. When a company regularly ignores personalized data, it undermines trust. Customers feel invisible, which lowers their loyalty and increases the chance that they will switch to competitors. Constant neglect of personalization leads to the loss of valuable relationships and worsens the company's reputation overall.
The Role of Artificial Intelligence in Personalization
AI drastically changes the approach to personalization in marketing. This shift from intuitive methods to precise algorithms is not just a step forward—it's a quantum leap in business.
First, AI actively utilizes big data analysis to identify complex user behavior patterns. A classic example is product recommendations on e-commerce platforms and marketplaces. For example, AI analyzes not only what you bought before but also how often you interact with certain product categories. This allows for creating hyper-personalized offers that are truly interesting to a specific user.
Manual methods that used to take weeks are now completed in minutes. AI simplifies this process, allowing to quickly adapt offers to changing user preferences.
Moreover, AI allows for predicting user behaviors with high accuracy. It not only helps understand what the client needs now but also anticipates their future needs, facilitating the creation of long-term relationships.
Studying the Audience with AI
One of the main advantages of AI in audience study is segmentation. AI can analyze massive amounts of data and identify specific user groups based on their behavior, preferences, and demographic characteristics. This allows for creating more effective marketing campaigns, targeting specific audience segments.
User behavior analytics is another important aspect of audience study where AI can help. AI can analyze data on user behavior on the site, in social networks, and other online platforms, identifying patterns and trends that may not be obvious to humans.
AI can also help discover hidden preferences and trends that may be unobvious to marketers. For example, AI can analyze purchasing data and identify patterns in user behavior that may indicate new market opportunities.
Conclusion
Using artificial intelligence for content personalization and process automation is not the future but a reality. Many companies already implement AI into their business processes to increase efficiency and improve customer interaction.
However, it is important to remember that implementing AI should be done wisely and cautiously. AI is not a magic wand. It is necessary to clearly understand which tasks can be automated and which require human intervention.
