SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez nos Conditions d’utilisation et notre Politique de confidentialité.
SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez notre Politique de confidentialité et nos Conditions d’utilisation pour en savoir plus.
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
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 timeAs 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 - https://www.ibm.com/marketplace/cloud/cognitive-customer-engagement/us/en-us
Deliver Cognitive Customer Service with IBM Watson
Cognitive Customer Service
with IBM Watson
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.
• 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
• 100 years in combined experience in voice and
• 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
• Locations in Tulsa, LA, NY and the UK
Director, IBM Watson
Director, Product Management
with IBM Watson
• What is Watson?
⎼ Cognitive Computing
⎼ Structured vs. Unstructured Data
⎼ Customer Service Usage Patterns
• Case Studies
• Getting Started with Watson
• Highly demanding of seamless and
• Less loyal to singular brand
• Have omni-channel expectations
• Social media gives individual voices great
Self-Service Channels are Key to
Winning the Future of Customer Service
What is Watson?
A tablet you talk to? A giant server? A robot?
The ability to understand
structured and unstructured
data, text-based or sensory in
context and meaning, at
astonishing speed and
The ability to form
hypotheses, make considered
arguments and prioritize
recommendations to help
humans make better
Ingest and accumulate data and
insight from every interaction
continuously. Trained, not
programmed, by experts to
enhance, scale and accelerate
Watson: A Cognitive Platform
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
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
Patient Joe Brown
Date of Birth 02/13/1972
Date Admitted 02/05/2014
High Degree of organization, such as a
“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.”
Information that is difficult to organize using
Structured vs. Unstructured Data
In May, Gary arrived in India after
he celebrated his anniversary in
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
Weak evidenceRed Text
Answering complex natural language questions requires more than keyword evidence
Analyzing Unstructured Content
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
In May 1898 Portugal celebrated the
400th anniversary of this explorer’s
arrival in India.
Leverage Multiple Algorithms
The Watson Difference
Customer Service and Engagement
• Provide 360° views
• Deliver consistent and accurate answers
• Efficiently scale expertise to novice agent
• Personalize the customer experience
• 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
• 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
Applications and Data Sources
Watson Developer Cloud
Empower agents to better respond to requests and improve conversion rates
Watson Agent Assist
Watson listens and
conversation between a
customer and an agent
Watson understands the
intent of the customers
questions and surfaces
relevant information to
payment plan to ensure
that he’s always covered
relationship management is
essential in industries where
infrequent interactions have
substantial impact on customer
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
The Cognitive Customer Experience
SELF-SERVICE LEVEL 1 LIVE AGENTS
LEVEL 2 LIVE
AGENTSSELF-SERVICE LEVEL 1 LIVE AGENTS
LEVEL 2 LIVE
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
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
• 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
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 is the only IVR to certify IBM’s
MRCP connector for Watson.
Speech to text
to an existing
resolution to a
directly to the
are freed up
Levels of contact center autonomy
Blueworx Gives Watson its Own Voice
Level 1 – New Speech Engines
• Better quality speech
• Cloud based; no hardware or software maintenance
• Pay-per-use pricing
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
Step-4 (Optional) Access client systems (Web Services, Database, Legacy
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
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.
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
• Provides an interaction that delivers
on customer expectations based on
the cognitive ability to understand,
reason and learn from every
• Communicates with fluid, natural
language through speech or text.
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
Improved consistency and customer service
A Watson Digital Concierge
Reshaped the user experience
Autonomously handles tier-1 requests
(60% Upon Initial Release)
Supports software activation and
300% increase in web traffic
North American Software Company
Interactive Agent for Healthcare Providers
Cognitive agent converses with providers to
Seamlessly manages member information
Transformed a tedious IVR system
Drastic reduction in live agent requests
Call time reduced from 8 to 3 minutes
Rapidly iterate through Watson’s
application in your organization, define
measurable goals for your cognitive
analytics implementation, and begin your
Ideate on and discover the
possibilities of cognitive analytics
and industry applications for your
organization. Rapidly prototype and
illustrate the art of the possible.
3-4 WeeksHalf-to-Full Day
GOALS & KPIS
Watson Innovation Lab
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
• 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
• Absorb deflected contacts from higher cost channels
• Increased first-contact resolution
• Increased revenue through re-tasking human reps
• Decreased agent-to-agent transfers
• Satisfy customer demand through the channel they choose
• Consistent omni-channel customer experience
• Increases in lifetime value, net promoter score
Watson Virtual Agent Knowledge Base
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
80% of User Question Volume
20% of unique intents.
Low complexity, easy to answer derived
using context of the question itself