With Amazon Connect, a cloud-based contact center service, businesses can create dynamic contact flows that provide personalized caller experiences by taking history and past context into consideration to anticipate callers’ needs. Join us to learn how customers are executing successful strategies using Amazon Lex to add NLU chatbots into their Amazon Connect customer experience workflows. Learn how using Amazon Lex, an AI service that enables you to create intelligent conversational chatbots that can turn your contact flows into natural conversations using the same technology that powers Amazon Alexa. Learn how to automate repeatable routine tasks such as password resets, order status, and balance inquiries without the need for an agent.

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Bring the Power of AI to Your
Amazon Connect Contact Center
Dan Perry
Senior Product Owner
Liberty Mutual
Insurance
B A P 3 2 2
Laura McFarland
Technologist
Liberty Mutual
Insurance
Marc Rudkowski
Principal Consultant
AWS Professional
Services

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Agenda
• Business case
• Demo
• Architecture
• Best practices

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Improving contact centers with artificial
intelligence
A m a z o n
L e x
A m a z o n
T r a n s c r i b e
A m a z o n
C o m p r e h e n d
T R A N S C R I P T
A m a z o n
C o n n e c t
Analytics

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Improving contact centers with artificial
intelligence
A m a z o n
L e x
A m a z o n
T r a n s c r i b e
A m a z o n
C o m p r e h e n d
T R A N S C R I P T
A m a z o n
C o n n e c t
Analytics

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Making the business case
Meets an emerging technology
• Within our US operation, we receive
millions of calls annually across claims
& service call centers
• Some of which are repetitive & direct
• Offering up an opportunity for
automation
• Our customers expect 24/7 access
• No hold time
• Quick resolution of their inquiry
When a customer’s needs
External
Internal
The big bang …
Voice
revolution
Chatbot
evolution

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= Intents
Building a chatbot roadmap
Conversational complexity
Frequency
Building a data-driven
framework
… from a wide breadth of
resources

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Designing an MVP
Can we build a conversational chatbot that:
1. Interacts with our customers
2. Can answer a subset of claims rental car-related inquiries
3. Touches upon all KPIs needed to address scalability
Business
viability
Does the platform
integrate with our systems
... is it stable, scalable,
secure?
Technical
feasibility
Customer
desirability
Is the forecasted ROI
economically sound?
Do customers want to
engage with the chatbot &
can it embody our
company, our values?

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AWS services

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A new way of developing the design
GUI
CSS
Home screen
Screen flow
Screen components
VUI
Persona
Introduction sentence
Script Flow
Conversation script

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VUI designing
Professional call center persona
“Hi there, I can help you with your
rental car today …”
Amazon Lex only able to
misunderstand twice
Intents to handle “Can you repeat that”
After Intent is fulfilled, “What else can I
help you with today”
VUI
Persona
Introduction sentence
Script flow
Conversation script

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VUI designing Text to speech (TTS) prompts include
punctuation marks to add emphasis or
pauses.
Use SSML (Speech Synthesis Markup
Language) for additional controls over
speech generation. Amazon Polly
console great for learning SSML.
<break time="1s"/> 1 second pause
Initial scripts—reviewed with UX
psychologist.
Held user experience testing session.
VUI
Persona
Introduction sentence
Script flow
Conversation script

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VUI designing—Script and flows

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A new way of developing the design
Partnered with AWS Professional Services
to get access to:
Amazon Connect & Amazon Lex expert
VUI designer from VoiceFoundry
UX psychologist
PhD in cognitive and neural sciences
Partnered with AWS in a consultancy
Their responsiveness and fast feedback kept our MVP
project on a fast pace

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Learning and evolving architecture
We had an emerging serverless architecture
AWS Cloud

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Evolving architecture—Top tips
AWS Cloud
Get out of the console early & start
developing AWS CloudFormation
templates
Reporting, alerting, …

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Evolving architecture—Top tips
AWS Cloud
https://aws.amazon.com/about-aws/whats-
new/2018/

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Evolving architecture
Amazon S3 Alarm
CALL
CENTER
software
JSON
JSON
JSON
AWS Cloud
Solid architect pattern
established
ProxyGateway
Logs, Alarms & Reports
Rental Bot FeedbackBot
Lambda Data Gathers

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Call flow and data flow in action
Amazon S3 Alarm
CALL
CENTER
software
JSON
JSON
JSON
AWS Cloud
ProxyGateway
2. New call event sent
4. Data pulled & front loaded into
DynamoDB
6. Amazon Lex accesses caller data
to have intelligence about caller
…
JSON
… … …
JSON
Logs, Alarms & Reports
Rental Bot FeedbackBot
Lambda Data Gathers

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Amazon Lex under the hood
Has my rental been
authorized?
According to your claim
file, your rental is
scheduled to begin on 30
March and is approved for
10 days
Are there other rental car
questions I can help you
with today?
Rental authorized intent
Caller’s utterance matches an intent
Fulfillment lambda populates the static
text with caller data & passes it to
Amazon Lex via a PostText
Follow-up prompt to
continue or end call
I see we have a rental car
already set up for you. Do
you want to hear the
details?
Amazon Lex calls the fulfillment lambda
with caller’s session data in the request
Lambda uses the session data to get the
front-loaded caller data from DynamoDBYes

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Amazon Lex intents

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Amazon Lex intents, sample utterances, and slot usage

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Amazon Lex intent and sample utterances testing
Automated utterances tested using Amazon
Lex runtime API PostText
https://docs.aws.amazon.com/lex/
latest/dg/API_runtime_PostText.html
Can send text or SSML

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Amazon Lex intent and sample utterances testing
Request
POST /bot/botName/alias/botAlias/user/userId/text HTTP/1.1
Content-type: application/json
{
"inputText": "the utterance text to send",
"requestAttributes": { "string" : "string" },
"sessionAttributes": { "string" : "string" }
}
Response
{
"dialogState": "Fulfilled",
"intentName": "IntentNameUsed",
"message": ”Response message from intent",
...
}

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Amazon Lex intent and sample utterances testing
For rental authorization intent
{ "Authorized" },
{ "Authorization" },
{ "Authorized rental" },
{ "Rental authorized" },
{ "Rental authorization" },
{ "Has my rental been authorized" },
{ "Is my rental authorized" },
{ "I need to know if my rental car is authorized" },
{ "I want to know if my rental has been authorized" },
{ "Trying to get my reservation with approved" },
{ "Send an authorization out" },
{ "You guys haven't approved the rental yet" },
...
assertEquals(response.intentName, "Authorized_Rental”)

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Amazon Lex intent and sample utterances testing
Automated CI/CD pipeline
Code check in followed by pull request
Static analysis runs—Sonar, Lint, CheckStyle
Built & deployed to AWS dev account
Bamboo job kicks off Java app tests with each
bot’s intents test utterance text
AWS SDK included to call Amazon Lex runtime API
Results pulled into Allure

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Utterance refinement procedure
Used 3,000+ example rental queries from our call center
transcripts to create our utterance test cases
Each utterance test case has a single Amazon Lex intent it
must match to
Advice was to aim for 75% match for MVP
First run of the tests was not good news

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Utterance refinement procedure
But we just gamified utterance testing!

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Utterance refinement procedure
one intent at a time

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Utterance refinement procedure
Before refinement
"sampleUtterances": [
"Has {Rental} been {Authorized}",
"Has my {Rental} been {Authorized}",
"Is my {Rental} {Authorized}
]
After refinement
"sampleUtterances": [
"{Authorized} {Rental}",
"Has {Rental} been {Authorized}",
"Has my {Rental} been {Authorized}",
"Is my {Rental} {Authorized}",
"Send an {Authorized}",
"If {Rental} has been {Authorized}”
]
Enums
"Rental": [
{"value": "rental vehicle"},
{"value": "rental car"},
{"value": "car rental"},
{"value": "vehicle rental"},
{"value": "rented car"},
{"value": "rented vehicle"}
]
"Authorized”: [
{"value": "authorized"},
{"value": "allowed"},
{"value": "approved"},
{"value": "authorize"},
{"value": "approval"},
{"value": "approve"},
{"value": "authorization"}
]

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Caller interactions with Amazon Lex
what i wanna know
is when i rent a car
say for her does my
insurance cover it
do i qualify for a rental
car with my claim
rental set
up
rental car
schedule
rental car
extension
rental
details
how much money do
I have to rent a car
no but thank
you very much
for the
information
that’s all
thank you
very much
can you repeat
that for me
That’s good
thank you
yes
ma’am

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MVP project timeline
Team—seven + AWS Professional
Services
Continue evolving fast & create new
intent areas

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Rental chatbot results
10-15%Call deflection
Utterance
recognition
Customer
<5%Callbacks
>90%
Call volume
experience
7/10
- “A good experience.”
- “You’re very good
help.”
- “Pleased with my
service.”
Wk1 Wk2 Wk3 Wk4 Wk5 Wk6

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Polyglot programming
Pair programming
Mob code reviewing
The squad
Serverless architecture
Event-driven design
Cloud native
CI/CD pipelines
TDD
Fully automated testing

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Building your next customer self-service voice
experience—Best practices
Building the customer experience
• Pick a use case that allows you to vet some of your key business
requirements, but be pragmatic about complexity
• Customer experience is as important as the technology:
• Delight customers
• Leverage your data and anticipate why they’re calling
• Iterate and validate with real end-user/customers
• Leverage automated testing to improve effectiveness and containment
of your experience

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Building your next customer self-service voice
experience—Best practices
Learn from existing approaches:
• Build a Q&A bot
• Understand how to leverage Lambda with Amazon Lex
• Build an automated AI experience with Amazon Connect and Salesforce
• Build your own text-to-speech applications with Amazon Polly
• Explore and build experiences with Alexa and Alexa for Business

42. Thank you!
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AWS console—
Amazon Lex
https://aws.amazon.com/blogs/machi
ne-learning/building-better-bots/
Niranjan Hira and Harshal
Pimpalkhute

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The console—Amazon
Connect
https://aws.amazon.com/blogs/aws/n
ew-amazon-connect-and-amazon-lex-
integration/
Randall Hunt