[et_pb_section fb_built=”1″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Brands that prioritize their customers will never worry about going out of business. Happy customers are more likely to stay loyal, make repeat purchases, and recommend your products or services to others.\u00a0<\/span><\/p>\n Artificial intelligence (AI) has emerged as a powerful tool to improve how brands approach customer satisfaction.<\/span><\/p>\n In this blog, we\u2019ll:<\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n AI is a computer algorithm that can be trained to perform tasks that typically require human intelligence. These tasks include things like:\u00a0<\/span><\/p>\n Brands can use AI\u2019s vast data processing capabilities to analyze customer-related data to make informed decisions and provide better online experiences.<\/span><\/p>\n Let\u2019s explore seven methods on how you can implement customer-centric AI into your customer interactions.\u00a0<\/span><\/p>\n Brands can instruct AI algorithms to sift through a wealth of customer data, such as browsing history, purchase behavior, and preferences.\u00a0<\/span><\/p>\n The goal? Offer individualized product or content suggestions.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2024\/02\/understanding-customer-needs.png” alt=”Understanding Customer Needs: 66% of customers expect brands to understand their needs expectations” title_text=”understanding customer needs” align=”center” _builder_version=”4.21.0″ _module_preset=”default” width=”78%” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n (<\/span>Image source<\/span><\/a>)<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The customer is always right. Understanding their needs and expectations from the first interaction helps the customer find what they need quickly. The result? You guessed it. Better sales opportunities and higher conversion rates.\u00a0<\/span><\/p>\n By presenting customers with items or content they\u2019re more likely to be interested in, brands can effectively cross-sell related products and upsell higher-value options.<\/span><\/p>\n Such data-driven decisions are a win-win scenario.\u00a0<\/span><\/p>\n AI algorithms can instantly analyze the content and context of a <\/span>customer support query<\/span><\/a>.<\/span><\/p>\n With the help of natural language processing (NLP), AI can comprehend the query\u2019s intention and importance. From there, it\u2019s great at categorizing it accordingly (such as a request for information, a complaint, or technical support).\u00a0<\/span><\/p>\n The technology can direct it down the proper support path by either:\u00a0<\/span><\/p>\n This process drastically reduces response times and guarantees customer inquiries are promptly and accurately addressed.<\/span><\/p>\n Over time, the AI system can become more adept at responding to frequently asked questions by learning from previous queries and responses. It can even anticipate customer needs based on similar past inquiries.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2024\/02\/customer-reaction-to-bad-customer-service.jpg” alt=”consumer reaction to bad customer service” title_text=”customer reaction to bad customer service” align=”center” _builder_version=”4.21.0″ _module_preset=”default” width=”53%” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n (<\/span>Image Source<\/span><\/a>)<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The truth is customer service can make or break your brand. When customers receive prompt and relevant responses, they\u2019re happier and <\/span>more likely to recommend you<\/span><\/a> to their friends and family.\u00a0<\/span><\/p>\n Chatbots<\/span><\/a> and virtual assistants aren\u2019t new, but when powered by AI, their customer service capabilities are amplified.\u00a0<\/span><\/p>\n AI chatbots learn from customer interactions. They analyze data from past conversations to improve their responses over time. As a result, they become more accurate and helpful as they consume more data (a branch of AI known as machine learning).<\/span><\/p>\n When equipped with NLP, they understand human language with all its nuances and handle queries in a more human-like manner.<\/span><\/p>\n For example, if a customer typed:<\/span><\/p>\n \u201cI bought a blue sweater last month, and it\u2019s too small. Can I exchange it for a larger size, and do you have it in green?\u201d<\/span><\/i><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.17.4″ _module_preset=”default” link_option_url=”https:\/\/www.ometrics.com\/ochatbot-for-enterprise\/” border_radii=”on|3px|3px|3px|3px” border_width_all=”3px” border_color_all=”#68AB37″ global_module=”6437″ saved_tabs=”all” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2022\/08\/Ochatbot_ecomm-cart-420×420-1-300×300.png” alt=”shopify plugins” title_text=”Ochatbot_ecomm-cart 420×420″ align=”center” _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.17.4″ _module_preset=”default” custom_margin=”|3px||3px|false|false” border_width_all=”0px” global_colors_info=”{}”]<\/p>\n We Guarantee Results… Or Work For Free!<\/b><\/strong><\/span><\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/www.ometrics.com\/ochatbot-for-enterprise\/” button_text=”Learn More” _builder_version=”4.17.4″ _module_preset=”default” custom_button=”on” button_text_size=”24px” button_text_color=”#000000″ button_bg_color=”#E09900″ button_border_width=”0px” button_icon=”=||divi||400″ global_colors_info=”{}”][\/et_pb_button][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n A non-AI chatbot would pick up on set keywords like \u201cexchange\u201d and provide a generic response about the company\u2019s exchange policy.<\/span><\/p>\n On the other hand, an AI chatbot would understand:<\/span><\/p>\n The AI-powered chatbot can also pick up any hint of frustration and dissatisfaction in the customer\u2019s message to avoid generating a tone-deaf response.\u00a0<\/span><\/p>\n For example, it might offer a discount or free delivery on the next purchase to apologize for any inconvenience caused.<\/span><\/p>\n Here\u2019s a quick glance at what customers expect from chatbots.\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2024\/02\/what-do-customers-expect-from-chatbots.jpg” alt=”chart of what do customers expect from chatbots” title_text=”what do customers expect from chatbots” align=”center” _builder_version=”4.21.0″ _module_preset=”default” width=”54%” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n (<\/span>Image Source<\/span><\/a>)<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n By understanding the context of a customer\u2019s query, generative AI tools vastly improve the customer experience.<\/span><\/p>\n Brands who want to implement customer-centricity and build strong customer relationships must know how their customers feel and respond appropriately.<\/span><\/p>\n The old ways of deriving customer sentiment involve counting the number of times certain words appear across social media channels and product reviews. These methods are outdated and ineffective.<\/span><\/p>\n AI-driven <\/span>customer sentiment analysis<\/span><\/a> quickly helps brands derive valuable customer insights.<\/span><\/p>\n For example, a bookstore could plug customer feedback from all sources into a sentiment analysis tool. It\u2019ll highlight the quantity of positive vs. neutral vs. negative comments and the reason behind those scores.<\/span><\/p>\n The bookstore might find people are happy with the availability of books but not the delivery times. Identifying and correcting areas of weakness will naturally boost customer engagement.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2024\/02\/customer-sentiment-analysis.png” alt=”a chart of customer sentiment analysis: boosts revenue, relevant responses, increase customer satisfaction, customer lifetime value, enhance brand reputation, streamline customer experience, customer insights in sessions, profound market research, better customer support, brand performance.” title_text=”customer sentiment analysis” align=”center” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n (<\/span>Image Source<\/span><\/a>)<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n The accuracy of AI sentiment analytics is unparalleled because it understands human slang and sarcasm. These small details allow brands to understand the extent to which customers feel happy\/unhappy.<\/span><\/p>\n Thanks to the sheer power of AI, this analysis is done at a large scale, at high speeds, and in real time to create a robust customer feedback loop.<\/span><\/p>\n AI breaks down language barriers for brands that serve a global customer base in the following ways:<\/span><\/p>\n AI language translations are fluid and easy to understand on both sides, drastically reducing misunderstandings while boosting customer loyalty.<\/span><\/p>\n AI-driven accessibility tools are designed with sophisticated algorithms that learn and adapt, offering a more personalized and accurate experience for users with different needs.<\/span><\/p>\n For instance, AI-powered speech-to-text services can accurately transcribe spoken language into written text, accommodating:<\/span><\/p>\n This level of precision and adaptability is a leap forward from non-AI speech recognition systems, which often had limited vocabulary and struggled with anything outside of standard speech patterns.<\/span><\/p>\n Similarly, AI-text-to-speech services can generate natural and expressive speech far surpassing earlier non-AI systems\u2019 robotic and monotone outputs.\u00a0<\/span><\/p>\n This technology is particularly beneficial for visually impaired users or those with reading difficulties, as it provides a more engaging and easier-to-understand auditory experience.<\/span><\/p>\n And because the <\/span>best TTS tools<\/span><\/a> continually learn from interactions, they can offer a level of customization and effectiveness that non-AI solutions simply can\u2019t match.<\/span><\/p>\n AI algorithms are adept at <\/span>session replay<\/span><\/a> \u2014 the art of scanning and analyzing a wealth of data generated by user interactions on a website.\u00a0<\/span><\/p>\n This analysis includes tracking all aspects of the customer journey, including:\u00a0<\/span><\/p>\n AI can draw insightful conclusions about individual user preferences and interests from this rich dataset.<\/span><\/p>\n For instance, AI can detect frequent points of use<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2024\/02\/user-behavior-analysis.png” alt=”user behavior analysis” title_text=”user behavior analysis” align=”center” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n (<\/span>Image Source<\/span><\/a>)<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n With <\/span>\u200b\u200b88%<\/span><\/a> of customers less likely to return to a website where they experience a bad UX, preventing simple website issues works wonders in boosting customer sentiment.\u00a0<\/span><\/p>\n By automating the analysis of countless user sessions, AI saves time and provides more accurate and objective insights.<\/span><\/p>\n Furthermore, AI can categorize sessions based on user behavior. Now, you can access the right data to quickly find and study relevant sessions \u2014 like those leading to a purchase or those ending in a user dropping off.<\/span><\/p>\n This targeted analysis helps you make data-driven decisions to optimize user experience, improve website functionality, and enhance overall customer satisfaction.<\/span><\/p>\n At this point, it\u2019s clear that customer-centric AI helps you work smarter, not harder, to improve customer interactions with your brand.<\/span><\/p>\n However, some customers are cautious about how their data is used to provide these personal interactions.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.ometrics.com\/blog\/wp-content\/uploads\/2024\/02\/how-to-use-ai-securely.png” alt=”How concerned are you that companies’ usage of AI for recommendations, customer service, and technical support could potentially compromise your online privacy?” title_text=”how to use ai securely” align=”center” _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n\n
What is Artificial Intelligence (AI)?<\/span><\/h2>\n
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1. Personalized recommendations<\/span><\/h2>\n
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2. Customer query management<\/span><\/h2>\n
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3. Chatbots and virtual assistants<\/span><\/h2>\n
True AI to engage customers for eCommerce, business leads, and customer support.<\/b><\/h4>\n
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5% to 35% Increase in AOV*<\/h4>\n<\/li>\n
20% to 40% Increase in Revenue*<\/h4>\n<\/li>\n
25% to 45% Reduction in Tickets with a Customer Service Chatbot<\/h4>\n<\/li>\n<\/ul>\n
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4. Sentiment analysis<\/span><\/h2>\n
5. Language translations\u00a0<\/span><\/h2>\n
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6. Enhanced accessibility<\/span><\/h2>\n
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7. User behavior analysis<\/span><\/h2>\n
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How to Use AI Securely<\/span><\/h2>\n