Virtual Assistant Update

 

We recently published “Virtual Assistant Update.” It’s a broad and not too deep update on virtual assistant technologies, products, suppliers, and markets from the perspective of the five leading suppliers: [24]7, Creative Virtual, IBM, Next IT, and Nuance. These are the leaders because they:

  • Have been in the virtual assistant business for some time (from 16 years for [24]7 via its acquisition of IntelliResponse to four years for IBM).
  • Have attractive and useful virtual assistant technology
  • Offer virtual assistant products that are widely used and well proven.
  • Want to be in the virtual assistant business and have company plans and product plans to continue.

The five suppliers are quite diverse. There’s the public $80 billion IBM and the public $2 billion Nuance. Then there are the private [24]7, a venture backed company big on acquisitions and the more closely held Creative Virtual and Next IT. Despite these big corporate-level differences, the five’s virtual assistant businesses are quite similar. Roughly they’re all about same size and the five compete as equals to acquire and retain virtual assistant business.

By the way, across the past 12 to 24 months, business has been good for all of the five suppliers. Customer growth has been very good across the board. Our suppliers have expanded into new markets and have introduced new and/or improved products.

Natural Language Processing and Machine Learning

Technologies are quite similar, too. All five have built their virtual assistant offerings with the same core technologies: Natural Language Processing (NLP) and machine learning.

Virtual Assistants use NLP to recognize intents of customer requests. NLP implementations usually comprise an engine that processes customer requests using an assortment of algorithms to parse and understand the words and phrases in a customer’s request. An NLP engine’s processing is guided by customizable and/or configurable deployment-specific mechanisms such as language models, grammars, and rules. These mechanisms accommodate the vocabularies of a deployment’s business, products, and customers.

Virtual assistants use machine learning technology to match actual customer requests with anticipated customer requests and then to select the content or execute the logic associated with the anticipated requests. (Machine learning algorithms learn from and then make predictions on data. Algorithms learn from training. Analysts/scientists train them with sample, example, or typical deployment-specific input then with feedback or supervision on correct and incorrect predictions. A trained algorithm is a deployment-specific machine learning model. The accuracy of models can improve with additional and continuing training. Some machine learning implementations are self-learning.)

Complex and Sophisticated Work: Consultant-led or Consultant-assisted

The work to adapt NLP and machine learning technology implementations for virtual assistant deployments is sophisticated and complex. This is work for experts: scientists, analysts, and developers in languages, data, and algorithms. The approach to this is work differentiates virtual assistant suppliers and products. The approach drives virtual assistant product selection. Here’s what we mean.

All the virtual assistant suppliers have built tools and package predefined resources to make the work simpler, faster, and more consistent. Some suppliers have built tools for the experts and these suppliers have also built consulting organizations with the expertise to use their tools. Successful deployments of their virtual assistant offerings are consultant-led. They require the services of the suppliers’ (or the suppliers’ partners’) consulting organizations.

Some suppliers have built tools that further abstract the work and make it possible for analysts, business users, and IT developers to deploy. While these suppliers have also built consulting organization with expertise in virtual assistant technologies and in their tools, successful deployments of their virtual assistant offerings are consultant-assisted and may even approach self-service.

So, a key factor in the selection of a virtual assistant product is deployment approach: consultant-led or consultant-assisted. Creative Virtual, Next IT, and Nuance offer consultant-led virtual assistant deployments. [24]7 and IBM offer consultant-assisted deployments. For example, IBM Watson Virtual Agent includes tools that make it easy to deploy virtual assistants. In the Figure below, we show the workspace wherein analysts specify the virtual assistant’s response to the customer request to make a payment. Note that the possible responses leverage content, tool, and facilities packaged with the product.

ibm watson va illos

© 2017 IBM Corporation

Illustration 7. This Illustration shows the Watson Virtual Agent workspace for specifying responses from the bot/virtual assistant.

 

Which is the better approach? Consultant-assisted is our preference, but we’ve learned over our long years of research and consulting that deployment approach is a function of corporate, style, personality, and culture. Some businesses and organizations give consultants the responsibility for initial and ongoing technology deployments. Some businesses want to do it themselves. For virtual assistant software, corporate style could very well be a key factor in product selection.

 

 

 

 

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.

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.

A Good Quarter for Customer Service in 3Q2013

This week, continuing our tenth year of quarterly updates on the suppliers and products in customer service, we published our 3Q2013 Customer Service Update Report. Just a reminder, these reports examine customer service suppliers and their products along the dimensions of customer growth, financial performance, product activity, and company activity. We currently cover ten leading customer service suppliers. They lead in overall market influence and share, in market segment influence and share, and/or in product technology and innovation.

3Q2013 was a good quarter for customer service. Customer growth was up and improved customer growth resulted in improved financial performance. Product activity was light. Six of our suppliers did not make any product announcements, but remember that third quarters are summer quarters. They’re usually never big for products. Company activity was also on the light side but what company action we saw was highlighted by expansion into new markets by four of our suppliers. That’s a key customer service trend and a solid indicator of customer service growth in the quarters ahead. Here’s a bit more detail:

  • On July 17, IntelliResponse and BolderView, a Melbourne, AU-based consultancy specializing in virtual agent solutions for large enterprises in utilities, banking, technology, higher education and government markets, jointly announced that BolderView had become a value-added reseller of IntelliResponse VA for Australia and New Zealand. Within the release, IntelliResponse also announced the opening of its own office in Sydney, AU.
  • On September 5, KANA and Wipro jointly announced a partnership that will apply Wipro’s consulting, systems integration, and insurance industry expertise and experience to accelerate deployments of KANA Enterprise for large global insurers and financial services providers. The companies will form a dedicated, joint deployment team to work on customer deployments.
  • On September 17, Clarabridge announced the expansion of its global operations into Latin America. A sales team will use Miami, FL offices and will leverage Clarabridge’s partnerships with Accenture, Deloitte, and Salesforce.com initially to focus on opportunities in Argentina, Brazil, Chile, Colombia, Mexico, and Peru.
  • On September 25, Moxie announced the expansion of its operations in Europe. The expansion includes opening an office in Reading, UK, forming partner ships with Spitze & Company in Denmark and IZO in Spain, and appointing Andrew Mennie General Manager for EMEA.

This expansion is a win for customer service suppliers, a win for their customers, and a win for their customers’ customers.

It’s already winning for customer service suppliers. For example, Moxie claims to have doubled its European customer base in the last six months. New customers include Allied Irish Bank and the British Army. IntelliResponse and BolderView recently launched “Olivia,” their first joint virtual agent deployment. Olivia is the virtual agent for Optus, Australia’s second largest telecommunications provider. And, Creative Virtual, a UK-based virtual agent software supplier that we’ve been covering in our quarterly reports for the past four quarters, recently announced Sabine, the Dutch-speaking virtual agent for NIBC Direct, the online retail unit of The Hague, NE-based bank. Sabine’s deployment is supported from Creative Virtual’s new Amsterdam office. See Sabine at the bottom right of NIBC Direct’s home page, below.

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Expansion demonstrates the strength and viability of customer service suppliers. Their products have reached the level of maturity and reliability that their deployment “far from home” carries little or no risk. They have the resources to open offices and hire the staff to promote, sell, and support their products in new markets. And they recognize the potential for new and additional business in those markets.

Our suppliers’ customers and their (end) customers in Australia and New Zealand, Latin America, and Europe benefit, too. Customer service applications like Clarabridge Analyze, a CEM (Customer Experience Management) app, Creative Virtual V-Person and IntelliResponse VA (Virtual Agent) virtual agents apps, and Moxie Social Knowledgebase, a social customer service app have been proven to lower cost to serve and to improve customer experiences. Companies in expanded markets that deploy these apps will have more satisfied, more profitable customers. These apps will help answer customers’ questions and solve customers’ problems more quickly and more easily.

We’ve been ready for this expansion. Language support has long been a criterion in our frameworks for evaluating customer service applications. We examine the languages that the apps support for internal users and the globalization/localization facilities to deploy the apps to end customers. Generally, we’ve found that most customer service apps can be localized to support locale-specific deployments. On the other hand, the tools and reporting capabilities for internal users tend to be implemented and supported only in English.