Redefining customer experience: The role of AI in customer support

As digital platforms continue to disrupt traditional business models, companies across various sectors are focusing on customer engagement. Engaged customers demonstrate more loyalty, interact more with their preferred brands, and deliver higher value over their lifetime. Financial institutions, however, often face the challenge of achieving this level of deep customer engagement.

Today’s customers have higher expectations. Some time-pressed customers, for instance, expect real-time customer support, while the majority of customers expect a consistent cross-channel service experience. As both cost pressures and service expectations rise, adding more well-trained employees isn’t a sustainable solution.

This is where AI comes in. Businesses are leveraging AI to provide proactive, personalized services the way customers want them – sometimes even before they know they want it. For those who have adopted this approach, AI-enhanced customer support can boost customer engagement, create more opportunities for cross-selling and upselling, and reduce service costs.

AI-driven customer support gives companies the power to gain deeper insights and create superior user experiences. This not only enhances the online customer experience but also improves customer retention, reinforces brand reputation, facilitates proactive support, and even boosts revenue generation. In fact, it is predicted that AI could enhance company productivity by up to 40% by 2035.

While chatbots are a commonly known form of AI in customer service, latest AI solutions provide a whole lot more. Modern technologies like Machine Learning (ML) and Natural Language Processing (NLP) offer customers and customer service professionals alike a fresh approach.

Maximizing the potential of AI tools to transform customer service isn’t just about implementing the latest technology. Companies face several challenges, from choosing the right use case for AI to integrating it with existing systems, and finding the appropriate talent and organizational governance structures.

However, when executed correctly, an AI-powered customer support service can unlock substantial value for a business, forming a positive cycle of improved service, increased satisfaction, and enhanced customer engagement.

This article delves deeper into the use cases of AI in customer support across industry verticals and explores other aspects of this emerging technology in the field of customer service.

• How does AI work in customer support?
  • Machine learning
  • NLP
• Use cases of AI in customer support across major industry verticals
  • E-commerce and Retail
  • Banking and Financial Services
  • Telecommunications
  • Healthcare and Pharmaceuticals
  • Supply chain and logistics
  • Travel and Hospitality
  • Manufacturing
  • Insurance
  • Automotive
  • Education
  • Real Estate
• What are the benefits of AI in customer support?
  • Benefits for customers
  • Benefits for backend users
  • Operational benefits
• Getting started with AI-powered customer support automation
  • Data ingestion
  • Data categorization
  • Model training
  • Defining the action path
  • Workflow integration
• Best practices for AI in customer support

How does AI work in customer support?

When it comes to customer support, the application of artificial intelligence typically involves the use of NLP and ML.

Machine learning empowers computers to accomplish tasks without explicit programming. Instead, it relies on algorithms to carry out certain actions, recognizing patterns from past data to make predictions on new data. It’s capable of processing massive amounts of data to extract insights quickly. Customer service professional first define the rules, and the machine learning model takes it from there.

For instance, customer service interactions produce vast quantities of relatively organized data from customer inquiries and staff responses. Machine learning enables a program to accumulate and analyze this data, training itself to understand and respond to customer requests. Often, this process requires additional technologies, like NLP software.

NLP allows machines to comprehend spoken and written language. Technologies like chatbots and virtual assistants like Siri and Alexa use NLP to interpret user inputs and provide appropriate responses.

AI in customer support typically leverages these methodologies to aid both users and customer service representatives. The specific use of AI models in customer support often hinges on whether we are dealing with structured, unstructured, or semi-structured data.

Let’s explain it.

Machine learning

Machine learning is emerging as a significant technology in today’s business landscape. Its capacity to discern patterns and make forecasts can assist companies in enhancing all aspects of their functions, including customer service. Here are a few ways machine learning can enhance customer service operations.

• Streamlining customer service: Machine learning algorithms enable companies to develop chatbots capable of interacting with customers on a variety of topics, from responding to common queries to conducting straightforward transactions. This allows human agents to concentrate on more intricate issues.
• Customizing interactions: Machine learning algorithms can detect trends and patterns by analyzing customer data. This understanding can aid companies in delivering personalized suggestions and content to customers. These insights can be employed to foster a more engaging, customer-centric experience.
• Forecasting customer actions: Predicting customer needs is essential for companies aiming to improve customer retention rates or optimize customer service. Machine learning can comprehend customer behavior and predict when assistance might be required. Companies can then proactively approach customer engagement, resulting in seamless interactions and greater customer satisfaction.
• Determining customer sentiment: Machine learning can also be utilized to observe and assess customer sentiment. By scrutinizing customer feedback, reviews, and other forms of data, valuable insights into customer sentiment can be garnered. This knowledge allows for the adjustment of your customer service approach as needed.

NLP

Natural language processing is a powerful tool that is significantly enhancing customer service. It meets evolving consumer demands by offering immediacy, self-reliance, and 24/7 accessibility. NLP provides a more personalized customer experience, allowing businesses to understand and act upon customer feedback better. However, it’s more of a support tool than a replacement for customer service teams, aiding in automating various tasks.

NLP can significantly assist in backend data analysis. It allows businesses to understand customer sentiments and uncover themes in communication, enabling them to fill gaps in their service.

The field of NLP is ever-evolving, with transformer-based architectures emerging as a game-changer. These models can understand and perform predictive analytics based on textual analysis. Models like BERT (Bidirectional Encoder Representations from Transformers) and ELMO (Embeddings from Language Models) are expected to redefine the performance on various NLP tasks.

The combination of supervised and unsupervised learning methods has shown promising results in model training. Reinforcement learning is also becoming crucial, speeding up tasks like translation and summarization.

Use cases of AI in customer support across major industry verticals

E-commerce and retail

AI enhances customer support in the e-commerce and retail sectors by streamlining responses and personalizing customer experiences. Utilizing AI technologies like chatbots, online stores can deliver immediate, round-the-clock assistance, boosting response rates and accessibility. Furthermore, AI’s ability to analyze customer data and anticipate their requirements allows online retailers to provide tailor-made support and suggestions, heightening customer satisfaction.

Here are some use cases:

• AI chatbots: AI-powered chatbots have emerged as vital tools for online retailers, facilitating efficient customer service round-the-clock. AI chatbots can aid customers with product inquiries, order tracking, and procedures for returns and refunds. H&M, for instance, employs a chatbot for guiding shoppers through product discovery and order placement.
• Product suggestions: AI can generate personalized product suggestions by scrutinizing customer data. It enables e-commerce businesses to offer products that align with customers’ preferences and buying history, thus improving user experience. Amazon, for example, utilizes AI to recommend products based on a user’s browsing and purchasing patterns.
• Fraud prevention: AI assists in identifying and forestalling fraudulent activities in e-commerce businesses. It identifies potential fraud risks by examining patterns of suspicious behavior and activities. For example, PayPal uses AI to detect fraudulent transactions and unauthorized account access.
• Voice assistants: The advent of voice assistants like Amazon Alexa and Google Home has allowed AI to streamline the e-commerce customer experience. Shoppers can use voice commands to order products, track orders, and find answers to queries. Walmart has collaborated with Google Home to enable voice-command shopping, permitting customers to add items to their carts and place orders.
• Image recognition: With AI-powered image recognition, e-commerce companies can improve their product search and discovery processes. By analyzing product images, AI identifies attributes like color, shape, and texture, simplifying product searches for customers. For instance, Wayfair employs image recognition technology to aid customers in finding furniture and home décor items that match their preferences.

Banking and financial services

In the rapidly evolving banking and finance landscape, excellent customer service remains a critical determinant of success. Satisfied customers tend to remain loyal, often sharing their positive experiences and venturing into additional bank services. Thus, it’s crucial for banks to adopt advanced technology to meet evolving customer expectations. By leveraging the powerful capabilities of AI and machine learning, banks can deliver swifter, more efficient services customized to meet their clientele’s varied needs and preferences.

Some of the use cases:

• AI chatbots and virtual assistants: These AI entities in banking facilitate communication with customers, understanding natural language to provide quick and precise answers. Banks use them to handle routine inquiries, freeing human resources for complex tasks. Notable examples include Bank of America’s Erica and HDFC Bank’s EVA, which manage tasks ranging from checking account balances to giving personalized financial advice.
• Intelligent Process Automation (IPA): This technology, a blend of AI and automation, streamlines repetitive tasks in banking, enhancing operational efficiency, minimizing human error, and improving response times. It simplifies processes like loan applications, account openings, and customer onboarding, providing a seamless customer experience. JPMorgan Chase and Goldman Sachs have incorporated IPA, significantly accelerating their banking operations.
• Sentiment analysis and emotion recognition: AI-powered sentiment analysis and emotion recognition technologies analyze customer interactions to decipher underlying emotions, enabling banks to understand better and cater to their customers. They help create personalized customer experiences and proactively address issues. Bank of America uses this technology to scrutinize customer feedback and identify improvement areas.
• AI-driven financial advice: AI platforms analyze customer data like account balances, spending patterns, and financial goals to offer customized advice on budgeting, investing, and retirement planning. Banks such as Wells Fargo and U.S. Bank have employed this technology, aiding customers in making informed financial decisions while also boosting customer engagement.
• AI-powered risk assessments: AI algorithms analyze vast amounts of data, including credit scores and financial histories, to conduct detailed risk assessments for loans and other financial products. This results in more informed decision-making and reduces default risks. Institutions like JPMorgan Chase and Goldman Sachs use AI to enhance risk management strategies.
• AI fraud detection and prevention: AI systems analyze transaction data and customer behaviors to detect potentially fraudulent activities in real-time, providing secure financial transactions. Mastercard’s Decision Intelligence and PayPal’s fraud detection system are prime examples of these tools, significantly reducing fraud losses.

Telecommunications

Today’s highly competitive economic environment has posed significant challenges to the telecom industry, and investing in new solutions seems formidable. However, the potential benefits are considerable. Top-tier telecom companies have already initiated the deployment of AI in their operations. Similarly, emerging digital competitors, capitalizing on software-defined and cloud-based networks, have also started leveraging AI. Keeping pace with both these technological advancements will be essential for businesses to stay competitive.

Potential use cases:

• AI-powered customer communication: AI algorithms help manage customer interactions on a large scale, processing historical and real-time data. In the telecom sector, this takes the form of virtual assistants or chatbots, while some algorithms work behind the scenes to streamline customer service operations. Key applications include routing customer requests, analyzing network data to troubleshoot customer problems, and identifying sales opportunities.
• AI as a customer service agent: Many telecom companies, such as AT&T and Spectrum, use AI to enhance their customer service, making it more cost-efficient. For instance, Spectrum’s AI assistant can handle a wide range of inquiries, from identifying service outages to ordering services, allowing their customer service team to focus on more complex issues.
• Sales and personalized user experience: AI can also help improve customer retention and increase revenue. This is achieved through personalized recommendations based on user behavior, timely cross-sell and upsell offers, and proactive problem detection and resolution. For example, Comcast uses AI to recommend relevant content to its users.
• Accurate product recommendations: AI can recommend the most suitable data packages to customers based on their usage patterns, thereby improving the sales process’s efficiency. This can lead to quicker service and higher satisfaction for customers, potentially boosting retention rates.
• Call center automation: AI can streamline call center operations by automating repetitive tasks and reducing manual errors. Examples of technologies that can be used for automation include self-serve systems, conversational IVR that understands customer voice commands, and agent assist tools that provide real-time guidance to agents during calls.
• AI in network analysis & predictive maintenance: AI plays a crucial role in network maintenance, especially with the ongoing 5G rollout worldwide. AI can help optimize networks to handle increased data usage and improve network quality and internet connectivity. Predictive maintenance can also help avoid unplanned check-ups and potential service disruptions, enhancing the customer experience.

Healthcare and pharmaceuticals

Despite projections that the global healthcare sector would create over 40 million jobs by 2030, it was anticipated that a shortage of nearly 9 million staff members would occur. This deficit was due to various long-standing issues, including inadequate recruitment strategies and a scarcity of available personnel. Such shortcomings led directly to subpar patient care management. This situation forced healthcare providers to seek alternative solutions to enhance patient care experiences. The answer came in the form of automation.

Here are some of the use cases of AI-enabled automation:

• AI-powered appointment scheduling: AI can streamline appointment management, reducing wait times and enhancing patient satisfaction. For instance, an AI assistant can set up, reschedule, or cancel appointments. It can also provide reminders, specify necessary documentation, and provide detailed instructions for appointment preparation.
• Customer care: AI helps manage a high volume of inquiries, automating routine and repetitive tasks and freeing up human support staff. Enterprises can leverage AI to scale up their customer support capacity, offering 24/7 assistance and letting their staff focus on more critical tasks.
• Health management: AI enables patients to manage their health proactively by providing tools and information at their fingertips. Patients can set and meet their health goals and receive routine tips to lead a healthier lifestyle.
• Virtual health assistants: Virtual health assistants manage several tasks, including responding to queries, managing medical records, and scheduling appointments with doctors. They send follow-up reminders and clinical appointment reminders to the patients.
• Automation of redundant healthcare tasks: AI also helps automate redundant tasks, freeing up administrators to focus on other important tasks.
• Medical records management: AI helps break down medical data and connect necessary data points. This data-driven approach in healthcare allows the development of new medicines and drugs.

Supply chain and logistics

In the supply chain and logistics realm, the relationship between customer service and logistics operations has traditionally been complex and challenging. AI emerges as a potent tool in this context, bridging the divide between the two areas. Enhancing customer service in the logistics industry through AI allows us to harmonize processes and resolve long-standing friction points. Let’s delve into how AI is reshaping customer service within the logistics sector.

• Streamlined information processing: AI plays a vital role in processing and managing vast information streams in the logistics industry. Automation tools can effectively handle data from customer service, making error-prone manual tasks easier to perform. For instance, AI can process customer complaints and notify the logistics team of quality issues, enabling them to enact timely preventative measures.
• Enhancing feedback mechanisms: Automated feedback tools can drastically improve the quality and reliability of customer responses. These tools use AI to offer a comprehensive, unbiased view of customer reactions to products or services, providing invaluable insights for the logistics team.
• Trend prediction: AI can analyze customer data to anticipate future trends. Logistics can proactively manage their stocks and predict successful product lines by understanding customer preferences and market trends.
• Facilitating reverse logistics: Automation in customer service can support the complex reverse logistics process. Whenever a return occurs, AI systems record the reasons and send the data to the logistics department for further action, ensuring efficient handling of returned items.
• Elevating customer service: AI can greatly reduce communication errors, providing accurate and prompt responses to customer issues. As AI tools can be updated instantly with the latest product information, they can answer customer queries more effectively than human agents, reducing wait times and improving customer satisfaction.
• Efficient resource allocation: By automating certain aspects of customer service, such as email queries, companies can redistribute their human resources to areas where human interaction is more valued, like phone support. This optimizes resource allocation and shortens the training period for employees, saving valuable time and resources.
• Inventory management: The use of AI in inventory management is especially beneficial for businesses that have to deal with a large volume of inventory. The AI system can monitor and control the amount of inventory in real-time, ensuring no shortages or overstocks. This can save businesses a lot of money and prevent them from losing sales due to a lack of stock.
• Demand forecasting: AI can also predict customer demand, allowing businesses to adjust their inventory levels accordingly. This can prevent businesses from running out of stock and help them avoid having too much inventory on hand, which can lead to wasted resources and money.
• Transportation optimization: AI can optimize transportation routes, making deliveries quicker and more accurate. This technology can also predict market trends, allowing companies to adjust their strategies accordingly. This allows businesses to save money and increase customer satisfaction.
• Warehouse automation: Warehouses can also benefit from the use of AI technology. AI can automate many tasks typically performed by human employees, freeing up those employees to focus on more strategic tasks. This can increase productivity and reduce labor costs.
• Supply chain optimization: AI can also optimize the supply chain process. This means that businesses can anticipate customer needs and adjust their strategies accordingly. This allows businesses to save money and increase customer satisfaction.

Travel and hospitality

Artificial intelligence technologies are continuously advancing, becoming increasingly reliable and efficient tools across a multitude of sectors. The travel and hospitality industry is no exception and progressively incorporates AI and machine learning into various operations. These state-of-the-art technologies aid businesses in executing diverse administrative tasks while simultaneously enhancing customer service experiences.

One of the significant advantages of AI implementation in this industry is its ability to increase personalization, offer valuable recommendations, and ensure prompt responses, even without human staff. As a result, many hotels and resorts worldwide heavily rely on AI solutions to deliver their services and maintain a robust reputation in the highly competitive tourism sector. Forecasts predict that the travel AI market could surpass $1.2 billion by 2026, indicating AI’s growing significance within the travel industry.

Here are some use cases –

• AI for streamlined travel reservations: Artificial intelligence has made it easy for travelers to plan their trips without needing a physical travel agency. AI-enabled assistants and smart chatbots have taken over the role of traditional travel agents. They assist travelers in booking flights, arranging accommodations, and even renting vehicles online. Renowned travel booking platforms such as Booking.com, Skyscanner, and Expedia have integrated such chatbots into their systems. Users just input their journey details, and the bot searches the various booking sites to present the best deals.
• Robot-assisted customer services: Robots are gradually taking over customer services in the travel industry, reducing the dependency on human agents. Some airports and hotels have employed robots to assist customers, such as guiding passengers in busy terminals or streamlining check-in and check-out processes. For instance, the Henn-na Hotel in Nagasaki is the world’s first hotel entirely staffed by multilingual robots. Another notable application of AI is ‘Connie,’ a robot employed at Hilton McLean in Virginia. Connie uses the IBM Watson AI program to answer hotel services and local attractions queries.
• AI applications for flight price forecasting: AI-powered smart price prediction applications have made significant improvements in flight booking. An example is Hopper, which employs machine learning algorithms to predict optimal hotel prices and flights, providing users with personalized recommendations on the ideal time to book a flight.
• Voice assistants and robots in the travel industry: The travel industry has been significantly affected by the Covid-19 pandemic, prompting the need for contactless check-in and check-out procedures. Voice assistants are already heavily utilized in guest rooms, cruise ships, and airport security. As the travel industry adapts to the new normal, the use of robots and voice assistants is expected to increase.
• Voice-based digital assistants: The evolution of AI-powered voice recognition technologies has brought about a new era of digitalization in the hospitality industry. Many hospitality companies globally employ voice assistants that offer immense benefits to guests and hotels. For example, these voice-activated devices can automate room controls, provide regional weather forecasts, and facilitate hands-free calling, among other things.
• Personalized recommendations for travelers: Machine learning in the travel industry is leveraged to offer personalized recommendations to travelers. These recommendations are based on user behavior data collected from users’ actions on travel-related websites. This allows airlines, hotels, and restaurants to offer services tailored to customer preferences.
• Sentiment analysis: Reviews and feedback provided by travelers on different platforms, travel blogs, or social media are valuable data sources for travel agencies, hoteliers, and tourists. Machine learning algorithms and natural language processing technologies are employed to undertake sentiment analysis of these comments to understand people’s opinions and sentiments about tourism services and products.
• AI solutions for baggage handling: The automation of baggage systems at airports demonstrates the successful application of AI in the travel industry. Modern AI tools employ photo identification to track baggage for travelers, forecasting the number of bags a particular person may have based on previously obtained travel data. Automated luggage handling technology increases the efficiency and reliability of luggage check-in, fostering a more sustainable and convenient travel experience.
• Facial recognition for check-ins: Facial recognition technology is increasingly being employed across various businesses to expedite check-in processes and save time. In the hospitality context, this technology facilitates contactless check-in for hotel guests, rendering physical room keys or cards unnecessary. AI-driven facial recognition systems can also simplify the identity verification process for flight boarding, permitting facial scans to replace physical boarding passes. Despite privacy and data usage concerns, this technology has been broadly implemented at airports worldwide, aiming to streamline traditional boarding procedures and enhance check-in and check-out experiences.

Manufacturing

The US manufacturing industry is predicted to reach $6 trillion in revenue. The pandemic has only served to accelerate changes already underway. As a result, the manufacturing industry’s customer service sector is undergoing significant changes. It is necessary to understand the use cases in order to stay ahead of customer expectations.

• Inventory management: AI plays a pivotal role in managing inventory in the manufacturing sector. With natural language processing (NLP) powered chatbots, employees can access supply status and other pertinent details seamlessly. These intelligent systems can also interface with ERP solutions for rapid data access and can analyze supplies’ usage to curtail waste. This leads to streamlined operations and prompt follow-ups on orders.
• Automated customer support: AI chatbots are enhancing customer care in the manufacturing industry by automating responses to routine queries, service tickets, and complaints. During product recalls or delivery issues, these virtual assistants act as human agents, addressing customer concerns and providing relevant information. If needed, they can also offer recommendations on problem-solving steps. If the bot can’t address a particular query, it can smoothly hand over the conversation to a human agent.
• Workshop query management: In bustling manufacturing plants spread across multiple floors, AI chatbots can effectively manage floor queries, enhancing efficiency. They can monitor workload distribution, production capabilities, and recorded maintenance issues, providing quick insights compared to traditional monitoring systems. These AI assistants can also play a critical role in quality control by offering managers detailed procedure insights.
• Delivery notifications: AI chatbots can inform all stakeholders, from employees to customers, about delivery updates. These systems can provide real-time information about delivery status, timing, and condition, saving valuable time for customer support teams and enhancing customer satisfaction.
  Product recommendation: AI chatbots can assist manufacturing companies in recommending products. Using historical data and machine learning algorithms, these bots can understand customer preferences and suggest suitable products, enhancing the buying experience.

Insurance

The insurance sector has recently begun emphasizing exceptional customer service, shifting towards a model that places the consumer at the center of its operations. This transition comes as the industry witnesses a surge in clientele propelled by technological advances. This increased demand has spurred the adoption of modern technologies to expedite insurance processes. AI, particularly through cloud-based solutions, stands at the forefront of these technological advancements, profoundly enhancing customer service in the insurance industry.

Potential use cases

• AI-enhanced claims management: Historically, manual claims processing methods were susceptible to errors and inefficiencies, inflating insurers’ costs by approximately 50%. The introduction of AI dramatically improved the efficiency and accuracy of claims management, facilitating the swift processing of high volumes of claims and notices, leading to enhanced customer satisfaction. Today, multiple vendors offer AI solutions for handling claims that can be integrated with CRM, email, and document archival systems. With minimal training, these solutions can comprehend and extract data from claims and insurance notices, performing actions like categorization, replies, updates, and archiving. They can be particularly effective in claims management audits, initial claims routing, and fraudulent claims detection. Over time, these AI solutions continually learn and refine their efficiency in claims handling, contributing to enhanced customer service.
• Document digitization: Using AI In the traditional insurance industry, extensive reliance on paper documents and hand-written texts often hampered efficiency. However, the advent of rapid document digitization using Optical Character Recognition (OCR) has transformed this landscape. AI-driven automation solutions employing OCR enable automated data entry by integrating with third-party business systems. Capable of processing different types of documents, including images, these solutions leverage Natural Language Processing (NLP) techniques to facilitate communication between humans and operational systems. These AI solutions’ accurate interpretation of unstructured data contributes significantly to improved customer service through optimized customer onboarding and Know Your Customer (KYC) processes.
• AI in underwriting management: The application of AI in underwriting procedures ensures a more seamless and assured experience for customers when they apply for insurance policies. Advanced AI solutions can perform evaluations based on predefined rules and decisions, providing customers with precise estimates in case of events necessitating the use of insurance benefits. These AI systems can also update third-party systems with real-time information based on incoming data, thereby expediting the processing time of customer information and enhancing customer service.
• AI for fraud management: Given their vulnerability to fraudulent activities, insurance companies often incur considerable financial losses each year. However, integrating AI within the insurance industry has improved fraud detection and prevention capabilities. AI solutions, powered by machine learning and deep learning, can identify patterns of unusual customer behavior that could indicate fraud. These systems aid in conducting comprehensive background checks of customers before onboarding, enhancing customer service.

Automotive

Over recent years, the automotive industry has been transformed by four groundbreaking trends, often referred to by the acronym ACES: Autonomous driving, Car connectivity through data sensors, Electrification, and Shared mobility. The concept of ACES has become the driving force for growth in the automotive sector, with AI at its core. AI’s applications within the automotive world are vast, spanning from vehicle design and manufacturing to sales and maintenance. The extensive influence of artificial intelligence across the vehicle lifecycle is why leading automotive manufacturers are keen to integrate AI into their vehicle offerings.

Some of the use cases:

• Assistance for drivers: One of the most noteworthy applications of AI in the automotive sector involves enhancing driver assistance. AI-powered systems, equipped with various sensors, aid with tasks such as steering and pedestrian detection, monitoring blind spots, and providing alerts to the driver. Such measures help avoid potential road mishaps.
• Driver surveillance: AI algorithms can discern if the individual in the driver’s seat is the vehicle’s usual operator or a different person. When the recognized user is driving, these systems automatically adjust mirrors, seats, and even the temperature to the driver’s preferences. Further, AI technologies can track the driver’s eye movement and head position to detect signs of fatigue, alerting the driver if necessary.
• Onboard personal assistants: While many automotive manufacturers opt for integrating third-party voice assistants like Alexa or Siri, others choose to develop their own voice-recognition software. These AI-enabled personal assistants can make calls, regulate temperature, change radio stations, play music, and inform the driver about the fuel level. Moreover, these voice recognition tools have robust personalization capabilities, meaning they can recall users’ preferences and suggest modifications based on their history.
• Enhanced passenger experience: With a focus on improving passenger experience and road safety, automotive manufacturers strive to incorporate various AI technologies, including IoT, image data, Natural Language Processing (NLP), and object identification, into their vehicles. Specific commands allow passengers to listen to their favorite music, order food, and engage in other activities while traveling, enriching their overall travel experience.

Education

In the era of digital learning, the edtech industry faces unique communication challenges among educators, students, and parents. However, these issues are being addressed with the introduction of artificial intelligence especially, conversational AI. Conversational AI, through unified messaging and AI-enabled chatbots streamlines interactions, facilitating academic progress tracking, and enhancing the overall virtual learning experience.

Here are some of the notable use cases:

• AI for student services: AI-driven chatbots can help provide important information to students, ranging from class schedules to scholarship deadlines. This automation of administrative tasks allows institutions to focus their resources on other essential matters.
• AI for diagnostic assessment: Pre-assessment of courses can be automated using conversational AI, which assesses students’ familiarity with the course content in a relaxed, conversational manner. Post-assessment, AI can suggest personalized learning pathways to address their knowledge gaps.
• Personalized learning content: AI can curate a personalized learning experience by asking students about their interests, career goals, and preferred learning methods. This enables students to enjoy a tailored learning experience designed specifically for them.
• Virtual teaching assistant: AI-powered virtual assistants can enhance distance learning by providing real-time assistance to students. These assistants can also engage students with quick pop-up quizzes, fostering microlearning and improving retention.
• AI for grading: AI can simplify grading by evaluating student responses based on pre-set grading criteria. Not only can it grade objective assessments, but conversational AI can also evaluate essay-like responses and provide detailed feedback to students.
• Feedback collection: Conversational AI can streamline the feedback process by facilitating easy collection and dissemination of feedback. This two-way evaluation provides valuable insights to both students and instructors.
• Automating the admission process: Conversational AI can guide students through course selection and admission procedures. From choosing a course and completing payment to uploading necessary documentation, AI makes the onboarding experience seamless. It also answers any query regarding the process or institution without human intervention.

Real Estate

Artificial intelligence’s role is significant in the real estate industry. It provides indispensable technological assistance to both buyers and sellers by leveraging powerful analytical tools and AI-powered chatbots.

• Round-the-clock availability: Many real estate businesses are adopting AI-powered chatbots, allowing them to be available 24/7 across various platforms. This ensures customers can interact with the agency at their preferred time, enhancing customer satisfaction.
• Streamlining property search: AI can quickly and accurately answer recurrent queries regarding property attributes like location, price, size, and the number of bedrooms. The swift provision of AI-driven search results enhances customer experience and potentially leads to increased conversions.
• Predictive assistance: Due to the fluctuating nature of the real estate market, AI assistants can be instrumental in keeping buyers informed about market changes and suggesting new potential deals that align with their needs.
• Multilingual support: Recognizing the diverse linguistic backgrounds of customers in real estate, AI can help agencies interact with customers in various languages and communication modes, including chat and voice. This tailored approach ensures better customer engagement and brand loyalty.
• Virtual tours: The real estate sector has embraced AI technology to stay afloat, with virtual tours emerging as a significant innovation. This technology, driven by AI, enables customers to experience immersive 3D views of properties without physically visiting them. Features include zooming in, listening to spoken notes, and enjoying panoramic views, proving particularly useful for overseas properties or those under construction. This advancement also facilitates regular virtual meetings between landlords and tenants, further supporting contactless interactions.

What are the benefits of AI in customer support?

Benefits for customers

Let’s delve into how customers’ experiences can be enhanced by incorporating AI into customer support processes.

• Instantaneous support: AI can be programmed to offer automated responses to customer queries, leading to immediate replies. More complex problems are efficiently directed to the right support agents’ inboxes, accelerating the resolution process.
• Around-the-clock support: AI-powered tools can offer consistent customer support even without live representatives. Thanks to AI-facilitated customer service bots, customers can resolve issues anytime, thus improving the overall service experience.
• Minimized human errors: While humans are prone to making mistakes, AI systems are designed for precision. The more data they process, the more precise they become, promising enhanced accuracy in addressing evolving customer support needs and eventually leading to an improved customer experience.
• Efficient issue resolution: The primary concern of customers is a swift resolution of their issues. If an AI-powered chatbot can guide them to a solution faster than a live agent, it means a positive experience for the customer.
• Streamlined user experience: Well-implemented AI can assist support teams in refining front-end processes and tackling common user issues before they escalate, leading to a cleaner user experience.
• Predictable user experience: AI gives customers well-defined paths for issue resolution, empowering them to select their preferred resolution methods with accurate estimates of response times.
• Lowered costs: By reducing overhead costs through AI implementation, businesses can maintain lower customer price points, further enhancing their experience.

Benefits for backend users

Let’s explore the numerous benefits that AI-powered customer support can offer to your organization and the employees.

• Lead identification: AI-empowered customer service processes facilitate the identification of potential leads that could convert into valuable customers. When a contact enters your system, AI tools can initiate marketing flows that guide them throughout the customer journey. Automated reminders can be set up for unresponsive leads, enhancing conversion rates and increasing revenues.
• Data collection and analysis: AI technologies can collect vast amounts of data swiftly and with minimal effort, transforming this data into actionable insights to improve your product and customer service process. Automated text analysis can identify recurring words or phrases in customer messages, possibly pointing toward consistent product issues. Recognizing these trends can guide product enhancements that resolve customer problems.
• Enhanced brand reputation: Efficient and timely support, facilitated by AI, leads to happier customers who can act as advocates for your brand. Automation ensures all customer messages are addressed, boosting customer satisfaction and enhancing brand reputation.
• Reduced workload: By leveraging AI to resolve basic customer issues, the ticket volume for customer service agents is substantially reduced. This frees up agents to focus on more complex, higher-value tasks.
• Cost efficiency: As companies grow, so do customer needs. AI allows companies to manage these growing demands while keeping costs under control.
• Insightful Forecasting: Machine learning can analyze extensive data to predict needs, suggest proactive actions, and maintain inventory, providing more actionable insights to the business.

Operational benefits

Let’s delve into how AI in customer support can enhance operational benefits for your organization.

• Brand’s voice: Recently, messaging apps have become increasingly popular for customer engagement with brands. Implementing AI-powered, real-time messaging bot services provides an excellent opportunity for businesses to connect with customers and foster a new revenue stream. Industries such as fashion, tourism, food chains, airlines, and e-commerce are already capitalizing on this trend.
• AI-powered action plans: AI is disrupting the customer service space with its capability to multitask and offer quick responses. AI-powered customer service platforms can generate accurate and speedy responses, significantly reducing research time and offering efficient action plans. By learning from the customer’s context, history, and preferences, AI can anticipate specific customer needs, enhancing the customer experience.
• One-time investment: Traditional call center practices involve high costs and time in hiring and training staff for customer service. In contrast, automating responses via AI platforms significantly reduces these burdens. Once configured, these platforms continually handle routine customer requests, freeing human agents for more complex tasks.
• Multi-channel support: An intelligent support system can direct customers toward parallel support channels when issues get complicated. This ensures a comprehensive balance in the support system, enhancing customer experience and relieving loaded support channels from the hectic rush.
• Machine learning for enhanced support: By learning from frequently resolved issues, machine learning enables customer support to anticipate and prepare for challenging problems. This indirectly benefits both customers and agents, ensuring more efficient problem resolution.
• Predictive insights: AI can predict customers’ wants based on their choices, likes, and visited content. These insightful predictions translate into future actions that customers may take, guiding agents in their interactions and enhancing customer service.
• The momentum of service: AI-driven customer service is not bound by time constraints or holidays, providing customers with 24/7 assistance. This means that customers can interact with AI robots to resolve issues anytime, enhancing customer satisfaction and brand reputation.
• Reliability: With its ability to surpass human limitations and biases, AI offers a level of reliability difficult for human counterparts to achieve. From escalating customer inquiries when unable to solve themselves to surpassing negative human emotions, AI ensures a smooth and reliable customer experience.
• Smarter email support: AI constantly evolves but lacks certain human-like sensibilities. This is particularly evident in handling email support, where automated responses may not be sufficient. With its learning ability, AI can offer meaningful solutions through email support, similar to chatbots.
• Precision at its best: AI-assisted service solutions adhere to predetermined standards, delivering high-quality, straightforward customer experiences with minimal Average Handling Time (AHT).
• Personalized customer experience: Harnessing the combined power of big data, AI, and its machine learning capabilities, businesses can offer personalized customer experiences. This level of personalization greatly impacts customer service interaction, engagement level, customer satisfaction, customer retention, repeat business, and conversion metrics.

Getting started with AI-powered customer support automation

Leveraging AI for customer support is a smooth-sailing task with the right tools in your arsenal. Here, we consider a hypothetical tool using which we will show how to kick-start this process.

Data ingestion

You first need to feed data into the system. This data could either be:

• In-house data: Extracted from existing files such as a spreadsheet.
• Third-party data: Sourced from other integrated applications like your CRM software.

Data categorization

The subsequent phase involves labeling your data appropriately for effective categorization. Your labels act as a reference point for the algorithm, signifying the data points inputted and the result you aim to obtain.

For instance, if you are utilizing AI in customer service, potential labels could be:

• Support ticket classification: Billing, feedback, query, complaint.
• Email categorization: Urgent, internal, personal, confidential.
• Sentiment analysis: Positive, neutral, negative.
• Email attachments classification: Invoice, CV, quote, contract.

Your labels are determined by your data and what you aim to identify.

Model training

The core of AI operations lies in training your model. The “training data,” which can be text or images, is linked to one of your predefined labels, providing the AI with learning examples. It’s crucial to include at least two labels and a minimum of 20 data points for your model to produce accurate results. As you provide more data, the AI model improves precision and accuracy.

Once the AI tool has analyzed your data to make predictions, you will need to assess the summary, performance score, and suggested improvement measures. Depending on your needs, you may choose to keep a “Human-in-the-Loop” for continuous model performance monitoring or allow the AI to function independently.

Defining the action path

Having trained your AI model, it’s time to lay out the roadmap for its operation. Here, you need to define the actions your model should undertake once it has evaluated and categorized each piece of data.

Workflow integration

The final frontier involves integrating your model into your existing workflow. This can be achieved through various integration tools.

At this stage, you specify where the machine sources its data from and the course of action post-data evaluation and categorization. Depending on your requirements and processes, you can either store the output data in a Google Sheet or transport it to another tool.

These steps lay the groundwork for the effective use of AI in your customer support solutions, setting your business up for success.

Best practices for AI in customer support

Laying the foundation with research

To successfully implement AI in customer support, thorough research is imperative. Ask crucial questions that can guide your strategy, such as:

• What are the preferred channels of your customers for support (e.g., messaging, SMS, social media platforms, phone calls)?
• What are the major pain points for your customers, and how can AI assist in alleviating them?
• Where do you find gaps in your current customer service? Are there frequent issues your agents grapple with or is there high turnover in your contact center?
• What proportion of agents spends time interacting with customers versus conducting post-call activities?

Answers to these questions will steer you towards AI-powered customer service features pertinent to your needs.

Harnessing the power of customer feedback

Customers are the lifeline of your business, making their feedback integral to shaping your customer support strategy. Use their insights to enhance your service continually. For instance, are they interested in self-service options or favor certain channels? A customer satisfaction survey can be an effective method to gather such data and gauge the quality of your service.

Personalizing the customer journey across channels

In the current customer-centric landscape, personalization is key. Customers anticipate tailored conversations and seek a swift understanding of their needs, eliminating the need for repetitive information sharing with different agents. Having real-time customer data is essential, but its utility lies in making it easily accessible for your entire customer service team.

Periodic performance evaluation

Maintaining a status quo in your customer service processes is not an option in an ever-changing business environment. Schedule regular performance reviews, whether monthly, quarterly, or annually. This will enable you to identify your strengths and areas for improvement, leading to necessary modifications based on the available data. Metrics such as churn and customer retention rates can provide useful insights. Comparing these metrics before and after the incorporation of AI in your customer support can shed light on the impact of your AI initiatives.

Endnote

The impact of artificial intelligence in customer support is proving to be transformative across a wide array of industries worldwide. The synergy between AI and customer service has opened new avenues for efficient communication, personalized service delivery, and valuable insights into customer behavior.

The advantages that AI brings to customer support are multi-fold. From automating repetitive tasks to enabling 24/7 service availability and providing personalized assistance to predicting future trends, AI technologies are significantly enhancing operational efficiency while dramatically improving customer experience.

Moreover, with the introduction of machine learning and data analysis, AI-powered customer service platforms cannot just react to consumer queries but proactively anticipate them. This means that businesses are not just solving problems as they arise, but they are also capable of predicting potential issues and taking preventive action, thereby achieving superior customer satisfaction.

As we look toward the future, the role of AI in customer service will undoubtedly become more pronounced. As AI technologies evolve, we can expect to see even more sophisticated applications emerging, such as virtual assistants that can understand and respond to complex human emotions or predictive systems that can anticipate market trends with even greater accuracy.

Ultimately, integrating AI into customer service is not just an enhancement of existing capabilities but a game-changer that is redefining how businesses interact with their customers. As we navigate this exciting landscape, the fusion of AI and customer support holds the promise of propelling industries into a future where customer satisfaction is not just an objective but a guaranteed outcome.

Take your customer service to the next level with AI-powered solutions. Leverage LeewayHertz’s expertise in AI and redefine your customer interactions. Get in touch with us today!