Recommendations are an important component of numerous successful online services. Users of platforms offering books, music, hotel and restaurant reservations or other products will already have noticed such offers — and probably made use of them, too. The fact that these recommendations are extremely reliable is due to the use of artificial intelligence to optimize them. When applied correctly, this marketing instrument has proven to be extremely efficient and can now be used by companies of almost any size.
AI-based recommendations can be found everywhere on the internet: users shopping on Amazon, for example, receive recommendations below the product currently displayed: “Customers who bought xxx also bought xxx”. Or in video streaming, when they select a movie that is recommended to them.
The reason why many internet service providers and e-commerce giants display these recommendations is the enormous added value they generate. With the right recommendation at the right time, many customers can be encouraged to discover complementary offers and ultimately spend more money than originally planned. Or users spend more time on the platforms because of these recommendations. This development goes so far that many users state that they generally rely on the recommendations rather than searching for new content themselves.
These examples show how worthwhile the use of recommendations is. And they become a true killer application when they are tailored to customers through artificial intelligence, because this decisively improves the accuracy of these recommendations. The good news is that artificial intelligence is no longer accessible only to the IT giants. Thanks to the enormous progress made in recent years, more and more of these applications are becoming accessible to small and medium-sized enterprises as well. With the expertise of companies like ours, you too can use AI-based applications to your advantage in a fiercely competitive market.
The basis for recommendations tailored to the user is data. The more you know about your users, the more accurately an artificial intelligence can anticipate their inclinations and needs. Artificial intelligence is far more efficient than manually curated recommendations: thanks to machine learning, the application can process huge amounts of data within a short time and thus create particularly accurate user profiles. The AI compares the user behavior of the entire customer base. In this way, patterns are found that can be applied to specific user groups — right down to truly individual recommendations. The accuracy is astonishing and achieves better results than an entire armada of analysts could achieve manually. Artificial intelligence thus makes tailor-made recommendations affordable — thanks to efficient algorithms, low-cost computing power and the availability of well-structured data.
AI-supported applications use different methods to arrive at recommendations that are as accurate as possible. Three of them are currently used most frequently, bearing in mind that hybrid forms are normally used to achieve the best possible results.
Content-based AI algorithms find recommendations by evaluating the products that users purchase. The information, such as tags and metadata of those products, is analyzed. This method can achieve good results especially when not much other information is available about the customer. For example, in an online shop, a customer could be offered special kitchen utensils right after moving a particular cookbook into their shopping cart.
Recommendations based on popularity are frequently used. Here, users are shown those products that are the most popular overall. Popularity-based recommendations are particularly suitable when only little information is available about both the user and the products.
This term describes those AI recommendation algorithms that achieve results by comparing different user profiles. The customer’s user history is analyzed and compared with that of all other users. Given the availability of usable data, the AI learns better and better through machine learning which users buy similar products or, for example, which movies they prefer to watch.
In principle, the decision as to which algorithms are best suited to optimizing recommendations for your customers must be made depending on the business model and available data. In the vast majority of cases, however, we use hybrid approaches. Collaborative algorithms, for example, are frequently combined with the popularity of the respective products. It requires a thorough analysis of the respective situation, as well as an extensive test phase, to determine which methods can be applied most effectively for your particular business.
Overall, it seems clear that AI-based recommendations are far superior to manual methods — both in terms of individual results and costs. Our experts are happy to advise you if you are considering using AI-based recommendations for your company as well.
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