“Welcome to our chat! You can ask me anything or request real human help any time. Ask me anything!” Whether you’re looking to buy a car, ordering pizza, or browsing research publications, the average internet experience tends to feature some form of a user-oriented chatbot experience. Whether you find the omnipresence of chatbots maddening or inspiring, trends suggest they’re here to stay. Chatbots empower your users by automating question answering and guiding troubleshooting. Many organizations choose to feature a chatbot on their company’s home page and, after ironing out the initial wrinkles, see a positive drop in customer service requests. Intrigued? This blog is an introductory overview of the how of chatbots and considers the various elements at-play behind the curtain.
Chatbots are most commonly used for information gathering, user-empowering customer service assistance, or request mapping (think of the phone prompts you go through each time you call your internet services provider – if you’re looking to cancel your service because you’re moving, it would be a waste of both parties’ time to connect you to someone who provides service bundling packages). Chatbots also vary in their capabilities, with some looking for responses to Boolean questions (‘Did you say you want to cancel your internet services?’) while others are primed to mimic human conversation by ‘responding’ to users via full, natural language-sounding sentences (though no known chatbots have yet to convince their audience that they are capable of autonomous thought, otherwise known as the Turing test).
In addition to their varying abilities, chatbots also tend to take two forms. This blog considers a ‘real’ chatbot and how they work. Specifically, these ‘smart’ chatbots include components of machine learning that work hand-in-hand with natural language processing capabilities, allowing the bot to ‘understand’ human language and the forms that questions or requests take per the structure of language. And in addition to intaking human language queries, these bots also produce responses mimicking human language, allowing them to provide contextualized answers or share actionable options with the user. Alternatively, a simpler bot (or ‘dumb’ bot) that’s commonly seen across the web functions only as another avenue to the organization’s search. These bots process incoming requests or questions as search queries, and, as their ‘responses,’ return links to content determined most relevant per the keywords identified in that query.
From a development perspective, we can shed some light on the technical tasks necessary to successfully implement a chatbot by reviewing one of our past projects. At a global development bank, executive leadership knew their colleagues were missing information because they were routinely unaware of it or couldn’t find it. The bank wanted a solution that collected all of their institutional wisdom and learned people’s areas of knowledge and need so it could automatically assemble and send targeted information in two instances: the first being to connect individuals to that information at the moment of request and the second being sending appropriate information to the right people prior to important events, like a topic-specific board meeting.
To address this two-pronged need, the EK team built a semantic hub. This solution uses machine learning to automatically deliver content to bank employees when and where they need it through email and related webpage widgets. The hub interfaces with a graph database and knowledge graph platform, and a semantic technology tool that manages both taxonomies and ontologies that’s provided by the Semantic Web Company. The hub then provides contextualized recommendations to deliver relevant content on any bank-managed website. Although we were tackling a project that required collaboration between both the bank and two service providers, we delivered a complex solution without having the project managers feeling like they were managing three different, disparate organizations. As a finished product, our chatbot recognizes that a user is requesting documents, and their message is forwarded to the recommendation engine to generate results, scouring the engine for metadata that matches, or is akin to, the user’s query. The tool is in active daily use, providing timely recommendations on three different web applications and in advance of important meetings.
Additionally, at the outset of all Chatbot development projects, before you can connect users to content, you have to first affix descriptive metadata to that content so that the chatbot can find it. A dedicated taxonomy and ontology development effort is necessary, as are subsequent validation sessions throughout a series of project phases. Such sessions can and should include a content cleanup process and a series of focus groups to validate user needs and your prioritized use cases to be addressed by the bot. Once your content is nice and NERDy, a selection of that content should go through a text extraction process which further informs and validates the taxonomy/ontology, ensuring you are working with metadata that best reflects your organization’s information. Content from around the organization is auto-tagged (using a taxonomy management tool) and collected within the graph database.
The Why for Implementation
If the above example seems at all daunting to you and you’re wondering if chatbots are worth the effort to implement at your own organization, consider this recent stat:
Chatbots allow for a customized user experience, and not only allow users to get the information they need more quickly, but can be designed and oriented toward each user’s unique intent and interest. And for an additional convincing statistic: a survey done in 2016 by Oracle showed that 80% of business decision-makers said they already used chatbots or plan to use them by the end of 2020. All kinds of users are interfacing with chatbots, whether they’re service users, potential customers, or those with executive decision-making abilities. And while chatbot development can be complex, firms like ours, with deep experience in data and ontology mapping and user experience design, can facilitate the design and development process for your organization. Additional benefits to chatbot implementation include increases in revenue as easy-to-answer inquiries are automated, decreases in overstaffing on your CX team, and greater customer satisfaction for users on your site, increasing the likelihood that they become repeat users.
If you think chatbots could benefit your organization (and they probably can), don’t hesitate to reach out to us at email@example.com. We can assist you at any stage of the chatbot process, from the design, data mapping, and the development process from beginning to end.