Companies around the world are constantly evaluating how to best reach and/or serve their customers, exploring ways to better connect with customers, while always keeping a keen eye on the resources needed to do so. Higher education is no different. It too needs to cut through the clutter and create a connection with potential students, and continue to build the relationship even after those students are part of their university environment.
EDSCOOP, the leading media featuring latest information technology for higher education, published a story about Juji and Juji's cognitive AI assistants for the higher education sector.
SARATOGA, CA - April 22, 2021 - Juji issues a press release and announces its offering of a new generation of chatbots that makes AI accessible to universities and brings human-like engagement to students without requiring IT resources.
In his podcast, Brent Csutoras at Search Engine Journal had a conversation with Dr. Michelle Zhou, co-founder and CEO of Juji on how cognitive intelligence will reshape the chatbot industry.
As indicated by this post, 84% consumers want to be treated as a unique individual not just a number, while 95% of companies saw 3X ROI from their personalization efforts. Now with the rise of conversational commerce, chatbots become a natural and private channel for brands to engage with users and offer personalized services in one-on-one conversations. In such a conversation, a chatbot can naturally elicit users' needs and wants and then provide personalized help or guidance based on the gathered information.
The Chatbot Tsunami has brought us a flood of chatbots to help automate various business functions, including customer service chatbots that automate customer Q&A, marketing research chatbots that automate customer interviews, and HR chatbots that automate job interviews. Although a chatbot is often made to serve one purpose, users expect the chatbot to perform multiple related functions while serving the main purpose.
As indicated by this Forbes article, 76% of customers contact businesses to make inquiries and get support via text messaging. Now with the required social distancing during the COVID-19 pandemic, more and more consumers choose to interact with brands online.
Since one of the most popular chatbot uses is to automate customer service, a chatbot's abilities to answer user questions is directly related to brand image and customer satisfaction. Previously, I talked about how to create a Q&A chatbot in a few minutes to answer users' free-text questions and handle complex, multi-turn Q&As, all without coding. Because no chatbot is perfect, I also mentioned how to teach a chatbot handle unknown user questions and further improve customer satisfaction.
In my Clojure/north 2020 talk on "diffing-based software architecture patterns", I mentioned that Juji is using Editscript, a library I developed, to diff Clojure data structures. During the Q&A session of the talk, someone brought up another Clojure diff library, called deep-diff2, which I was unaware of. Then on Youtube, a comment asking the difference between Editscript and deep-diff2 appeared again. This prompted me to do an investigation on Clojure data diff libraries. Given how the Clojure community places such an emphasis on data oriented programming, a comparison of data diff alternatives appears to be of interest.
A while back, I wrote a blog on "Chatbot Tsunami", mentioning that our world is now inundated with chatbots, including both "good ones" that can help us fulfill our requests and "bad ones" that can hardly understand users or achieve anything.
One of the most intuitive uses of chatbots is perhaps to automate customer service, where a chatbot automatically answers user questions, 24x7. In fact, in almost every chatbot application, a chatbot's ability to answer user questions, especially free-text questions, is always desirable.
