4 Ways That AI Is Improving the Customer Experience
As one of the leading trends in technology, Artificial Intelligence (AI) continues to gain in popularity for marketers and sales professionals, and has grown to be an essential tool for brands that wish to provide a hyper-personalized, exceptional customer experience. The availability of AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software has brought AI to the enterprise without the high costs that were previously associated with the technology.
A report on the Future of Work from RobertHalf indicated that 39% of IT leaders are currently using AI or machine learning, 33% said that they expect to use AI within the next three years, and 19% expect to use it within five years. AI has many applications for enterprise businesses, and in this article, we will discuss 4 ways that it can be used to improve the customer experience.
AI Facilitates a Better Understanding of the Customer
The combination of AI and machine learning for gathering and analyzing social, historical and behavioral data enables brands to gain a much more accurate understanding of its customers. Unlike traditional data analytics software, AI is continuously learning and improving from the data it analyzes, and is able to anticipate customer behavior. This allows brands to provide highly relevant content, increase sales opportunities, and improve the customer journey.
Sven Feurer, senior director of engineering and operations at SAP Customer Experience, shared his thoughts on using AI to enhance CX. “When it comes to customer experience, there is promise for broad impact. With the exponential growth of data arises an opportunity for both B2B and B2C brands to utilize it along with AI to improve everyday experiences for customers,” said Feurer.
“To deliver truly excellent experiences, all customer-focused business units—like sales, customer service and marketing—must work together and efficiently leverage AI tools for common goals. By doing this, AI has the potential to help brands connect with customers on a more personal level, thus increasing loyalty and securing trust not just for now, but post-pandemic as well,” he said.
CRM platforms such as C2CRM, Salesforce Einstein, and Zoho have integrated AI to provide functionality including real-time decisioning, predictive analysis, conversational assistants and other functionality that helps sales teams more easily understand and engage their customers. CDPs such as Amperity, BlueConic, Adobe’s Real-Time CDP, and ActionIQ have also integrated AI into traditional CDP elements to unify customer data and provide real-time functionality and decisoning for marketers, allowing them to gain a deeper understanding of what their customers want, how they feel, and what they are likely to do.
According to Mike Orr, CEO of Grapevine6, in an increasingly digital world, customer engagement often centers on digital content, but by adding natural language processing (NLP) actionable insights can be gleaned. “[By] combining natural language processing (NLP) to the content we can develop insights into each individual customer experience and commit those to a customer data platform — not only do these insights into the interests of the customers provide context for the next human interaction, but also the next content experience,” he said.
He added that AI can also be applied to recommend next best actions for the customer by learning how interests and insights reflect their needs from similar customers.
Real-Time Decisioning and Predictive Behavior Analysis
Real-time decisioning is defined as the ability to make a decision based on the most recent data that is available, such as data from the current interaction that a customer is having with a business — with near-zero latency. Precognitive’s Decision-AI, for instance, features a sub-200 millisecond response time to assess any event in real-time using a combination of AI and machine learning. Decision-AI is part of Precognitive’s fraud prevention platform, and can be integrated on a website using an API.
Real-time decisioning can be used for more effective marketing to customers. One example of real-time decisioning is to identify customers that are using ad blockers, and provide them with alternative UI components that can continue to engage them. Another is personalized recommendations, which are used to present more relevant content to the customer. By using AI and real-time decisioning to recognize and understand a customer’s intent through the data that they produce, in real-time, brands are able to present hyper-personalized, relevant content and offers to customers.
Predictive analytics refers to the process of working with statistics, data mining, and modelling to make predictions. Because AI is able to analyze large amounts of data in a very short amount of time, it uses predictive analytics to produce real-time, actionable insights that guide the next interactions between a customer and a brand. This is often referred to as predictive engagement, and it requires the knowledge of when and how to interact with each customer, something that AI is very good at.
AI and predictive analytics are able to go further than historical data alone, providing deeper insights into what has already occurred, and what can be done to facilitate a sale through suggestions for related products and accessories, making the customer experience more relevant and more likely to generate a sale, as well as providing the customer with a greater sense of emotional connection with a brand.
AI Chatbots Come of Age
According to a 2020 MIT Technology Review survey of 1,004 business leaders, customer service (via chatbots) is the leading application of AI being deployed today. 73% of respondents indicated that by 2022, it will still be the leading use of AI in companies, followed closely by sales and marketing at 59%. A recent report from Capgemini entitled AI and the Ethical Conundrum indicated that 54% of customers said they have daily AI-enabled interactions with businesses, including chatbots, digital assistants, facial recognition, and biometric scanners, and 49% of those customers found AI interactions to be trustworthy, up from only 30% in 2018.
Feurer recognizes the usefulness of AI chatbots for providing personalized assistance to customers, but does not believe that they are a replacement for human contact. “Modern businesses should view chatbots not as a replacement for humans but rather as supplementing the human workforce to help their employees be as efficient as possible,” he said. “Notably, organizations must strike the important balance between self-service and human interaction to deliver the most convenient experience possible. For example, AI-powered chatbots are a valuable tool that can save businesses money while allowing customers to take care of minor issues on their own time. It’s important to remember, though, that chatbots won’t perform as well if they try to understand everything; rather, they should be used to tackle a select number of topics such as invoice management, order tracking and account management. In fact, research shows that chatbots are able to accelerate the handling of queries regarding invoice management by 2-3X.”
Orr understands the value that AI chatbots bring to customer interaction, but reiterated what Feurer said about the need for customer service reps and chatbots to work in conjunction with each other. “Chatbots and really all autonomous customer experience ‘robots’ have the potential to solve a bunch of transactional problems, often related to information discovery. Natural Language Processing has made answering simple questions that rely on complex data easy for users — for example finding a financial advisor near you that specializes in estate planning,” Orr said. ”There’s an inflexion point though where the complexity of the answer requires a person as a trusted intermediary — you may find the advisor using a robot, but you’re not going to take financial advice from a robot unless it’s very simple.”
According to Vikram Khandpur, CPO at Sinch, a cloud communications platform provider AI-based chat isn’t just about customer service. “Chatbots don’t only have to be used for customer service inquiries. For example, by analyzing customer history, a chatbot can create a proactive personalized offer for a customer, and depending on the channel, can also share rich imagery and product photos or a link along with it. Chatbots can be used to predict when a customer may need a new service, and proactively offer it up to them,” Khandpur explained.
“Chatbots of today won’t be confused by a customer changing the topic of conversation. “They can jump from topic to topic — and even channel to channel, such as starting a conversation on the brand’s website and then transitioning to WhatsApp if the customer needs to leave their desktop—to meet the customer where they are and provide service that rivals that of their human counterparts,” he said.
Chris Radanovic is a conversational AI expert at LivePerson, in his experience, with the help of conversational AI, consumers can connect with brands right in the same channels they use most. “Intelligent virtual concierges and bots instantly greet them, answer their questions and carry out transactions, and if needed connect them to agents with all of the contextual data they’ve collected over the course of the conversation,” he said.
AI for Hyper-Personalization
Hyper-personalization combines AI and real-time data to deliver content that is specifically relevant to a customer. According to Radanovic, consumers and brands are embracing conversational AI because it provides personalized experiences that are also much quicker and convenient than traditional ways of interacting with businesses, think waiting on hold for a phone call or clicking through tons of pages to find the right info. Along with a more personalized experience, AI can also help to eliminate the pain points in the customer journey.
Radanovic offered this example, “A giant source of frustration for consumers is repeating information they’ve already shared, like re-confirming a phone number or having to re-explain a problem to multiple agents. As brands adopt tools like LivePerson’s Conversational Cloud, which allows conversational AI to connect to conversation histories, customers’ previously stated intentions, and other data, the conversations they have with consumers feel far more personalized.”
Geoff Webb, VP of strategy at PROS, thinks that AI-driven personalization can facilitate a more personalized, consistent customer experience. “AI and machine learning are key to creating personalized offers because they allow vendors to analyze huge amounts of data quickly in order to present the best offer to their customers,” he said. “That data, on previous interactions and real-time market dynamics, is how businesses unlock the potential of a seamless, personalized, and consistent customer buying experience. And that experience is likely to be the most powerful differentiator for businesses in the future.”
The Challenges of AI
Currently, the greatest challenge for brands using AI is that customer data is spread out among many different channels and disparate systems, and much of it is siloed. All of the data needs to be unified before it can be analyzed by AI. Given the exponential amount of data that is produced throughout the customer journey, many brands are using a Customer Data Platform (CDP) to unify and analyze that data. Blueconic’s CDP uses AI to enable brands to enhance profiles with customer scores, create more effective customer segments, and design new data visualizations.
Another challenge is that many brands do not have funds for AI in their budget, or they have the misconception that AI is overly costly. Similarly, many brands do not feel that they will get a valid ROI from AI. The truth is that when AI is used effectively for customer experience, be it for real-time decisioning, personalization or customer service, the return on investment can easily be validated through analytics.
For those brands that are considering the use of AI chatbots for handling customer enquiries and general customer service requests, they must recognize that AI will never replace human interaction. There will always have to be customer service agents to deal with requests that the AI chatbot cannot fulfill. A realistic goal is for the AI chatbot to handle half of the requests, and the other half will be handled by an employee.
Orr brought up a great point about recognizing the limitations and scope of AI and how it should, and shouldn’t, be used. “One of the big challenges for AI today, especially as it relates to customer experience, is where to draw the boundary. AI often lives as an external service and organizations are struggling to find the balance between sharing enough information to get a meaningful, positive impact on customer experience while respecting the privacy and data risk of their customers. On the technology side, we’ve learned that AI needs to be tuned and optimized for each specific application and that’s where a lot of the investment is right now. Of course in time, AI will be able to tune itself to the application and we’ll need to think of new challenges!”
Artificial intelligence isn’t the boogie man that sci-fi movies scared people with for years, nor is it the job killer that employees have worried about, and it provides many valuable business opportunities. By leveraging AI to understand the customer better, taking advantage of real-time decisioning and predictive analysis, providing a hyper-personalized experience, and using AI chatbots to engage the customer, the customer journey can be improved through all touchpoints and across all channels.
This article is written by Scott Clark and originally published here