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.

Customer Service Integration

This week’s report is our 1Q2014 Customer Service Update. Briefly, 1Q2014 was a quiet quarter for customer service. Customer growth was down. Only Clarabridge improved significantly in both customer acquisition and repeat business. Product activity was light. Five of our suppliers did not make any product announcements. Company activity was light. Four suppliers did not make any company announcements. Most significantly, Verint acquired KANA. Clarabridge earned a Customer Service Star for 1Q2014 for outstanding customer growth, for significant company activity, and for earning an excellent product evaluation.

We observed one customer service trend—customer service integration. Very important. Customer Service Integration is one of the key criteria in all of our frameworks for evaluating customer service products. Customer service integration can reduce cost to serve and increase customer satisfaction. Integration expands and streamlines the customer service experience. It makes it easier for customers to get answers to their questions and solutions to their problems. It makes it easier for customer service agents to help customers.

For example, from our framework for evaluating virtual agent/virtual assisted-service products, we state, “Through integration with external customer service applications, virtual agent software product deployments can escalate to assisted-service chat or contact center telephone channels, deliver virtual assisted-service on social networks, and/or can answer a wider range of questions, questions that involve the data in cases and accounts, for instance. Integration makes virtual agents more powerful, creating a richer, broader, and deeper virtual assisted-service experience. Integration lowers cost to serve, deflecting/avoiding high-cost interactions with live agents.”

The important integration targets for several types of customer service applications are shown in the Table below. Our evaluation frameworks are the source.

Customer Service Integration
Customer Service Application Type Integration Targets
Virtual agent
  • Account management
  • Case management
  • Contact center
  • Knowledge management
  • Live chat
  • Social networks
Social customer service
  • Account management
  • Case management
  • Contact center
  • Communities
  • Knowledge management
Contact center/Case management
  • Account management
  • Communities
  • Knowledge management
  • Live chat
  • Social networks/Social customer service
  • Virtual agents

Table 1. In this Table we present the key integration targets for several types of customer service applications.

In practice, we’ve seen broad and deep customer service integration within CRM suites and customer service suites such as Oracle Service Cloud and Salesforce Service Cloud. For example, Salesforce Service Cloud and Salesforce Sales Cloud are both implemented on the Salesforce1 platform. Platform resources include account data so account management is built in to Service Cloud. The Service Cloud Console gives agents access to cases. Salesforce Knowledge, the firm’s knowledge management offering, Salesforce Communities, the firm’s internal communities offering, and Live Agent, the firm’s live chat offering, are Service Cloud features. Salesforce Social Hub, a feature of the Radian6 component of Salesforce Marketing Cloud, which provides social listening and interaction capabilities, integrates social customer service. While many of these features are separately packaged and separately priced, all are very tightly integrated and that integration is “in the box.”

Individual customer service applications typically do not package integration with external customer service applications. We’ve heard from suppliers of these applications that integration can be accomplished by their professional services organizations, that it’s a “simple matter of programming,” and that they’ve written this code for many of their customers. That may be so, but professional service programming is not product. New releases on either side of the integration interface mean additional custom programming. Programming is never simple.

Alternatively, licensees of these products commonly do integration “at the desktop.” Customer service agents’ desktops have a window open for each of the applications they need to help answer their customers’ questions or solve their problems. Integration at the desktop is complicated. The integration burden is on agents.

This quarter, eGain, IntelliResponse, and Oracle announced new customer service integration. The eGain SAP Certified integration allows contact center agents to search and access the eGain Knowledge Base from the SAP CRM agent console using eGain’s FAQs, natural language and keyword search queries, topic trees, and guided help search methods. The IntelliResponse Virtual Agent (VA) for Salesforce integrates IntelliResponse VA with Salesforce Service Cloud, adding virtual assisted-service to the Service Cloud Console, the Customer Portal, and Service Cloud Communities. In the Oracle Service Cloud February 2014 release, the dynamic forms API for the Customer Portal enables developers to configure a page that asks the customer for additional information, dynamically, before submitting the incident.

We hope that more customer service suppliers will recognize the value in customer service integration. Customer service integration makes their offerings more attractive. It helps their customers create and deliver a better customer service experience, reducing cost to serve and increasing customer satisfaction. It makes it easier and faster for their customers’ customers to get answers and solutions. That’s’ a win, win, win, a no-brainer for sure.

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.

Back to the Future in Customer Service

This week’s report is a product evaluation of V-Person, the virtual agent offering of Creative Virtual, a privately held supplier that was founded in 2003 and is based in London, UK with offices in the US, India, the Netherlands, and Australia. The report updates our September 19, 2012 evaluation. Since that time, Creative Virtual has made significant, attractive, and very useful improvements to V-Person. Personalization is the most significant, most attractive, and most useful set of capabilities.

Personalized Customer Service

Creative Virtual takes the familiar rules-based approach to personalization, matching customer profile attributes with attributes of knowledgebase answers. Personalization V-Person is the first virtual agent offering to implement personalization. This is a big step forward for virtual agent technology and a big step for customer service.

As we stated in our report, virtual agents that can deliver personalized customer service are especially attractive for account management scenarios in financial services, government, healthcare, telecommunications, and travel. JP Morgan Chase Bank is the first user of V-Person’s personalization. Here’s a screen shot. Note that Richard Simons is CEO Creative Virtual USA.

jpmc&co copy

Rules-Based Personalization

We first saw this rules-based personalization approach in ecommerce systems back in the late 1990s. Rules executed at runtime matched customer/account profile attributes with product attributes to select and deliver content. Ecommerce systems stored and managed all of this account and product data. The stored and managed web content, too. The challenge for analysts and administrators was to minimize the number of rules processed. Too many times, in their zeal to deliver exactly the right content, they would ask the ecommerce systems to execute dozens of rules for every customer interaction. The time needed to process the rules might slow response time so much (Remember that this is 1990s era processing speed and power.) that customers frequently abandoned leave the site. Analysts learned this lesson and reduced the number of rules. Personalization was still pretty good.

Customer service applications have a different challenge to perform rules-based personalization. While their knowledgebase answers or cases/tickets have plenty of predefined attributes, they do not store and manage customer/account data. The customer/account profile attributes needed for personalization are typically stored and managed in CRM systems, billing systems, or account management systems—systems external to customer service applications. Application integration is required to collect the customer profile attributes that personalization rules use to select content.

Customer service applications, particularly virtual agent applications, have not been so good at application integration. Most are missing high-level integration tools and/or packaged integration facilities. (Note though that IntelliResponse, whose IntelliResponse Virtual Agent is among the leading virtual agent offerings, just announced packaged integration with Salesforce Service Cloud. Major progress there.) Some don’t even expose web services or lower level APIs. That makes customer service application integration, the application integration necessary for personalized customer service, low-level programming work, work for the consultants of customer service application suppliers. That’s not a very attractive, very repeatable, or very cost effective approach.

Collecting Customer/Account Profile Attributes in V-Person

Creative Virtual offers two approaches for collecting customer/account profile data. The first is that low-level programming integration. Python scripting is V-Person’s integration mechanism. The second is cookies. Cookies can store the customer/account profile attributes collected by Python scripts in the first approach or analysts can use V-Person’s facilities to collect these attributes through a (virtual agent) question and (customer) answer dialog and then pass the data to web developers to set the cookies.

When use of virtual agent applications requires logging in to a customer support site, and it should for any account management activity, then online collection of customer profile attributes is a decent approach. Using cookies to store them has some advantages but, in this time of high sensitivity to privacy and security, certainly some disadvantages, too.

Back to the Future for Application Integration, too

Through its personalization capabilities, Creative Virtual has broken new ground and raised the bar for customer service. Props to them. The firm’s customers will be able to deliver a better customer service experience to their customers. That’s a terrific first step.

The next step has to be the application integration improvements to make the implementation of personalized customer service faster, easier, and more extensible. Programming by consultants within professional services engagements with customer service suppliers won’t cut it for the long term. What’s needed is exemplified by IntelliResponse’s new packaged integration with Salesforce Service Cloud—packaged integration with the leading CRM and account management products and higher-level integration mechanisms for integration with custom CRM and account management apps. Like rules-based personalization, packaged integration is widely used and well proven. Application integration standards have been around for years. Customer service application providers should go back to the future again.

 

 

 

 

 

Voices of Customers

With this week’s report, the 4Q2013 Customer Service Update, we complete our tenth year of quarterly updates on the leading suppliers and products in customer service. These updates have focused on the factors that are important in the evaluation, comparison, and selection of customer service products.

  • Customer Growth
  • Financial Performance
  • Product Activity
  • Company Activity

Taking from the framework of our reports, for Company Activity, we cover company related announcements, press releases, and occurrences that are important to our analysis of quarterly performance. In 4Q2013, three of our suppliers, Creative Virtual, KANA, and Nuance, published the results of surveys that they had conducted or sponsored over the previous several months. All of the surveys were about customer service and the answers to survey questions demonstrated customers’ approach, behavior, preferences, and issues in their attempts to get service from the companies with which they’ve chosen to do business. The responses to these surveys are the Voices of the Customers for and about customer service. This is wonderful stuff.

Now, to be sure, suppliers conduct surveys for market research and marketing purposes. Suppliers’ objectives for surveys are using the Voice of the Customer to prove/ disprove, validate, demonstrate, or even promote their products, services, or programs. Certainly, all of the surveys our suppliers published achieved those objectives. For this post, though, let’s focus on the broader value of the surveys, the Voice of the Customer for Customer Service.

Surveys

The objectives in many of the survey represent the activities that customers perform, the steps that customers follow to get customer service from the companies with which they choose to do business. By getting customer service, we mean getting answers to their questions and (re)solutions to their problems. Ordering our examination and analysis of the surveys in customers’ typical sequence of these steps organizes them into a Customer Scenario. Remember that a Customer Scenario is the sequence of activities that customers follow to accomplish an objective that they want to or need to perform. For a customer service Customer Scenario, customers typically:

  • Access Customer Service. Customer login to their accounts or to the customer service section of their companies’ web sites, or call their companies’ contact center and get authenticated to speak with customer service agents
  • Find Answers and (Re)solutions. Use self-service, social-service, virtual-assisted service, and/or assisted-service facilities to try to help themselves, seek the help of their peers, seek the help of customer service agent for answers and (re)solutions.
  • Complain. If customers cannot get answers or (re)solutions using these facilities, they complain to their companies.

Here, in Table 1, below, are the surveys that examine how customers perform these activities and how companies support those activities. Note that these surveys are a subset of those surveys that were published by our suppliers. Not all of their surveys mapped directly to customer activities. Note that our analyses of survey results are based on the content of the press releases of the surveys. This content is a bit removed from the actual survey data.

Sponsor Survey Objective Activity Respondents
Nuance Privacy and security of telephone credentials Access Smartphone users
Nuance Telephone authentication issues and preferences Access US consumers
KANA Email response times for customer service Find answers and (re)solutions N/A
KANA Twitter response times for customer service Find answers and (re)solutions N/A
Nuance Resolving problems using web self-service Find answers and (re)solutions Web self-service users, 18–45 years old
Nuance Issues with Web self-service Find answers and (re)solutions Windstream Communications customers
KANA Usage of email vs. telephone for complaints Complain N/A
KANA Customer communication channels for complaints Complain UK consumers
KANA Customer complaints Complain US consumers, 18 years old and older

Table 1. We list and describe customer service surveys published by KANA and Nuance during 4Q2013 in this Table.

Let’s listen closely to the Voices of the Customers as they perform the activities of the customer service Customer Scenario. For each of the surveys in the Table, we’ll present the published survey results, analyze them, and suggest what businesses might do to help customers perform the activities faster, more effectively, and more efficiently.

Access

If questions and problems are related to their accounts, before customers can ask questions or present problems, they have to be authenticated on the customer service system that handles and manages questions and problems. Authentication requires usernames and passwords, login credentials. In these times of rampant identity theft, security of credentials has become critically important.

Nuance’s surveys on privacy and security of telephone credentials and on telephone authentication shed some light on customers’ issues with authentication.

  • 83 percent of respondents are concerned or very concerned about the misuse of their personal information.
  • 85 percent of respondents are dissatisfied with current telephone authentication methods.
  • 49 percent of respondents stated that current telephone authentication processes are too time consuming.
  • 67 percent of respondent have more than eleven usernames and passwords
  • 80 percent respondents use the same login credentials across all of their accounts
  • 67 percent of respondents reset their login credentials between one and five times per month.

Yikes! Consumers spend so much time and effort managing and, then, using their credentials. We’ve all experienced the latest account registration pages that grade our new or reset passwords from “weak” to “strong” and reject our weakest passwords. While strong passwords improve the security of our personal data, they’re hard to remember and they increase the time we spend in their management.

In voice biometrics, Nuance offers the technology to address many of these issues. On voice devices, after a bit of training, customers simply say, “My voice is my password,” to authenticate account access based on voiceprints and  voiceprints are unique to an individual.

Find Answers and (Re)solutions

KANA’s surveys on email response times for customer service and Twitter response times for customer service examine response times for “inquiries.” When customers make inquiries, they’re looking for answers or (re)solutions. In the surveys, KANA found:

  • According to Call Centre Association members, response times to email inquiries was greater than eight hours for 59 percent of respondents and greater than 24 hours for 27 percent of respondents.
  • According to a survey by Simply Measured, a social analytics company, the average response times to Twitter inquiries were 5.1 hours and were less than one hour for 10 percent of respondents.

While it’s dangerous to make cross-survey analyses, it seems reasonable to conclude that customer service is better on Twitter than on email. That’s not surprising. Companies have become very sensitive to the public shaming by dissatisfied customers on Twitter. They’ll allocate extra resources to monitoring social channels to prevent the shame. Customers win.

However, remember that these are independent surveys. The companies that deliver excellent customer service on Twitter might also deliver excellent customer service on email and the companies that deliver not so excellent customer service on email might also deliver not so excellent customer service on Twitter. The surveys were not designed to gather this data. That’s the danger of cross-survey analysis.

If your customers make inquiries on both email and social channels, then you should deliver excellent customer service on both. Email management systems and social listening, analysis, and interaction systems, both widely used and well proven customer service applications, can help. These are systems that should be in every business’s customer service application portfolio.

Email management systems help business manage inquiries that customer make via email. These systems have been around for way more than ten years, helping businesses respond to customers’ email inquiries. Businesses configure them to respond to common and simple questions and problems automatically and to assign stickier questions and problems to customer service staff. Business policies are the critical factor to determine response times to customers’ email inquiries.

Social listening, analysis, and interaction systems have been around for about five years. They help businesses filter the noise of the social web to identify Tweets and posts that contain questions and problems and the customers who Tweet and post them. These systems then include facilities to interact with Tweeters and posters or to send the Tweets and posts to contact center apps for that interaction.

Find Answers and (Re)solutions Using Web Self-Service

Nuance’s surveys about web self-service really show the struggles of customers trying to help themselves to answers and (re)solutions.

In the survey about consumers’ experiences with web self-service, the key findings were:

  • 58 percent of consumers do not resolve their issues
  • 71 percent of consumers who do not resolve their issues spend more than 30 minutes trying
  • 63 percent of consumers who do resolve issues, spend more than 10 minutes trying

In Nuance’s survey of Windstream Communications’ customers about issues with web self-service, the key finding were:

  • 50 percent of customers who did not resolve their issues, escalated to a live agent
  • 71 percent of customers prefer a virtual assistant over static web self-service facilities

The most surprising and telling finding of these surveys was the time and effort that customers expend trying to find answers and (re)solutions using web self-service facilities. 30 minutes not to find an answer or a solution seems like a very long time. Customers really want to help themselves.

By the way, Windstream’s customers’ preference for a virtual assistant is not a surprise. Windstream Communications, a Little Rock, AK networking, cloud-computing, and managed services provider, has deployed Nina Web, Nuance’s virtual agent offering for the web. Wendy, Windstream’s virtual agent, uses Nina Web’s technology to help answer customers’ questions and solve their problems. The finding is a proof point for the value of virtual agents in delivering customer service. Companies in financial services, healthcare, and travel as well as in telecommunications have improved their customer services experiences with virtual agents. We cover the leading virtual agent suppliers—Creative Virtual, IntelliResponse, Next IT, and Nuance—in depth. Check out our Product Evaluation Reports to find the virtual agent technology best for your business.

Complain

Customers complain when they can’t get answers to their questions and (re)solutions to their problems. KANA’s surveys about complaints teach so much about customer’s behavior, preferences, and experiences.

  • In KANA’s survey on usage of email or telephone channels for complaints, 42 percent of survey respondents most frequently use email for complaints and 36 percent use the telephone for complaints.
  • In KANA’s survey of UK consumers on communications channels for complaints, 25 percent of UK adults used multiple channels to make complaints. Fifteen percent of their complaints were made face-to-face.

The surprising finding in these surveys is the high percentage of UK consumers willing to take the time and make the effort to make complaints face-to-face. These customers had to have had very significant issues and these customers were very serious about getting those issues resolved.

The key results in KANA’s survey about customer complaints by US consumers were:

  • On average, US consumers spend 384 minutes (6.4 hours) per year lodging complaints
  • In the most recent three years, 71 percent of US consumers have made a complaint. On average, they make complaints six times per year and spend one hour and four minutes resolving each complaint.
  • Thirty nine percent of US consumers use the telephone channel to register their complaints. Thirty three percent use email. Seven percent use social media.
  • Millenials complained most frequently—80 percent of 25 to 34 year old respondents. Millenials are also most likely to complain on multiple channels—39 percent of them.
  • Survey respondents had to restate their complaints (Retell their stories) 69 percent of the time as the responsibility to handle their complaints was reassigned. On average, consumers retold their stories three times before their issues were resolved and 27 percent of consumers used multiple channels for the retelling.

The surprising findings in this survey are the time, volume, and frequency of complaints. Six and a half hours a year complaining? Six complaints every year? Yikes!

No surprise about the low usage of social channels to register complaints. Customers want to bring our complaints directly to their sources. They may vent on the social web, but they bring their complaints directly to their sources, the companies that can resolve them.

Lastly and most significantly, it’s just so depressing to learn that businesses are still making customers retell their stories as their complaints cross channels and/or get reassigned or escalated. We’ve been hearing this issue from customers for more than 20 years. Customers hate it.

Come on businesses. All the apps in your customer service portfolios package the facilities you need to eliminate this issue—transcripts of customers’ activities in self-service apps on the web and on mobile devices, threads of social posts, transcripts of customers’ conversations with virtual agents, and, most significantly, case notes. Use these facilities. You’ll shorten the time to solve problems and resolve customers’ complaints. Your customers will spend less time trying to get answers and (re)solutions (and more time using your products and services or buying new ones).

4Q2013 Was a Good Quarter for Customer Service

By the way, Customer Service had a good quarter in 4Q2013. Customer growth was up. Financial performance was up as a result. Product activity was very heavy. Nine of our ten suppliers made product announcements. Company activity was light. Five suppliers did not make any company announcements. Most significantly, KANA was acquired by Verint. And of course, three suppliers published customer service surveys.

Nuance Nina Web

Flexible and Accurate Answers to Customers’ Questions

We published our Product Evaluation Report on Nina Web from Nuance Communications this week. Nina Web is virtual assisted-service software for web browsers on desktops, laptops, and mobile devices. Type a question in a text box and a Nina Web-based virtual agent will deliver an answer or will engage you in a dialog when it needs more information to answer your question. Answers are text, images, links, URLs, and/or data from external applications.

Nina Web was originally developed as VirtuOz Intelligent Virtual Agent by VirtuOz, Inc., a privately held firm founded in France in 2002. Nuance acquired VirtuOz in March 2013. Nina Web became the third member of the Nina family of customer self-service offerings from Nuance’s Enterprise division, joining Nina IVR and Nina Mobile.

Nuance has made and continues to make significant improvements to the VirtuOz IVA. A bit less than a year after the acquisition, Nina Web is stronger and more attractive virtual agent offering, earning good grades on our Report Card for Virtual Assisted-Service. (See the Product Evaluation report for the details.)

Most significantly, Nuance’s Enterprise division developers have just about completed what they call a “brain transplant” for Nina Web, replacing the question analysis and matching technology built by VirtuOz with Nuance’s Natural Language Understanding (NLU) technology, the same technology used by Nina IVR and Nina Mobile. NLU combines Natural Language Processing (NLP) with statistical machine learning. NLP does some parsing and linguistic analysis of customers’ questions. Statistical machine learning, which Nuance implements in neural networks, matches customers’ questions with typical and expected “User Questions” and variations of User Questions that analysts create and store in Nina Web’s knowledgebase. Analysts also create knowledgebase answers and associate an answer with each User Question. When NLU matches a customer’s question with a User Question, Nina Web presents the answer associated with the User Question to the customer.

Analysts “train” NLU’s machine learning algorithms with User Questions and their variations. Nina Web provides the facilities and tools for initial training and ongoing refinement/retraining. Analysts add, delete, and modify User Questions as the intent and the vocabulary of customers’ questions changes to ensure that their Nina Web virtual agent delivers accurate answers. They must refine answers, too.

As you might infer by our description, NLU is a black box. Train it with a set of User Questions and it will match customer’s questions with them. The critical tasks for a Nina Web deployment are the initial specification and continuing refinement of User Questions and of answers. Nina Web insulates deployment work from NLU, from the complexity of NLP and statistical machine learning. Analysts do not specify language models or matching rules. They do not (and cannot) configure and/or customize neural network processing. Knowledge management is the focus deployment efforts. That can make for easier and faster deployment, a strength and differentiator for Nina Web.

One more thing. We mentioned that NLU is the analysis and matching technology in Nina IVR and Nina Mobile as well as in Nina Web. One set of User Questions can match customer questions with one set of answers across telephone, web, and mobile channels. Together, Nina IVR, Nina Mobile, and Nina Web can deliver a consistent cross-channel customer self-service experience, but, today, that consistency requires creating and managing three copies of the set of User Questions and three copies of the set of answers because the products are not integrated. Each Nina deploys independently of the others. But, cross-Nina integration is on Nuance’s product roadmap. An integrated, cross-channel Nina will be quite a customer service offering.