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.

 

 

 

 

 

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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.

Next IT Alme

This week’s report is a product evaluation of Next IT’s virtual agent offering Alme (All me). The report updates our November 28, 2012 product evaluation. Just a reminder, Alme is the software behind about 20 deployments, all for B2C organizations. You’ve might have had some of your travel questions answered by Jenn of Alaska Airlines or Alex of United Airlines. Next IT is one of the pioneers in virtual agent technology. The firm was founded in 2002 in Spokane, WA and introduced its first product in 2004.

Remember that Alme uses Natural Language Processing (NLP) to analyze customers’ question and to match them with answers in its knowledgebase and in external applications. Key components are an NLP engine and a language model. The language model specifies language constructs that adapt the Alme to the lexicon of the deployment’s domain. Analysis of customers’ questions by the engine, using the language model allows Alme’s virtual agents’ answers to be dynamic and personalize-able through the access and analysis of data from external applications.

So what’s new in Alme? Lots. In the year or so since our last evaluation, Next IT has been quite busy. Its developers have made Alme a more attractive, more powerful offering that’s easier to deploy and to manage through significant improvements to its language model and its tools.

  • Language model improvements help virtual agents deliver more accurate and more personalized answers and solutions to customers’ questions and problems. For example, Alme can use information within customers’ questions to establish a context for their “conversations” with virtual agent. This context makes conversations more natural and helps virtual agents deliver answers and solutions more quickly. Also, Alme now has a new conversational model that helps virtual agents perform complex tasks for customers. And, another new language model feature helps virtual agents handle ambiguous questions and questions that contain idiomatic phrases.
  • New and improved tools make virtual agents faster and easier to deploy and manage and make Next IT’s clients more self-sufficient. In our previous evaluation, we had identified limitations in change management and team support. Next IT has addressed those limitations quite nicely in the tools of the current version. Also, the new Response Management toolset decouples the complex work of language model design, specification, and maintenance from simpler content/knowledge management work. As a result, organizations that license Alme can do more of the work to deploy and manage Alme virtual agents and become less dependent of Next IT professional services.

Alme’s key strength and most significant differentiator has been its capability to deliver very sophisticated answers to complex questions. Language model improvements make Alme stronger. For example, healthcare companies might use the new conversation model to collect the information required to complete an insurance application, a referral to a specialist, or a follow-up reminder to a prescription. On the topic of healthcare, Next IT has begun a major and very timely initiative in that market segment. On October 10, 2013, the firm announced Alme for Healthcare. Alme for Healthcare uses all of the new language model capabilities, especially the new conversational model for both of its applications—a clinical application that helps inform, coach, and engage patients and an administrative application that helps patients and administrative/support staff with forms, processes, and information retrieval. Look for announcements about the companies using Alme for Healthcare soon.

Improved tools make Alme more attractive and more competitive. Time and cost to deployment have been issues for all customer service applications. Deploying virtual agent products has been particularly expensive because language models are complex, domain-specific, deployment-specific, and proprietary. Companies that license virtual agent software depend on their suppliers to design, specify, implement, test, and manage language models and knowledgebases. Time to deployment can be pretty long, approaching a year in some cases. Next IT has provided all the services for initial virtual agent deployment and ongoing management. Some of its customers use those services. However, new tools and tools improvements give customers the opportunity to do much of this work themselves and give Next IT’s professional services consultants the facilities that speed and simplify the tasks that they perform for customers. The results: shortened time and reduced cost to deployment, faster ROI, and faster and easier ongoing management.

Virtual agents have become far more than avatars and FAQs in a box on your support page. Alme demonstrates and proves that virtual agents can do serious customer service work and Next IT continues to make Alme more attractive. A virtual agent should be an integral component of every customer service application portfolio.

IntelliResponse VA

Accurate Answers with Fast and Easy Deployment

We’ve just published our Product Review of IntelliResponse Virtual Agent (IntelliResponse VA), the virtual assisted-service offering from IntelliResponse Systems, Inc., a privately held supplier founded in 2000 and based in Toronto, ON Canada. The report completes our latest research series on virtual agents/virtual assisted-service.

We’ve published evaluations of the four leading virtual agent offerings:

  • Creative Virtual V-Person
  • IntelliResponse VA
  • Next IT Active Agent
  • Nuance Nina Web (VirtuOz Intelligent Virtual Agent when we published. Nuance acquired VirtuOz earlier this year.)

Virtual agents implemented on all four can deliver a single answer to a customer’s question on web, mobile, and social channels. Expect the answer to be correct about 90 percent of the time.

Virtual agents deliver bottom line benefits. They can lower cost to serve as compared to live agents and they can improve customer sat by improving the speed, accuracy, and consistency of the answers to customers’ questions.

 Contrast virtual agents with search and knowledgebase approaches that deliver many answers and leave it to the customer to pick the correct one. This single correct answer makes virtual agents useful for answering many kinds of customers’ questions, certainly customer service questions but also questions about your business and about your business policies, processes, and practices, about your products, and everything about your customers’ relationships with you—accounts, orders, bills, and passwords, for example. They can be your agents for marketing, for sales, and for service.

 Like your live agents, it takes time and effort to get virtual agents ready to engage with your customers. You have to give them the knowledge about the business areas that they support. You have to train them to understand your customers’ questions and to correlate or match those questions with the correct answers. The knowledge is contained in/represented by the items in their knowledgebases, their store of predefined answers. Anticipate the questions that your customers will ask, specify the answers, and store them in the virtual agent’s knowledgebase. All four virtual agent products have knowledgebases and provide tools and facilities for creating and managing answers. Your customers’ questions will change and evolve with their relationships and with changes to your offerings of products and services and to your business. Virtual agent’s knowledge has to keep up with those changes (just like live agents’ knowledge).

Training virtual agents to understand your customers’ questions and to correlate/match them to correct answers is the harder part. Virtual agents use very sophisticated and complex technology to analyze customers’ questions and to match them with the answers in their knowledgebases. Analysis and matching is the core processing that virtual agents perform. Analysis and matching technology is the virtual agent supplier’s core IP, its secret sauce. Each of the four has patented some or all of this technology. The suppliers want you to appreciate the sophistication and power of the technology. They don’t give you much detail of what it does or how it works.

(We describe and evaluate how a virtual agent analyzes and matches questions in our product review. We actually read many of the suppliers’ patents to help us understand the technology. In our reports, we describe it a bit, but we focus on what you’ll have to do to use it effectively.)

Creative Virtual V-Person, Next IT Active Agent, and Nuance Nina Web use Natural Language Processing (NLP) technology for their analysis and matching. Each has its own NLP implementation. NLPs perform computational linguistic analyses on customers’ questions, parsing for subjects, verbs, object, and qualifiers, extracting entities, identifying actors and roles, and codifying relationships. This is sophisticated and complex processing.

For a successful virtual agent deployment, you provide critical input to your virtual agent supplier’s NLPs, for example:

  • Words that your customers will likely include in their questions
  • Misspellings, typos, slang, idioms, ad stems for those words
  • Conditions/rules/expressions for how your customers combine words into phrases
  • Parameters for configuring the NLP processing

If you don’t specify the actual words and their various alternative forms that your customers use in their questions, then your virtual agent cannot deliver answers. Complete specification of your customers’ vocabularies is critical. Virtual agent products help considerably with packaged dictionaries of common industry and application terms, but it’s on you to provide the vocabulary specific to your business and your products.

Virtual agent suppliers also provide consulting services to help the NLP specification. These services are essential for a successful virtual agent deployment. These services are also essential for ongoing management of your virtual agent. Remember that customers’ questions are always changing. So is your business.

IntelliResponse VA uses machine learning technology for its analysis and matching. Machine learning is an algorithmic approach. The algorithm learns by training it with sample data in a controlled environment. It applies its learning when it goes live. The sample data that you provide to train an IntelliResponse VA virtual agent are the typical questions that you want it to answer, not the words and phrases in those questions, not various forms of those words, not the relationships between them, just the questions. IntelliResponse VA can do the rest of the work, even to accommodate the ongoing changes in customers’ questions and in your business. IntelliResponse VA virtual agent deployment is easier and faster than NLP-based deployments and delivers answers with the same level of accuracy. Read our report for the details and note that IntelliResponse can also provide those consulting services to help you deploy and manage virtual agents. The details of the work will be a bit different and there will be less work to do, but the objective will be the same—deploying virtual agents that answer customers’ questions.