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.

 

 

 

 

Analytics in Radian6

This week’s report is our evaluation of Radian6, the component of Salesforce Marketing Cloud that does social monitoring, analysis, and interaction. Its tight integration with Salesforce Service Cloud—automatic creation of Cases and Contacts—makes it the obvious social-service choice to add to the customer service application portfolio of Salesforce CRM users

Customer social-service is all about monitoring customers’ conversations in the social cloud, identifying customers with questions, problems, and issues, and then interacting with those customers to answer questions, solve problems, and address issues. The number of customer posts and conversations in the social cloud that may be relevant to a business can be very large, ranging to thousands or even tens of thousands per week and, in the extreme, hundreds of thousands per day. Monitoring and analyzing all of them, identifying the (few) posts that require attention, and then handling each one individually and handling all of them consistently are daunting and complex tasks, daunting because of the sheer volume and complex by the diversity and nuance of language, breadth of topics, and depth of emotion (sentiment).

Most social-service products use third parties to monitor social posts, to crawl and search the key social networks and the hundreds of millions of blogs and forums where customers ask questions, get answers, and make comments.  The value-add of these products is in their analytic capabilities, capabilities that can “understand” the content of social posts. Natural Language Processing (NLP), sometimes called text analytics, is the technology that they most commonly use. And, also most commonly, each of them is built its own NLP implementation. Their companies are built on it, too. These NLP implementations are frequently patented and almost always proprietary. They’re the crown jewels of analytics companies. So, the selection of a social-service application usually involves the evaluation and comparison of NLP implementations, a difficult selection of sophisticated and complex technology.

Not the case for Radian6. It takes the opposite approach. Rather than leverage the data collection capabilities of third parties and apply its own analytics, Radian6 does its own data collection (The current version searches and crawls over 650 million social sources.) and leverages the analytic capabilities of third-party analytics suppliers to understand the content of social posts. (Radian does a bit of its own analytics, too, although its analytics are a bit basic and are not built on NLP.) These 14, third-party analytics suppliers comprise what Salesforce.com calls the Radian6 Insights Ecosystem, Insights for short. They apply their analytic technologies to the social posts collected by Radian6.

The 14 are:

  • Bitext
  • Communication Explorer
  • Clarabridge
  • EpiAnalytics
  • Hottolink
  • Klout
  • LeadSift
  • Lymbix
  • OpenAmplify
  • Open Calais
  • PeekAnalytics
  • Soshio
  • The SelfService Company
  • Trendspottr

Let’s take a little closer look at three Insights to get an idea of their capabilities.

  • The Bitext Sentiment analytic perform Entity extraction and sentiment analysis for posts in Spanish (European and Latin American), Portuguese (Brazilian and European), Italian, and English using natural language processing technology (NLP).
  • Clarabridge provides two analytics. Clarabridge Link Sentiment provides sentiment analysis of social posts in Chinese, Dutch, English, French, German, Italian, Portuguese, Russian, and Spanish using NLP; Clarabridge Link Classification applies a Universal Category and Classification model to social posts in Chinese, Dutch, English, French, German, Italian, Portuguese, Russian, and Spanish using NLP.
  • OpenAmplify also provides two analytics. OpenAmplify Cust Svc uses NLP to identify social posts containing potential customer service issues and the topics of those potential issues. OpenAmplify uses NLP to identify sentiment, intention, and topics of social posts.

Salesforce.com offers these Insights like usage-priced cell phone minutes within the subscription licenses and their monthly fees for Radian6 Editions. (Editions are licensing tiers that bundle applications resources.) More specifically, Radian6 Editions include blocks of Insights partner credits. The analysis of one social post by a one analytic application from one partner costs one partner credit. At the low end, Marketing Cloud Radian Basic Edition includes 1,000 Insights partner credits. At the high end, Marketing Cloud Radian Enterprise Edition includes 500,000 Insights partner credits. Blocks of 10,000 additional Insight partner credits are available for a fee of $100 per month. Credits are expire every month (like cell phone minutes).

Insights’ suppliers set up pre-configured deployments of their analytic applications for access and usage by Radian6 licensees at runtime. That approach can be a disadvantage. For NLP based Insights, runtime access means that language models and processing configurations are those implemented by their suppliers for general-purpose usage, not language models and configurations of deployments tailored to the applications and vocabularies of specific businesses and their customers. For example, the Clarabridge Link Classification Insight uses a “Universal Category and Classification” to classify social posts. Analytic processing will still be quite useful, just not custom tailored.

There are also advantages to Radian6’s Insights approach of runtime access to analytic applications. Most significantly, Radian6 lets businesses easily combine and nest these analytics. For example, analysts might use the entity, fact, and event extraction capabilities of Open Calais to find posts relevant to a product launch and then use PeekAnalytics to identify the demographics of those posters. Also, specifying language models and processing configurations for NLP-based analytic applications is complex work, work that Radian6 users do not have to do to get much of the benefits of these sophisticated applications.

The approach to analysis in Radian6 is a significant differentiator and a key factor for selection. Radian6 delivers most of the power of a wide array of third-party analytic applications and the flexibility to use them separately or to combine their processing. Pricing is based on usage. Value is very good.