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.

© 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.

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.

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.

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.

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.