Next IT Alme: Helping Customers Do All Their Work

On September 2, 2004, we published my article, “May I Help You?” It was a true story about my experience as a boy working in my dad’s paint and wallpaper store. The experience taught me all about customer service.

The critical lesson that I learned from my dad and from working in the store was customers want and need your help for every activity that they perform in doing business with you from their first contact with you through their retirement.

That help was answering customers’ questions and solving customers’ problems. That’s the usual way that we think of customer service, helping with exceptions, the times that customers can not do their work. But, that help was also performing “normal” activities on customers’ behalves—providing the right rollers, brushes, and solvents for the type of paint they wanted to use, for example, or collaborating with customers to perform normal activities together—selecting a paint color for trim or a wallpaper pattern.

At Kramer’s Paint, my dad or I delivered all of that help—normal work and exceptions work. In your business, you deliver the help to perform customers’ normal planning, shopping, buying, installing/using, and (account) management activities through the software of self-service web sites and/or mobile apps or through the live interactions of your call center agents, in-store associates, or field reps. And, you deliver the help for customers’ exception activities through customer self-service apps on the web, social networks, or mobile devices or through the live interactions of customer service staff in call centers, stores, and in the field.

Virtual Assistants Crossover to Perform Normal Activities

Recently, in our on customer service research, we’ve begun to see virtual assistant software apps crossover from helping customers not only with the exception activities to performing normal activities on customers’ behalves, activities like taking orders, completing applications, and managing accounts. We wrote about this crossover a bit in our last post about IBM Watson Engagement Advisor’s Dialog facility. And, we provided links to crossover examples of Creative Virtual V-Person at Chase Bank and Nuance Nina Mobile at Domino’s.

Alme, the virtual assistant software app from Spokane, WA based supplier Next IT, can crossover to help customers perform normal, too. In fact, Alme has always performed normal activities for customers. One of our first reports about virtual assistants, a report that we published on March 13, 2008, discussed Jenn, Alaska Airlines’ Alme-based virtual assistant. We asked Jenn to find a flight for us through this request, “BOS to Seattle departing December 24 returning January 1.” Jenn did a lot of work to perform this normal activity. Her response was fast, accurate, and complete. We asked Jenn again in our preparation for this post. “She” prepared the “Available Flights” page for us. Once again, her answer was fast, accurate, and complete. All that’s left to do is select the flights. The illustration below shows our request and Jenn’s response.

alaska airlines blog

Next IT Alme Provides Excellent Support for Normal Activities

Alme provides these excellent facilities for performing normal activities, facilities that are one of its key strengths and competitive differentiators:

  • Support for complex, multi-step interactions
  • Rules-based personalization
  • Integration with external applications
  • Let’s take a closer look at them.

Support for Complex, Multi-Step Interactions

For normal activities, complex, multi-step interactions help virtual assistants collect the information needed to complete an insurance or loan application, order a meal, or configure a mobile device and the telecommunications services to support it, for example. Alme supports complex, multi-step interactions with Directives and Goals.

Directives

Directives are hierarchical dialogs of prompt and response interactions between Alme virtual assistants and customers. They’re stored and managed in Alme’s knowledgebase and Alme provides tools for building and maintaining them. Directive’s dialogs begin when Alme’s processing of a customer’s request matches the request to one of the nodes in a Directive. The node presents its prompt to the customer as a text box into which the customer enters a text response or as a list of links from which the customer makes a selection. Alme then processes the text responses or the link selections. This processing moves the dialog:

  • To another node in the Directive
  • Out of the Directive
  • Into a different Directive.

That customers’ requests can enter, reenter, or leave Directives at any of their nodes is what makes Directives powerful, flexible, and very useful. Alme’s analysis and matching engine processes every customer request and response to Directive prompts the same way. When the request (re)triggers a Directive, Alme automatically (re)establishes the Directive’s context, including all previous text responses and link selections. For example, financial services companies might use Directives to implement retirement planning for their customers. The customer might leave the Directive to gather information from joint accounts at the bank with the customer’s spouse before returning to the Directive to continue the planning, opening, and funding of an Individual Retirement Account (IRA).

Goals

Goals let virtual assistants collect a list of information from customers through prompt and response interactions to help perform and personalize their activities. Virtual assistants store the elements of the list of information that the customer provides within virtual assistant’s session data for use anytime within a customer/virtual assistant session. Alme can also use its integration facilities to store elements of the list persistently in external apps.

Goals have the ability to respond to customers dynamically, based on the information the Goal has collected. For example, if the customer provides all of the Goal’s information in one interaction, then Goal is complete or fulfilled and the Alme virtual assistant can perform the activity that is driven by the information. However, if the customer provides, say, two of four required information items, then the Goal can change its responses and request the missing information, leading the customer through a conversation. Goals are created by authors or analysts who specify a list of variables to store the information to be collected and the actions to be taken when customers do not provide all the information in the list. In addition, Goals can be nested, improving their power and giving them flexibility as well as promoting their reuse.

Healthcare providers (Healthcare is one of Next IT’s target markets.) might use Goals to collect a list of information from patients prior to a first appointment. Retailers might use them to collect a set of preferences for a personal e-shopper virtual assistant.

Rules-Based Personalization

Personalization is essential for any application supporting customers’ normal activities. Why? Because personalization is the use of customer information—profile attributes, demographics, preferences, shopping histories, order histories, service contracts, and account data—to tailor a customer experience for individual customers. Performing activities on customers’ behalves requires some level of personalization.

For example, virtual assistants use a customer’s login credentials to access external apps that manage account or order data and, then, use that order data to help customers process a refund or a return. Or, to complete an auto insurance application, virtual assistants need profile data and demographic data to price a policy.

Alme’s rules-based personalization facilities are Variables, Response Conditions, and AppCalls. They are implemented within the knowledgebase items that contain the responses to customers’ requests.

  • Variables provide personalization and context. They contain profile data, external application data, and session data, for example.
  • Response Conditions are expressions (rules) on Variables. Response Conditions select responses and/or set data values of their Variables.
  • AppCalls (Application Calls) pass parameters to and execute external applications. They use Alme’s integration facilities to access external apps through JavaScript and Web Services APIs. For example, Jenn, Alaska Airlines’ virtual assistant, uses AppCalls to process information extracted from the customer’s question—departure city, arrival city, departure date and return date—and normalizes and formats the information for correct handling by the airlines’ booking engine. This AppCall checks city pairs to ensure the flight is valid and formats and normalizes dates so that the booking engine can display appropriate choices. AppCalls also integrate Alme with backend systems. Ann, Aetna’s virtual assistant, uses AppCalls to collect more than 80 profile variables from Aetna’s backend systems to facilitate performing tasks and to personalize answers for Aetna’s customers after they log in and launch Ann. (See the screen shot of Ann, below.)

Integration with External Applications

The resources that virtual assistant applications “own” are typically a knowledgebase (of answers and solutions to expected customers’ questions and problems) and accounts on Facebook and Twitter to enable members of these social networks to ask questions and report problems. So, to perform normal activities, virtual assistants need to integrate with the external apps that own the data and services that support those activities.

Alme integrates with external customer service applications through JavaScript (front end) and Web Services (back end) interfaces. New in Alme 2,2, the current Alme version, Next IT has introduced a re-architected Alme platform that is more modular and more extensible. The new platform has published JavaScript and Web Services interfaces to all Alme functionality and support for JavaScript and Web Services to external resources.

AppCalls use Alme’s integration facilities. To process an AppCall successfully, developers must have established a connection between Alme and an external application. Jenn integrates Alme with Alaska Airlines booking engine. Ann integrates Alme with Aetna’s backend systems. Here’s a screen shot.

aetna blog

Virtual Assistants Are Doing More of the Work of Live Agents

Next IT Alme was one of the first virtual assistant software products with the capabilities to perform normal activities. Its facilities are powerful and flexible. While integration with external applications will always require programming (and Next IT has simplified that programming), Alme’s facilities for supporting normal activities are built-in and designed for business analysts. They’re reasonably easy to learn, easy to use, and easy to manage.

By performing normal activities, virtual assistants are doing more of the work that live agents have been doing—quickly, accurately, consistently, and at a lower cost than live agents. That frees live agents to handle the stickiest, most complex customer requests, requests to perform normal activities and requests to answer questions and resolve problems. It’s also a driver for your organization to consider adding virtual assistants to your customer service customer experience portfolio.

The Dialog Feature of IBM Watson Engagement Advisor

We just updated our Product Evaluation Report on IBM Watson Engagement Advisor. It’s an update to our July 10, 2014 report. Both the scientists at IBM Research and the developers in the IBM Watson Group have been busy improving Watson and Watson Engagement Advisor, busy and productive enough to drive us to do the update. Here are the highlights:

  • Dialog is a new feature of Watson Engagement Advisor that provides facilities to support complex interactions between virtual assistants and customers. These interactions include prompt and response conversations as well as business processes, transactions, and supplementary questions. Dialogs can guide customers through the necessary steps to an outcome or help answer customers’ vague and ambiguous questions.
  • Knowledgebase additional input file support. MHT and ZIP files can be ingested into Watson’s knowledgebase, adding to HTML, Microsoft Word, and PDF file formats.
  • Watson Experience Manager is the visual toolset that subject matter experts use to configure, train, test, and administer Watson Engagement Advisor deployments. Improvements include new tools to configure Dialog conversations.
  • The Cognitive Value Assessment (CVA) is a consulting offering designed to help organizations identify use cases and benefits through examination of issues and pain points in their customer and end user business processes.
  • Product positioning. First contact self-service resolution

Dialog is the most important product improvement. Watson Group used technology from its May 2014 acquisition of Chatswood, New South Wales, AU virtual assistant software supplier Cognea to help build Dialog. The feature helps IBM catch up with its virtual assistant software competitors. The leading suppliers—Creative Virtual, IntelliResponse, Next IT, and Nuance—had all been offering Dialog-like capabilities for some time. Prompt and response conversations have become a key customer service requirement for virtual assistants. These conversations been the approach for answering vague or ambiguous customer questions, helping virtual assistants gather information from customers that concretizes or disambiguates questions (or problems) like, “I can’t get my printer to work,” or “What’s my balance?”

And, on that “What’s my balance?” example, prompt and response conversations are also an approach for supporting personalized tasks and transactions, a hot trend and an emerging requirement for virtual assistants to “act” more like live agents within their interactions with customers. Personalized tasks and transactions take a bit of application integration—therefore some programming—to access, retrieve, and use customer profile data and account data, but, once a virtual assistant has that data, it can perform a wide range of activities for customers in addition to delivering answers and solutions. Think of the Nuance Nina virtual assistant taking pizza orders for Domino’s (https://www.youtube.com/watch?v=noVzvBG0GD0) or Sara, a Creative Virtual V-Person virtual assistant helping consumers with online and mobile banking presenting account balances at Commercial Bank of Dubai (https://www.youtube.com/watch?v=rCvMJYQ0OT0).

Dialog is the mechanism that enables Watson Engagement Advisor virtual assistants to act more like live agents and web concierges. With that bit of application integration/ programming, they can perform personalized and/or transactional tasks for customers. Watson Engagement Advisor’s virtual assistants also have the advantages of Watson’s cognitive technology, which lets them have more flexible, more varied conversations. Watson can answer many types of questions, question that have answers like a simple fact, the definition of a term, the description of a topic, yes/no or true/false, and the steps in a procedure, or an approach to trouble shooting a problem. Dialog lets customers interject relevant but out of band questions within prompt and response conversations then, after the virtual assistant delivers the answer, either return to the Dialog or continue out of band interactions, perhaps, entering other Dialog flows. For example, in a customer/Watson Engagement Advisor virtual assistant session about property and casualty insurance, the customer might interject a question about extra coverage for jewelry in the middle of a Dialog implementing the application for a standard property policy. The virtual assistant can answer the jewelry question, answer any additional jewelry coverage questions, return to the policy application conversation, or even complete an application for a jewelry rider. For new customers, the policy application conversation will collect appropriate customer data and pass it to the external app. For existing customers, the virtual assistant will access the appropriate external app for the customer data. Live agents might do that data access manually from their desktops. Virtual assistants must do it with programming.

In the current Watson Engagement Advisor release, Dialog functionality and the tools are essentially what Cognea had built and offered. Watson Group’s developers are working hard to integrate the functionality more seamlessly within Watson Engagement Advisor and to integrate and improve the tools on the Watson Experience Manager toolset. We don’t think that Dialog is part of any of the five or so live Watson Engagement Advisor deployments, but going forward, we think that it will become part of most deployments. In fact, every virtual assistant should provide the capabilities to perform actions on behalf of customers.

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.

Salesforce Service Cloud

Evaluation of Service Cloud Winter ’15

This week’s report is our evaluation of Salesforce Service Cloud and its collection of tightly integrated but variously packaged and priced features and add-on products—Service Cloud, itself, for case management and contact center support, Salesforce Knowledge for knowledge management, Live Agent for chat, Social Studio for social customer service, and Salesforce Communities for communities and for customer self-service. Winter ’15 is the current release of the offering and the release that we evaluated in this report.

The offering earns an excellent evaluation against the criteria of our Framework for Customer Service Applications. We found no areas where significant improvement is required.

We had last published an evaluation of Service Cloud Winter ’13 on January 24, 2013. Winter ’15 is the sixth of the regular cycle of Winter, Spring, and Summer releases since that date. Every new release has included significant new and/or improved capabilities.

Salesforce Communities – a New Platform for Customer Self-Service

Salesforce Communities is one of the new capabilities in Winter ’15. It packages an attractive set of facilities, facilities that let customers perform a wide range of collaboration and self-service activities and tasks. However, none of these facilities use new technology; all of them have been existing features of Salesforce applications. What’s new and what’s innovative is their use as the platform for customer self-service. With Communities, Salesforce.com has extended the customer service provider-centric, web content-intensive self-service of portals with social and collaborative self-service that lets customers (and customer service agents) answer and solve customers’ questions and problems. Here’s what we mean.

Customers can use Communities’ packaged, portal-style facilities to perform these self-service tasks:

  • Search a Salesforce Knowledge knowledgebase to find existing answers and solutions for similar questions and problems
  • Browse a hierarchy of “Topics” to find existing answers and solutions to their problems in the knowledgebase or within community content.
  • Create new Service Cloud Cases when they can’t find answers and/or solutions via searching or browsing a knowledgebase, or by browsing Topics and community content.
  • Note that during the case creation process, Communities uses Automatic Knowledge Filtering, a Salesforce Knowledge feature, that automatically suggests knowledgebase Articles relevant to the content of the fields of the new Case.
  • Contact support for escalation to assisted-service

Customers can also use Communities’ packaged social and collaborative facilities to perform self-service tasks.

  • Post their questions or problems on a threaded, post-and-reply forum to solicit answers and solutions from other customers or from customer service staff members who monitor community activity. Note that Communities’ threads are implemented with Salesforce Chatter Feeds. Feeds are Twitter-like stacks of posts and replies/comments.
  • Search post-and-reply Feeds to find existing answers and solutions or previously posted questions and problems and replies/comments about them.

You may have read these lists of bullet points and said, “So, what. There’s nothing new here. We already have these facilities on our portal and on our community.” Exactly right, but that separate portal and community approach forces customers to go to two places to find answers and solutions, and, based on the experience that you’ve given them, they go to one place or the other depending on the type of question or problem they have or the quality and usefulness of answers and solutions that they’ve found. Salesforce Communities gives customers one place to go for self-service answers and solutions. One place not two makes it easier and faster for them to do business with you and makes it easier and more efficient for you to do business with them.

community.seagate.com

For example, Seagate Technology LLC, the provider of hard disk drives and storage solutions based in Cupertino, CA, has a Salesforce Communities-based self-service site. Its home page is shown in the screen shot below.

seagate blog1

As a Mac user needing some advice on drives for backups, I clicked on the Mac Storage Topic and was taken to the Mac Storage products page shown below in the next screen shot. This page presents a list of combined questions, (Salesforce Knowledge) Articles, Solved Question, Unsolved Questions, and Unanswered Questions in the center with a drop-down at top of the list to filter the presentation. Links to product-specific pages are at the left.

seagate blog 2

At the bottom of the Mac Storage Product Page are links to additional customer service facilities, including, “Get Help from Support.” We show them in the screen shot below.

seagate blog 3

The Seagate community offers a complete set of easy-to-use self-service facilities. Community-style self-service gives customers everything they need for customer service—finding answers and solutions or getting assisted-service when answers and solutions don’t exist or can’t be found.

Tools and Templates

By the way, Salesforce Communities includes tools and reusable templates that can make it easy and fast to deploy customer self-service communities. Community Designer is the toolset for building and managing the web pages of Communities deployments. Community Designer can also customize the three web page templates packaged with Communities—Koa, Kokula, and Napili. For example the web pages for the Koa self-service template contain facilities that let customers search for or navigate to Salesforce Knowledge Articles by categories called Topics or contact support if they can’t find answers or solutions.

Salesforce.com is changing and improving self-service with Salesforce Communities. What a good idea!

 

 

Framework for Evaluating Customer Service Products

This week’s report is a new version of our Framework for Evaluating Customer Service Software Products. We had two goals for its design. First, we wanted your evaluation, comparison, and selection processes to be simpler and faster. Second, we wanted shorter and more actionable Product Review Reports. The new Framework eliminates evaluation criteria that do not differentiate. For example, we no longer analyze and evaluate web content management for a product’s self-service and assisted-service UIs. These UIs have become a bit static. They’re configurable and localizable, but they’re no longer as customizable and manageable as they had been. The new Framework also decreases the number of factors (sub-criteria) that we consider within an evaluation criterion. For example, the Knowledge Management criterion now has two factors: Knowledge Model, and Knowledge Management Services. The previous version of the Framework examined these and six others.

We also added a criterion—Case Management. When we began evaluating customer service products back in 1993, we felt that case management, while a critical customer service process, was well understood, did not differentiate, and was not really customer-centric. We’ve changed our point of view. We still believe that the purpose for customer service is answering customers’ questions and solving customers’ problems. However, we also recognize that at the point in time that a customer asks a question or poses a problem you might not have an answer or solution available. You create a case to represent that question or problem, your process to resolve the case is a process to find or develop an answer or solution, and its resolution is, itself, the answer or solution. Our evaluation of case management considers four factors that focus on a product’s packaged services and tools for performing the tasks of the case management process. The process includes finding and using case resolutions in communities and social networks.

Customer Service Best Fit and Customer Service Technologies are the Framework’s two top-level evaluation criteria. Customer Service Best Fit presents information and analysis that classifies and describes customer service software products. Customer Service Technologies examines the implementation of a product’s customer service applications. The graphic below shows the Framework, its top-level criteria, and their sub-criteria.

framework

We plan to use the Framework to evaluate every type of customer service product within our current research—case management, knowledge management, virtual assistant, and social network monitoring, analysis, and interaction. The Customer Service Best Fit criterion applies very nicely to any product. The application of the Customer Service Technologies criterion is product-type dependent. Look for our Product Review Report on Salesforce Service Cloud. It will be the first against the new Framework. Based on the draft of that report, the Framework works very nicely.

Desk.com from Salesforce.com

Very Good Customer Service Capabilities in an Fast and Easy to Deploy Package

This week’s report is our Product Evaluation of Desk.com, Salesforce.com’s customer service offering for very small, small, and mid-sized businesses as well as for small departments in larger organizations. The product is based on technology that came to Salesforce.com In its acquisition of Assistly in September of 2011.

From positioning, pricing, and packaging perspectives, Desk.com is an entry-level customer service application. The smallest organizations can purchase subscription licenses for its core case management capabilities on email, telephone, Facebook, and Twitter channels for $3 per user per month for up to three users. Its next price point is $30 per user per month to add chat, knowledge management, community, web self-service, and reporting capabilities and an API for integrating external apps. $50 per user per month adds more users and support for multiple languages and multiple brands.

Desk.com’s customers are exactly small and fast growing companies. The list of reference customers provides a good idea about the kinds of companies that are best fits. Some of them are:

  • BarkBox
  • HotelTonight
  • One Kings Lane
  • SoundCloud
  • Susty Party
  • ZenPayroll
  • Volotea

For example, here’s the web self-service site for Volotea, a low cost and charter airline based in Barcelona, Spain that serves small and medium sized cities in Europe.

volotea

© 2014 Volotea

Top Two Takeaways

Our top two takeaways from our research are Desk.com offers:

  • Fast and easy deployment
  • Rich customer service capabilities

Let’s take a closer look at why.

Fast and Easy Deployment

As we expected for an entry-level app, Desk.com is easy to learn and easy and fast to deploy.

We took the “Free Trial” offered to any business on http://www.desk.com, learned how to deploy and use the product, configured it to support our evaluation, and used its facilities (almost) as if we were a customer service organization. Note that trial deployments are preconfigured for a one-agent customer service operation. It was absolutely fast and easy. Case management on a packaged agent UI works out-of-the-box.

Knowledge management capabilities are built-in, too, but, of course, you’ll have to populate the knowledgebase with relevant knowledge items. Desk.com helps with samples and predefined, configurable “Topics” for categorizing them. Web self-service also works out-of-the-box on a packaged but configurable UI. We did some very basic configuration for the self-service UI below.

kramermitch support center

Rich Customer Service Capabilities

We found Desk.com’s customer service capabilities to be way more than entry-level, especially case management. For example, the Table, below, shows the predefined fields of Desk.com’s Case objects. The product packages application services that support all of the fields. The large number of date/time fields demonstrates the breadth and depth of case management capabilities.

Desk.com Case Objects
Field Description
ID String identifier for this object
External_ID Unique identifier to reference this case to an external system
Blurb Short summary of, or excerpt from, the case
Subject Subject of this case
Priority Number between 1 and 10 (1 being lowest priority)
Description Case description or background information
Status Current state of the case: new, open, pending, resolved, closed
Type Channel of the case source: chat, Twitter, email, Questions and Answers, Facebook, or telephone
Labels Labels associated with this case
Label_IDs Label ids associated with this case
Language The case’s ISO language code: EN, FR, DE, IT, JA, SP
Custom_fields Deployment-specific fields
Created_at Date/time this case record was created
Updated_at Date/time this case record was last updated by any action
Changed_at Date/time this case was last updated by a user
Active_at Date/time this case was last active
Received_at Date/time the most recent message was received
Locked_until Date/time the lock on this case will expire
First_opened_at Date/time this case was first opened
Opened_at Date/time this case was most recently opened
First_resolved_at Date/time this case was first resolved
Resolved_at Date/time this case was most recently resolved

Table 1. Predefined fields in Desk.com’s Case objects

Worth a Closer Look

Desk.com is an impressive offering. Its fast and easy deployment and rich customer service capabilities could make it a best fit as your business’s first cross-channel customer service app. And, it can grow with you to the point where you have a staff of several dozen-customer service agents. Take a closer look. Read our Product Evaluation Report.

Virtual Agents that Can Think!

IBM Watson and IBM Watson Engagement Advisor

If you’re a fan of Jeopardy! (The TV game show), then for sure you remember the IBM Challenge on February 14 – 16, 2011 when a supercomputer app from IBM named Watson (after Thomas J. Watson, IBM’s founder) played the game against its two biggest (multi-million dollar) winners and beat them handily. Watson delivered so many more correct responses so much faster than the former champions. It really was no contest. Check out this video if you don’t remember or if you’re not a Jeopardy! fan (https://www.youtube.com/watch?v=K0GD8w0k0UA).

Well, IBM had made Watson a product—IBM Watson Engagement Advisor. While IBM positions and markets Watson Engagement Advisor more broadly, from our perspective, this very new offering can be the technology behind very useful and very intelligent virtual agents, virtual agents that can learn and (almost) think, virtual agents that can transform customer service. This week’s report is our evaluation of Watson Engagement Advisor against our Framework for Evaluating Virtual Assisted-Service (virtual agent) Products.

Cognitive Technology Makes the Difference

Cognitive technology is Watson Engagement Advisor’s most significant strength, advantage, and differentiator. Watson Engagement Advisor is the only customer service product that uses it. Watson Engagement Advisor’s approach to analyzing customers’ questions and matching them with knowledgebase answers uses a combination of cognitive technology, Natural Language Processing (NLP) technology, and machine learning technology. Similarly to alternative virtual agent approaches, Watson Engagement Advisor uses NLP technology to parse and understand the intent of customers’ questions. And, also similarly to alternative approaches, it uses machine learning technology to match canonical, representative, or expected forms of customer’s questions with knowledgebase answers. (Analysts train Watson’s machine learning model with question and answer pairs.) Cognitive technology uniquely enables Watson Engagement Advisor’s virtual agents to think. In analyzing customers’ questions, Watson’s cognitive technology 1) generates a number hypotheses, which are possible answers, 2) compares the language of the hypotheses with the language of the customer’s questions, and 3) scores each hypothesis for how well the question infers it. The hypotheses with the highest scores are delivered back to customers as the answers to their questions. By the way, Watson thinks fast. There’s no performance penalty for the “extra” work to perform hypothesis generation, comparison, and scoring. Watson won at Jeopardy! because it delivered correct answers faster than its human competitors.

Watson Engagement Advisor Can Answer Many Types of Customers’ Questions

Cognitive technology also enables Watson Engagement Advisor to answer many types of questions, another of its strengths and differentiators.

• Simple facts
• Definitions of terms
• Descriptions of topics
• Yes/no or true/false
• Steps in a procedure, or approaches to troubleshooting.

If analysts have includes the content that contains the answers to the questions in the knowledgebase and if they’ve trained their machine learning model with appropriate question and answer pairs, then Watson Engagement Advisor will deliver the correct answers. On the topic of knowledge, Watson Engagement Advisor has an excellent approach to knowledge management. Its knowledgebase is its corpus. Analysts create a corpus by uploading (existing) HTML, PDF, Word, or XML documents. Watson Engagement Advisor organizes, indexes, and manages this content as a knowledgebase. No authoring, editing, and managing knowledge items. No explicit indexing or categorization, either. Watson Engagement Advisor does the work. Pretty good, eh?

Work in Progress

As we mentioned above, Watson Engagement Advisor is a very new offering. While IBM Research developed (and continues to develop) Watson’s core NLP, cognitive, and machine learning technologies several years ago, Watson Engagement Advisor was introduced on May 21, 2013. It’s a bit immature and a bit incomplete. For example, the current version supports only English, does not support speech, does not have reporting capabilities, and does not integrate with external customer service apps. IBM told us that its product developers are working to deliver capabilities in all of these areas. Also, while approximately ten end-customers and ten partners have licensed Watson Engagement Advisor, none have yet deployed live apps. Remember, though, Watson Engagement Advisor is a new product but an offering from a very experienced and very established supplier. In fact, IBM has established the Watson Group to support the development and commercialization of cloud-delivered cognitive applications and announced that it would invest more than $1 billion in it, including $100 million available for venture investments to support its ecosystem of partners that are building and will be building “powered by Watson” cognitive apps. No question that IBM will deliver the missing pieces. No question that live powered by Watson apps will be coming soon.