The Helpdesks: Desk.com, Freshdesk, Zendesk

We’ve added our Product Evaluation Report on Freshdesk to our library of in-depth, framework-based reports on customer service software. We put this report on the shelf, so to speak, next to our Product Evaluation Reports on Desk.com and Zendesk. The three products are quite a set. They’re similar in many ways, remarkably so. Here are a few of those similarities:

The products are “helpdesks,” apps designed to provide an organization’s customers (or users) with information and support about the organization’s products and services. Hence, their names are (alphabetically) Desk.com, Freshdesk, and Zendesk.

They have the same sets of customer service apps and those apps have very similar capabilities: case management, knowledge management and community/forum with a self-service web portal and search, social customer service supporting Facebook and Twitter, chat, and telephone/contact center. Case management is the core app and a key strength for all of the products. Each has business rules-based facilities to automate case management tasks. On the other hand, knowledge management and search are pretty basic in all of them.

The three also include reporting capabilities and facilities for integrating external apps. Reporting has limitations in all three. Integration is excellent across the board.

These are products that deploy in the cloud. They support the same browsers and all three also have native apps for Android and iOS devices.

All three are packaged and priced in tiers/levels/editions of functionality. Their licensing is by subscription with monthly, per user license fees.

Simple, easy to learn and easy to use, and cross/multi/omni-channel are the ways that the suppliers position these offerings. Our evaluations were based on trial deployments for each of the three products. We found that all of them support these positioning elements very well.

Small (very small, too) and mid-sized businesses across industries in all geographies are their best fits, although the suppliers would like to move up market. The three products have very large customer bases—somewhere around 30,000 accounts for Desk.com and Zendesk and more than 50,000 accounts for Freshdesk per a claim in August from Freshdesk’s CEO. Note that Desk.com was introduced in 2010, Freshdesk in 2011, and Zendesk in 2004.

Suppliers’ internal development organizations design, build, and maintain the products. All three suppliers have used acquisitions to extend and improve product capabilities.

While the products are similar, the three suppliers are quite different. Salesforce.com, offers Desk.com. Salesforce is a publicly held, San Francisco, CA based, $8 billion corporation founded in 1999. Salesforce has multiple product lines. Freshdesk Inc., offers Freshdesk. It’s a privately held corporation founded in 2010 and based in Chennai, India. Zendesk, Inc. offers Zendesk. This company was founded in 2007 in Denmark and reincorporated in the US in 2009. It’s publicly held and based in San Francisco, CA. Revenues in 2015 were more than $200 million.

These differences—public vs. private, young vs. old(er), large vs. small(er), single product line vs. multiple product line—will certainly influence many selection decisions. However, all three are viable suppliers and all three are leaders in customer service software. The supplier risk in selecting Desk.com, Freshdesk, or Zendesk is small.

Then, where are the differences that result in making a selection decision? The differences are in the ways that the products’ developers have implemented the customer service applications. The differences become clear from actually using the products. Having actually used all three products in our research, we’ve learned the differences and we’ve documented them in our Product Evaluation Reports. Read them to understand the differences and to understand how those differences match your requirements. There’s no best among Desk.com, Freshdesk, and Zendesk but one of them will be best for you.

For example, here’s the summary of Freshdesk evaluation, the grades that the product earned on our Customer Service Report Card. “Freshdesk earns a mixed Report Card—Exceeds Requirements grades in Capabilities, Product Management, Case Management, and Customer Service Integration, Meets Requirements grades in Product Marketing, Supplier Viability, and Social Customer Service, but Needs Improvement grades in Knowledge Management, Findability, and Reporting and Analysis.”

Case Management is where Freshdesk has its most significant differences, differences from its large set of case management services and facilities, its support for case management teams, its automation of case management tasks, and its easy to learn, easy to use case management tools. For example, Arcade is one of Freshdesk’s facilities for supporting case management teams. Arcade is a collection of these three, optional gamification facilities that sets and tracks goals for agents’ customer service activities.

  • Agents earn Points for resolving Tickets in a fast and timely manner and lose points for being late and for having dissatisfied customers, accumulating points toward reaching six predefined skill levels.
  • Arcade lets agents earn “trophies” for monthly Ticket management performance. In addition,
  • Arcade awards bonus points for achieving customer service “Quests” such as forum participation or publishing knowledgebase Solutions.

Arcade lets administrators configure Arcade’s points and skill levels. Its Trophies and Quests have predefined goals; however, administrators can set Quests on or off. The Illustration below shows the workspace that administrators use to configure Points.

arcade points

Freshdesk can be a Customer Service Best Fit for many small and mid-sized organizations. Is it a Best Fit for your? Read our Report to understand why and how.

Nuance Nina Virtual Assistants

We evaluated Nina, the virtual assistant offering from Nuance, for the third time, publishing our Product Evaluation Report on October 29, 2015. This Report covers both Nina Mobile and Nina Web.

Briefly, by way of background, Nina Mobile provides virtual assisted-service on mobile devices. Customers ask questions or request actions of Nina Mobile’s virtual assistants questions by speaking or typing them. Nina Mobile’s virtual assistants deliver answers in text. Nina Mobile was introduced in 2012. We estimate that approximately 15 Nina Mobile-based virtual assistants have been deployed in customer accounts.

Nina Web provides virtual assisted-service through web browsers on PCs and on mobile devices. Customers ask questions or requests actions of Nina Web’s virtual assistants questions by typing them into text boxes. Nina Web’s virtual assistants deliver answers or perform actions in text and/or in speech. Nina Web was introduced as VirtuOz Intelligent Virtual Agent in 2004. Nuance acquired VirtuOz in 2013. We estimate that approximately 35 Nina Web-based virtual assistants have been deployed in customer accounts.

The two products now have common technologies, tools, and a development and deployment platform. That’s a big deal. They had been separate and pretty much independent products, sharing little more than a brand. Nuance’s development team has been busy and productive. Nina also has many new and improved capabilities. Most significant are a new and additional toolset that supports key tasks in initial deployment and ongoing management, PCI (Payment Card Industry) certification, which means that Nina virtual assistants can perform ecommerce tasks for customers, support for additional languages, and packaged integrations with chat applications.

Nina Evaluation Process

We did not include an evaluation of Nina’s Ease of Evaluation. Our work on the Nina Product Evaluation Report was well underway before we added that criterion to our framework. So, we’ll offer that evaluation here.

For our evaluation, we used:

  • Product documentation, which was provided to us by Nuance under an NDA
  • Demonstrations, especially of new tools and functionality, conducted by Nuance product management staff
  • Web content of nuance.com
  • Online content of Nina deployments
  • Nuance’s SEC filings
  • Discussions with Nuance product management and product marketing staff
  • Thorough (and very much appreciated) review of report draft

We also leveraged our knowledge of Nina, knowledge that we acquired in our research for two previously published Product Evaluation Reports from July 2012 and January 2014. We know the product, the underlying technology, and the supplier. So we were able to focus our research on what was new and improved.

Product Documentation

Product documentation, the end user/admin manuals for Nina IQ Studio (NIQS) and the new Nuance Experience Studio (NES) toolsets, was they key source for our research. We found the manuals to be well written and reasonably easy to understand. Samples and examples illustrated simple use cases and supported descriptions very well. Showing more complex use cases, especially for customer/virtual assistant dialogs, would have been very helpful. Personalization facilities could be explained more thoroughly. Also, there’s a bit of inconsistency in terminology between the two toolsets and their documentation.

Nina Deployments

Online content of Nina deployments helped our research significantly. Within the report, we showed two examples of businesses that have licensed and deployed Nina Web are up2drive.com, the online auto loan site for BMW Financial Services NA, LLC and the Swedish language site for Swedbank, Sweden’s largest savings bank. The up2drive Assist box accesses the site’s Nina Web virtual assistant. We asked, “How to I qualify for the lowest rate new car rate?” See the Illustration just below.

up2drive

Online content of Nina Mobile deployments show how virtual assistants can perform actions for customers. For example, we showed how Dom, the Nina Mobile virtual assistant, could help you order pizza from Domino’s in our blog post of May 14, 2015. See https://www.youtube.com/watch?v=noVzvBG0GD0.

Take care when using virtual assistant deployments for evaluation and selection. They’re only as good as the deploying organization wants to make them. Their limitations are almost never the limitations of the virtual assistant software. Every virtual assistant software product that we’ve evaluated has the facilities to implement and deliver excellent customer service experience. Virtual assistant deployments, like all customer experience deployments, are limited by the deploying organization’s investment in them. The level of investment controls which questions they can answer, which actions they can perform, how well they can deal with vague or ambiguous questions and action requests, and their support for dialogs/conversations, personalization, and transactions.

No Trial/Test Drive

Note that Nuance did not provide us with a product trial/test drive of Nina. In fact, Nuance does not offer Nina trials/test drives to anyone. That’s typical of and common for virtual assistant software. Suppliers want easy and fast self-service trials that lead prospects to license their offerings. Virtual assistant software trials are not any of these things. They’re not designed for self-service deployment either for free or for fee.

Why not? Because virtual assistant software is complex. Even its simplest deployment requires building a knowledgebase of the answers to the typical and expected questions that customers ask, using virtual assistant facilities to deal with vague and ambiguous questions, engaging in a dialog/conversation, escalating to chat, or presenting a “no results found” message, for example, and using virtual assistant facilities to perform actions that customers request and deciding how to perform them. (Performing actions will likely require integration apps external to virtual assistant apps.) This is not the stuff of self-service trials and test-drives.

In addition, most virtual assistant suppliers have not yet invested in building tools that speed and simplify the work that organizations must perform for the initial deployment and ongoing management of virtual assistants software even after it has been licensed. Rather, suppliers offer their consulting services instead. (That’s changing for Nuance with toolsets like NES and for several other virtual assistant software suppliers and that’s certainly a topic for a later time.)

Thank You Very Much, Nuance

One more point about Ease of Evaluation. Our research goes into the details of customer service software. We publish in-depth Product Evaluation Reports. We demand a significant commitment from suppliers to support our work. Nuance certainly made that commitment and made Nina Easy to Evaluate for us. We so appreciate Nuance’s support and the time and effort taken by its staff.

Nina was very easy for us to evaluate. The product earns a grade of Exceeds Requirements in Ease of Evaluation.

Zendesk, Customer Service Software That’s Easy to Evaluate

Zendesk Product Evaluation

Zendesk is the customer service offering from Zendesk, Inc. a publicly held, San Francisco, CA based software supplier with 1,000 employees that was founded in 2004. The product provides cloud-based, cross-channel case management, knowledge management, communities and collaboration, and social customer service capabilities across assisted-service, self-service, and social customer service channels.

We evaluated Zendesk against our Evaluation Framework for Customer Service and published our Product Evaluation Report on October 22. Zendesk earned a very good Report Card—Exceeds Requirements grades in Product History and Strategy, Case Management, and Customer Service Integration, and Meets Requirements grades for all other criteria but one, Social Customer Service. Its Needs Improvement grade in Social Customer Service is less an issue with packaged capabilities than it is a requirement for a specialized external app designed for and positioned for wide and deep monitoring of social networks.

Evaluation Framework

Our Evaluation Framework considers an offering’s functionality and implementation, what a product does and how it does it. It also considers the supplier and the supplier’s product marketing (positioning, target markets, packaging and pricing, competition) and product management (release history and cycle, development approach, strategy and plans) for the offering.

We rely on the supplier for product marketing and product management information. First we gather that info from the supplier’s website and press releases and, if the supplier is publicly held, from the supplier’s SEC filings. We speak directly with the supplier for anything else in these areas.

For functionality and implementation, the supplier typically gives us (frequently under NDA) access to the product’s user and developer documentation, the manuals and help files that licensees get. In this era of cloud computing, we’ve been more and more frequently getting access to the product, itself, through online trials. We also read any supplier’s patents and patent applications to learn about the technology foundation of functionality and implementation.

In addition, we entertain the supplier’s presentations and demonstrations. They’re useful to get a feel for the style of the product and the supplier and to understand future capabilities. However, to really understand the product, there’s no substitute for actual usage (where we drive) and/or documentation.

Our research process includes insisting that the supplier reviews and provides feedback on a draft of the Product Evaluation Report. This review process ensures that we respect any NDA, improves the accuracy and usefulness of the information in the report, and prevents embarrassing the supplier and us.

Ease of Evaluation, a New Evaluation Criterion

Our frameworks have never had an Ease of Evaluation criterion. We’ve always figured that we’d do the work to make your evaluation and selection of products easier, faster, and less costly. Our evaluation of Zendesk has us rethinking that. We’ve learned that our Product Evaluation Reports can speed and shorten your evaluation and selection process but that your process doesn’t end with our reports. You do additional evaluation, modifying and extending our criteria or adding criteria for criteria to represent requirements specific to your organization, your business, and/or application for a product. Understanding Ease of Evaluation can further speed and shorten your evaluation and selection process.

So, beginning with our next Product Evaluation Report, you’ll find that Ease of Evaluation criterion in our framework.

Zendesk Was Very Easy to Evaluate

By the way, Zendesk would earn an Exceeds Requirements grade for Ease of Evaluation. We did a 30-day trial of the product. We signed-up for the trial online—no waiting. During the trial we submitted cases to Zendesk Support and we used the Zendesk community forums. In addition, Zendesk.com provided a wealth of detailed information about the product, including technical specifications and a published RESTful API.

Scroll down to the bottom of Zendesk.com’s home page to see a list of UNDER THE HOOD links.

under the hood

Looking at the UNDER THE HOOD links in a bit more detail:

  • Apps and integrations is a link to a marketplace for third party apps. Currently there are more than 300 of them.
  • Developer API is a link to the documentation of Zendesk’s RESTful, JavaScript API. It lists and comprehensively describes more than100 services.
  • Mobile SDK is a link to documentation for Android and iOS SDKs and for the Web Widget API. (The Web Widget embeds Zendesk functionality such as ticketing and knowledgebase search in a website.)
  • Security is a link to descriptions of security-related features descriptions lists of Zendesk’s security compliance certifications and memberships.
  • Tech Specs is a link to a comprehensive collection of documents that describe Zendesk’s functionality and implementation.
  • What’s new is a link to high-level descriptions of recently added capabilities
  • Uptime is a link to info and charts about the availability of Zendesk Inc.’s cloud computing infrastructure
  • Legal is a link to a description of the Terms of Service of the Zendesk offering

We spent considerable time in Tech Specs and Developer API. We found the content to be comprehensive, well organized and easy to access, and well written. The combination of the product trial and UNDER THE HOOD made Zendesk easy to evaluate. And, we did not have to sign an NDA for access to any of this information.

Many suppliers make their offerings as easy to evaluate as Zendesk, Inc. made Zendesk for us. On the other hand, many suppliers are not quite so willing to share detailed information about their products and, especially their underlying technologies. Products and technologies are, after all, software suppliers’ key IP. They have every right to protect this information. They don’t feel that patent protection is enough. Their offerings are much harder to evaluate at the level of our Product Evaluation Reports.

Consider Products That Are Easy to Evaluate

We feel as you should feel that in-depth evaluations are essential to the selection of customer service products. You’ll be spending very significant time and money to deploy and maintain these products. You should never rely on supplier presentations and demonstrations to justify those expenditures. Certainly rely on our reports and use them as the basis for your further, deeper evaluation, including our new Ease of Evaluation criterion. Put those suppliers that facilitate these evaluations on your short lists.

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!

 

 

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.

Using Clarabridge to Deliver Social Customer Service

What Is Social Customer Service?

This week’s report is our evaluation of the social customer service capabilities of Clarabridge Analyze and Clarabridge Act. Here’s what we mean by social customer service:

The social web has hundreds of millions of users who spend incredible amounts of time posting and responding about any and every possible aspect of their personal and professional lives. Many of these users are prospects and/or customers who use the social web to get help in evaluating and selecting the products and services that they want to or need to buy and in installing and using those products and services after they’ve bought them.

These users want to leverage the experience and expertise of their peers, who are also social web users, who have already made these purchasing decisions or have already encountered these installation and usage problems. These users have also come to expect that the products’ and services’ suppliers are listening to their social conversations and will contribute timely and accurate answers and solutions. Get it?

Clarabridge Analyze and Clarabridge Act Deliver Social Customer Service

Clarabridge Analyze and Clarabridge Act comprise a product suite that can help suppliers deliver social customer service.

We had published an evaluation of Clarabridge 5.5 on March 28, 2013. Since that date, Clarabridge, Inc. has made significant improvements to the offering within two new versions: Clarabridge 6.0, which was introduced in April 2013 and Clarabridge 6.1, the version we evaluate in our report, which was introduced in November 2013.

This is a strong offering that earns a very good report card—Exceeds Requirements grades for the critical Monitoring and Analysis criterion and Meets Requirements grades in Product Viability and Company Viability. The Needs Improvement Grade in Customer Service Integration should improve soon through planned enhancements in future product versions.

Three factors, all product strengths, differentiate Clarabridge Analyze and Clarabridge Act and drive toward its selection. One of those factors is powerful and flexible monitoring and filtering of customer conversations in multiplelanguages.

The Social Web Is Noisy and Getting Noisier All the Time

Filtering is critical. The social web is very, very noisy. It’s a major challenge for suppliers to identify the social conversations that questions and problems that require customer service. Why? Because:

  • Huge and increasing volumes of customer conversations on the social web
  • The number of social web users continues to increase
  • The number of social apps continues to increase
  • Customers are increasingly social

Also, the conversations of social web users about products and services, even named products and services, can be ambiguous and misleading. Product category, product, and company mentions might be:

  • Geographical. Make a left turn at the Publix.
  • Ambiguous. Is “Asics” a brand of running shoes or Application Specific Integrated Circuit(S)? Is “4X4” a product category for automobiles or dimensional lumber?
  • Conversational. “I’ll pick you up at the airport. I’ll be driving a black Volvo.” Lost: North Face backpack.

Querying social conversations by keywords or SQL-like keyword expressions, the approach of many social customer service products, results in a very large number of results, maybe tens of thousands of results that will not require any social customer service action. Querying by keyword collects every conversation that contains the keywords—relevant or not, misleading, and ambiguous. Querying by keyword puts the onus on social customer service staff to filter the noise and to identify the social conversations that need attention through manual investigation of reports that list these results.

Recently, as social networks have begun to (try to) generate revenue, social noise now includes ads, coupons, and spam—more noise, more results from keyword querying.

Clarabridge Filters the Noise

The filtering capabilities of Clarabridge can reduce the noise—big time. Specifically, Clarabridge Analyze can filter customer conversations by language, structured data attributes, social media attributes, data type, and/or content type. Here’s how.

Language. Clarabridge Analyze automatically detects the language of customer posts, messages, and feedback. Language filtering is useful, but it won’t reduce noise.

Structured data attributes/metadata. Clarabridge Analyze lets analysts filter customer conversations by any available attributes. If a social conversation includes tagged content data, then Clarabridge Analyze can filter based on those tags. For example, ecommerce web pages might be tagged with product categories, product names, or company names. Case/incident content might be tagged with customer identifiers and product identifiers. Metadata filters can cut through noise quickly and easily.

Social media attributes. Social media attributes are social source-specific. Analyze instruments all available social data attributes in customer posts, comments, and replies for the social sources that it supports. Attributes may include the poster’s full name, username, and/or locale/location. This is information that can help find a poster’s customer record, if one exists. With a customer record, suppliers can find information about current offers, purchased products, and historical cases/incidents, information that can determine whether the conversation is noise.

Data types. Data type attributes filter customer conversations by a data source ID, which is defined by the deployment, and by verbatim type. Verbatim types are post, tweet, reply, and comment. Data type filters can focus social customer service efforts on the posts and tweets that include questions and problems.

Content type. Content type filtering distinguishes between “contentful” posts, messages, and feedback and “noncontentful” posts, messages, and feedback. Noncontentful content types are ads, coupons, links to articles, and spam, and Clarabridge Analyze automatically recognizes and flags them. Analysts can configure content type filtering to discard or to retain noncontentful content. Content type filtering is new. This is an excellent noise reducer.

The monitoring and filtering capabilities of Clarabridge Analyze help businesses collect customer conversations across all social and internal channels in a wide range of languages and then filter the ever-increasing levels of noise to identify and analyze the most meaningful and important customer conversations. Filtering is a key strength and differentiator.

Find and Customers’ Questions and Problems in Social Conversations

Customers are talking about companies and their products on the social web. They’re making comments, asking questions, posing problems. It’s critical but increasingly difficult to find those conversations that include questions and problems then to deliver answers and solutions to their posters. That’s social customer service. Clarabridge offers tools to help.