Finding the story qualitative

Finding and Communicating the Story – Qualitative InformationRay Poynter, 2016

Finding andCommunicating the Story

Working with Qualitative Information

Ray Poynter

April 2016


Agenda

• Overview of the Frameworks approach

• Qualitative information

• Qualitative analysis

• Finding the story in qualitative information

• Communicating qualitative messages


Intuitionor

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Finding the Story

Intuitive Genius– Great at finding the message in the data, just

seems to play around with the numbers and out pops the story

– Usually bad at training others and often poor at working in teams

Systematic approach– Frameworks


The Frameworks Approach

1. Define and frame the problem

2. Establish what is already known– And, what is believed/expected

3. Organise the data to be analysed

4. Apply systematic analysis processes

5. Extract and create the story


Define the Problem

“A problem defined is a problem half-solved”

Sources of information:– The request for a study

– The proposal

– Discussions between sponsor, insight team and supplier• What is the background to the project?

• What would success look like?

• What actions 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 onjust 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 existing knowledge:– Within your organisation

– Within the agency/supplier

– In the public realm


Who is the project for? _________________

What is the business issue/problem that is being addressed?__________________________________________________

What does the business want to do, once it has addressed this issue?______________________________________________________

What do we already know?Item Held by: Description

1 ______ ______ ______________2 ______ ______ ______________3 ______ ______ ______________

Assumptions and predictionsWho What

1. ______ ______2. ______ ______

Simplified


What is Qualitative?No single, perfect description

– Definitions often a matter of degree

• Qual includes human judgements as part of the analysis– Quant is algorithmic, removing or minimising the human role

• Qual is about meaning and understanding– Quant is about quantification

• Qual deals with all sorts of information, including unstructured– Quant requires the data to become structured/operationalised

• Qual looks at within case information (≈ lots of information about a few people)– Quant looks at across cases information (≈ small amount of information about

lots of people)


What is Qualitative?Which is the best door for our building?

Focus Group or IDIsDetermine A is preferred by left-handed people, and B by right-handed people. Perhaps find out that one group is more insistent than the other - Qual

A BEthnographical approachWatch people tackling a variety of doors, plus other objects. Determine people who tend to favour their left prefer A and visa versa - Qual

Usability ProfessionalAssesses the options based on experience and criteria - QualOr, apply a fixed scoring system - Quant

Survey PeopleDiscover 90% prefer B –QuantOr, include left/right handed variable, find right-handed people prefer B –QuantOr, include open-ended question on why, some people cite handedness –Quant with some Qual

Picking the best door? Qual


Quant starts as Qual

A. How many drinks did you have today?– What is a drink?

2 sips from a bottle versus 2 sips from a fountain?2 separate glasses of wine versus a glass of wine that was topped up?

B. Agree Strong, Agree, Neither Agree Nor Disagree, Disagree, Disagree Strongly?– In the mind of the participant there are no numbers,

they pick an answer which they believe best reflects their view

MRS Menopausal Rating Scale

15


OperationalizingFrom Qual to Quant

Qual is analysed by a human*, quant employs an algorithm

If we code qual data and count the codes, we convert from qual to quant, via operationalizing

– Brand mentions

– Likes and Dislikes

– Sentiment

– Marking an essay

– Evaluating people for mental health disorders

Tendency to treat this quant as ‘hard’ data, and the underlying qual as ‘soft’


Computers ＆ Qualitative Analysis

• Scissors & coloured pens Word, Excel etc

• CAQDAS – Computer Aided Qualitative Data Analysis Software, e.g. Nvivo

• Text analytics, from word clouds to Leximancer

• Social Media analysis, e.g. Brandwatch & Radian 6

• Coding software, e.g. Ascribe

• Photos and Video organising, e.g. Google Photos and Living Lens

Your organisation’s Framework should specify the tools to be used, storage protocols, and approaches to things like memos, tags, and notes.


AI and Qual

At some point in the future, and maybe somewhere in the world today, it might be possible for qual data to be analysed by AI instead of, or as well as, humans.


Organising Existing Knowledge

• Include qual and quant knowledge

• Stakeholders summarise what is known and what they think the research will show

• Make the data accessible– Transcripts, translations, video libraries, photo

galleries

– Consider computer tools like NVivo


Qualitative Data?

• Notes created by researchers when observing, listening, discussing with participants

• Open-ended comments in interviews, focus groups, surveys etc

• Posts in Social Media

• Letters

• Videos, recordings, transcripts

• Art

• Meals, clothes, trash

• Theatre, cinema

• Play, activities, interactions

• Objects

• Photographs & recordings

• Observation & passive data

Many of these can also be called artefacts (artifacts in North America)


Symbiosis of Collection and Analysis

Establish the Question and

what is Known,Plan Research

Do Research

AnalyseUpdate

plan

Analyse Story


Academic versus CommercialAnalysis of Qualitative Data

Many techniques are used by both, e.g. conversation analysis, grounded theory, etc

But! – Timelines vary, commercial one day to one week,

academic can be months

– Success can vary, commercial = better business decision, academic = advancing knowledge (academic definition of knowledge)

– Purity of methodology, academic more pure, commercial more pragmatic (which often means using hybrids)


SemioticsSemiotics was developed from the work of Ferdinand de Saussure from the later 19thCentury onwards. Semiotics is the study of meaning-making by looking at the use of signs and symbols (which can be any form of data, including worlds, brands, images, sounds etc.) Semiotics does not require the collection of data from research participants; semiotics if frequently conducted with artefacts that exist in the ‘real world’ rather than in an MR created world. However, semiotics can be applied to MR data, just as it can be applied to any other data.

Sign

Signified

Signifier

Sign

Rose

Sign

Passion

Rose

Sign

Passion


Overarching StructureNo uniform

No books

Travel costs

School fees

Worry

Mind elsewhere

Tired in School

Headaches

Lack school materials

Unable to pay school costs

Worry about dependents

Feelingexhausted

Physically & emotionally

stressed

Can’taffordschool

These childrenhave tangible

problems

Adapted fromwww.open.edu/openlearnworks/mod/resource/view.php?id=52658


Deciding What to Believeand What to Interpret

Less believable– Yes, I always give my

children healthy snacks

– Yes, I will buy this new product

– I always remember to take my medicine

– I buy on value, not because of the advertising

More believable– I have two children

– No, I did not like it

– I think men will like this more than women

– Which of these three is the odd one out?

– Why is it the odd one out?


Popular Internet meme

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Why ‘Just Say No!’is Not so Easy

Just Say No? The Use of Conversation Analysis in Developing a Feminist Perspective on Sexual Refusal, Celia Kitzinger, 1999


Common Analytical Elements

• Saturated analysis – keep going until you stop finding new/useful things

• Structure – find/create an architecture to what you find

• Make notes of what you find, linking back to the data, highlighting examples

• Look to support AND break hypotheses


Finding the Story

• Use the client’s question as the lens

• Tag, code, memo the material as you analyse

• Challenge what is known/believed

• Find the main story

• Find the relevant exceptions/differences

• Create an overall structure, the plot

• Is it good news or bad news?


Finding the Story

Use the client’s question as the lens– What does success look like?

– What actions are pending on the results?

– What do people think is true?

– What do people think the results will be?

Testing all the hypotheses is key– Supporting and breaking


Different Perspectives

Sales think it is a duck

NPD think it is a rabbit


5 Cs of Insight

• Connections

• Coincidences

• Curiosities

• Contradictions

• Creative jump

} Build on the current picture

Rethink an assumption

Discard an assumption


Connections

• Charles Darwin and Evolution

• Aware of how farmers bred different sizes and shapes of cows, horses, pigs etc – selective breeding

• Visited Galapagos and saw varieties of sizes/shapes

• Read Malthus’ essay on population growth and competition for resources

• Made the connection to realise that competition for resources was the hand behind the selective breeding


Connections

“The smell makes me think hospitals”

What do we know about hospitals, smells and implications for this type of product

– Cleaning – strong, clean, but not homely

– Food – not so good

– Technology – good but cold


Coincidences

• US doctor Michael Gottlieb

• 31 year old patient, unusual symptoms, auto-immune disorder. Being gay irrelevant.

• 2 more patients, similar unusual symptoms. Coincidence – they were also gay.

• Gottlieb explored the coincidence (with a prepared mind) and found AIDS


Coincidences

• In-home placement test for a new cooking product

• Looking at the diaries we notice that three people said they forgot where they had put the ingredient and could not readily find it

• Explore whether this points to a usability problem for the project


Curiosities

• British research Alexander Flemming

• Researching Staphylococcus

• Went on holiday in August, leaving petri dishes with the bacteria

• On return, one had developed a mould and near the mould the bacteria had died

• This curiosity, connected with a prepared mind, led to penicillin


Curiosities

• Researching a new brand of strong, dark chocolate

• One participant discusses buying it for her sister as a present BECAUSE her children won’t like it

• Hmm, is this an insight, let’s dig deeper


Contradictions

• 19th Century London, John Snow is investigating cholera

• Predominant theory = cholera is airborne

• But! He looks at corpses. The lungs look good, but their digestive system looked damaged CONTRADICTION

• He looked for ingestion routes and discovered cholera was spread via drinking water


Contradictions

• Client has a product which is liked by children, but is not successful – they believe parents are not buying it

• Research brief, find out how to persuade more parents to buy it for their children

• But, looking in trash cans finds lots of uneaten product – CONTRADICTION many children do not actually like it


Creative Jump

4 straight linesNot leaving the paper


Creative Jump

4 straight linesNot leaving the paper

Outside the box - really


Creative Jump

• When people use it they love it– Make it free and charge for the refills

• These candles (etc) are too nice to burn (use)– See them as gifts not consumables

• Growth in people wanting to split bills– App payment designed to help diners and

restaurants


Good and Bad News

• There are four typical stories– Good news– Good news with caveats– Bad news with some options– Bad news

• The storytelling for these four cases is different

• Good news and bad news is defined by what the client wanted AND what the research finds


Bad News

• 5 stages of grief– Anger, Denial, Bargaining, Depression, Acceptance

• One presentation/report rarely tackles all the stages of bad news

• ‘Facts’ are rarely enough to persuade– Emotions are the key – a customer video can be more

powerful than any amount of analysis

• Go back to a point where the expectations match the findings and build from there


Conveying Confidence

• Confidence is created by the researcher

• Don’t convey more confidence than you have– Don’t convey less confidence

• Utilise– Triangulation– Testable predictions– Consistency– Coherence


The Big Picture

• Frameworks for reliable / effective stories

• Define the problem

• Organise the data according to the Framework –everybody using the same tools and approaches

• Find the main story and build out from there

• Is it good or bad news, confirming or challenging expectations/beliefs

• Engaging, memorable, simple story


Thank You!

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