Virtual Assistant Update

 

We recently published “Virtual Assistant Update.” It’s a broad and not too deep update on virtual assistant technologies, products, suppliers, and markets from the perspective of the five leading suppliers: [24]7, Creative Virtual, IBM, Next IT, and Nuance. These are the leaders because they:

  • Have been in the virtual assistant business for some time (from 16 years for [24]7 via its acquisition of IntelliResponse to four years for IBM).
  • Have attractive and useful virtual assistant technology
  • Offer virtual assistant products that are widely used and well proven.
  • Want to be in the virtual assistant business and have company plans and product plans to continue.

The five suppliers are quite diverse. There’s the public $80 billion IBM and the public $2 billion Nuance. Then there are the private [24]7, a venture backed company big on acquisitions and the more closely held Creative Virtual and Next IT. Despite these big corporate-level differences, the five’s virtual assistant businesses are quite similar. Roughly they’re all about same size and the five compete as equals to acquire and retain virtual assistant business.

By the way, across the past 12 to 24 months, business has been good for all of the five suppliers. Customer growth has been very good across the board. Our suppliers have expanded into new markets and have introduced new and/or improved products.

Natural Language Processing and Machine Learning

Technologies are quite similar, too. All five have built their virtual assistant offerings with the same core technologies: Natural Language Processing (NLP) and machine learning.

Virtual Assistants use NLP to recognize intents of customer requests. NLP implementations usually comprise an engine that processes customer requests using an assortment of algorithms to parse and understand the words and phrases in a customer’s request. An NLP engine’s processing is guided by customizable and/or configurable deployment-specific mechanisms such as language models, grammars, and rules. These mechanisms accommodate the vocabularies of a deployment’s business, products, and customers.

Virtual assistants use machine learning technology to match actual customer requests with anticipated customer requests and then to select the content or execute the logic associated with the anticipated requests. (Machine learning algorithms learn from and then make predictions on data. Algorithms learn from training. Analysts/scientists train them with sample, example, or typical deployment-specific input then with feedback or supervision on correct and incorrect predictions. A trained algorithm is a deployment-specific machine learning model. The accuracy of models can improve with additional and continuing training. Some machine learning implementations are self-learning.)

Complex and Sophisticated Work: Consultant-led or Consultant-assisted

The work to adapt NLP and machine learning technology implementations for virtual assistant deployments is sophisticated and complex. This is work for experts: scientists, analysts, and developers in languages, data, and algorithms. The approach to this is work differentiates virtual assistant suppliers and products. The approach drives virtual assistant product selection. Here’s what we mean.

All the virtual assistant suppliers have built tools and package predefined resources to make the work simpler, faster, and more consistent. Some suppliers have built tools for the experts and these suppliers have also built consulting organizations with the expertise to use their tools. Successful deployments of their virtual assistant offerings are consultant-led. They require the services of the suppliers’ (or the suppliers’ partners’) consulting organizations.

Some suppliers have built tools that further abstract the work and make it possible for analysts, business users, and IT developers to deploy. While these suppliers have also built consulting organization with expertise in virtual assistant technologies and in their tools, successful deployments of their virtual assistant offerings are consultant-assisted and may even approach self-service.

So, a key factor in the selection of a virtual assistant product is deployment approach: consultant-led or consultant-assisted. Creative Virtual, Next IT, and Nuance offer consultant-led virtual assistant deployments. [24]7 and IBM offer consultant-assisted deployments. For example, IBM Watson Virtual Agent includes tools that make it easy to deploy virtual assistants. In the Figure below, we show the workspace wherein analysts specify the virtual assistant’s response to the customer request to make a payment. Note that the possible responses leverage content, tool, and facilities packaged with the product.

ibm watson va illos

© 2017 IBM Corporation

Illustration 7. This Illustration shows the Watson Virtual Agent workspace for specifying responses from the bot/virtual assistant.

 

Which is the better approach? Consultant-assisted is our preference, but we’ve learned over our long years of research and consulting that deployment approach is a function of corporate, style, personality, and culture. Some businesses and organizations give consultants the responsibility for initial and ongoing technology deployments. Some businesses want to do it themselves. For virtual assistant software, corporate style could very well be a key factor in product selection.

 

 

 

 

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Oracle Service Cloud Virtual Assistant

Oracle Service Cloud Virtual Assistant is a relatively new brand (November 2013) that dates back to 2001 when it was known as Q-go, a product with the same name as its privately held, Amsterdam, NE-based and supplier. Q-go, the product, was first commercially deployed in 2001.

Neither RightNow, which acquired Q-go in 2011, nor Oracle, which acquired RightNow in 2013, has done very much to enhance the old Q-go other than to rebrand it. (RightNow branded it RightNow Intent Guide and Natural Language Search at the time of the 2011 Q-go acquisition. As we mentioned just above, Oracle branded it Virtual Assistant in November 2013.) Today, Oracle Service Cloud Virtual Assistant doesn’t support voice. It deploys on web browsers in HTML. Its deployments are language-specific and only Western European languages are supported.

However even after all this time, the product technology remains reasonably attractive and quite useful. The product’s core NLP technology, the technology developed by Q-go that analyzes customers’ questions and matches them with knowledgebase answers remains fresh and innovative. This is the technology that makes virtual assistants that are deployed on Virtual Assistant powerful and flexible.

Briefly, here’s how it (still) works. Customers enter questions as natural language sentences or phrases or as individual keywords or short “telegram-style” phrases. Virtual Assistant uses NLP technology to analyze customers’ questions and to match them with Questions in its knowledgebase.

  • If Virtual Assistant finds a Question that is a best match with a customer’s question, then it presents the knowledgebase Answer that is linked to the Question.
  • If Virtual Assistant cannot find a best match, it presents a short list of Questions that are likely matches to customer’s question. From this list, the customer selects the Question that best represent her/his question. Virtual Assistant delivers the Answer associated with the Question selected by the customer.
  • Alternatively, the matching Question may trigger a Prompt and Response Dialog with the customer to arrive at an Answer through conditional sequence of steps.
  • Virtual Assistant presents the knowledgebase Answers that are linked to the Questions that best match the customers’ questions.

Here’s an example from klm.com. KLM Royal Dutch Airlines had deployed Q-go. The Customer Support tab on klm.com continues to use the NLP technology to answer customers’ questions.

I asked the question, “golf clubs,” two keywords, certainly not a full sentence or even a good phrase. Virtual Assistant did not find a single best match. It found five likely matches and presented them as links as shown in the screen shot below.

blog klm 1

Question 3, “Can I take a golf bag with me,” represents our question. I clicked it and klm.com presented the Answer shown in the screen shot below.

blog klm 2

That’s exactly the information I was looking for, a “Very useful” Answer. I then clicked the “Read more about taking a golf bag” link to get additional information. See the screen shot below for that information.

blog klm 3

Pretty good, no? Flexible and powerful. When Virtual Assistant can’t find a best match, finding and presenting a short list of likely, possible matches is very useful. It’s a reasonable and fast extra step to take to get to that best match. Note though that this example was not implemented by a completely automatic process. No surprise there. Analysts and administrators had some work to do to specify and manage the keywords that customers would likely use and to associate those keywords with knowledgebase Questions, like those in the first screen shot,  to help Virtual Assistant find likely matches. They also had to specify and manage the Questions.

Oracle certainly has some work to do to bring Virtual Assistant up to its competition, but the work builds on a very good foundation. Read our Product Evaluation Report to get the details.

Nuance Nina Web

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.