Flexible and Accurate Answers to Customers’ Questions
We published our Product Evaluation Report on Nina Web from Nuance Communications this week. Nina Web is virtual assisted-service software for web browsers on desktops, laptops, and mobile devices. Type a question in a text box and a Nina Web-based virtual agent will deliver an answer or will engage you in a dialog when it needs more information to answer your question. Answers are text, images, links, URLs, and/or data from external applications.
Nina Web was originally developed as VirtuOz Intelligent Virtual Agent by VirtuOz, Inc., a privately held firm founded in France in 2002. Nuance acquired VirtuOz in March 2013. Nina Web became the third member of the Nina family of customer self-service offerings from Nuance’s Enterprise division, joining Nina IVR and Nina Mobile.
Nuance has made and continues to make significant improvements to the VirtuOz IVA. A bit less than a year after the acquisition, Nina Web is stronger and more attractive virtual agent offering, earning good grades on our Report Card for Virtual Assisted-Service. (See the Product Evaluation report for the details.)
Most significantly, Nuance’s Enterprise division developers have just about completed what they call a “brain transplant” for Nina Web, replacing the question analysis and matching technology built by VirtuOz with Nuance’s Natural Language Understanding (NLU) technology, the same technology used by Nina IVR and Nina Mobile. NLU combines Natural Language Processing (NLP) with statistical machine learning. NLP does some parsing and linguistic analysis of customers’ questions. Statistical machine learning, which Nuance implements in neural networks, matches customers’ questions with typical and expected “User Questions” and variations of User Questions that analysts create and store in Nina Web’s knowledgebase. Analysts also create knowledgebase answers and associate an answer with each User Question. When NLU matches a customer’s question with a User Question, Nina Web presents the answer associated with the User Question to the customer.
Analysts “train” NLU’s machine learning algorithms with User Questions and their variations. Nina Web provides the facilities and tools for initial training and ongoing refinement/retraining. Analysts add, delete, and modify User Questions as the intent and the vocabulary of customers’ questions changes to ensure that their Nina Web virtual agent delivers accurate answers. They must refine answers, too.
As you might infer by our description, NLU is a black box. Train it with a set of User Questions and it will match customer’s questions with them. The critical tasks for a Nina Web deployment are the initial specification and continuing refinement of User Questions and of answers. Nina Web insulates deployment work from NLU, from the complexity of NLP and statistical machine learning. Analysts do not specify language models or matching rules. They do not (and cannot) configure and/or customize neural network processing. Knowledge management is the focus deployment efforts. That can make for easier and faster deployment, a strength and differentiator for Nina Web.
One more thing. We mentioned that NLU is the analysis and matching technology in Nina IVR and Nina Mobile as well as in Nina Web. One set of User Questions can match customer questions with one set of answers across telephone, web, and mobile channels. Together, Nina IVR, Nina Mobile, and Nina Web can deliver a consistent cross-channel customer self-service experience, but, today, that consistency requires creating and managing three copies of the set of User Questions and three copies of the set of answers because the products are not integrated. Each Nina deploys independently of the others. But, cross-Nina integration is on Nuance’s product roadmap. An integrated, cross-channel Nina will be quite a customer service offering.