Chatbots have been on the communications scene since the early days of Instant Messaging.
Early uses included virtual entertainment; spawning simple answers to user-submitted questions – activated via screenname within one of several online messaging platforms (like AOL Instant Message within the AOL service or its web-based version, AIM).
These bots, now known as declarative, or task-oriented bots, delivered canned information based on specific rules set on the backend, spawned by a user’s questions. In the early days, they lacked flexibility, depending on specific entry commands that had little room for interpretation or variety. They were updated by programmers and editorial teams on a regular basis to refresh responses or add additional functionality – including pertinent news and information – and later, in-chat gaming.
And while rudimentary in their initial iterations, these simple-use chatbots set the stage for the advancement of enhanced communication within messaging products throughout the digital space – whether built into software or in the public domain for universal use.
Chatbots have become standard across multiple industries, meeting the needs of consumers across nearly every iteration of online tasking. Banking, purchases, weather, news and more all are served up in quick-hit format to a hungry audience, expecting immediate responses and rewards.
In fact, according to Business Insider, 44 percent of consumers surveyed in 2016 prefer interacting with Chatbots over humans for customer relations – an increase of 4 percent since 2015.
As Artificial Intelligence technology expands, the prospect of smarter, more interactive Chatbots has given way to the ever-expanding possibility beyond a Chatbot’s initial, declarative use. Arguably, some predict Chatbots will take the place of mobile applications as the main means for consumers receiving important, daily information.
According to AdAge, "Chatbots will replace the search window and there will be a rapid decline in app usage. In the last three (3) years, we have gone from brands' desire to have mobile-friendly websites, then apps, and [we] now expect to see app activity get cannibalized by bots."
In the top-level, generic understanding of chatbots, this communication tool falls into two (2) simple categories: Task-oriented chatbots (declarative) and Data-driven & Predictive Chatbots (conversational).
Understanding these two (2) categories clears the way for businesses to understand what type of bot application is best-suited for a business and its consumers.
Task-Oriented, Declarative Chatbots
As the most common chatbot use to date, the Declarative Chatbot is described as an interactive Frequently Asked Questions (FAQ) tool that responds with preprogrammed, predetermined ‘if’ and ‘then’ conditional statements – or rules programming.
Declarative Bots adapt to the demands of a user-initiated query and serve up automated responses and conversational menus to further assist and engage its human user. One of the simplest ways to think about a declarative chatbot is as the curator of interactive information – not necessarily in-depth conversation.
In short, declarative bots are a conversation moderator, giving responses based on information supplied on the back-end that is either discovered through its connection to other platforms or its database of responses.
Some of today’s most popular declarative chatbots include interactive chat sports information, movie ticket purchasing bots and food-ordering/delivery bots. By connecting to programs/software these organizations already use, businesses have created a direct experience that no longer requires funneling through several channels to provide the consumer with meaningful information – it is instantaneous, efficient and maintains buyer satisfaction.
Based on its most popular uses, declarative chatbots are highly specialized, structured interactions and achieve great success in the support and services industries. Their interactions can be analyzed through in-depth reporting based on use and adapted with programming enhancements on the backend.
Data-Driven, Predictive Conversational Chatbots
Arguably the latest, most popular iterations of successful data-driven, predictive chatbots can be found in Amazon’s Alexa or Google Assistant.
The data-driven, predictive conversation chatbot can monitor data, calculate intent, initiate conversation and are – amazingly enough – culturally aware. These bots adapt as new information is developed – they intuitively stay current with relevant information – as long as the internet keeps growing, so does their database of limitless knowledge.
Conversational bots utilize user personalization based on profiles and past behavior to customize a simulated relationship with its user in order to garner further utility and dependability. One might say it is attempting to build ‘trust’ to ensure its further use and dependability. Conversational chatbots of this magnitude are designed and programmed to become an in-home, irreplaceable tool of value and ease.
The beauty of a predictive, conversational chatbot is in the complexity of its use of natural language processing and machine learning. These attributes give it a sense of ‘personality’ and ‘friendliness,’ attempting to set users free to make inquiries at unpredictable intervals, based on when interaction is engaged – available 24/7 and always ready to respond.
Through the use of predictive, artificial intelligence, an ability to collect an evolving set of analytic data and storing dynamic user interface information, conversational chatbots depend on Big Data to collect data across all use cases so updates can be developed and deployed.
In such a complicated, growing form of technology, these two (2) simple breakdowns define an entire specialty within artificial intelligence that is quickly redefining conversation & interaction at unprecedented levels and it shows no sign of stopping.
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