Deliver Cognitive Customer Service with IBM Watson


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Self-service channels are becoming increasingly popular as customers look for convenient communication and instant information. To meet this demand, more than half of global organizations are using or are planning to use automated customer interaction tools. It’s estimated that customers will manage 85% of their enterprise relationship without interacting with humans by 2020.

As self-service solutions and customer experiences incorporate cognitive capabilities, satisfaction and success rates increase significantly. IBM Watson harnesses the power of cognitive exploration, machine learning, and natural language processing to deliver exceptional solutions for customer service.

We joined Blueworx, a leading IVR provider, for an informative conversation on cognitive customer service. We covered:

-The benefits of self-service and cognitive solutions to your customer service organization

-Use cases for Watson, including virtual agents, customer service assistance, integrated voice solutions, and customer service interaction analysis

-High-level Watson introduction and overview

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  • We faced a lot of technical challenges but at the center of the problem is dealing with the many was you can express the same meaning in natural language.

    NL is often very sensitive to context and is often incomplete, tacit and ambiguous. Simplified approaches can easily lead you astray. These next two examples should help motivate our approach.

    Consider this question. <Read it>

    Now consider that based simply on keywords it would be straight-forward to pick up this potentially answer-bearing passage.

    <read green passage>

    This is a great hit from a keyword perspective in shares many common terms – May, Arrived, Anniversary, Portugal, India etc.

    and by using keyword evidence should give good confidence that Gary is the explorer in question.

    And whose to say Garry is not an Explorer. After all, we are all explorers in our own special way.

    In fact, the next sentence might read – and then Gary returned home to explore his attic looking for a lost photo album. Such a sentence would be legitimate evidence that Gary can be classified as an Explorer.

    Classifications are tricky, we humans are very flexible in how we classify things – we are willing to accept all sorts of variations in meaning to make language work. Of course in this case, the famous explorer Vasco De Gama is the correct answer but how would a computer know that for sure.

    A computer system must learn to dig deeper, to find, evaluate and weigh different kinds of evidence – ultimately finding the answer that is best supported by the content.

    Consider this…<next slide>

  • Here we see the same question on the right <read it again> To identify and gain confidence in better evidence, the system must parse the question, determining its grammatical structure and identify the main predicates like celebrated and arrived along with their main arguments (that is their subjects and objects, etc) for example -- who is doing the celebrating, and who is doing the arriving AND for each of these actions where and when are they happening. This would further require the system to attempt to distinguish places, dates and people from each other and from other words and phrases in the question.

    On the right side, we see a passage containing the RIGHT answer BUT with only one key word in common -- “MAY”.
    <read the green passage>

    Given just that one common and very popular term, the system must look at a huge amount of unrelated stuff to even get a chance to consider this passage and then must employ and weigh the right algorithms to match the question with an accurate confidence, for example in this case <click>
    Temporal reasoning algorithms can relate a 400th anniversary in 1898 to 1498,
    Statistical Paraphrasing algorithms can help the computer learn from reading lots of texts that landed in can imply arrived in and
    finally with Geospatial reasoning using geographical databases the system may learn that Kappad Beach is in India and if you arrive in Kappad Beach you have therefore arrived in India.
    And still, all of this will admit numerous errors since few of these computations will produce 100% certainty in mapping from words, to concepts to other words. Just as an example, what if the passage said “considered landing in” rather than “landed in” or what if it the question said “arrival in what he thought to be India?”.
    Question Answering Technology tries to understand what the user is really asking for and to deliver precise and correct responses. But Natural language is hard … the authors intended meaning can be expressed in so many different ways. To achieve high levels of precision and confidence you must consider much more information and analyze it more deeply.
    We needed a radically different approach that could rapidly admit and integrate many algorithms, considering lots of different bits of evidence from different perspectives, AND that could learn how to combine and weigh these different sorts of evidence ultimately determining how strongly or weakly they support or refute possible answers.

  • CSC Customer Service Representative 360 degree Dashboard Application utilizing Watson Explorer technology within CSC call centers. The primary objective of the Project for CSC is cost take-out.  Specifically, CSC aimed to achieve the following cost reductions: •        Reduce average handle time (“AHT”) •        Reduce call close-out time •        Reduce repeat calls   In this Project, IBM provided Services to build a solution that provided the following functionality: •        Unified view of structured and unstructured data (“CSR Dashboard”) •        Enhanced search capability against structured and unstructured data •        Ability to Initiate over 50 types of work orders with auto-populated form fields to reduce data entries •        Call notes and transaction history records providing insights to the customer service representative (CSR) to quickly resolve issues •        Automated the call notes summary and closure process to reduce the call close-out time As a result of the implementation, CSC achieved:   • Reduction in average call handling time which includes the time authenticating a caller as well as the time spent talking to the client to resolve their issue. Total Average Handle Time reduced by 43% (10 % from Caller validation and authentication,  14% reduction in call time, 19% reduction in time required to create call notes and close call) • Single Page Architecture allows access to relevant data to effectively handle the call first time and avoid repeat callbacks. • Centralized location to enter various kinds of transactions from the dashboard avoiding the CSR to login to multiple systems to complete the call. Prior to this, the CSR needed to log in to over 6 different systems. • Data quality improvement by automatically saving authenticated caller/role information and provide CSR ability to select various Policy details to include in Call notes. Reduces time spent typing and lets the CSR focus on the call flow. • Next major release that is currently being worked on, includes implementing Death Claim Transactions. Approximately 10% of the calls are Death Claims. This is the most critical, complex and time consuming transaction a CSR has to complete - after this is implemented the call durations will continue to decrease and the CSRs will work entirely within the dashboard for call servicing.   Additional Benefits: • Client – Improved user experience and customer satisfaction • Employees – Reduced training period for new CSR – Improved user experience – Reduced attrition • Operations – Formatted transaction requests lays the foundation for automation of back office transactions – Lays foundation for Conversational Self Service • Management – Greater consistency in call notes and call dispositions – Additional insight into Business Process Service operations

  • Watson Virtual Agent on IBM Marketplace -
  • Deliver Cognitive Customer Service with IBM Watson

    1. 1. Cognitive Customer Service with IBM Watson September 2017
    2. 2. 2 About Perficient Perficient is the leading digital transformation consulting firm serving Global 2000 and enterprise customers throughout North America. With unparalleled information technology, management consulting, and creative capabilities, Perficient and its Perficient Digital agency deliver vision, execution, and value with outstanding digital experience, business optimization, and industry solutions.
    3. 3. 3 Perficient Profile • Founded in 1997 • Public, NASDAQ: PRFT • 2016 revenue $487 million • Major market locations: Allentown, Atlanta, Ann Arbor, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Lafayette, Milwaukee, Minneapolis, New York City, Northern California, Oxford (UK), Southern California, St. Louis, Toronto • Global delivery centers in China and India • Nearly 3,000 colleagues • ~95% repeat business rate • IBM Watson Talent Partner • 2017 Beacon Award Winner for an Outstanding Watson Cognitive Solution • Vast portfolio of Watson-based accelerators, quick starts and assessment offerings
    4. 4. 4 Blueworx Profile • 100 years in combined experience in voice and mobile applications • Legacy of innovation since 1986 in IBM labs • BVR is rock solid and massively scalable • 100,000+ ports deployed • Top telco’s in the world have run on BVR for 10+ years • Cloud, on-premises or a combination of both • Obsessed with delivering amazing customer experiences • Locations in Tulsa, LA, NY and the UK
    5. 5. 5 Speaker Introduction CHRISTINE LIVINGSTON Director, IBM Watson Perficient DEAN UPTON Director, Product Management Blueworx
    6. 6. 6 Deliver Cognitive Customer Service with IBM Watson
    7. 7. 7 • Introduction • What is Watson? ⎼ Cognitive Computing ⎼ Structured vs. Unstructured Data ⎼ Customer Service Usage Patterns • Case Studies • Getting Started with Watson Agenda
    8. 8. 8 Changing Consumer Expectations • Highly demanding of seamless and frictionless experience • Less loyal to singular brand • Have omni-channel expectations • Social media gives individual voices great power
    9. 9. 9 Self-Service Channels are Key to Winning the Future of Customer Service
    10. 10. 10 Customer Experience Gap
    11. 11. To drive customer loyalty you must invest in customer experience.
    12. 12. What is Watson?
    13. 13. 13 What is Watson? A tablet you talk to? A giant server? A robot?
    14. 14. 14 Understand The ability to understand structured and unstructured data, text-based or sensory in context and meaning, at astonishing speed and volume. Reason The ability to form hypotheses, make considered arguments and prioritize recommendations to help humans make better decisions. Learn Ingest and accumulate data and insight from every interaction continuously. Trained, not programmed, by experts to enhance, scale and accelerate their expertise. Watson: A Cognitive Platform
    15. 15. 15 The volume, variety and veracity of data – 80% of it unstructured – is growing at a rate impossible to keep up with. Customers have a wider range of choices than ever before and are expecting innovative, relevant and personalized engagement. Why is Cognitive Important? Companies must engage customers on their terms in a consistent, natural, and intuitive way. Cognitive is the new competitive advantage for enterprises focused on enhancing the customer experience.
    16. 16. 16 Column Value Patient Joe Brown Date of Birth 02/13/1972 Date Admitted 02/05/2014 Structured Data High Degree of organization, such as a relational database “The patient came in complaining of chest pain, shortness of breath, and lingering headaches … smokes 2 packs a day … family history of heart disease…has been experiencing similar symptoms for the past 12 hours.” Unstructured Data Information that is difficult to organize using traditional mechanisms Structured vs. Unstructured Data
    17. 17. 17 explorer India In May 1898 India In May celebrated anniversary in Portugal In May, Gary arrived in India after he celebrated his anniversary in Portugal Portugal 400th anniversary celebrated Gary In May 1898, Portugal celebrated the 400th anniversary of this explorer’s arrival in India This evidence suggests “Gary” is the answer BUT the system must learn that keyword matching may be weak relative to other types of evidence arrived in arrival in Legend Keyword “Hit” Reference Text Answer Weak evidenceRed Text Answering complex natural language questions requires more than keyword evidence Analyzing Unstructured Content
    18. 18. 18 27th May 1498 Vasco da Gama landed in arrival in explorer India Para- phrases Geo- KB Date Match Stronger evidence can be much harder to find and score … … and the evidence is still not 100% certain  Search far and wide  Explore many hypotheses  Find judge evidence  Many inference algorithms On the 27th of May 1498, Vasco da Gama landed in Kappad Beach 400th anniversary Portugal May 1898 celebrated In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India. Kappad Beach Legend Temporal Reasoning Reference Text Answer Statistical Paraphrasing GeoSpatial Reasoning Leverage Multiple Algorithms The Watson Difference
    19. 19. 19 Customer Service and Engagement Agent Assist • Provide 360° views • Deliver consistent and accurate answers • Efficiently scale expertise to novice agent • Personalize the customer experience Virtual Agents • Provide self-service options • Guide customers through transactions • Engage customers through several mediums Integrated Voice Solutions • IVR Replacement/Enhancement • “Active Listening” Customer Service Interaction Analysis • Support Multiple Channels (social media, call center, email exchanges) • Understand customer tone and sentiment • Uncover hidden trends and relationships
    20. 20. Agent Assist
    21. 21. 21 • Improve self-service options through natural language interfaces, reducing the number of calls received • Provide 360° insight into customer, product, tickets, etc. • Personalize the client experience with deep insights into preferences and interaction history • Deliver consistent and accurate answers • Efficiently scale expertise to novice agents • Additional insights identified through analysis of all existing knowledge and problem history – Which problems / issue areas take long to solve? – Trends and deviations? Peaks? – Has the same or a similar problem already occurred? – Any issues known with this entity / product / …? – Who do I need to contact (Who solved it before?) – Related cases / workarounds Contact Center Agents Watson Explorer Applications and Data Sources Watson Developer Cloud Empower agents to better respond to requests and improve conversion rates Watson Agent Assist
    22. 22. 22 Active Listening Watson listens and transcribes the conversation between a customer and an agent Watson understands the intent of the customers questions and surfaces relevant information to the agent
    23. 23. Virtual Agents
    24. 24. 24 Schedules recurring payment plan to ensure that he’s always covered Consistent, effective relationship management is essential in industries where infrequent interactions have substantial impact on customer satisfaction. Currently customers have limited self-service options available to them for servicing their accounts but choose to navigate through phone-based systems answered by local agents or call centers Watson Offers customers an elevated, intuitive self service experience that allows them to easily achieve what they set out to do. The Cognitive Customer Experience
    25. 25. 25 From: To: SELF-SERVICE LEVEL 1 LIVE AGENTS LEVEL 2 LIVE AGENTSSELF-SERVICE LEVEL 1 LIVE AGENTS LEVEL 2 LIVE AGENTS FIRST CALL RESOLUTION - Self-service solutions unable to resolve calls - Customers want to be passed to Live Agents quickly FIRST CONTACT RESOLUTION - Watson offers better user experience - Able to resolve calls through integrated actions From First Call to First Contact Resolution
    26. 26. 26 Scripted vs. Cognitive Conversations • Driven by a pre-defined conversation flow • Expects key phrases or words • Functions best on structured data • Best for short and simple tasks • Relatively quick to implement Scripted Conversations • Driven by conversational intents rather than expected flow • Trained to understand natural language • Operates on both structured and unstructured data • Learns over time • Capable of a wide range of tasks • Training time varies by complexity Cognitive Conversations
    27. 27. 27 Virtual Agent Knowledge Base Expansion
    28. 28. Cognitive Contact Center
    29. 29. 29 Blueworx Delivers Watson’s Capabilities to Your Contact Center • Blend Watson’s fluid conversation with traditional directed dialog • Build a cognitive contact center at a pace that suits your business • Continuously improve the quality of every customer interaction • Transform calls into a more relevant and relational experience • …with the proven reliability of Blueworx Blueworx is the only IVR to certify IBM’s MRCP connector for Watson. Level 1 New speech engines Example Speech to text instead of grammars. Level 2 Fluid Conversation Example Watson Conversation guides caller to an existing Directed Dialog application. Level 3 Agent-assist Example Cognitive application attempts to provide resolution to a human agent. Level 4 Highly automated Example Cognitive application attempts to provide resolution directly to the caller. Agents are freed up for more challenging calls. Level 5 Fully autonomous Example Caller uses freeform speech to request information, continuously improved resolution derived from machine learning. Directed Dialog Directed Dialog Directed Dialog Directed Dialog Levels of contact center autonomy Cognitive Conversation Natural Speech Cognitive Information
    30. 30. 30 Blueworx Gives Watson its Own Voice Applications & Data Sources Watson Developer Cloud Contact Center Agents Watson Explorer
    31. 31. 31 Level 1 – New Speech Engines Benefits: • Better quality speech • Cloud based; no hardware or software maintenance • Pay-per-use pricing
    32. 32. 32 Level 2 – Fluid Conversation Benefits: • Fluid speech interaction • Omnichannel • Easy application development
    33. 33. 33 Watson + Blueworx Step-1 Call arrives to SIP gateway. (SIP or TDM initiated calls) Step-2 Call is routed to the IVR. Step-3 Access VXML Application layer transformation to interface with Watson Cognitive. Step-4 (Optional) Access client systems (Web Services, Database, Legacy Systems) Step-5+ Access Watson Services (i.e. WVA, Conversation, Natural Language Classifier, etc) and more. Establish and manage ongoing dialog with either Watson Virtual Agent, and / or Watson Conversation. Step-6 Interact with the MRCP server to access Watson Speech-To-Text & Text-To-Speech. Step-7 MRCP server manages session, and transformation between MRCP-v2 Protocol and Watson speech services <-> Speech-To-Text & Text-To-Speech. Step-8 User interfacing with WVA using Chat Bot Widget.
    34. 34. 34 Cognitive Contact Center • A center that unlocks the customer experience potential by leveraging data from external, internal, structured, unstructured, voice and visual sources…making them work together. • Provides an interaction that delivers on customer expectations based on the cognitive ability to understand, reason and learn from every interaction. • Communicates with fluid, natural language through speech or text.
    35. 35. Case Studies
    36. 36. 36 360° Customer Perspective  Unification of structured and unstructured data in a 360° dashboard  Consolidated data platform enhances search and eliminates multiple system logins  Automated call notes summary and closure process  Improved consistency and customer service transcript analysis 43%reduced AHT training period and attrition customer satisfaction Life Insurer
    37. 37. 37 A Watson Digital Concierge  Reshaped the user experience  Autonomously handles tier-1 requests (60% Upon Initial Release)  Supports software activation and maintenance tasks  300% increase in web traffic 90% 99% lower support costs shorter resolution times North American Software Company
    38. 38. 38 63%reduced AHT Interactive Agent for Healthcare Providers  Cognitive agent converses with providers to verify benefits  Seamlessly manages member information inquiries  Transformed a tedious IVR system  Drastic reduction in live agent requests  Call time reduced from 8 to 3 minutes live agent requests Healthcare Insurer
    39. 39. Getting Started
    40. 40. 40 Available Workshops Rapidly iterate through Watson’s application in your organization, define measurable goals for your cognitive analytics implementation, and begin your cognitive journey. Ideate on and discover the possibilities of cognitive analytics and industry applications for your organization. Rapidly prototype and illustrate the art of the possible. IBM Watson Workshop IBM Watson Innovation Lab 3-4 WeeksHalf-to-Full Day
    42. 42. 42 Questions?
    43. 43. Appendix
    44. 44. 44 Channel proliferation has consumers expecting instantaneous personalized, high-quality interactions regardless of the contact channel the consumer chooses. Watson Virtual Agent offers customers a cognitive, conversational self-service engine that can provide answers and take action through a variety of channels at scale. What is Watson Virtual Agent, and what can it do for you and your customers? Watson Virtual Agent on IBM Marketplace Watson Virtual Agent Business Problem: Solution: Learn More: • Personalized, contextual digital assistant that can take action on customer’s request • Pre-trained natural language understanding conversations for customer service domain • Customer service-focused dialog flows across a range of complexities • Conversation tooling and dashboard for managing customer experiences • Software-as-a-Service solution with continuous delivery of enhancements and new content Quantitative Benefits • Absorb deflected contacts from higher cost channels • Increased first-contact resolution • Increased revenue through re-tasking human reps • Decreased agent-to-agent transfers Qualitative Benefits • Satisfy customer demand through the channel they choose • Consistent omni-channel customer experience • Increases in lifetime value, net promoter score
    45. 45. 45 Watson Virtual Agent Knowledge Base Frequency Question Intent Complexity 20% Of User Volume, much larger number of singleton (unique) intents. High complexity, answer depends on a number of variables (knowing the intent is not enough to answer), requires deep QA search. Body Long Tail Pilot Phase1 Phase2 Phase 3 80% of User Question Volume 20% of unique intents. Low complexity, easy to answer derived using context of the question itself