Remember the time when we used to spend hours in a shopping mall, waiting just to try on a shirt? And how about the time we would spend browsing through different stores to simply compare the prices? Thanks to digitalization and the advancement of e-commerce, we do not have to do that anymore. Customer behavior has undergone a notable change over the years and continues to evolve. The enormous data collected by brands fuels this evolution of customer behavior. 

 “Digital transformation is not about technology at all. It is about People.” 

 Brands and companies have shifted from a product-centric to a customer-centric approach. Their focus is to deliver meaningful and relevant experiences to their customers. The more impactful the experiences, the higher the chances of a customer choosing the brand in question over others. It boosts customer loyalty and in turn, overall revenue. Brands can leverage technologies powered by AI and ML to enhance the customer experience. For instance, the recommendation algorithm will recommend products based on a customer’s previous purchases and preferences. It can be enabled by the data brands collect.  

 AI (Artificial intelligence) continues to grow as a key enabler for brands to provide hyper-personalized and impactful customer experiences. AI-based technology helps brands to drive sales, increase customer engagement, improve brand loyalty, and understand customer needs better. AI has several applications for enterprise businesses, and mentioned below are ways in which it can be used to improve customer experience journey in the coming future. 

  AI-driven features on apps will drive better product discovery and hence conversion. 

 Discovering a product on an e-commerce platform is driven by either the search feature or browsing the product catalog on the navigation pane. As brands onboard more and more products onto their platform, discovering the right product becomes laborious. Customers do not possess the time or attention span to go through this process. ML improves this experience by suggesting related products based on what the customer views. This is done using a class of methods called recommender systems. These systems are an advantageous alternative to traditional search algorithms. It helps the customer to make the right choices quickly and provides a better experience by predicting users’ choices based on their previous behavior. 

  Marketing to customers is no longer about mass emails but about personalization. 

 Mass communication through emails, SMS, and calls has now proven to yield low ROI. Some of these can negatively impact the experience as consumers are more conscious of their time. Consumers want their preferred brands to understand their needs and provide relevant solutions, in other words, they want customized experiences. It is driven by combining a large set of customer attributes and behavioral data to predict their needs. The usage of ML methods can take in these large sets of inputs (features) and enhance the accuracy of these predictions to ensure relevance to consumers. This technology is transforming the way brands interact with their customers through personalization. 

  Measuring the voice of customers will drive broad policy decisions. 

 Brands have always tried to gauge what the customers feel and say through research methods like surveys and group discussions. But these methods lack coverage and efficiency. Customers want brands to be sensitive to their varied needs and provide quick responses. The collection of such data has been scaled through experience management platforms- a post in which ML methods like NLP are used for analysis to discover themes and their importance. It helps brands in capturing customers’ emotional responses in real time and determine the root cause. It also assists the brand in training the employees based on what matters to the customers. These help brands to be agile about their policies on returns, delivery, etc., in a matter of days, which otherwise would have taken months to figure out, causing a lousy customer experience. Identifying low-frequency but high-impact themes these are also more likely to nip precarious issues in the bud.  

  Omni channel customer service is here to stay. 

 As we head towards a digital-first world, brands need to be able to connect with their customers in more than just one way. Today, the shopping experience of a customer is not restricted to going to physical stores or placing an online order. The customer wants all these options throughout the entire experience. Hence, you find physical store brands establishing their online presence and online brands having physical stores. In addition, the store executives are empowered to be directly in touch with customers through convenient apps (like WhatsApp, Instagram, etc.) throughout the order cycle. It helps address customer needs with the right channel in real time. Online helps easier product discovery, stores give the high touch to help the decision, and messaging services support the tactical considerations of availability, ordering, and delivery.  

 Not only does omnichannel customer service help to increase revenue, but it also helps the brand identify opportunities to satisfy customers and resolve issues faster. While technology stitches these experiences together, AI/ML enhances this experience by connecting the dots and anticipating the customer's needs at any point in the order cycle. This will be done through a combination of methods of predicting event likelihood, language processing, and even computer vision. 

  With the shifting landscape, customer experience trends are going to develop continuously. AI-powered platforms help brands stand out from the crowd, quickly adapt to changing customer needs and deliver meaningful experiences. These trends will help brands keep up with the ever-evolving environment and stay relevant in the industry. 

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