What influence does AI have on the customer experience?

Customer experience has become a central element of online sales. Indeed, the majority of stores today adopt user-centric strategies, which means that they orient their actions around their users: the users and the customers.

Artificial intelligence has brought a great deal to many different professions. In this article, we'll be focusing on retailers and the opportunities offered by AI in terms of user experience.

What types of AI are used in e-commerce?

Artificial intelligence uses a variety of techniques and technologies to enhance the online shopping experience. Among these, three main methods stand out for their effectiveness and impact: Machine Learning (ML), Natural Language Processing (NLP) and Recommendation Algorithms.

Machine Learning

Machine Learning is a branch of AI that enables systems to learn and improve autonomously from data without being explicitly programmed for each specific task. 

In the context of e-commerce, ML algorithms analyze user data to identify patterns and make predictions that help personalize the user experience.

Natural Language Processing

Natural Language Processing enables machines to understand, interpret and generate written and spoken text in a similar way to humans. 

This technology is particularly useful in e-commerce for improving communication with customers and offering recommendations based on user opinions.

Recommendation algorithms

Recommendation algorithms are intelligent systems designed to suggest relevant products to users based on their browsing and purchasing behavior. 

These algorithms are essential for delivering a personalized shopping experience and increasing conversion rates.

Personalized recommendations

AI-based recommendation systems work by analyzing data collected from users. This data can include browsing history, past purchases, searches performed and even social network interactions. 

Using machine learning techniques, algorithms process this information to identify patterns and preferences. This enables them to predict which products, services or content might be of interest to each user on an individual basis. For example, if a user has often purchased sports goods, the algorithm can recommend new sports equipment or clothing suited to his or her activities.

What are the benefits for users?

Time-saving

Recommendation systems enable users to find relevant products more quickly, without having to scroll through numerous pages or carry out extensive manual searches. Suggestions based on their previous preferences and behavior reduce the time spent searching for items.

Discovering new products

Personalized recommendations can introduce consumers to products they might not otherwise have found. By presenting items that are similar or complementary to those they have already viewed or purchased, algorithms help users discover new options suited to their tastes.

Enhanced shopping experience

Personalization makes the shopping experience smoother and more enjoyable. Relevant recommendations offer an experience that feels tailor-made, better meeting consumers' individual needs and expectations. This increases not only customer satisfaction, but also the likelihood of repeat purchases.

Personalizing the customer experience

There are several techniques for personalizing the customer experience: 

Personalized emails

Companies use behavioral and preference data to send targeted emails. These emails may contain product recommendations based on previous purchases, special offers for frequently consulted products or reminders for items left in the shopping cart. This personalization increases the relevance of communications and can improve open and click rates, as well as conversions.

Targeted advertising

AI enables the creation of hyper-targeted ads by analyzing user data, such as browsing history, product interactions and purchasing behavior. This information is used to display relevant ads across various channels, including social networks, search engines and partner websites. Targeted ads increase the likelihood of engagement and conversion by presenting products or services that closely match users' interests.

Dynamic product sheets

Product pages on e-commerce sites can be personalized in real time according to user behavior. For example, products recently viewed or similar to past purchases can be highlighted. Recommendations can also be based on seasonal trends or current promotions. This dynamic personalization makes site navigation more intuitive and relevant, helping consumers to find what they're looking for more quickly and discovering items that match their specific tastes and needs.