Conversational AI: What’s The Key Differentiator
While “chatbots” are usually presented as a monolithic group, there are actually two different kinds: traditional chatbots and conversational AI chatbots. Each type differs significantly in its conversational capabilities and intelligence.
Understanding What is a key differentiator of conversational AI and the difference between traditional and conversational AI chatbots is vital for businesses aiming to enhance customer interactions and deliver exceptional user experiences.
By ensuring any chatbot the brand deploys is powered by AI, the business can leverage intelligent chatbots to engage customers, streamline processes, and drive overall business success.
1What is A Key Differentiator of Conversational AI
Conversational chatbots are the natural evolution of traditional chatbots. By leveraging advanced technologies like machine learning and natural language processing, these conversational AI chatbots can better understand user intent and context, and thus respond in a more personalized and human-like manner. Because of this ability, these chatbots excel at:
- Handling complex queries.
- adapting to user preferences.
- Continuously learning from interactions to improve performance.
Traditional chatbots rely on predefined replies in response to specific keywords or commands. For example, customers can effortlessly place food orders through Domino’s Pizza’s chatbot on Facebook Messenger, sparing them the need to call or visit the store.
Since the chatbot operates within Messenger, it retains a customer’s order history and provides estimated delivery times and updates. The one downside to traditional chatbots is that they may come across as generic and impersonal, especially when the customer needs more specialized assistance.
Brands like renowned beauty retailer Sephora are already implementing conversational AI chatbots into their operations. The brand implemented a chatbot called Sephora Virtual Artist. After users upload their photos or use the live camera feature to try on different makeup looks virtually, the chatbot uses AI algorithms to analyze the person’s facial features, suggest suitable products, and offer personalized beauty advice. In this way, the chatbot is not just regurgitating predefined responses but offering customized beauty consultations to users at scale.
2Traditional Chatbots
Traditional chatbots refer to the early generation of chatbot systems that were primarily rule-based and lacked advanced natural language processing capabilities. These chatbots have a long response time, ranging from 0.1 seconds to 10 seconds of delay, during which the user will commonly see a typing indicator.
These chatbots follow a predefined set of replies in responding to the users, often based on a set of given choices. To return to the example of Domino’s Pizza, when a user chooses the “Order via Messenger” option, the chatbot will respond with a pre-programmed message that includes instructions and information needed for them to process the customer’s order.
Traditional chatbots have several limitations, beginning with their inability to handle complex or ambiguous queries.
In some cases, certain questions may fall completely outside the scope of the traditional chatbot’s knowledge or capabilities. For example, perhaps a customer may inquire about what food will be available for sale at the venue, but the chatbot’s programming is limited to providing details on the location’s address, detailing show times and prices, and selling tickets.
Now, what happens when a query stumps these chatbots? They resort to the most infuriating of responses: "Not understood," “I’m sorry, I don’t understand the question,” or “I’m unable to provide an answer at the moment.” You see, these chatbots have their Achilles heel: the art of understanding conversation context or interpreting the user's intent correctly still eludes them. This kind of response provides a poor user experience to customers, who expect traditional chatbots to be able to handle all the queries that a human agent can.
The inability of traditional chatbots to understand natural language is as disappointing to businesses as it is to users.
According to a recent market study surveying IT professionals at companies, 48% of respondents stated their existing chat technology did not accurately solve customer issues or regularly got their intent wrong. 38% of these respondents said that the chatbots are time-consuming to manage and they do not self-learn.
Finally, 29% of IT professionals stated they must manually load intent and answer pairs into the chat platform. Because this process essentially comes down to content moderation, it is extremely cumbersome. The content must be first manually created within the organization, vetted for accuracy and style, and then finally uploaded. When users begin to ask questions not already covered by these intent and answer pairs, the process must begin anew, with IT teams again creating content to address the new queries. It should be no surprise why so many enterprises have soured on the idea of chatbots: They are using severely outdated versions of this technology.
3Conversational AI Chatbots
Welcome to the era of Conversational AI chatbots, the fresh-faced upstarts of the chatbot dynasty. They're armed with machine learning, artificial intelligence, and natural language processing (NLP). What is a key differentiator of conversational AI? Their dialogue is more human-like, interactive, and engaging. This sophistication of conversational AI chatbots may be difficult to imagine until you look at a specific use case.
For example, there is a mental health chatbot known as Weobot. This chatbot operates in a minefield. The stakes are high, with users potentially wrestling with mental health challenges, including depression or even thoughts of self-harm. The demand for Weobot? To provide context-aware and empathetic responses. Failure to do so could result in catastrophic consequences. Can you imagine if Weobot replied “Not understood” to a user confessing suicidal thoughts?
Fortunately, Weobot can handle these complex conversations, navigating them with sensitivity for the user’s emotions and feelings. Weobot is effectively stepping in as a friend in less serious situations and as a counselor in more serious ones. This is made possible through the underlying technology of conversational AI chatbots.
Conversational AI chatbots utilize machine learning algorithms to improve their understanding of natural language. They can process and analyze large amounts of data to learn patterns, meanings, and context from user interactions. This level of information processing enables them to recognize user intent and extract relevant information from the conversation.
Conversational AI chatbots are not only hyper-intelligent - they are also convenient. These conversational chatbots can be integrated on messaging platforms (Facebook Messenger, WhatsApp, or Slack), voice assistants (Amazon Alexa, Google Assistant, or Apple Siri), or even websites. This breadth enables businesses to deploy these touchpoints across company assets, allowing the users to interact with them through their preferred channels.
4Conversational AI vs. Traditional Chatbots - Key Differences
The key differences between traditional chatbots and conversational AI chatbots are significant.
In terms of how they work, traditional chatbots rely on a keyword-based approach, where predefined keywords or phrases trigger specific responses. As a result, traditional chatbots can only comprehend what they have been pre-programmed on when it comes to understanding user input.
On the other hand, conversational chatbots utilize Natural Language Processing (NLP) to understand and respond to user input more conversationally. Conversational AI chatbots also use Automatic Semantic Understanding, allowing them to understand a wide range of user inputs and handle more sophisticated conversations.
Then, there are the traditional chatbots, poor creatures with their narrow horizons and limited scalability. They're specialists, tailored to work within specific use cases and prone to fumbling when flooded with user queries it can’t comprehend. Here lies the difficulty - either the IT team tirelessly updates its content, or users face the music with a less-than-ideal solution that leaves their needs unanswered. In contrast, conversational chatbots offer significantly higher scalability. They can handle a vast number of interactions and adapt to different user needs.
Regarding updates, traditional chatbots require explicit pre-programming to learn new responses or improve their performance. This involves inputting intent and answer pairs into the chatbot based on current or anticipated use queries. Naturally, this process represents a major opportunity cost: Instead of focusing on higher level tasks, the IT team must now spend man-hours on menial content management.
Conversational AI chatbots, on the other hand, continuously learn and improve from each interaction they have with users, allowing them to update and enhance their knowledge and capabilities over time.
In terms of customer interaction, traditional chatbots typically rely on option-based interactions. Users select options from a predefined set of buttons or menus. Conversational AI chatbots, however, support text and even voice interactions, enabling users to have more natural and flexible conversations with the bot.
5Evaluating the User Experience of Conversational AI /Traditional Chatbots
Traditional chatbots are analogous to a directory presented in a chat interface. In some cases, this directory will be invisible. People from older generations who used AOL Instant Messenger (AIM) may be familiar with this format because some of the earliest chatbots appeared on this medium.
The user will simply type in a specific comment or question, and if any of those words or phrases match with the chatbot’s logic, a predefined response will be given. This user interface is understandably poor: Users have to rely on guesswork to discover further content, and failure to do so will result in a conversational dead end. When the chatbot has no response to match the user’s query, it may revert to a catch-all phrase, such as “I’m sorry. I don’t understand.”
In other cases, the directory is visible to users, as in the case of the first generation of chatbots on Facebook. Users will type in a menu option to see more options and content in that information tree.
While this user interface for traditional AI is marginally better, it is still mainly on the rails: Users cannot seek answers to queries beyond what is presented. This limitation is problematic because it would be impossible for businesses to foresee all the possible queries that customers may have, leaving many customers without proper assistance.
This lack of assistance is compounded by the fact that those with uncommon questions often need help the most. For example, an irate customer may want to process a refund for a defective product, a differently abled person may want to know about available accommodations for their in-store experience or a bride-to-be may want to initiate the registration process.
These customers may likely fall outside the scope of the brand’s traditional chatbot - it takes significant man-hours to load this intent and answer pairs, after all - leaving these customers even worse than before: They still don’t have the right answer, and now they have wasted their time.
Conversational AI chatbots represent a quantum leap over traditional chatbots. AI chatbots can have human-like conversations in the chat interface powered by cutting-edge technologies, such as generative AI, machine learning, and natural language processing.
This functionality is ideal because it mirrors how humans are accustomed to communicating in our day-to-day life: People ping, chat, and text. By enabling people to interface with conversational chatbots in the most familiar way, they get an unrivaled user experience: There is no learning curve to get accustomed to the tool, so they can get immediate value.
6Use Cases and Examples
Conversational AI chatbots have a diverse range of use cases across different business functions, sectors, and even devices.
Customer support
In customer service and support, conversational AI chatbots can handle customer inquiries, provide accurate information, and offer timely assistance, improving response times and customer satisfaction. They can also escalate complex problems to human agents when necessary, such as when an irate customer may need to be calmed down.
For example, American Express has integrated a chatbot named Amex Bot within their mobile app and website. The chatbot is designed to handle customer inquiries related to account information, transactions, rewards, and even process certain transactions.
In ecommerce, many online retailers are using chatbots to assist customers with their shopping experience. Conversational AI provides personalized recommendations based on customer preferences and behavior, past purchases, browsing history, and user feedback. The conversational AI chatbot will then suggest relevant products or services, which not only enhances the shopping experience but increases conversions.
Voice-enabled devices
Conversational AI chatbots are also ideal for some devices, such as virtual assistants and voice-enabled devices, where they can provide users with hands-free, voice-activated interactions. Using only voice commands, a user can perform such tasks as set reminders, control smart home devices, conduct research, and even initiate online purchases, making daily life more convenient and efficient.
Critical industries
Conversational AI also finds applications in healthcare and medical assistance. Chatbots can provide patients with information about symptoms, schedule appointments, recommend wellness programs, and even offer general healthcare advice. By assisting healthcare providers in triaging patient inquiries and providing preliminary assessments, conversational AI chatbots improve access to healthcare services.
For example, digital healthcare provider Babylon Health employs chatbots and virtual assistants to deliver medical assistance and support to patients. Their conversational AI technology allows patients to receive accurate information about symptoms, book appointments, receive recommendations for wellness programs, obtain general healthcare advice, and even facilitate a direct consultation with a doctor or clinician.
In the financial services sector, conversational chatbots can handle routine inquiries about account balances, transaction history, and application status. They can assist in financial planning, provide budgeting advice, and even start financial transactions, offering customers a seamless and efficient banking experience.
For example, Bank of America has implemented an intelligent virtual assistant called Erica, which operates through their mobile app. In addition to handling basic queries, Erica can also provide financial guidance, such as budgeting advice and tips for improving overall financial health. Erica can also help customers transfer funds or pay bills with the app, further enhancing the user experience for BoA’s customers.
Final Thoughts
As is evident from these examples, we can learn what is a key differentiator of conversational AI. It represents a significant leap from traditional chatbots. Freed from the limitations of pre-defined rules and intent and answer pairs, enterprises can pursue more advanced use cases with AI chatbot. These include transforming existing functions, such as customer support; innovating new touchpoints, such as voice-enabled devices; and creating industry-specific use cases to serve customers better. In the end, business leaders should remember that the phrase “AI chatbots” is short for artificial intelligence chatbots: These tools will make your operations, marketing, and service significantly smarter, in turn driving growth, revenue, and success.
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