Customers increasingly expect to engage with brands through self-service channels and not with tedious and frustrating traditional call centers that use IVR. They want the convenience and timely, streamlined interactions that a virtual agent offers.
More than 50% of enterprises have invested in virtual agents for customer service, and it’s estimated that by 2020 approximately 85% will manage the customer relationship with no human interaction at all.
But not all virtual agents are created equal. IBM Watson harnesses the power of natural language processing, machine learning, and cognitive computing to deliver an exceptional virtual agent experience.
Perficient and IBM took a closer look at intelligent virtual agents, including:
-The benefits of intelligent virtual agents
-Considerations when selecting a virtual agent
-IBM Watson Assistant introduction and demonstration
-Practical ways to get started with Watson
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
Perficient Profile
• Founded in 1997
• Public, NASDAQ: PRFT
• 2017 revenue $485 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), Phoenix, Seattle, Southern California, St. Louis,
Toronto, Washington, D.C.
• Global delivery centers in China, India and Mexico
• 3,000+ colleagues
• Dedicated solution practices
• ~95% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth
awards
4. 4
• Practice Overview
• Virtual Agents Trends and Forecast
• Cognitive Virtual Agent Review
⎼ Conversational Agents
⎼ Conceptual Architecture
• Training Approach
• PoT Execution
⎼ Sprint Plan
⎼ Assumptions
• Perficient Case Studies
Agenda
5. 5
• Over 30 delivery
professionals
• Leveraging experience in
Analytics, Big Data,
Unstructured Content
Management, Enterprise
Search, Digital Experience
and Business Optimization
• IBM Watson Talent Partner
• IBM Watson
• Microsoft Azure
• Google Cloud Platform
• Amazon
• Open Source (R, Python, etc.)
• Platform Selection Engagements
• Cognitive Readiness Evaluations
• Solution Business Case Development
• Cognitive Search Implementations
• Text and Content Analytics Solutions
• Virtual Agents and Chatbots
• Predictive Modeling
• Machine Learning Models
• Decision Support Solutions
Practice Overview Platform Support Offerings and Services
2017 Beacon Award Winner for an
Outstanding Watson Cognitive Solution
Artificial Intelligence Practice Overview
7. 7
Looking Forward
by 2022
25% of customer service and support operations will integrate virtual agent
technology across engagement
40% of customer-facing employees and government workers will consult
daily an AI virtual support agent for decision support
over 50% of organizations have already invested in VAs for customer
service, as they realize the advantages of automated self-service
20% of brands will abandon their mobile apps in favor of building
presence in consumer messaging apps, such as Facebook Messenger
85% of the enterprise relationship to a customer will be managed
without human interaction
30% of all B2B companies will employ artificial intelligence (AI) to
augment at least one of their primary sales processes.
by 2019
in 2018
by 2020
9. 9
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.
10. 10
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
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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
12. 12
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:
14. 14
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
16. 16
Virtual Agent Knowledge Base
Frequency
Complexity
High complexity, answer depends on a number of
variables (knowing the intent is not enough to
answer), requires Deep QA search.
Short Tail Long Tail
Proof-of-Technology
Phase1
Phase2
Phase 3
Low complexity, easy to answer derived
using context of the question itself
Phase 4 Phase 5
19. 19
Virtual Agent Vocabulary
I need to add my daughter to my auto policy.
utterance
entity entity
intent: addDriver
verb noun
20. 20
Intent Training
I need to add my daughter to my auto policy.
Training Set: intent: addDriver
My son just turned 16 and I need to add him to my policy.
I have to update my policy to include my nanny.
Make sure my account covers my twins also.
Test Set
Please add my son.
21. 21
Entity Training
I need to add my daughter to my auto policy.
Training Set: entity: PolicyType - auto
My son just turned 16 and I need to add him to my policy.
What does my automotive insurance cover?
My twins are new drivers please add them to my policy.
Test Set
Does my car insurance cover theft?
requires inference
25. 25
Contextual Entities
Enable entities to be
contextually aware and
expand value
Recently Released Features
• Dialog Folders
Stay organized as
you scale your bot.
• Digressions
Dynamically answer
questions in the midst
of a business process
• Rich Response
Types
Provide buttons,
images, videos, pauses,
etc. into the response to
the end user
Set Context in
the Dialog UI
Avoid having to set
context in the advanced
JSON editor.
Conflict Detection
(Premium)
Find conflicts
between intents and
resolve them
6
Entity Synonym
Recommendations
Disambiguation
(Premium)
Prompt user for
clarification on which
intent was intended
7 8
Separate Log Files
Separate workspace from
your log file, allowing you
to improve a bot while in
production.
9
Based on a particular
value, recommend
other synonyms for
the user
27. 27
A 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 Provider
28. 28
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
Major Health Insurer
Stats from Gartner
2019: By 2019, 20 percent of brands will abandon their mobile apps.
Many brands are finding that mobile applications are not delivering the level of adoption and customer engagement they expected. Original return-on-investment (ROI) calculations are missing the mark due to the cost of support, maintenance, upgrades, customer care and marketing to drive downloads. Brands are now investing to build presence in consumer messaging apps, such as Facebook Messenger and WeChat, to reach customers where they spend a high percentage of their time.
Source: Gartner Predict 2018: Artificial Intelligence
https://www.gartner.com/newsroom/id/3858564
Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020
Analysts Present Top Predictions for Customer Experience Leaders at the Gartner Customer Experience Summit 2018 in Tokyo, February 19-20
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.
https://chatbotsmagazine.com/chatbot-vocabulary-10-chatbot-terms-you-need-to-know-3911b1ef31b4
Utterance: Anything the user says. For example, if a user types “show me yesterday’s financial news”, the entire sentence is the utterance.
Intent: An intent is the user’s intention. For example, if a user types “show me yesterday’s financial news”, the user’s intent is to retrieve a list of financial headlines. Intents are given a name, often a verb and a noun, such as “showNews”.
Entity: An entity modifies an intent. For example, if a user types “show me yesterday’s financial news”, the entities are “yesterday” and “financial”. Entities are given a name, such as “dateTime” and “newsType”. Entities are sometimes referred to as slots.
https://chatbotsmagazine.com/chatbot-vocabulary-10-chatbot-terms-you-need-to-know-3911b1ef31b4
Utterance: Anything the user says. For example, if a user types “show me yesterday’s financial news”, the entire sentence is the utterance.
Intent: An intent is the user’s intention. For example, if a user types “show me yesterday’s financial news”, the user’s intent is to retrieve a list of financial headlines. Intents are given a name, often a verb and a noun, such as “showNews”.
Entity: An entity modifies an intent. For example, if a user types “show me yesterday’s financial news”, the entities are “yesterday” and “financial”. Entities are given a name, such as “dateTime” and “newsType”. Entities are sometimes referred to as slots.
https://chatbotsmagazine.com/chatbot-vocabulary-10-chatbot-terms-you-need-to-know-3911b1ef31b4
Utterance: Anything the user says. For example, if a user types “show me yesterday’s financial news”, the entire sentence is the utterance.
Intent: An intent is the user’s intention. For example, if a user types “show me yesterday’s financial news”, the user’s intent is to retrieve a list of financial headlines. Intents are given a name, often a verb and a noun, such as “showNews”.
Entity: An entity modifies an intent. For example, if a user types “show me yesterday’s financial news”, the entities are “yesterday” and “financial”. Entities are given a name, such as “dateTime” and “newsType”. Entities are sometimes referred to as slots.