The role of neural networks and AI in improving customer experience

Modern market conditions require companies to take an increasingly personalized approach to their customers. The introduction of neural networks and artificial intelligence (AI) is becoming the key to improving customer experience, providing faster and more accurate satisfaction of user needs.

Neural networks make it possible to process huge volumes of data and identify hidden patterns, preferences, and behavioral tendencies among users. This approach helps companies understand their customers more deeply and develop personalized recommendations, products, and offers.

Example: in the eCommerce sector, the ASOS platform uses neural networks to analyze user preferences. The system studies not only purchase data but also on-site behavior, including time spent on specific pages and the number of viewed products. Thanks to this information, ASOS can offer products that match the customer’s style and show additional recommendations that increase the likelihood of a purchase.

Artificial intelligence can not only analyze customer behavior but also predict their preferences and actions in real time. AI algorithms can track which products customers view most often, what they add to favorites, and how they react to discounts and promotions. This allows companies to adjust offers in a timely manner and adapt their marketing strategies to audience needs.

Example: Starbucks implemented a personalized recommendation system based on AI, which analyzes data on customer preferences, visit times, order history, and even weather conditions. If a customer usually orders a cold drink when it’s hot outside, AI will offer them the relevant product at the right moment, improving the customer experience and increasing sales.

AI-powered chatbots and virtual assistants have become an essential part of customer service, offering round-the-clock support and instant answers to questions. These systems can automatically process a large number of requests and provide fast assistance with typical questions, order placement, returns, and delivery tracking. Virtual assistants can even recognize returning customers and take their previous interactions into account.

Example: Capital One bank launched the virtual assistant Eno, which helps customers manage finances by answering questions about their balance, transaction history, and upcoming payments. If a customer makes an unusual transaction, Eno can immediately notify them and ask for confirmation, increasing both security and convenience.

AI can analyze purchasing cycles and predict customer needs, allowing businesses to offer necessary products or services in advance. In retail, for example, AI can analyze how often a customer buys a certain product and offer it beforehand, just when the customer is likely to need to restock.

Example: Walmart uses AI to forecast demand for specific items. The system analyzes seasonal fluctuations, weather conditions, and customer behavior to offer relevant products. For instance, before holiday weekends, Walmart may start advertising items traditionally purchased for that period to engage customers and boost sales.

AI enables companies to create and display personalized content tailored to the interests and needs of each user. Such content includes articles, videos, product selections, special offers, and discounts. This helps keep users on the website, encourages repeat visits, and increases the likelihood of conversion.

Example: in the travel industry, Booking.com analyzes user preferences to recommend the most suitable hotels and excursions. If a client searches for hotels with specific amenities, the system recommends only those options and even suggests similar alternatives in terms of style and price. This helps customers find the right option more quickly and improves the convenience of interacting with the platform.

Modern AI technologies are beginning to incorporate a user's emotional state to create a deeper level of personalization. For example, analyzing on-site behavior such as scroll speed, time spent on specific pages, or even facial expressions while watching videos can help determine the user’s mood. This data can then be used to tailor content to emotional preferences.

Example: major learning platforms like Coursera use algorithms that analyze students’ emotional reactions to video courses. If a student starts speeding up playback or skipping certain sections, AI records this and can suggest similar courses with different presentation styles, helping improve engagement and retention.

Voice assistants such as Amazon Alexa, Google Assistant, and Siri are becoming increasingly popular among users and are used to search for information, control devices, and even make purchases. AI analyzes voice commands to offer helpful information and adapt recommendations based on user requests.

Example: large retailers like Walmart and Target integrate their services with Amazon Alexa and Google Assistant. A customer can add items to their cart via voice command, receive product recommendations, and complete purchases. This simplifies the process and enhances convenience, especially when the user doesn’t have time to browse manually.

Artificial intelligence and neural networks significantly transform customer interaction with brands, enabling the creation of personalized solutions and high-quality service. Personalized recommendations, chatbots, behavioral analysis, need prediction, and new technologies such as voice and emotional assistants help companies not only improve customer experience but also strengthen loyalty and increase sales. Implementing AI and neural networks is becoming an essential element of strategies crucial for successful business development in the digital world.

Keywords: AI-driven personalization, neural networks for improving customer experience, AI chatbots, customer needs analysis, voice assistants for business, predicting customer needs, AI recommendation systems, emotional responses and AI.