Personalization through AI: How deep does it go?

Nowadays, personalization through AI is crucial for companies to remain competitive. Customers expect customized experiences that are tailored to their individual needs and preferences.

A personalized customer approach not only leads to greater customer satisfaction, but also strengthens customer loyalty and can significantly increase conversion rates and sales.

Studies show that companies that rely on personalization strategies achieve measurable success and perform better than their competitors.

What is personalization?

The term “personalization” is often used in marketing in particular, but what exactly does it mean?

Personalization refers to the adaptation of products, services or content to the individual needs of a customer. This is done by analyzing user data such as demographic information, purchasing behavior, interaction patterns, user profiles and other data to create a unique and relevant experience for each customer. In the best case scenario, this results in customer loyalty and trust.

Don’t we all know that feeling of appreciation that arises in us when we receive something personal and individual? Before the digital revolution, there were numerous such examples that explain in very simple terms and in essence what personalization means: Tailored clothing made exactly to your own wishes and measurements was more than just a piece of clothing – it was an expression of style and individuality. Or putting together a mix tape on a home-burned CD for a friend was a creative way of expressing feelings and sharing a common passion for music.

So how can these examples be transferred to digital marketing?

Examples of personalization in the digital age:

Email marketing: Personalized advertising is based on customer preferences and behavior to provide relevant content and offers.

When your favorite online bookseller sends you a personalized book recommendation with a loyalty discount, you feel truly appreciated.

Websites and apps: Dynamic content and real-time adaptation of products and recommendations based on user behavior.

After your last vacation, your travel app discreetly suggests new destinations that match your previous preferences exactly.

E-commerce: Recommendation systems such as “Customers have also bought” are based on previous purchasing behavior and serve to increase the shopping basket value.

You regularly buy coffee beans and receive a perfectly matching coffee grinder as a recommendation, which enriches your shopping experience.

Streaming services: Platforms such as Spotify use personalization to make music recommendations based on individual listening history.

Like Spotify’s popular “Mix of the Week” playlist, which many users love, you get a personalized weekly selection that hits your favorite genres while introducing you to new artists – a familiar and welcome form of personalization.

Why is personalization important?

The well-known statement “Markets are conversations” by Karl-Heinz Land puts it in a nutshell:

Markets consist not only of transactions, but also of dialogs between companies and customers. At a time when customer reviews and social media are playing an increasingly important role, it is crucial to maintain a dialog with customers. This means responding to their needs, reacting authentically and building genuine relationships.

The “four Cs” are of great importance in order to implement this successfully:

Contact – It starts with permission to contact the customer in the first place, be it through double opt-in (DOI) or other legal declarations of consent.
Context – Where is the customer? Knowing the context in which the customer operates is crucial in order to convey the right message at the right time.
Content – The content must be appealing and relevant, the “bait” must be attractive in order to arouse the customer’s interest and motivate him to take action.
Community – Ideally, the company creates a community in which customers can interact with each other and share their experiences. This is where personalization becomes particularly important in order to fulfill the feeling of “me, everything immediately and everywhere” and to put the customer at the center.

Technical aspects of AI-based personalization

The basis for personalized experiences lies in the way AI algorithms work. These algorithms go through several steps to generate relevant content for the user. Let’s take a look at this step by step using a specific example:

Data collection: The first step is to collect data about the user. Let’s assume we are talking about an online bookseller. It collects data about the books that a user looks at, adds to the shopping cart or buys. This data can also be combined with information about browsing behavior (e.g. which pages were visited) and demographic data (e.g. age, gender, place of residence).
Data analysis: In the next step, the AI algorithm analyzes this data to identify patterns. For example, the algorithm could determine that the user frequently buys science fiction novels, but also shows an interest in biographies. This is done using machine learning techniques, in which the AI learns from the data without being explicitly programmed what exactly it should search for.
Prediction: Based on the recognized patterns, the AI can make predictions. In our example, the algorithm could predict that the user would most likely be interested in a new science fiction book that has recently been published. These predictions are made by models such as decision trees or neural networks that are able to understand complex relationships in the data.
Personalization: The AI uses these predictions to give the user personalized recommendations. The bookseller could send the user a personalized email with book recommendations or adapt the homepage of the online store accordingly. This is where real-time customization comes into play: The next time the user visits the website, they may directly see recommendations based on their recent activity.
Feedback loops: After each interaction of the user with the personalized content (e.g. whether they click on a book recommendation or not), the model is further improved. This is done through continuous feedback to the AI, whereby the algorithms learn to make even more precise predictions.

Conventional personalization vs. hyper-personalization

Traditional personalization in marketing involves adapting content and offers based on basic customer information such as name, purchase history or demographic data. This form of personalization is relatively simple and limited in scope, as it is often based on static data and does not take place in real time.

Hyper-personalization, on the other hand, goes one step further by using technologies such as artificial intelligence, machine learning and real-time data to create much more precise and individual experiences. This strategy enables companies to take into account subtle customer preferences and behavioral patterns and adapt content or offers in real time.

Comparison table between traditional personalization and hyper-personalization in various aspects such as data basis, use of technology, responsiveness, degree of customization and complexity.

A table that compares the differences between traditional personalization and personalization through AI by comparing aspects such as data basis, use of technology, responsiveness, degree of adaptation and complexity.

However, hyper-personalization also brings challenges. Excessive personalization can be perceived as intrusive and undermine customer trust. There is also the question of how companies can ensure that they maintain a balance between useful personalization and the protection of user privacy. Compliance with data protection guidelines and transparent communication about how customer data is used are crucial here.

Personalization through AI: In the application

Artificial intelligence is now taking this to the next level by analysing big data and recognizing patterns in customer behaviour. AI algorithms can process data in real time to provide personalized content and recommendations. Through machine learning, companies can make accurate behavioral predictions and adapt their marketing strategies accordingly. Technology trends such as adaptive technology make it possible to dynamically adapt content to user preferences.

Hyper-personalization in SMEs: Tailor-made solutions for small and medium-sized enterprises

In addition to the well-known applications of major players such as Amazon, Netflix, Spotify and co., hyper-personalization also offers considerable advantages for SMEs. However, smaller companies often face the challenge of generating sufficient data volumes and providing the necessary technical infrastructure. This is where specialized service providers and tailor-made AI solutions can build a bridge by enabling SMEs to implement their personalization strategies efficiently and cost-effectively. Through the targeted use of artificial intelligence and real-time data analysis, companies can create customized experiences that are specifically tailored to the needs and preferences of their customers. Here are some application examples that show how SMEs can benefit from hyper-personalization

Why the end of “dumb” loyalty programs has come

The traditional form of loyalty programs has reached its limits. These programs are based on limited data sets and therefore offer only limited opportunities for personalization, as they mainly rely on simple purchase data. This lack of data significantly limits the effectiveness of these programs.

The future belongs to intelligent AI assistants

Intelligent AI assistants are fundamentally changing this picture. They use modern technologies such as machine learning and big data to create comprehensive customer segmentation that goes far beyond simple transaction data. This allows them to recognize behavioral patterns and offer personalized experiences in real time. This AI personalization far surpasses traditional loyalty programs and creates a tailored customer experience that significantly strengthens customer loyalty.

Anonymous data platforms: Data protection and personalization combined

Data protection and privacy are key aspects of AI-based personalization. Platforms such as Perfect-ID enable users to manage and consciously share their data themselves, while companies benefit from precise personalization strategies that comply with data protection regulations.

Integration and optimization of existing programs

Another advantage of these AI assistants is their easy integration into existing programs. Companies can seamlessly add these technologies to enhance existing programs through deeper data analysis and more customized feedback loops.

Conclusion

The future of customer loyalty lies in intelligent AI assistants that are replacing traditional loyalty programs. These technologies offer companies the opportunity to create customized customer experiences that are not only effective, but also ethical and legally compliant.

It is important to emphasize that these technologies are ultimately tools. How well they work depends heavily on how they are used. The quality of personalization depends on the right combination of data, algorithms and human expertise. Companies need to realize that it’s not just about having the latest technology, but also about using it responsibly and purposefully. Let’s use these technologies to create truly valuable experiences that serve customers while adhering to ethical and legal standards. Let’s make something good out of it.

How do you assess this development? Could your company benefit from these technologies? Please contact us.