In another example, a student might ask a chatbot about their tuition balance. McNasby says there are chatbots designed to access that student’s financial aid file, provide them with the balance, and links to payment options.
For more complex or specific questions involving named entities, like “Who is my student adviser?” McNasby says chatbots find creative algorithmic solutions to best answer students’ questions.
How it works
Computers excel in various natural language tasks such as text categorization, speech-to-text, grammar correction, and large-scale analysis. ML algorithms have been used to help make significant progress on specific problems such as translation, text summarization, question-answering systems and intent detection and slot filling for task-oriented chatbots.
For example, that grammar plugin many of us use at work, and the voice note app you use while driving to send a text, is all thanks to Machine Learning and Natural Language Processing. However, as smart as these bots may appear, humans are smarter, for now. When it comes down to it, human language is nuanced and often ambiguous. This presents serious challenges for ML systems. Training on massive datasets — for example: every Wikipedia article — and creating large language models does not lead to an understanding of language.
If you are wondering whether a chatbot could be a solution to a problem within your organization, this post will talk about the problems that chatbots, conversational agents, and virtual agents can solve.
Businesses that struggle with customer engagement can often utilize digital transformation solutions to help discover more information about what the market needs from their organization. Chatbots can be one part of this multi-faceted solution. With Alexa and Siri leading the effort in helping change the paradigm for how consumers should perceive chatbots, the industry as a whole has caught a lot of attention because of the ability to help inform businesses about their customers using data. This is the first small step into using “big data” in a way that is digestible and consumable. For example, this data includes understanding how humans use language in both subtle and non-subtle ways as well as being able to discern information from their initial request.
Organizations that quickly adopt and iterate on their chatbots can often find a treasure trove of information that can be used to quickly help inform how to better engage with their customers, learning how to trend and using it as a predicting service , and resulting in an increased bottom line. Chatbots with speech recognition have also drastically improved over the years, which has enhanced the interaction with various applications.
What Can A Chatbot Do?
- Act as a customer service chatbot to answer queries: Has the ability to answer basic questions from customers, often with button-based bots used to direct visitors down a triage of questions to reach the appropriate answer or page.
- Allow for transactional purchases: Enables customers to make transactions within the context of the conversation
- Enforce lead generation strategies: Has the ability to gather data, identify prospects and direct them to products or services of interest
You want to broaden your appeal to different audiences.
Consumers want instant gratification. Fewer and fewer people are willing to pick up the phone to get answers. If knowledge is difficult to find, abandonment rates rise and organizations lose the opportunity to compete for prospects’ dollars.
When a chatbot is connected to a knowledge repository and is available for public use, the pressure is off and the process is simplified. Prospective customers can assess the products and services offered, determine if their needs will be met, and access organizational knowledge, all without a phone call or support ticket.