Every day, artificial intelligence (AI) helps us accomplish more than we ever thought possible. One such example is the way we access information. We expect immediate answers to our questions, and AI makes that possible. Likewise, we see that the obvious application of AI is scale; we reach far more students and stakeholders with a simulated human experience than with staff alone. In fact, according to IBM, AI chatbots can cut customer service costs by 30% and resolve 80% of routine inquiries. But, we shouldn’t think of AI as a one-way street where information is passed from an algorithm to an end-user. Instead, we should recognise that valuable information also flows back to us from the end-user. If we’re paying attention to what, when and how people are communicating with AI chatbots, we can make better informed decisions that enhance the service experience.
Sophisticated AI systems are supported by various algorithms, and a common example is the classification algorithm. In such a case, Natural Language Processing (NLP) technology essentially matches an input against a stored label. So, if a user asks “what’s your name?” and the AI has knowledge stored against labels such as “what-is-your” and “name”, it will return the information associated with that specific pair of labels. We can then measure the frequency with which users ask about specific topics to identify which subjects are actually most important to our users. Similarly, if the AI is unable to return information, we gain insight into questions users are asking that we might not have addressed in our existing resources. Understanding the specific language people use to ask questions is also valuable, as this reflects the way they search for information on your website or via a search engine.
An accurate representation of what our students want to find on our website can help us create more intuitive, informative web content. For example, a website designed to guide users through a process based on their school status (undergraduate versus graduate) is certainly well-intended, but if users primarily seek informational resources independent of their status within your institution, then it may actually detract from the service experience. Evaluating the ‘what’ of conversational AI analytics makes it possible to produce responsive versus static content.
It’s also important to remember that conversational AI data is fluid – that is, what’s relevant to students today may take a backseat in their minds tomorrow. Using conversational AI data, we can listen to our students and other stakeholders, and manage our content to better meet their seasonal or changing needs.
Another valuable insight gleaned from transaction data is when students tend to seek information. Traditional models rely on an assumption of customer preferences, since service centres might have operational hours that will otherwise influence behaviour. For example, a call centre that’s open from 7:00am to 6:00pm might experience peak volumes between 12:00pm and 1:00pm. This may certainly represent the most convenient time for people during those specific hours. However, when AI is in place to assist students 24/7, we may find that students are most engaged between 7:00pm and 8:00pm, guiding us to shift our operational hours to accommodate that preference. Likewise, transaction data provides a more accurate representation of when users prefer to communicate with us, and we can leverage that data to improve their experience.
We can also make improvements by analysing how users interact with us. AI systems support nearly unlimited deployments; they can live on your phone system, your website, your SMS texting platform, your social media pages, and even your virtual assistants like Amazon’s Alexa. Conversational AI analytics offer a glimpse into which channel is most important to users. With that information, we can make informed decisions about where to invest additional resources, and which channels to promote to our customers. Additionally, you might find that it’s best to highlight certain information on your social media pages but other information on your website, since users that tend to frequent one or the other might have different interests and needs. If we understand how students seek information, we can tailor our delivery systems to reflect those preferences.
Colleges and universities are diverse from one to another, and tend to have unique qualities, so it’s unlikely that these examples offer a comprehensive summary of how AI informs higher education providers. The important thing is that we pay attention to what the data is telling us. When we do, the decisions we make to shape the service experience are better informed, and more likely to add value for our stakeholders.
Ivy.ai is the leading provider of conversational, artificially intelligent chatbots for higher education. Ivy.ai’s rich feature set expands student access, reduces staff workload, and increases operational efficiency with 24/7, omni-channel access to information. Ivy.ai deploys state of the art technology to elevate the learning experience and empower academic achievement. Clients receive many AI-powered features such as Live Chat, SMS Texting, Social Messaging, Unlimited API Integrations, Analytics, and more. With Ivy.ai, you can reach students anytime and anywhere, offer better support and information, eliminate student runaround, and boost recruitment, retention and engagement.
To learn more about AI chatbots from Ivy.ai and how they can benefit your institution, visit www.ivy.ai and schedule a demo.
You might also like: More of us should be adapting to life alongside AI – here’s why