What are chatbots? As a portmanteau, the name already explains itself. But technically defined, chatbots are computer programs that use natural language to create responses that simulate human conversation. An “artificial intelligence for human communication”, so to speak.
Generally, such a description should already answer the main question of the title. However, the comprehensive nature of chatbots, as well as the slew of technologies used to achieve their features, make them much more than simple automated response machines. Because at the heart of every chatbot design rests the most important factor of (convincing) human interaction.
Chatbot Service Prerequisites
The Turing test is a well-known inquiry method for determining if a computer is capable of human-level free thought. But more specifically, the test attempts to see how effective an AI system would be in convincing a human user that they are interacting with an actual human being.
That is one of the primary ideas of chatbots, or at least the main spirit. Obviously, we as users know that chatbots are not real humans. However, their intention is to provide the informative guidance necessary to perform nearly as effectively as a human operator within the same level of baseline interaction for a particular query.
Thus, according to this principle, the three basic prerequisites of chatbots (as a service) are:
- Interactivity – how versatile its responses get
- User Data – how much “experience” it has (for the aforementioned interactivity)
- Guidance – how refined its performance becomes
Of course, all three of these are determined by the size of the information source, standard analytical algorithms, and “decision-making” capabilities. However, while it’s possible to create a workable chatbot by prioritizing any one prerequisite (compared to the other two), a good balance of the three usually forms the basic outer shell of its capabilities.
This is what transforms a chatbot from a mere “communication AI” to “smart communication AI”. Natural language processing (NLP) features serve not only to make it intuitive but also to make the service flow more smoothly since a lot of people still prefer the inherent convenience of interfacing using their language.
How Chatbots Operate
The basic operation of a chatbot, like most other tiered tasks, is built from a workflow. This is where the outline of interactions is created from a framework, serving as a basic guide for its step-by-step actions.
In general, the questions generated from building each step of the workflow are as follows:
- How does the user communicate?
- What is the user initially asking for?
- What is the user truly asking for?
- Does it have an accurate response for anything else in between?
- Does it (still) have an accurate response if it doesn’t have one?
While features that answer these questions can be manually programmed to deal with every single query possible, it often becomes an impractical task. This is simply due to the sheer number of different ways that natural language can be formed in communication.
Pre-training and basic algorithm-based programming aside, chatbot systems typically use the power of machine learning technologies to solve the adaptation issue. The objective is not just to actively learn from user interactions but to improve the quality of its responses over time.
Deep learning models are a step up in this game, but it includes even more complex algorithms, often generated by the system itself, to learn and adapt strategies on their own.
In fact, the advancement of machine learning technologies, in general, has increased the user’s level of expectation towards chatbot performance. To operate, it would no longer simply answer quantitative queries (when? How much? How long?) but would also need to tackle and analyze (also sort, if appropriate) far more varied indirect interactions. This would be within a significantly wider margin before it eventually chooses to refer the user to a human representative.
Efficient Characteristics of Chatbots
For chatbots, the necessary characteristics that bring out their efficient qualities are as follows:
- Convincingly Eloquent – as mentioned earlier, the increased performance expectation for chatbots today means that they should interact in the most “conversationally mature” way possible. A chatbot should leverage its NLP to find the shortest route to the answer while maintaining its human-like responses.
- Universally Accessible – chatbots should be available across any communication platform available to a human user as much as practically possible. This is especially important for physically impaired users, who may only have hearing, sight, or touch as their main method of communication.
- Empathetic – chatbots must be able to interact with a slight degree of personalization towards the user instead of just using robotic responses to well-explained inconveniences. Artificial as this may seem, it actually functions as an important service layer or buffer to user satisfaction.
- (Self) Diagnosable – when chatbots make mistakes, as they would eventually do, they need to be able to provide an explainable diagnosis to developers, regardless of the level of complexity of the developed neural network algorithms.
- Limit-conscious – Even when deploying deep learning algorithms, a chatbot should still be smart enough to quickly know the moment when a user requires human expertise (instead of fiddling through question loops). This both saves time and shortens the interaction processing required.
- Freely knowledgeable – in human terms, the chatbot should allow itself to learn from any relevant resource available: structured information, global databases, even personal billing statements, as well as previous interactions with the same user.
- Secure – lastly, all information sources used by the chatbot system should be secure from data tampering. Even better for social PR, make the data collected transparent to the users themselves.
General Applications of Chatbots
Much more than just answering your questions on your phone, many integrated applications of chatbots are at the core of customer-related procedures of businesses today:
- Showing real-time information – delivering more details on observed updates via queries. Asking Alexa about the weather today is one very straightforward example of this.
- Providing directions – asking for certain routes, pinpointing locations, as well as optimizing distance. Google Maps integrates with Google Now for this type of functionality, even providing step-by-step instructions as you progress.
- Processing transactions – filling electronic applications or creating alternative process suggestions. Whenever necessary, chatbots like CBOT may fill in for certain payment procedures (ordering, canceling, status updates, etc.). However, processing the transaction itself is conducted by another system.
- Filtering basic queries – completely removing the need for human operators on simpler questions, making the service available 24/7. For example, in real-estate marketing, Roof AI proves the versatility of such an application by automating potential leads, even throwing back simple queries to the users themselves to eventually lead them to the appropriate sales agent.
- Preliminary Analysis – chatbots can usually function as the first line of service when providing analysis for any type of service. Babylon Health, for example, provides a casual and friendly NLP interface for its symptom checkers, though it functions better as a referral tool for quickly (and accurately) linking patients to the required medical professionals.
Other Tactical Advantages of Chatbots
Indeed, providing a human-like layer of interaction is an already revolutionary feature of chatbots. But mechanically speaking, the development of such a customer engagement system also brings other coinciding benefits, such as:
Constant Service Availability
Pretty self-explanatory. Since chatbots are already considered to be automated programs, they can function 24/7. The convenience alone is usually enough to favor its use over human operators or agents in situations where they can be reliably replaced.
Can Automatically Gather and Organize Data
While your operator can rely on assistant software to register queries or to categorize interactions with their customers, a chatbot could do it almost instantaneously without any active actions required. Of course, this is already moving towards the realm of robotic process automation (RPA), but it is nonetheless an important service integration factor for chatbots.
As a bonus, chatbots can even use direct interactions to learn and adapt, though with a separate learning system, and perhaps also depending on whether neural network algorithms are primarily employed.
Increases Lead Generation
Having a guide to help customers with their purchases makes it far more likely that they would eventually choose or avail of any of your products or services rather than not buying anything at all. This is perhaps the simplest layperson explanation of why chatbots are very capable of producing significantly increased sales conversion rates.
Of course, the design still has to follow the aforementioned efficiency factors. But when built up to proper specification, a sales chatbot would always provide the assistance needed for the customer to take those next several logical steps into buying.
Lowers Overall Costs
Finally, it all boils down to reductions in cost. A chatbot that is capable of providing a consistent level of service all day long definitely needs way fewer resources, as opposed to staffing customer support employees, who might also require things other than just a paycheck. Even better, certain businesses that have no actual economic capacity for such investment in human resources are now able to utilize such services.
“Chattier-bots” of the Future?
Despite the apparent advancement of chatbots towards near-perfect replication of human-like interactions, they are still not entirely human enough to pass the Turing test in any situation without fail. This, as mentioned at the beginning of the article, we already understand. The first-level functionality of chatbots when it comes to interacting and filtering users is arguably the more important use as a technological application anyway.
With the growing complexity of digital information around us, we might just witness the advent of economically practical sentient-like chatbots in the next few decades. These chatbots would most likely have finally perfected the emotional interaction factor, capable of passing the Turing test with admirable consistency.
Their prevalence would perhaps become most especially apparent on social messaging platforms, a place where the emotional and personalization factors of chatbots are expected to play the most important role in streamlining any service today.
