The first serious attempts to personalize marketing and customer service can be traced back to the introduction of Customer relationship management (CRM) systems in the 1980. At that time, companies began to recognize the importance of effective customer engagement and the need to store data on each client’s needs, preferences, and purchase history [20]. Such systems became the first significant step in this process. They allowed companies to track data on transactions, customer preferences, and brand interactions, enabling the creation of targeted offers and the optimization of marketing efforts (fig. 7).
Figure 7. Personalization process using CRM systems
However, despite their importance, CRM systems were limited in terms of flexibility and accuracy. Typically, simple market segmentation methods were used based on basic customer characteristics, such as purchase frequency or product types. Within this approach, personalization remained at the level of basic offers tailored to user groups rather than to each individual client. Their main function was to collect data, not to conduct in-depth analysis. This was an important step toward personalization, but it still did not provide sufficient depth in processing information to predict future customer needs.
In the next stage, personalization took a new form through automated marketing. This became possible thanks to the development of platforms capable not only of storing and processing customer data but also of initiating communications with users based on their actions. During this period, digital marketing began to shift away from universal mass mailings toward more flexible and targeted interactions.
The newly introduced tools enabled automatic messages to be triggered in response to specific events in the user environment. If a customer made a purchase, browsed certain categories, or abandoned a shopping cart, the system could independently respond with an appropriate message – whether a thank-you email, a reminder, or a personalized offer. This approach significantly increased the relevance of communications, as the messages were based on actual consumer behavior rather than assumptions.
However, despite the obvious progress, these scenarios remained predictable and static. They relied on pre-defined templates and could not adapt to changes in context or customer behavior in real time. Interaction with the user was based on simple cause-and-effect relationships, without accounting for nuances such as motivation, temporal preferences, or emotional state. The logic of personalization within automated marketing was limited by pre-established conditions and did not include the system’s ability to learn or self-update.
Nevertheless, this stage marked an important milestone in the history of personalization. It allowed marketing systems to move beyond data storage and toward the active use of data in communication processes. Moreover, it was during this period that the concept of sequence and continuity in the customer journey was introduced – where each client action could prompt a response from the brand. This laid the groundwork for further algorithmic complexity and led to the transition from template-based solutions to intelligent personalization systems capable of analyzing behavior in a broader and more contextually rich framework.
As development continued, the technology reached a qualitatively new level due to the increasing use of machine learning algorithms in marketing strategies. Unlike previous stages, where user interaction was based on fixed triggers and linear scenarios, the new approach allowed for the analysis of complex dependencies between various behavioral parameters and the delivery of individualized solutions in real time.
Recommendation systems became the core of this transformation. Their operation was based on the ability of algorithms to detect patterns not only in the behavior of a single user but also across massive datasets accumulated from millions of others (fig. 8).
Figure 8. The principle of how recommendation systems work
This made it possible to build personalization not only based on direct indicators – such as «what you purchased» – but also through similarity with other users who performed similar actions, searched for similar content, or displayed comparable activity on platforms. As a result, content, offers, and communications began to be shaped by probabilistic models that take into account the statistical closeness between behavioral patterns.
This type of personalization proved especially effective in areas where the choice of a product or service involves a high level of uncertainty – for example, in entertainment, e-commerce, or streaming content industries. Platforms could suggest movies, music, or products even before the user consciously recognized the need to make a choice, thereby shortening the decision-making process. Moreover, the system considered not only past behavior but also the context of the current session – day of the week, time of day, device used, and duration of interaction. All of this enabled a more finely tuned communication, where the user received suggestions tailored not just to their general interests, but to their current state and expected actions.
User interaction became continuous and synchronized across different touchpoints: website, mobile app, social media, and email. Behavior in one channel influenced recommendations in another, creating the sense of a cohesive, individually tailored customer journey. Personalization ceased to be fragmented and evolved into a systemic adaptation of the brand to the unique rhythm of each customer’s life.
This became possible not only due to the growth of computing power and the accumulation of large volumes of data but also thanks to the increased accessibility of cloud storage and data processing technologies. Companies gained the ability to collect, synchronize, and analyze user data from multiple sources – something that previously required expensive infrastructure solutions. As a result, personalization evolved from a tool for local interaction into a full-fledged mechanism for strategic customer experience management, where every step was planned, predicted, and supported by data.
In the next stage, personalization continued to evolve, shifting from analytical behavior prediction to deep adaptation and interpretation of user context. A key development was the large-scale transition to systems based on neural networks and deep AI architectures. This made it possible not only to process vast datasets but also to work with their internal structure at a new level of complexity, uncovering subtle patterns in behavior, motivation, and emotional responses.
At this stage, personalization became multimodal – it combined textual, visual, auditory, and behavioral data to build a multilayered, dynamically updated user profile. For example, the analysis of text queries in search engines, combined with image or video viewing, as well as data on content reactions (viewing speed, scrolling depth, clicks, pauses), began to be used collectively to more accurately predict interests and intentions. This is no longer just a reaction to behavior – it is proactive modeling of intentions, in which the system strives not only to understand what the user wants, but also why they want it, what state they are in, and which forms of communication will be most appropriate.
Natural language processing (NLP) technologies gained significant importance at this stage. Systems learned not only to recognize queries but also to understand context, intonation, and even the emotional tone of a message. This enabled the creation of personalized dialogues, where the communication style was adapted to the user based on their speech patterns, writing style, or voice responses.
A major shift also occurred as personalization began to take into account not only behavioral and demographic characteristics but also more nuanced psychographic and emotional data. Marketing practices started to incorporate mood and emotional state analysis technologies based on the evaluation of text, audio, video, or even facial expressions. This allowed systems to adapt not only the content of offers but also their presentation: tone, visual design, timing, and delivery channels.
During this period, personalization acquired true cognitive capabilities – that is, the ability to learn,