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.

 

 

 

 

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Evaluating Customer Service Products

Framework-based, In-depth Product Evaluation Reports

We recently published our Product Evaluation Report on Desk.com, Salesforce’s customer service offering for small and mid-sized businesses. “Desk” is a very attractive offering with broad and deep capabilities. It earns good grades on our Customer Service Report Card, including Exceeds Requirements grades in Knowledge Management, Customer Service Integration, and Company Viability.

We’re confident that this report provides input and guidance to analysts in their efforts to evaluate, compare, and select those customer service products, and we know that it provides product assessment and product planning input for its product managers. Technology analysts and product managers are the primary audiences for our reports. We research and write to help exactly these roles. Like all of our Product Evaluation Reports about customer service products that include multiple apps—case management, knowledge management, web self-service, communities, and social customer service—it’s a big report, more than 60 pages.

Big is good. It’s their depth and detail that makes them so. Our research for them always includes studying a product’s licensed admin, user, and, when accessible, developer documentation, the manuals or online help files that come with a product. We read the patents or patent applications that are a product’s technology foundation. Whenever offered, we deploy and use the products. (We took the free 30-day trial of Desk.) We’ll watch suppliers’ demonstrations, but we rely on the actual product and its underlying technologies.

On the other hand, we’ve recently been hearing from some, especially product marketers when they’re charged to review report drafts (We never publish without the supplier’s review.), that the reports are too big. Okay. Point taken. Perhaps, tt is time to update our Product Evaluation Framework, the report outline, to produce shorter, more actionable reports, reports with no less depth and detail but reports with less descriptive content and more salient analytic content. It’s also time to tighten up our content.

Product Evaluation Reports Have Two Main Parts

Our Product Evaluation Reports have had two main parts: Customer Service Best Fit and Customer Service Technologies. Customer Service Best fit “presents information and analysis that classifies and describes customer service software products…speed(ing) evaluation and selection by presenting easy to evaluate characteristics that can quickly qualify an offering.” Customer Service Technologies examine the implementations of a product’s customer service applications and their foundation technologies as well as its integration and reporting and analysis capabilities. Here’s the reports’ depth and detail (and most of the content). Going forward, we’ll continue with this organization.

Streamlining Customer Service Best Fit

We will revamp and streamline Customer Best Fit, improving naming and emphasizing checklists. The section will now have this organization:

  • Applications, Channels, Devices, Languages
  • Packaging and Licensing
  • Supplier and Product
  • Best Prospects and Sample Customers
  • Competitors

Applications, Channels, Devices, Languages are lists of key product characteristics, characteristics that quickly qualify a product for deeper consideration. More specifically, applications are the sets of customer service capabilities “in the box” with the product—case management, knowledge management, and social customer service, for example. Channels are assisted-service, self-service, and social. We list apps within supported channels to show how what’s in the box may be deployed. Devices are the browsers and mobile devices the product supports for internal users and for end customers. Languages are two lists: one for the languages in which the product deploys and supports for its administration and internal users and one for the languages it supports for end customers.

Packaging and Licensing presents how the supplier offers the product, the fees that it charges for the offerings, and the consulting services available and/or necessary to help licensees deploy the offerings.

 Supplier and Product present high level assessments of the supplier’s and the product’s viability. For the supplier, we present history, ownership, staffing, financial performance, and customer growth. For the product, we present history, current development approach, release cycle, and future plans.

Best Prospects and Sample Customers are lists of the target markets for the product—the industries, business sizes, and geographies wherein the product best fits. This section also contains the current customer base for the product, a list of typical/sample customers within those target markets and, if possible, presents screen shots of their deployments.

 Competition lists the product’s closest competitors, its best alternatives. We’ll also include a bit of analysis explaining what make them the best alternatives and where the subject product has differentiators.

Tightening-up Customer Service Technologies

Customer Service Technologies is our key value-add and most significant differentiator of our Product Evaluation Reports. It’s why you should read our reports, but, as we mentioned, it’s also the main reason why they’re big.

We’ve spent years developing and refining the criteria of our Evaluation Framework. They criteria are the results of continuing work with customer service products and technologies and our complementary work the people who are product’s prospects, licensees, suppliers, and competitors. We’re confident that we evaluate the technologies of customer service products by the most important, relevant, and actionable criteria. Our approach creates common, supplier-independent and product-independent analyses. These analyses enable the evaluation and comparison of similar customer service products and results in faster and lower risk selection of a product that best fits a set of requirements.

However, we have noticed that the descriptive content that are the bases for our analyses has gotten a bit lengthy and repetitive (repeating information in Customer Best Fit). We plan to tighten up Customer Service Technologies content and analysis in these ways:

  • Tables
  • Focused Evaluation Criteria
  • Consistent Analysis
  • Reporting

Too much narrative and analysis has crept into Tables. We’ll make sure that Tables are bulleted lists with little narrative and no analysis.

Evaluation criteria have become too broad. We’ve been including detailed descriptions and analyses of related and supported resources along with resources that’s the focus of the evaluation. For example, when we describe and analyze the details of a case model, we’ll not also describe and analyze the details of user and customer models. Rather we’ll just describe the relationship between the resources.

Our analyses will have three sections. The first will summarize what’s best about a product. The second will present additional description and analysis where Table content needs further examination. The third will be “Room for Improvement,” areas where the product is limited. This approach will make the reports more actionable and more readable as well as shorter.

In reporting, we’ll stop examining instrumentation, the collection and logging of the data that serves as report input. The presence (or absence) of reports about the usage and performance of customer service resources is really what matters. So, we’ll call the criterion “Reporting” and we’ll list the predefined reports packaged with a product in a Table. We’ll discuss missing reports and issues in instrumentation in our analysis.

Going Forward

Our Product Evaluation Report about Microsoft Dynamics CRM Online Service will be the first to be written on the streamlined Framework. Expect it in the next several weeks. Its Customer Service Best Fit section really is smaller. Each of its Customer Service Technologies sections is smaller, too, more readable and more actionable as well.

Here’s the graphic of our Product Evaluation Framework, reflecting the changes that we’ve described in this post.

Slide1

Please let us know if these changes make sense to you and please let us know if the new versions of the Product Evaluation Reports that leverage them really are more readable and more actionable.

Nuance Nina Virtual Assistants

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

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

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

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

Nina Evaluation Process

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

For our evaluation, we used:

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

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

Product Documentation

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

Nina Deployments

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

up2drive

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

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

No Trial/Test Drive

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

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

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

Thank You Very Much, Nuance

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

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

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.

Salesforce Service Cloud

Evaluation of Service Cloud Winter ’15

This week’s report is our evaluation of Salesforce Service Cloud and its collection of tightly integrated but variously packaged and priced features and add-on products—Service Cloud, itself, for case management and contact center support, Salesforce Knowledge for knowledge management, Live Agent for chat, Social Studio for social customer service, and Salesforce Communities for communities and for customer self-service. Winter ’15 is the current release of the offering and the release that we evaluated in this report.

The offering earns an excellent evaluation against the criteria of our Framework for Customer Service Applications. We found no areas where significant improvement is required.

We had last published an evaluation of Service Cloud Winter ’13 on January 24, 2013. Winter ’15 is the sixth of the regular cycle of Winter, Spring, and Summer releases since that date. Every new release has included significant new and/or improved capabilities.

Salesforce Communities – a New Platform for Customer Self-Service

Salesforce Communities is one of the new capabilities in Winter ’15. It packages an attractive set of facilities, facilities that let customers perform a wide range of collaboration and self-service activities and tasks. However, none of these facilities use new technology; all of them have been existing features of Salesforce applications. What’s new and what’s innovative is their use as the platform for customer self-service. With Communities, Salesforce.com has extended the customer service provider-centric, web content-intensive self-service of portals with social and collaborative self-service that lets customers (and customer service agents) answer and solve customers’ questions and problems. Here’s what we mean.

Customers can use Communities’ packaged, portal-style facilities to perform these self-service tasks:

  • Search a Salesforce Knowledge knowledgebase to find existing answers and solutions for similar questions and problems
  • Browse a hierarchy of “Topics” to find existing answers and solutions to their problems in the knowledgebase or within community content.
  • Create new Service Cloud Cases when they can’t find answers and/or solutions via searching or browsing a knowledgebase, or by browsing Topics and community content.
  • Note that during the case creation process, Communities uses Automatic Knowledge Filtering, a Salesforce Knowledge feature, that automatically suggests knowledgebase Articles relevant to the content of the fields of the new Case.
  • Contact support for escalation to assisted-service

Customers can also use Communities’ packaged social and collaborative facilities to perform self-service tasks.

  • Post their questions or problems on a threaded, post-and-reply forum to solicit answers and solutions from other customers or from customer service staff members who monitor community activity. Note that Communities’ threads are implemented with Salesforce Chatter Feeds. Feeds are Twitter-like stacks of posts and replies/comments.
  • Search post-and-reply Feeds to find existing answers and solutions or previously posted questions and problems and replies/comments about them.

You may have read these lists of bullet points and said, “So, what. There’s nothing new here. We already have these facilities on our portal and on our community.” Exactly right, but that separate portal and community approach forces customers to go to two places to find answers and solutions, and, based on the experience that you’ve given them, they go to one place or the other depending on the type of question or problem they have or the quality and usefulness of answers and solutions that they’ve found. Salesforce Communities gives customers one place to go for self-service answers and solutions. One place not two makes it easier and faster for them to do business with you and makes it easier and more efficient for you to do business with them.

community.seagate.com

For example, Seagate Technology LLC, the provider of hard disk drives and storage solutions based in Cupertino, CA, has a Salesforce Communities-based self-service site. Its home page is shown in the screen shot below.

seagate blog1

As a Mac user needing some advice on drives for backups, I clicked on the Mac Storage Topic and was taken to the Mac Storage products page shown below in the next screen shot. This page presents a list of combined questions, (Salesforce Knowledge) Articles, Solved Question, Unsolved Questions, and Unanswered Questions in the center with a drop-down at top of the list to filter the presentation. Links to product-specific pages are at the left.

seagate blog 2

At the bottom of the Mac Storage Product Page are links to additional customer service facilities, including, “Get Help from Support.” We show them in the screen shot below.

seagate blog 3

The Seagate community offers a complete set of easy-to-use self-service facilities. Community-style self-service gives customers everything they need for customer service—finding answers and solutions or getting assisted-service when answers and solutions don’t exist or can’t be found.

Tools and Templates

By the way, Salesforce Communities includes tools and reusable templates that can make it easy and fast to deploy customer self-service communities. Community Designer is the toolset for building and managing the web pages of Communities deployments. Community Designer can also customize the three web page templates packaged with Communities—Koa, Kokula, and Napili. For example the web pages for the Koa self-service template contain facilities that let customers search for or navigate to Salesforce Knowledge Articles by categories called Topics or contact support if they can’t find answers or solutions.

Salesforce.com is changing and improving self-service with Salesforce Communities. What a good idea!