Ray poynter from data to storytelling

Ray Poynter, The Future Place – JMRX Lectures 2015
From
Data
to
Storytelling

Ray Poynter
The Future Place
JMRX
–
Tokyo
–
June,
2015

Agenda

1.  Why
storytelling?

2.  Finding
the
story
in
the
data

3.  CreaBng
the
story

4.  Conveying
the
story

5.  QuesBons

Why
are
we
interested
in
storytelling?

How
Good
Could
Our
Stories
Be?

An
Inconvenient
Truth

(Al
Gore
won
an
Oscar
and
a
Nobel
Prize)

What
is
Storytelling
in
MR?

It
is
not
the
same
as
ﬁcBon

In
market
research,
storytelling
means

1.  NarraBve
ﬂow

2.  InteresBng

3.  AUenBon
grabbing

4.  AUenBon
keeping

5.  Memorable

6.  Delivers
a
message
–
creates
impact

6

Storytelling

Narra@ve
Theme

Wake

Breakfast

Travel

Work

Lunch

Work

Dinking

Travel

Sleep

Get
changed

Warm
up

Run

Warm
down

Shower

Get
changed

•  A
boy
is
found
inside
a

giant
peach

•  Grows
up
big
&
strong

•  Goes
oﬀ
to
ﬁght
ogres

•  Befriends
pheasant,
dog

&
monkey
on
the
way

•  At
the
Ogres
Island
they

all
play
a
role
in
winning

•  A_erwards
the
family
live

happily

Primary
Narra@ve
Axis

The
storyline,
the
plot,
core
idea,
the
spine

•  A
boy
is
found
inside
a

giant
peach

•  Grows
up
big
&
strong

•  Goes
oﬀ
to
ﬁght
ogres

•  Befriends
pheasant,
dog

&
monkey
on
the
way

•  At
the
Ogres
Island
they

all
play
a
role
in
winning

•  A_erwards
the
family
live

happily

Boy
Found

Quest

Comrades

Victory

The
Old

Couple

Food

Fights

BaUle

A_er

Frameworks

Most
of
the
teams
that
reliably
produce
good

analysis
and
useful
stories
use
frameworks

– Individuals
are
less
dependent
on
frameworks

Elements
of
frameworks

– How
to
frame
the
problem

– Linking
the
project
to
a
wider
context

– A
standard
method
of
organising
the
data
(qual

and
quant)

– SystemaBc
methods
of
analysing
data

– A
preferred
method
for
extracBng
the
story
and

linking
it
the
wider
context

Further
Reading

Published
by
Wiley,
2004

From
Data
to
Stories

1.  Define
the
problem

2.  Establish
what
is
currently
known/believed

3.  Check
and
organise
the
data

4.  Find
the
message
in
the
data

5.  Cra_
and
tell
the
story

11

The
process
starts
when
the
request
for
a

proposal
is
received
and
conBnues
throughout

the
research
process,
it
does
NOT
start
when
the

fieldwork
finishes.

Deﬁne
the
Problem

“A
problem
deﬁned
is
a
problem
half-‐solved”

Sources
of
informaBon:

– The
RFP/RFQ
(request
for
proposal/quotaBon)

– The
proposal

– Discussions
with
the
client

•  What
is
the
background
to
the
project?

•  What
would
success
look
like?

•  What
acBons
should
follow
from
the
research?

•  What
do
people
think
the
results
are
going
to
be?

(Or,
what
are
the
prevalent
hypotheses?)

Smith
&
Fletcher,
2004

Establish
What
is
Already
Known?

•  The
frameworks
approach
avoids
focusing
on

just
the
current
research
project

•  The
analysis,
the
validity,
and
the
story
need

to
blend
research
with
the
wider
context

•  The
context
is
a
web
of
exisBng
knowledge:

– Within
the
client

– Within
the
agency

•  One
popular
route
is
to
commission
trend
studies/
reports
as
addiBonal
background

– In
the
public
realm

Is
My
Data
Right?

We
see
paUerns,
even

when
they
are
not
there.

Image
from
Viking
I,
1976

Mars
–
led
to
theories
of

intelligent
life.

Process
Errors
&
You
Will
Find
PaPerns

Check
and
Organise
Data?

Quant
Data

–  Standardise?

–  Missing
data?

–  Index
or
re-‐base

Qual
Data

–  TranslaBons

–  Transcripts

–  Notes

Assess
the
credibility
of
different
sources

Don’t
fixate
on
combining
datasets,
it
is
o_en

sufficient
to
access
the
messages

The
Surveyor
and
the
Journalist

The
Lead

Nora
Ephron

When
Harry
Met
Sally

Sleepless
in
SeaUle

1st
Day
in
Journalism
School

5
Ws
(Who,
What,
When,
Where
&
Why?)

Asked
to
write
the
Lead
for

“The
enBre
school
faculty
will
travel
to

Sacramento
next
Thursday
for
a
colloquium
in

new
teaching
methods.
Among
the
speakers

will
be
anthropologists
Margaret
Mead,

college
president
Dr.
Robert
Maynard

Hutchins,
and
California
Governor
Edmund

Brown.”

All
the
students
wrote
about
the
5Ws
–
good,

but
not
right.

The
Lead?

No
school
next
Thursday!

Hermeneu@c
circle

1.  The
parts
can
only
be
understood
in
the

context
of
the
whole

2.  The
whole
can
only
be
understood
in
the

context
of
the
parts

3.  Analysis
should
move
from
the
whole
to
the

parts,
and
from
the
parts
to
the
whole,

iteraBvely

Find
the
Total
Picture
First

Then
the
relevant
detail

Quant

•  Look
at
the
Total

Column

•  Look
for
big
numbers

and
big
paUerns

•  What
is
the
big
picture?

•  This
will
frame
the

detail

Qual

•  Read
all
the
transcripts

–  Unless
you
conducted
the

ﬁeldwork

•  Create
notes
and
memos

•  What
are
the
main

messages

In
the
context
of
the
Business
QuesBon

Where
does
the
best
MR
come
from?

Column
%
Which
of
the
following
best
describes
you?
Countries
Merged

Total
Research
or

Consultancy

Supplier

Supplier
to
the

research

industry

Research

Buyer/User

Academic
+

Other

English

Speaking

Non-‐English

Speaking

UK
63%
61%
60%
92%
40%
66%
60%

USA
51%
52%
50%
46%
60%
52%
50%

Germany
18%
13%
30%
15%
60%
16%
21%

Australia
15%
14%
15%
15%
20%
16%
12%

Canada
11%
8%
20%
0%
40%
9%
14%

France
7%
7%
10%
8%
0%
7%
7%

Japan
5%
3%
15%
0%
0%
3%
7%

Brazil
3%
3%
5%
0%
0%
3%
2%

China
2%
1%
5%
0%
0%
3%
0%

Italy
2%
1%
5%
0%
0%
0%
5%

Other
8%
10%
10%
0%
0%
9%
7%

None
of
these
11%
15%
5%
0%
0%
9%
14%

Column
n
109
71
20
13
5
67
42

The
wrong
approach
to
starBng
analysis

Where
does
the
best
MR
come
from?

Column
%
Which
of
the
following
best
describes
you?
Countries
Merged

Total
Research
or

Consultancy

Supplier

Supplier
to
the

research

industry

Research

Buyer/User

Academic
+

Other

English

Speaking

Non-‐English

Speaking

UK
63%
61%
60%
92%
40%
66%
60%

USA
51%
52%
50%
46%
60%
52%
50%

Germany
18%
13%
30%
15%
60%
16%
21%

Australia
15%
14%
15%
15%
20%
16%
12%

Canada
11%
8%
20%
0%
40%
9%
14%

France
7%
7%
10%
8%
0%
7%
7%

Japan
5%
3%
15%
0%
0%
3%
7%

Brazil
3%
3%
5%
0%
0%
3%
2%

China
2%
1%
5%
0%
0%
3%
0%

Italy
2%
1%
5%
0%
0%
0%
5%

Other
8%
10%
10%
0%
0%
9%
7%

None
of
these
11%
15%
5%
0%
0%
9%
14%

Column
n
109
71
20
13
5
67
42

0%

10%

20%

30%

40%

50%

60%

70%

Which
Country
Produces
the
Best
MR?

The
Big
Message

Big
step!

Ques@ons

Why
are
the
UK
&
USA
so
high/diﬀerent?

Is
this
true
for
everybody?

What
are
the
implicaBons
of
this?

Selec@ng
Variables
for
Analysis

1.  What
are
the
objecBves
of
the
research?

2.  What
does
your
experience
suggest?

3.  What
variables
are
‘acBonable’?

4.  Expect
to
develop
the
list
of
key
variables

during
the
analysis,
including:

– Dropping
variables

– Merging
variables

– CreaBng
variables

24

What
are
the
key
ﬁndings?

1.  Link
to
the
project
objecBves

2.  ‘Need
to
know’
not
‘nice
to
know’

3.  Supported
by
paUerns
or
themes
in
the
data

–  Not
just
a
single
data
point

4.  Clear
ﬁndings

–  e.g.
In
our
chart
UK
and
USA
are
a
long
way

ahead
in
terms
of
Best
Research

25

0%

10%

20%

30%

40%

50%

60%

70%

Want
to
see
more
speakers
from
Best
research
quality
Non-‐English

If
people
do
not
see
enough
speakers
from
your
country,

they
expect
the
research
to
be
of
a
lower
quality!

NewMR
Survey,
May
2015,
N=133
(Non-‐English
Speaking=43),
PopulaBon
=
English
speaking
followers
of
NewMR

Primary
Narra@ve
Axis
–
NewMR
Study

0%

10%

20%

30%

40%

50%

60%

70%

Want
to
see
more
speakers
from
Best
research
quality
Non-‐English

1.  People
want
to
hear
speakers
from
important
countries
that

they
rarely
hear
from.

2.  Key
Point!
They
think
the
quality
of
research
is
best
from

countries
who
produce
many
speakers.

3.  The
implicaBon
is
that
not
speaking
is
associated
with
low

expectaBons

4.  So,
researchers
from
Japan
should
speak
at
more
events

Workspace
Study

•  Staff
of
a
modern
office
in
North
America

•  What
is
success?

–  Making
our
workspace
a
more
fun,
collaboraBve
and
producBve

place

•  What
do
we
need
to
know?

–  Achievable
ways
to
make
it
more
fun,
collaboraBve
&

producBve

•  What
do
we
know?

–  Staff
retenBon
is
good

–  Downtown
locaBon

–  Most
people
work
from
about
9
Bll
5:30

–  Mixture
of
big
tables,
cubicles
and
meeBng
rooms
–
mix
of

siwng
and
standing
desks

•  Volume
of
noise
in
the
office
(by
meter)
is
quite
low

What
New
Data
Do
We
Have?

1.  A
survey
with
220
completes
(over
70%
of

the
staﬀ)

2.  Lots
of
open-‐ended
comments
in
the
survey

– E.g.
What
do
you
love
about
our
workspace?
&

What
are
the
challenges?

3.  A
forum
discussion
with
about
50
members

of
staﬀ
discussing
the
workspace
and
being

prompted
by
members
of
the
research
team.

The
Big
Picture

Overall
evaluaBon
of
the
oﬃce

– 25%
Great,
50%
Good,
25%
Poor

– This
compares
well
with
external
data

– This
does
not
compare
well
with
other
oﬃces
of

the
same
company

Everything
else
we
discover/report
will
be

framed
with
this
split
in
mind,
i.e.
25%
not

happy,
75%.

Noise
and
Distrac@ons

The
main
negaBve
about
the
oﬃce
relates
to

noise
and
distracBons

– 60%
of
people
menBoned
Noise,
Noises,
Noisy,
or

Loud
in
the
challenges
open-‐end

– 44%
say
their
area
is
too
noisy
-‐
quant

– 38%
they
need
more
privacy
-‐
quant

– 60%
regularly
use
headphones
-‐
quant

– The
forum
highlighted
problem
created
by
team

‘stand
up
meeBngs’
adjacent
to
other
teams

Even
though
noise
is
not
high
according
to
meters,
it
is

seen
as
annoying
and
distracBng

Studying
Noise
and
Distrac@on
Further

Study
and
reporBng
then
looking
into:

– PaUerns
in
who
were
most
impacted
by
noise

– Teams,
locaBons,
funcBons

– Type
of
space

– The
issues
that
the
staﬀ
thought
were
causing
the

problems

– The
soluBons
that
the
staﬀ
were
suggesBng

(Including
the
use
of
white
noise)

Other
Topics
and
Issues

Other
topics
were
explored,
such
as
standing

versus
siwng
desks,
ameniBes,
and
space/
locaBon

Linking
to

– The
main
staBsBc
75%
happy

– The
business
quesBon

– The
number
1
issue
of
noise
and
distracBon

– IdenBfying
problems
and
soluBons,
or
exisBng

strengths

Pulling
the
Story
Together

•  Two
useful
and
challenging
ideas
emerged
from

the
research

1.  The
company
was
between
a
rock
and
a
hard

place.

–  In
order
to
be
modern
and
funky
it
wanted
a

downtown
locaBon

–  But
that
is
expensive,
so
space
was
Bght.

–  An
out
of
town
locaBon
would
provide
space,
but

deprive
it
of
the
‘buzz’
and
status

2.  One
open-‐ended
comment
nailed
the
issue
“it
is

like
a
call
center”
–
that
is
the
essence
of
the

story
and
the
problem

The
Presenta@on

•  Started
with
the
75%:25%
observaBon
re
happiness

•  Followed
the
rock
and

a
hard
place
analogy

•  Went
through
the
main
themes
starBng
with
noise
and

distracBon

•  Closing
with
the
call
centre
descripBon

•  Summarising
the
key
points
in
the
story
and
the
key

acBons
recommended

•  About
40%
of
the
quesBons
in
the
survey
were

reported
in
the
presentaBon

–  The
ones
that
were
part
of
the
story

–  A
full
data
set
with
notes
and
verbaBms
was
made

available

Not
in
the
Presenta@on

A
list
of
points
that
were
not
relevant
to
the

presentaBon
were
sent
to
diﬀerent
stakeholders

– Problems
with
one
of
the
types
of
pedestals

– Problems
with
the
men's
toilets
on
one
of
the

ﬂoors

– The
need
to
provide
webcams
and
headsets
to

everybody
who
is
expected
to
Skype

– Issues
with
recepBon

– SuggesBons
for
food
and
drink

Developing
your
narra@ve
theme

•  Select
your
primary
axis

•  This
is
the
elevator
pitch

•  Use
a
structure
that
works
with
the
audience

•  Typical
US
structure

– The
main
ﬁnding
was
X
so
we
recommend
doing
Y

and
Z

– So,
let’s
tells
you
why
it
is
X,
and
why
are

recommending
Y
&
Z

Hans
Rolsing

1. What
is
his
key
message?

2. What
is
the
story?

3. What
has
he
le_
out?

Hans
Rosling
for
the
BBC

hUps://youtu.be/jbkSRLYSojo

Hans
Rolsing

1. What
was
his
key
message?

2. What
was
the
story?

3. What
had
he
le_
out?

Hans
Rosling
&
Narra@ve
Theme?

Key
Message:

– It
is
possible
to
tackle
world
health
problems

Key
Story:

1.  200
years
short-‐life
expectancy
was
the
norm,

then
the
West
moved
ahead,
but
over
the
last

50
years
most
countries
have
caught
up

2.  There
are
some
countries
sBll
behind,
and
some

regions
of
other
countries,
but
since
most
of
the

world
has
been
solved,
the
rest
can
be

Key
narra@ve
axis:

– 200
years
from
1810

What
Did
Hans
Rosling
Leave
Out?

Numbers:

– A
few
dates,
3
life
expectancies,
3
income
levels

– Based
on
200
countries
and
120,000
numbers

Deﬁni@ons:

– Which
200
countries?

– How
did
he
deal
with
country
amalgamaBon
and

fragmentaBon?

517
other
sta@s@cs:

– GapMinder
lists
519
key
global
stats,
over
Bme

Advanced
analy@cs
procedures

We
use
advanced
analyBcs
when
there
isn’t
an

easier,
faster,
cheaper
opBon

43

Make
things
as
simple
as
possible,

but
not
simpler.

Albert
Einstein

The
Big
Picture

•  Develop
a
framework
approach

•  Deﬁne
the
problem
before
you
try
to
ﬁnd
the

answer
to
it

•  Put
the
research
project
into
the
context
of

what
is
already
known

•  What
do
you
want
the
client
to
do
a_er

hearing
the
results?

– The
story
is
a
device
to
deliver
that
acBon

Thank
You!

Ques@ons?

Ray poynter from data to storytelling

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