AWS Think Big Workshop: Experimenting with Data

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
25 September 2018
Think Big: Experimenting with Data
A hypothesis based approach for experimenting with data
Facilitators: Vicky Falconer & Toni Knowlson

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Agenda
Time Section Description
5 min Welcome Introduction, agenda and house rules
5 min Approach for the Day Outline of key principles and ways of working for the day
30 min Morning Energiser – Norbert’s Challenge Start to experiment with a challenge we all face…
30 min A Culture of Experimentation
Introduction to conducting an experiment, importance of
experimenting within organisations and core principles required to
experiment successfully
20 min Case Study – Unicorn Air Challenge Walkthrough of the experimentation method in action
50 min Make it Real – Your Challenge Create your own experiment
5 min Wrap up Next steps, close

Approach for the day
Today we encourage you to…
Change up your
thinking
Stay on track Try new
things
Have funGo with the
flow

Morning Energiser
– Norbert’s Challenge

Purpose
The purpose of Norbert’s challenge is to…
Alter your
perspective
Try agile ways of
working
Experience testing
and failing fast

“Why are people always
running late for meetings?”
Identify Challenge:

State Question:
“How can we get people to
attend our meetings on time?”

Formulate Hypothesis:
“If there was an alternative
way to tell time, then people
would be more punctual to
meetings”
(An educated guess about something in the world around
you; testable either by an experiment or observation)

Define what measureable success looks like…
The new way of telling time
encourages people to be
punctual to meetings
Meeting punctuality increases by
5% over the next month

Help Norbert solve his challenge by proving /
disproving his hypothesis
“If there was an alternative way
to tell time, then people would
be more punctual to meetings”

New Hypothesis:
“If we pay people $5 to attend
meetings on time, then people
would be more punctual to
meetings”

Experimenting is really not rocket science…
State
Question
Formulate
Hypothesis
Identify
Challenge
Define Hypothesis
Analyse
Results
Draw
Conclusions
Conduct Experiment
Run TestsDefine Tests
Design Experiment
“What does measurable success look like?” “What did we learn and what happens next?”“What data do we have and what data do we need?”

Quote
“To invent you have to experiment, and if you know
in advance that it’s going to work, it’s not an
experiment”
- Jeff Bezos

A Culture of Experimentation

Credits:
Human After All:
www.humanafterall.com.au
Data Machine:
https://dribbble.com/shots/3687258-Data-machine
Freepix:
https://www.flaticon.com/authors/freepik
Smash Icons:
https://www.flaticon.com/authors/smashicons
Geotaatah:
https://www.flaticon.com/authors/geotatah
Idea Studio:
https://www.flaticon.com/authors/photo3idea-studio
Kawai:
https://www.ohmydollz.com/?p=fiche&pseudo=kawai95130
Animated Gif:
https://giphy.com/gifs/motionaddicts-animated-gif-motion-addicts-DS89v1NqpzCqA
https://giphy.com/gifs/processing-sphere-IPrjvZM5dMBdm
https://www.flaticon.com/authors/becris
My Name Pong:
https://www.flaticon.com/authors/mynamepong
Becris:
Pause 08:
https://www.flaticon.com/authors/pause08
Ddara:
https://www.flaticon.com/authors/ddara
Surang:
https://www.flaticon.com/authors/surang
Eucalyp:
https://www.flaticon.com/authors/eucalyp
Test Tube:
https://dribbble.com/shots/4900928-Test-Tube
From 3 months…
The story of Fonterra
To 48 hours…

Organisations that experiment…
Trust the data
Show and tell
Define what
success looks
like from the
start
Stay curious
Test and fail fast
Alter
perspective
Build a shared
commitment
Remain flexible

Case Study
– Unicorn Air Challenge

Experimenting is really not rocket science…
State
Question
Formulate
Hypothesis
Identify
Challenge
Define Hypothesis
“What does measurable success look like?” “What data do we have and what data do we need?”
Analyse
Results
Draw
Conclusions
Conduct Experiment
Run TestsDefine Tests
Design Experiment
“What did we learn and what happens next?”

Conducting your Experiment

Why? What? How?
Identify 2 challenges you / your organisation is
currently facing..
“Why is Unicorn
Air spending so
much on fees?”
?“Why are people
always running late for
meetings?”

How? What?
State 2 questions for your challenges…
“How can Unicorn
Air capture a
greater market
share of non-loyal
business
customers?”
?“How can we get
people to attend our
meetings on time?”

If ___________(cause), then ____________(effect)
Formulate 2 hypotheses for your challenges…
“If late passengers
were re-seated on
to future flights,
then Unicorn Air
could achieve
savings in Airport
enforced late fees”
?“If there was an
alternative way to tell
time, then people
would be more
punctual to meetings”
A hypothesis is an educated, testable prediction about what will happen
Criteria of a good hypothesis :
• Is it clear and understandable?
• Does it explain what you expect to happen?
• Can at least one clear prediction be made
from the hypothesis?
• Is it testable? Can you prove or disprove it?
• Is it safe and ethical (creepy factor)?

Success looks like…
What does measureable success look like for your 2
hypotheses?
10% increase in
customer satisfaction
5% increase in
market share across
non loyal business
travellers
?The new way of telling
time encourages
people be punctual to
meetings
Meeting punctuality
increases by 5% over
the next month

Choose 1 Hypothesis to test…

Does? Will? Can?
Define a minimum of 8 tests to test your hypothesis…
Does a large non loyal
business traveller segment
exist?
Who is likely to be wait?
Do customers respond
positively to app?
Which customers are running
late from the Melbourne to
Sydney route?
Does weather impact
customers being late?
?Does the new watch face
help people be more
punctual? - why and why
not?
What does this current
group think?

What data exists?
What data is missing?
What data can add value?
What data do you have and what data you need to test
your hypothesis?
Customer data
Flight path data
Non loyal customer status data
Unicorn app location data
Uber data
Fare class data
Seat occupancy availability data
Weather data
Late customer interviews
Observe late check-in with app
Observe late check-in without app
?Customer Data
Location Data
Market Data

How did your experiment go?

What’s next…

Becoming a change agent…
Experiment it…
+ Pilot it…
+ Produce it…
= Radical
Change

What now…
Creating a culture of experimentation
Conduct an experiment in the next 2 weeks

Thanks for joining us today

Credits:
Human After All:
www.humanafterall.com.au
Data Machine:
https://dribbble.com/shots/3687258-Data-machine
Freepix:
Smash Icons:
https://www.flaticon.com/authors/smashicons
Geotaatah:
https://www.flaticon.com/authors/geotatah
Idea Studio:
https://www.flaticon.com/authors/photo3idea-studio
Kawai:
https://www.ohmydollz.com/?p=fiche&pseudo=kawai95130
Animated Gif:
https://giphy.com/gifs/motionaddicts-animated-gif-motion-addicts-DS89v1NqpzCqA
https://giphy.com/gifs/processing-sphere-IPrjvZM5dMBdm
My Name Pong:
https://www.flaticon.com/authors/mynamepong
Becris:
Pause 08:
https://www.flaticon.com/authors/pause08
Ddara:
Surang:
Eucalyp:
https://www.flaticon.com/authors/eucalyp
Test Tube:
https://dribbble.com/shots/4900928-Test-Tube

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