e-Commerce is one of the most competitive sectors in the world and it seems to maintain its place with the transforming world. With the developing technology, it is becoming much more common and easy to increase the efficiency of business operations in this field, to provide better quality service to customers, and to gain a place in new markets. As such, we can easily see the most real-life and effective examples of technology in this field. One of these examples, “Personalized Recommendation Systems”, which will be mentioned in this article, is here!
Personalization is the strategy of a marketplace that includes services, experiences and products that create tailored approaches for each individual consumer based on their interests. Personalized recommendations can have a substantial impact on the customer experience. Businesses can create a sense of personalization by providing tailored suggestions that match each customer’s unique needs and preferences, and make the customer feel understood and valued. Furthermore, personalized recommendations can also improve customer satisfaction by reducing frustration that comes with sifting through countless irrelevant options. In general, the impact of personalized recommendations on the customer experience can be substantial and can significantly enhance the chances of customer acquisition and retention. Statistics show that it is also highly effective in terms of user psychology:
- Accenture reports that 91% of customers claim they are more likely to shop with brands that provide personally relevant offers and recommendations;
- A survey by SmartHQ shows that 72% of shoppers say they ONLY engage with personalized messaging
- According to a study by Epsilon, 80% of consumers are more likely to shop with a brand that offers a personalized experience
In other words, if companies offer a unique and relevant service to their consumers, they gain a successful status by the user. Thus, both parties leave the process with what they want and an experience in optimum conditions.
Artificial intelligence is one of the biggest actors in creating this environment, making the experience as “humanised” as possible within the limits of technology and presenting it to the consumer. Recommendation systems can be divided into multiple sub-headings and different methods, but in reality, all they do is to find the most likely scenario by making calculations from some numerical values generated from the results of user behaviour. Here are the 2 most used methods.
Collaborative filtering algorithms recommend items based on preference information from many users. This approach uses similarity of user preference behavior, given previous interactions between users and items, recommender algorithms learn to predict future interaction. These recommender systems build a model from a user’s past behavior, such as items purchased previously or ratings given to those items and similar decisions by other users.
Content filtering, by contrast, uses the attributes or features of an item (this is the content part) to recommend other items similar to the user’s preferences. This approach is based on similarity of item and user features, given information about a user and items they have interacted with, model the likelihood of a new interaction.
Future of Personalized Recommendation Systems
LLMs could be a game-changer in the future of recommender systems. In addition to traditional deep learning methods, it may be a natural outcome that more advanced systems will be designed using LLMs to incorporate the semantics of language into the process.
References
Accenture. (2018, May 3). Widening gap between consumer expectations and reality in personalization signals warning for brands, Accenture Interactive research finds.
SmarterHQ. (2019). Privacy & personalization report.Epsilon. (2018, January 9). New research indicates 80% of consumers are more likely to make a purchase when brands offer personalized experiences.
Epsilon. (2018, January 9). New research indicates 80% of consumers are more likely to make a purchase when brands offer personalized experiences.
