16 Examples of AI in Customer Service
As AI develops, more ways are being found to incorporate it into customer service. In this article, we’ll go through 16 examples of AI in many areas of customer service, including customer-facing technologies such as identification and call routing, as well as behind-the-scenes technologies such as contact center workload management and high-level data analysis.
The Rise of AI in Customer Service
Artificial intelligence (AI) – the science that deals with the creation of human-like learning and reasoning capabilities – has been catapulted into the spotlight in recent years. It seems like every company in every industry wants to harness the power of AI to enhance operations and positively impact the lives of their customers.
Applications based on AI are already visible in healthcare diagnostics, transportation, entertainment and education, to name but a few, and the customer service industry in particular has recognized AI technologies as having almost unlimited potential to meet consumers’ growing demand for better customer experience (CX), lower costs and decreased reliance on contact center agents.
A Tata Consultancy Services survey found that 32% of major companies around the world are currently using AI customer service technologies, the second most common use of AI after IT.
Investments in AI
This realization has seen investments in AI rapidly increasing. The two fields that were predicted to attract the most AI investment this year were automated AI-powered customer service agents, at $4.5 billion, and sales process recommendation and automation, at $2.7 billion.
According to IDC, “AI is the game changer in a highly competitive environment, especially across customer-facing industries such as retail and finance, where AI has the power to push customer experience to the next level with virtual assistants, product recommendations, or visual searches.”
Forward-thinking companies are increasingly turning to AI-powered customer service solutions to optimize CX and to streamline their back-office operations.
In this article, we take a deep dive into sixteen different AI customer service technologies that companies are employing to improve their customer-facing interactions, as well as to enhance their internal processes. As the technology matures, many companies will inevitably look for holistic AI solutions that unify customer and operational data to achieve the most valuable and actionable insights.
Customer-facing AI technologies
AI customer service technologies have given rise to a wide range of customer-facing platforms, all of which help companies provide a level of service beyond human capacity. Customer-facing AI technologies are especially relevant to assisting in customer identification, call classification/routing, chatbots and predictive personalization.
AI for Customer Identification
Biometrics refers to body measurements and calculations for the purpose of authentication, identification and access control. Physical biometric solutions analyze parts of the human body, such as a person’s face, iris or fingerprints, while behavioral biometric solutions analyze other characteristics, such as gait, voice, or interaction with a device. The field is going mainstream with a 2017 Tractica report predicting that biometric hardware and software revenue will grow into a $15.1 billion worldwide market by 2025, at a CAGR of 22.9 percent.
Face and voice recognition
Facial recognition identifies and verifies an individual by comparing facial features from a digital image or video to a database. For example, an AI-based algorithm may analyze the distance between the eyes, the shape of the jaw or the width of the nose, and then use the data to find a match. Voice recognition, meanwhile, digitizes words and encodes them with data such as pitch, cadence and tone, and then forms a unique voiceprint related to an individual. This voiceprint can then be used to identify and authenticate the speaker.
AI continues to make significant improvements to machines’ biometric recognition capabilities, especially when it comes to challenging lighting conditions, angles, and backgrounds. Using biometrics, agents can recognize customers, and greet them in a personal manner. Companies can use biometrics to verify warranties, ensuring that customers receive service for their devices without requiring them to save receipts or other documentation. Agents representing financial institutions or insurance companies can use biometrics to quickly authenticate customers while minimizing the risk of fraud. As biometrics become more reliable and cost-effective, more companies can be expected to take advantage of their benefits.
AI for Call Classification / Routing
Intent prediction refers to the science behind figuring out the customer’s next-step requirements. Customers signals – such as clicks, views and purchases – are translated into predictions that deliver value-added personalization before customers even request it. Predictive solutions combine customer data with AI to determine intent and select the right next step to deliver the relevant customer support.
For example, the technology can identify patterns that indicate a customer’s intent based on web activity or text and route the call or chat to the appropriate agent. Intent prediction enables contact centers to up their game by giving customers the assistance they need in the way they want.
Emotion analytics analyzes an individual’s verbal and non-verbal communication in order to understand their mood or attitude. For example, if someone is smiling and nodding their head, they are probably happy, whereas if someone’s eyes are wide and their mouth is hanging open, they are probably shocked.
Emotion analytics can be used to classify a customer’s mood with the right priority and route it to the right agent. For example, an angry customer might be routed to the customer retention team, while a happy, satisfied customer might be routed to the sales team to be pitched a new product or service. Emotion analytics generates data that can then be used to understand a customer’s experience with a product, new packaging or interaction with a representative of the company, as well as to uncover any weak links that cause negative customer reactions.
Conversational AI customer service platforms – known as virtual assistants or chatbots – represent a promising technology that is already projected to cut business costs by as much as $8 billion in less than five years (Juniper). This is likely one reason why Oracle found that 80% of sales and marketing leaders say they currently use or plan to deploy chatbots in the near future.
Major enterprises such as Apple, Microsoft, Facebook, Disney and Google are all actively engaged in the race to build virtual assistants and chatbots that can respond to customer queries and scale the delivery of quality AI-powered customer service. Customer service has clearly benefited from bots as these virtual assistants can store endless amounts of data, predict customer behavior and access relevant information in real time.
Today, humans and AI-based bots can collaborate to optimize interactions with customers. Collaboration can be applied in two primary ways: for the augmentation of human intelligence and the enhancement of human capacity.
Natural Language Processing (NLP) refers to the application of computation techniques to language used in the natural form – written text or speech – to derive analytical insights. For example, a company can employ NLP to determine whether the writer’s perception of a specific topic is positive, negative or neutral. This type of sentiment analysis has become a key tool for making sense of the multitudes of opinions expressed every day in texts on review sites, forums, blogs, and social media.
NLP analysis also allows companies to extract product suggestions and complaints from online product reviews in order to proactively address any issues. These technologies enable companies to gain insights on a micro level — by understanding the emotions of each customer – as well as on a macro level, by keeping their finger on the pulse of their customer base’s opinions.
Organizations now have access to huge amounts of data about their customers that can be used to provide personalized service and recommendations to targeted consumers.
Sprint uses an AI-powered customer service algorithm to identify customers at risk of churn and proactively provide personalized retention offers, a practice that has dramatically improved its retention rate.
Netflix’s AI-powered customer service algorithm uses data such as demographics, viewing history, and personal preferences to predict what the user would like to watch next, with a level of accuracy that saves the company $1 billion a year in terms of customer retention.
This technology can be used to predict technical and maintenance issues before they develop.
Here are some current examples of AI predictive maintenance in action:
ThyssenKrupp claims that its predictive maintenance solution has dramatically increased elevator availability by employing real-time diagnostics that reduce out-of-service time.
Meanwhile, Cisco uses predictive maintenance to optimize
network performance and troubleshoot issues faster.
AI technologies have come a long way toward optimizing back-end customer service processes, ensuring companies are as efficient and cost-effective as possible. Utilizing robotic process automation (RPA) in contact centers has been proven to reduce costs and increase operational efficiencies. Back office AI customer service technologies are especially relevant to assisting in workload management, agent productivity and high-level data analysis of contact center performance.
AI Can Improve Contact Center Workload Management
Computer Vision AI for object/issue recognition
Computer Vision AI technologies involve the processing and analysis of digital images and videos to automatically understand their meaning and context. Their accuracy for object recognition enables the system to identify an object within an image, classify and distinguish it from other objects, and identify parts within the object.
Computer Vision AI customer service technologies can reduce the workload of contact center agents by routing customer enquiries to self-service channels, where customers visually interact with visual assistants that visually guide them toward self-resolution.
Agent Decision Support
The same Computer Vision AI technology that interconnects humans with technology to provide superior CX can also be utilized to make contact center reps’ jobs easier. It enhances agent decision-making and company-wide knowledge sharing through the creation of dynamic visual knowledge bases. The agent and system collaborate during each customer interaction, with the agent’s performance enhanced by the computer’s ability to provide real-time resolution suggestions. This model is especially effective when the contact center is required to handle large call volumes or highly complex episodes.
Agent motivation and productivity
Research shows that disengaged employees cost U.S. companies up to $550 billion a year. With advanced AI technologies such as Computer Vision, agents can work faster and more efficiently. These tools include
smart agent monitoring and training
Providing agents with AI-powered tools and solutions to extend their abilities, enabling them to master complex device guidance processes and provide better service, is an effective way to improve job satisfaction and reduce attrition. Empowering agents with top-notch solutions and encouraging them to perform better using these tools raises their sense of self-worth and increases the pride they feel in their work. When agents are empowered, they become invested in every customer interaction. The results are reflected positively in the agent’s KPIs, further motivating them to use these innovative tools to succeed.
Contact center decision makers understand that better tools are the key to reducing agent training times. Contact Center Pipeline reports that increasing the focus on coaching and development for agents is a top priority for contact center managers. AI-based call center training tools such as gamification, visual assistance and self-monitoring, cut down agent onboarding time and ensure reps are fully engaged from day one.
Another solution, Virtual Employee Assistants (VEAs) – or digital buddies – have been tapped as an effective solution to help contact centers support their agents with on-demand learning, foster intra-company communication and assist with other administrative tasks. In early 2019, Gartner predicted that by 2021, a quarter of digital workers will be using a VEA on a daily basis, a significant rise from less than 2% in 2019.
When gamification is introduced into a call center environment, agents compete with each other to complete objectives and outpace other reps in specific KPIs such as hours worked, lessons learned or average speed to answer. Gamification can be an immersive, exciting experience that engages and motivates agents. Rewards may include recognition on leaderboards, physical prizes or alternative rewards like preferred shifts or free parking.
The success of a gamification system lies in full transparency and comprehensive reporting that ensures a fair competition, which can be based on any activity tracked by the platform, such as resolved cases, average handle times, or timesheet submissions.
AI for High-Level Data Analysis
Inefficient processes cost organizations as much as 20 to 30 percent of their revenue each year. As companies scale their customer care operations or respond to new marketplace realities, changes to their processes are inevitable and necessary. Rather than relying on instinct or team decisions, process improvements should be factually substantiated based on data analytics. AI helps companies harness their data to make useful decisions about process changes that will drive the organization forward.
Customer Lifetime Value (CLV) is a metric that tracks how valuable a customer is to a company throughout the relationship. CLV is based on the premise that retaining existing customers delivers a higher return on investment than acquiring new ones. Studies have found that the likelihood of selling to a first-time customer is 5-20%, whereas for an existing customer the probability is 60-70%.
Using high-level AI-driven data analysis to pinpoint where in their lifecycles customers are churning or to target customers with loyalty promotions helps to optimize CLV. Understanding CLV gives companies the data they need to continuously improve or to pinpoint areas of excellence; it is a number that should be top of mind for every contact center agent fielding calls from customers.
Companies are increasingly adopting AI to identify trends and gain insights from the huge volumes of data they hold in order to aid decision-making. AI-driven holistic solutions are being utilized to automate business intelligence and analytics processes based on transactional data found in their databases. By detecting patterns and changes, companies can use the resulting insights for a wide range of business applications, such as new service requirements, location-based trends or new product development.
The Future of AI in Customer Service
The tremendous impact these AI customer service technologies are making – on both customer-facing and back office applications – has already been felt by companies across multiple industries. It is a space where new and improved AI applications are being deployed at a rapid rate to provide omni-channel experiences for both customers and agents.
In the insurance industry, for example, leading companies are now using AI to power every aspect of the policyholder experience and the claims process. Companies use facial recognition to identify customers and Computer Vision AI to compare images of damage with photos of objects already in their systems to calculate the cost, while gauging the urgency of the scenario in order to process the claim as quickly as possible.
Today, AI is at the epicenter of technological convergence across multiple sectors, creating a seamless union of customer-facing and behind-the-scenes AI-driven systems.