What are product recommendations?
Product recommendations are personalized suggestions for products and services that are presented to users based on various aspects such as:
- Previous search behavior: e.g. “Based on your selection, you might also like these products”
- Popularity of articles: e.g. “Trending and currently popular” or “Popular with other customers who have viewed this product”
- Product news: e.g. “Just arrived and interesting for you”
- Similarities to previously purchased products: For example, “Based on your previous purchases, you might also like this item”
These suitable and interesting suggestions for the user have the potential to better accompany them in their customer journey and to meet their needs in such a way that customer loyalty and customer satisfaction be increased.
The role of AI in product recommendations
In the context of product recommendations, artificial intelligence an increasingly important role. Through the automated collection of information about customer interactions and user behavior as well as through specific algorithms and predictive models, AI is able to record the interests, needs and preferences of users and present personalized recommendations based on this.
Particularly popular in this context are AI chatbots, which can communicate with users via chat and find out what users' wishes and preferences are based on conversations and interactions. With this knowledge, the chatbot can make individualized recommendations and personalized suggestions.
How do AI-based product recommendations work?
So that an AI is targeted and user-specific Product recommendations can pronounce, it requires various mechanisms and technologies.
On the one hand, artificial intelligence collects various types of information in order to be able to better understand users and their needs. This may include the following data:
- User behavior: For example, which products are clicked on or searched for
- The purchase history: e.g. which items are added to the shopping cart and/or (repeatedly) bought
- The browsing history: e.g. which routes traveled on the website, which product categories are visited frequently and whether product reviews are given
On the other hand, algorithms combined with the collected data help to develop personalized product suggestions for users. A distinction is made between various filtering methods:
- Collaborative filtering: On the one hand, recommendations can be made based on the behavior of similar users, i.e. if person A bought an inflatable water bottle for running and person B has a similar user profile to person A, the same bottle could also be recommended to person B (User-based collaborative filtering). On the other hand, recommendations can be made on the basis of similar products: If sports watches and running shoes are often purchased in combination with each other, running shoes can be recommended to a person A who has bought a sports watch (Item-based collaborative filtering).
- Content-based filtering: In addition to collaborative filtering, content-based filtering is another effective filtering method for personalized product suggestions. For example, recommendations can be made based on the characteristics of the products that users have viewed or bought in the past. If person A frequently watches romantic films, she can be offered more films of this type (characteristic-based analysis). Auch Natural language processing can be used to analyze text descriptions and user reviews and to identify similar products.
- Hybrid filtering: It is also possible to combine the various filtering methods in order to be able to make more detailed recommendations. A streaming platform such as Netflix could, for example, consult both the history of films and series already watched (collaborative filtering) and the characteristics of the films and series (content-based filtering) to suggest new recommendations.
In addition, recommendations for products and services can be based on
- ofthe Contexts (location, time, weather, or device used)
- The demographics (age, gender, income, education)
- The geography (location)
- ofthe Social networks (taking into account the preferences of users' friends)
- The interactive product search (individual preferences and selection criteria that users specify in real time when browsing product catalogs),
- The vibes (sentiment analysis: positive reviews/reviews)
be given.
However, it is important to emphasize that the personalized product recommendations described require a significant amount of user data and detailed profiles in order to be effective.
Dynamic product recommendations through AI chatbots without user profiles
Although the use of AI-powered product recommendations offers significant benefits for companies and consumers, not all organizations have the same requirements. In particular, companies without extensive, fixed user profiles, such as those common on Amazon or Spotify, are confronted with challenges. Because if users are not logged in or the company does not save permanent profiles, important data such as purchase history, preferences or previous surfing behavior is often missing. Even if data is available, it can be incomplete and less meaningful for occasional or non-logged-in users. This limits the ability to make effective recommendations.
The challenges lie not only in limited data availability, but also in data protection regulations, which strictly regulate the way in which data is collected and used. Without appropriate permissions, these companies cannot offer detailed product recommendations based on historical user behavior.
But there are solutions for this too: AI chatbots that interact with customers in real time. This technology makes it possible to quickly and efficiently query the preferences and needs of users without having to resort to previously collected data. Based on this dynamically collected information, companies can then make ad hoc personalized product recommendations. Such AI chatbots offer a flexible and privacy-compliant way to take advantage of personalized recommendations, even without pre-saved user profiles.
Areas of application and application examples
The use of AI-based product recommendations has virtually no limits. That's actually how it can in every sector, in which products and services are sold online, be an advantage to use suggestions for customers: From the automotive sector, media and publishing to the tourism, financial, healthcare or food industries — in all industries, companies use AI-based recommendations to benefit from the numerous advantages.
Using specific examples and best practices, the diverse areas of application of AI-based suggestions can be illustrated particularly vividly:
- media industry: Companies like Spotify are masters of AI-based product recommendations. Depending on their listening habits, they suggest similar songs and playlists to users, which result in users staying on the platform even longer and being satisfied with the music selection.
- tourism sector: Travel websites such as Booking.com are also good at the art of AI-based product recommendations. In particular, they analyze users' browsing and search histories in order to make personalized suggestions for travel destinations, hotels or activities based on this.
- fashion industry: Companies such as Zalando or ASOS also use artificial intelligence to recommend clothing and accessories based on individual style preferences and previous purchases.
Benefits of AI-powered product recommendations
Optimized customer experience
By helping their customers individually and specifically with recommendations, they find exactly what they need and feel as a result appreciated. This improved Customer Experience It also has a positive effect on customer loyalty as well as the customer satisfaction because customers usually make follow-up purchases from brands that offer products and services tailored exactly to their needs and place these recommendations on the website in such a targeted way that customers don't even have to search for them.
Improved conversion rate
Thanks to AI-supported product recommendations, the likelihood that customers will have certain items click, Add to cart or buy, are significantly increased because customers are addressed directly through personalization. At the same time, the probability that customers will cancel their purchase is reduced and a seamless Customer journey be guaranteed.
Efficient resource management in marketing
The more targeted you target advertising and the more precisely you manage marketing measures, the more efficient resource management in the company becomes. So become Reduces stray losses and only reaches people who are actually interested in the respective products and services. This not only increases the efficiency, but also saves expenses one, which in turn can be used elsewhere.
Conclusion: The transformative power of AI in product recommendations
The use of artificial intelligence in the context of product recommendations is becoming increasingly popular among companies, as it can help improve the level of personalization significantly increase and therefore customer loyalty, improve satisfaction and experience as well as optimize conversion rates, efficiency, and costs.
The better companies are able to use recommendations individually tailored to the various customer segments, the more likely they are to have a competitive advantage compared to the competition.
If you want to know how AI-based product recommendations work in practice, then try the moinAI Product Advisor! In this way, you can find out, without obligation and free of charge, how chatbots specifically guide customers to the right product and thus increase your conversion rate.
