Sue Bell AAA 2016

A qualita)ve researcher’s perspec)ve on AI for qualita)ve – Agility, Automa)on and AI Sue Bell, Susan Bell Research, 2016

AI, Automa)on and Agile Market Research:

Sue Bell Susan Bell Research,

18 August 2016

A qualita)ve researcher’s perspec)ve on AI for qual


This presenta7on is about how AI might affect qualita7ve research

for beCer or worse

Knowledge worker

automa7on

Text analy7cs

Speech to text


Somewhere in all that talk is all the answers

‘New York Morning’ Elbow


1. The impact of speech to text (voice-‐controlled compu7ng)


Voice-‐controlled compu7ng – via bots -‐ developing very fast.


The impact? More text faster

•  Faster turnaround •  Poten7al for in-‐person research to become

more ‘agile’.

•  Likely to increase the amount of text produced in online qual.

•  Because people say more than when they write.

In person qualita7ve research

Online qualita7ve research

In person qual

Text-‐based online qual

Voice controlled

Next?

Amount of text to analyse


If we have too much text – perhaps text analy7cs can help?

Yes

Only if qual researchers develop our own products and prac7ces – and not borrow from Big Data.

But Machine-‐learning based text analy7cs can help us find the answers in all that talk.......


2. How to make text analy7cs work for qual


An important dis7nc7on

Qualita7ve research

Qualita7ve informa7on

Big data Survey open

ends

Focus groups IDIs

Online qual


Qualita7ve informa7on uses one style of language

•  Formal language, structured composi7on •  Or short – eg Tweets

“(Service provider) were completely professional. They rang me regularly

to see how my treatment was progressing.”


Qualita7ve research uses a different style

•  Shorthand language, or •  Complex construc7ons.

“Sunglasses? I must admit, I don’t put them on un)l I start squin)ng and ‘oo I can’t really see properly maybe I should put my sunglasses on now’ and it’s not like an automa)c thing that I will put them on.“

•  Conversa7onal •  Informal

“My primary role at Christmas is the walking ATM”


Sophis7cated natural language processing (NLP) models used for qualita7ve informa7on

Machine-‐learning based text analy7cs takes

Qualita7ve informa7on

Turns it into structured data

So that it can be analysed

quan7ta7vely


Different types of output: 1. Stats

Numbers and stats have no role to play in qualita7ve research


Different types of output: 2. Word Clouds

But Word Clouds look as if they might suit qualita7ve research don’t they?


An example of qualita7ve research data put into a word cloud

But this is an inaccurate summary of what was said


And why the Word Cloud was wrong

•  The first 7me •  At the same 7me •  It’s that 7me of year •  The peak of Xmas delivery 7mes •  Us 7me poor peeps •  An honest aCempt for everyone to truly

spend the 7me together.

“Christmas is a double ended sword, it

has its stressful fran7c indecisive rushed moments and on the other end a relaxed joyful loving and happy 7me.”

And it missed ‘moments’ in this The ‘7me’ theme seemed like single construct – but it wasn’t

If borrowing methods from Big Data won’t work, what can we do?


One solu7on is on its way Parsey McParseface

“Parsey McParseface is built on powerful machine learning

algorithms that learn to analyze the linguis7c structure of

language, and that can explain the func7onal role of each word

in a given sentence.”

hCps://research.googleblog.com/2016/05/announcing-‐syntaxnet-‐worlds-‐most.html


Solu7on 2. Leximancer

“Leximancer automa7cally analyses your text documents to iden7fy the high level concepts in your text.” For example, the sogware tracked the concept ‘7me’ (as in days and hours) and its synonyms but ignored other uses of the word.

documentshCp://info.leximancer.com/


But we need a new way to analyse

Then we find the insight

need

hCp://www.7meout.com/music-‐arts-‐culture/blog/10-‐amazing-‐birds-‐eye-‐view-‐photos-‐ci7es-‐around-‐world/

AI creates the overview

Models and frameworks

Then we read

Then we apply


AI in qual research will not replace reading or listening. What it does do is provide an almost-‐instant impar7al overview that

guides your inves7ga7on and a searchable resource for finding quotes and examples.


3. The impact of knowledge worker automa7on (aka will we

s7ll have a job?)


According to McKinsey, possibly not

•  “McKinsey & Company predict that knowledge work automa7on will be the most significant disrup7ve technology to influence the world over the next 10 years, second only to the addi7on of 2-‐3 billion new mobile internet users by 2025.”

hCp://rainbird.ai/content/uploads/2015/12/RainBird_WhitePaper_KnowledgeAutoma7on_Digital.pdf


IMHO

•  In person modera7ng s7ll has a

very clear role to play. •  Unlikely to replace the moderator/

interviewer, but AI may transform this role – in a good way.

In person modera7ng is not at risk Online modera7ng is at risk

•  Already part automated. •  In some cases, it is just data

collec7on. •  The excep7on will be online

moderators and plalorms which ac7vely engage ‘people as people’.


The opportunity is to transform modera7ng not replace it ....

Instantly accessible library

of human behaviour theories

Instantly accessible library of previous ads, products etc

worldwide

AI bot could tailor concepts to each par7cipant’s learning style

Wearables during in person research – a human

moderates a face to face group discussion while the AI bot measures the nonverbal

stuff


And how about improving interac7on?

‘SmileCatcher is an aCempt to lower stress and increase happiness by gamifying smiles. Players are challenged to capture smiles from other players or people they interact with in

their daily lives.’

SmileCatcher A gamifica7on of smiles to encourage social

interac7on

luid.media.mit.edu/projects/smilecatcher


Thank You!

Susan Bell Research www.sbresearch.com.au


Q & A

Sue York The Handbook of

Mobile Market Research

Susan Bell Susan Bell Research

Sue Bell AAA 2016

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