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

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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

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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

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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

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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?

Marketing

Ray poynter from data to storytelling