Gartner predicts that this 2022, 70% of customer interactions will involve emerging technologies such as machine learning (ML) applications, chatbots and mobile messaging. Mordor Intelligence, on the other hand, projects that the chatbot market will be valued at $102.29 billion by 2026.
Meanwhile, Salesforce's 'State of Service' report showed a 67% uptick in chatbot usage between 2018 and 2020, with 66% of the survey's 7,000 respondents agreeing that self-service chatbots have helped their organisations reduce case volume, especially during the pandemic.
As digital technologies become more prevalent in our lives, customer expectations have changed. Nowadays, people expect businesses to be available 24/7, ready to interact or reply to questions immediately. From ordering pizza to finding details about a hotel booking, chatbots can handle all sorts of tasks quickly and efficiently.
At their best, they make customer interactions more streamlined and efficient. They're making it possible for businesses to provide answers and support in real-time, and they've become essential for delivering a great customer experience.
When reading about chatbots, the sheer amount of terminology can sometimes be overwhelming for individuals without a technical background. Suppose you're a decision-maker in the retail sector who needs a custom chatbot or are interested in a solution that can help your company improve customer service response times. You might have a good idea of the issues you need to address, but the jargon-filled language of engineers and developers can make it difficult to understand what you're actually getting.
To make informed decisions about your technological investments, you need a grasp of the essential concepts and features associated with this customer support tool. This article will give you a better understanding of how chatbots operate for you to get a clearer idea of what to look for when considering a solution for your business.
In its most basic form, artificial intelligence is the simulation of human intelligence through computers programmed to mimic human thought processes and actions. The term also applies to the 'smart' machines capable to perform tasks that typically require human intelligence such as learning and problem-solving.
AI is used to process and interpret actual customer queries so that chatbots can understand the customer's intent and respond accordingly. Chatbots are constantly learning from past interactions and getting better at understanding customer needs over time.
Chatbots use natural language processing (NLP) algorithms to process the text input by customers and convert it into a format that the chatbot can understand to interpret customer queries, identify the key elements of the conversation, and determine how best to respond.
For instance, a customer trying to make a purchase on your website might ask, "Do you take credit cards?" or "Are you cash only?" while another might ask, "Can I pay with Mastercard?" By implementing NLP techniques, engineers can train their model and enable the chatbot to respond with a correct answer such as, "We accept VISA and Mastercard" regardless of the pattern in which the question was phrased.
Natural language understanding (NLU) is an AI-driven capability that uses syntax and semantics to analyse and understand user input and identify the intent behind the message. Computer code is structured, so it is the job of NLU to account for the subjective, messy human language it encounters. For example, if a customer types in "I want the Return of the Jedi DVD," without any additional context, the chatbot relies on NLU to understand that the customer is most likely buying a disc and not looking to return a purchase.
This understanding of the customer's intent is what enables the chatbot to provide a fitting response such as, "I'm sorry, we don't have that in stock. Would you like to see our other options?"
The intent is what the customer wants to achieve with their message. Helpshift offers a great explanation on the importance of intent in chatbots:
"Intent is a critical factor in chatbot functionality because the chatbot’s ability to parse intent is what ultimately determines the success of the interaction. In order for a chatbot to be good at this, it must be programmed well and trained with a useful model involving a lot of training data and take advantage of machine learning to constantly advance and improve."
A customer might start with, "I need a flight to Los Angeles." This statement is asking the bot to browse its database of available flights to LA.
Entities are the important pieces of information a chatbot can extract from a customer's input that are relevant to the user's intent and can be later on used in the conversation to generate useful responses or perform specific actions.
For example, a customer might say, "I want to book a flight from New York to Los Angeles on June 12th." In this case, the entities would be:
Utterances are the different ways in which a customer might phrase their query. A chatbot would need to derive intents and entities from this input. For example, customers looking to book a flight would have different ways of stating their intent, such as:
Self-service or self-serve chatbots are designed to enable customers to serve themselves without the need to contact a customer service staff. It uses NLP and NLU to parse customer queries and provide appropriate responses to simple inquiries such as, "How can I change the password on my account?", or more complex queries like, "I am experiencing an issue with my order. Can you help?"
The key advantage of self-service chatbots is that they can handle a large number of straightforward queries simultaneously. This results in lower operational costs for businesses and higher satisfaction levels for customers as they are able to get their issues resolved faster through the database of answers the chatbot can pull from.
ML is a subset of artificial intelligence that enables chatbots to learn from past conversations and improve their responses over time. The more data a chatbot has, the better it can become at understanding customer queries and providing accurate responses. This is why it is important for businesses to constantly train their chatbots with new data.
Chatbots, through ML, can also learn to recognise patterns in customer queries and offer proactive support before an issue arises. A chatbot might recognise that a customer who initiated a chat prior and frequently asks questions about returns is likely to make a return soon and could proactively offer assistance.
Conversational AI is the subset of AI that leverages concepts like machine learning, NLP, and NLU (among others) to power chatbots and digital humans across a range of industries. Leveraging conversational AI could mean further developing a conventional chatbot to support tasks in customer support, lead qualification, or even sales.
By taking advantage of conversational AI, businesses can create chatbots that are more natural and engaging, providing a better overall experience for customers.
A conversational user interface (CUI) is a type of user interface that allows users to interact with computers and digital devices through natural conversations. It can take form either through text (via chatbots) or voice (via voice assistants). Slack’s slackbot is a classic example of CUI in action on the chatbot front, while tech giants Apple, Google, and Amazon are shining examples of companies betting big on CUI through their voice assistants.
Chatbots aren't just engineered to provide the right answers; they are also designed to understand the emotional context of a customer's inquiries. This is where this subfield of computer science comes in. Sentiment analysis allows chatbots to evaluate how users feel, flag any issues, and align responses to the customer's emotions.
Sentiment analysis is important as it allows businesses to improve their chatbots by addressing areas that are causing negative sentiment. A customer might say, "I never received my order." The chatbot would be able to detect that the user's sentiment is negative and immediately offer the appropriate assistance.
There are a number of other key concepts associated with chatbots, which you'll probably hear a lot of from technical conversations or read about in project documents, such as:
As people become increasingly reliant on digital tools when interacting with businesses, it's essential for organisations like yours to look for more innovative ways to deliver outstanding customer service. By taking advantage of the various AI-powered capabilities available today, you can pave the way for your company to provide even better customer service tomorrow.
No one will be willing to invest in technology they don't fully understand, so at Spark 64 we take out the explainability gap and get stakeholders on the same page with our AI conversations. We'll work with your team to ensure that you get the best results from your chatbot project, regardless of the industry you're in.
Talk to us to learn more about how your business can leverage chatbots and other AI technologies today!
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