Conversational AI for Customer Service – Pros and Cons in 2021

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    As the world spent the last year shopping, socializing, and working online in record numbers, businesses have been scrambling to create exceptional online experiences for their customers. The COVID-19 pandemic exposed a greater need for hyper-flexible, cloud-based technology and solutions (such as NOW CX) to meet the NOW Customer’s skyrocketing demands in 2021 and beyond.

    More websites than ever are investing in Conversational AI for Customer Service, a core contact center automation technology, to optimize workflow and shift to a digital-first approach for customer service solutions like live chat.

    Should your company jump aboard the bandwagon, too? Like all cloud technologies, there are some pros and cons to consider. In this post, we will cover some of the advances in Conversational AI in 2021, why it matters, plus the pros and cons to consider. But first, let’s take a deeper look at the specifics of how it all works.

    What is Conversational AI?

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    Conversational Artificial Intelligence (AI) refers to technologies that use data, machine learning, and NLP to enable human-to-computer communication. These chatbot technologies are a thriving business as they solve many issues in making human-like communications possible by recognizing speech and text inputs and translating their meanings across human and computer languages.

    Conversational AI for Customer Service, such as online chatbots (bots) and voice assistants (VAs) are becoming more widely used by contact centers to imitate human agents as part of customer support services automation.

    How Does It Work? Does It Use NLP?

    The way Conversational AI works for CS/CX is it comprehends and engages in contextual dialogue with visitors using natural language processing (NLP) and additional AI algorithms. 

    Here is a simplified explanation: First, an AI chatbot tries to understand what a website user is trying to say or the intent of the question. The bot then uses natural language understanding (NLU) to decipher the meaning in the user’s words via text or voice. Next, the bot must determine the right response to deliver based on its understanding of the user’s intent using machine learning. Finally, using natural language generation (NLG), the AI chatbot generates a response and delivers it in a way that the user can easily understand.

    Why is Conversational AI Important in Customer Support?

    Here is an important stat: According to J.D. Power, 46% of customers reported they prefer live chat compared to just 29% for email and 16% for social media.  And we know that what NOW Customers really want is real-time help whenever they need it. In fact, they expect a live chat support channel during their website shopping and browsing experiences. This will continue to grow in 2021 and 2022.

    Conversational AI for Customer Service is important because it allows your business to engage with customers on the channels they prefer for immediate responses and proactive engagement. 

    The shift away from in-person interaction with contactless pickup, remote work, remote access to services, mobile streaming, and the like has increased expectations for highly effective, always-on, omnichannel access to customer service. Outsourcing CS to a contact center can help scale, but legacy contact center models come with fixed prices and inflexible contractual terms that don’t support the demands of the NOW Customer. This is one of the main reasons many B2C companies are interested in Conversational AI.

    The technology certainly has a place in customer engagement strategies and across various touchpoints in the customer journey. Channels to consider AI tools include live chat, mobile messaging apps (e.g., SMS/text, WhatsApp, WeChat), social media interfaces (e.g., Facebook Messenger and Twitter), and email. However, installing applications like these across a variety of devices can have high up-front costs and require a significant time investment, which may put these solutions out of reach of smaller companies. Let’s take a look at the pros and cons of Conversational AI applications in the digital age.

    The Pros

    A few pros of Conversational AI for Customer Service include:

    1. Maximize human resources
    2. Scale operational efficiency
    3. Increase engagement
    4. Leverage data for actionable insights

    Staffing a CS department can be very costly, especially with the NOW Customer’s rising expectations for always-on, rapid-fire customer service. Businesses struggle to train and retain agents, secure proper agent staffing levels for the peaks and valleys in inquiry volume, and find a contact center model that works for their business (pros and cons of contact center outsourcing). Additionally, humans can deliver varying responses, leading to an inconsistent customer experience. These are just some of the reasons why Conversational AI for Customer Service in 2021 and beyond looks like a promising solution. Let’s take a closer look at the benefits of this tech:

    • Maximize Human Resources – Even a basic AI platform, like a bot with limited problem-solving skills, can reduce time and improve cost efficiency on repetitive customer support interactions. Augmenting human agents with Conversational AI and giving customers a bot-assisted self-service option will free human agents up to focus on more nuanced inquiries and build relationships with customers when it matters most.
    • Scale Operational Efficiency – Conversational AI platforms and tools used across business units can help drive operational efficiency at scale. Platforms, such as chatbots and virtual assistants, can manage more customers and demand outside business hours for 24/7 coverage. For larger companies, adding infrastructure to support this technology is often cheaper and faster than hiring and onboarding new employees, making it highly scalable.
    • Increase Engagement – An advanced AI platform that uses natural language understanding can respond to an inquiry and anticipate something the customer has not asked for yet. Think of chatbots that mimic human communication. Businesses can give users an easy path to find answers and information via a chatbot or dynamic search bar. B2C companies can also keep customers moving along the customer journey by intervening at critical moments, such as when a user hesitates at checkout, to support consumers 24/7.
    • Leverage Data for Actionable Insights – This type of technology generates rich data that can inform businesses on what channel their customers are using, the time of day they’re looking for answers, and add context to a CX survey sentiment analysis. Companies can leverage data for actionable insights on customer activities, preferences, challenges, and opportunities to improve CX and optimize business processes.

    The promise of Conversational AI for CX is that businesses can engage consumers with personalized, contextual information at scale. Companies can effectively augment (not replace) human communications with customers and create opportunities to establish new relationships, convert more sales, and increase customer satisfaction. The technology works to actively engage with customers while giving businesses rich data, competitive advantage, and new opportunities and solutions to explore.

    The Cons

    A few cons of Conversational AI for Customer Service include:

    1. AI is NOT the same as humans!
    2. Human language not factored in
    3. Privacy and security mandates
    4. Adoption and willingness to engage

      Technology-enabled CX solutions, with or without Conversational AI, are not meant to replace humans. A customer needs to have an easy way to connect with a human representative. Furthermore, a human-centered approach to CX is especially important to the NOW Customer who seeks an emotional connection from the brands they frequent. So, before investing in this technology, be sure to consider the following:
    • AI ≠ Humans – While there have been significant improvements in Conversational AI in 2021, especially in ASR and NLP, the technology’s ability to recognize intent and express empathy is limited. Customers can see through an automated interaction, and in some cases, it leads to increased customer effort, frustration, and neglect. A human will feel instant embarrassment and other emotions if they let down someone they’re chatting with, while a machine will have no problem dumping them off with no resolution. NOW Customers are looking for real human connection and insight, not canned responses and answers from chatbots that don’t accurately solve their problems.
    • Human Conversations – A major pain point for Conversational AI and ASR is the factors in human language. Language input for text and voice must include native languages beyond English, dialects, accents, slang, and even emojis.  The systems also have to decipher emotions, tone, and sarcasm. This can make it challenging for applications, like a voice assistant or chatbot, to interpret the intended user meaning and respond appropriately.
    • Privacy and Security Mandates – The technology is dependent on collecting data to answer user queries. As a result, it is extremely important that Conversational AI applications meet security requirements and ensure that privacy is protected for all users. Sensitive personal information and all personal details must be kept confidential or redacted based on the channel being used.
    • Adoption and Willingness to Engage – Another disadvantage is the level of questions it can handle.  For the customer seeking an answer to a simple question about products or billing, this may not be an issue. However, consider complex queries and the potential revenue loss if a chatbot, for example, leaves the customer with a negative impression of your brand. People can also be apprehensive about sharing personal or sensitive information with a chatbot or other non-human applications due to security and privacy concerns.

    Although Conversational AI applications and tools allow customers to engage more quickly and frequently with brands, their expectations for customer experiences remain high.  Businesses need to carefully consider how limited intent recognition and empathy could impact an automated response’s accuracy and result in escalations that profoundly impact customer effort and overall experience.

    Looking Ahead

    One of the lessons we carry into our post-pandemic world is the importance of business continuity planning. In the digital age, it is pivotal for businesses to continue delivering products or services and ensure that an unprecedented incident (such as a pandemic) does not impact core business functions. 

    The pandemic has also contributed to the growing base of  NOW Customers who live online, shop at all hours, and expect exceptional experiences from every brand, every time – no excuses. For CX leaders, that means delivering positive experiences and rapid resolutions, 24/7, across every channel.

    In the coming years, one change we should expect to see is an increase in Conversational AI applications used across all contact center channels, including live chat, messaging across apps and SMS, platforms, social media, and email. 

    At Simplr, we believe a hybrid AI + Human solution is the best approach for providing NOW Customers with the answers they’re looking for – with the accuracy and judgment of a human and the speed of a bot. Our AI-enabled Conversational Commerce capabilities empower our network of actual human specialists to provide personal consultative communication with your potential customers offering recommendations that help them buy, add-on, and come back by creating an experience they’ll love.

    Interested in learning more about our Conversational Commerce Suite? Contact us today to learn more about our AI-enabled NOW CX solution that will drive increased conversions, revenue, and customer loyalty.

    What is an Example of Conversational AI in Customer Service?

    Like most technologies, there are varying maturity levels of Conversational AI for Customer Service. Use cases and maturity ranges from basic answer and response programming to advanced natural language generation that automatically transforms data into plain-English content and tells a story as a human would. An AI chatbot is the most popular form.

    A simple example of a low maturity Conversational AI customer service application is a FAQ chatbot or a traditional scripted chatbot. In this scenario, a user types in a question, and the answer programmed for the keyword or phrase is delivered as the response. These “answer and response” chatbots do not use NLP, dialog management, or machine learning. This means the chatbots can handle customer requests that follow a predicted path; however, they cannot improvise when there are unexpected turns. 

    The next level includes voice assistants and virtual personal alike Amazon Alexa, Apple’s Siri, and Google Assistant. These virtual personal assistants use automatic speech recognition (ASR), NLP, and have simple dialog management. They serve a general purpose, and their responses are linear, meaning the answers they provide do not build on one another. Advances in virtual personal assistants include offering personalized suggestions based on user history.

    The most advanced applications include virtual customer assistants (VCA) and virtual employee assistants (VEA). These systems serve a specific purpose, use ASR, NLP, and are well-integrated into back-office operations to provide a contextual and personal experience to customers and employees. These applications are programmed to carry context from one interaction to the next using sophisticated NLU, enhancing the user experience and taking action on behalf of the customer to perform a transaction. These custom-designed advanced applications are becoming a popular way for businesses to manage customer service communications at scale.