quant equals qual

August 2015

UCD CONFERENCE - HUMANITY IN DIGITAL LANDSCAPES

@LolaOye

Quant = Qual Why we should all love data

‣ My background is primarily Qual.

‣ Depending on your leaning (Qual or Quant), you may feel

my points are unfair.

‣ I’m more interested in what’s next, than what was.

As you listen, bear in mind:

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“I assumed that the time would come when there would be a science in which things could be predicted on a probabilistic or statistical basis.

Isaac Asimov, Author of the Foundation Series, inventor of Psychohistory

Quantitative

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/ˈkwɒntɪˌtətɪv,-ˌteɪtɪv/

Relating to, measuring, or measured by the quantity

of something rather than its quality.

In UX Research measured by:

• Numbers

• Amounts

• Trends

• Increments

• Statistics

We treat quantitative data as inherently summative.

It lack’s the nuance that experiences are built on.

Qualitative/ˈkwɒntɪˌtətɪv,-ˌteɪtɪv/

Relating to, measuring, or measured by the quality

of something rather than its quantity.

In UX Research measured by:

• Narratives

• Stories

• Superlatives

• Inferences

We treat qualitative data as both formative and

summative. But it is open to bias and slow to analyse.

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UX research is losing relevance.

It takes too long and is too expensive.

Too many “UX people” don’t have research skills.

We no longer have the right skills for the

emergent business & technology context

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“Given two or three data points, our minds can construct an alternate reality in which all of those data points make flawless sense. Five UX Research Pitfalls, UXMagazine. Elaine Wherry, 2010

Indication not Inference Causation not Correlation

“Gary King, Harvard University, Director of Institute for Quantitative Social Science, 2013

Big Data is not about the data.

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We have a lot of quantitative data

that we need to analyse better.

We have a lot of qualitative data

and research but we can’t harness

these insights in real time.

Our quantitative data and

qualitative data are not joined up.

We can’t keep waiting 3-6 weeks

for data analysis.

UX & Product people need to

stop burning budget on research!

We need big data. We got a

Hadoop, please bring instructions.

HEY, WHAT CAN YOU TELL ME ABOUT OUR CUSTOMERS’ BEHAVIOUR? WELL WHAT DO YOU WANT TO

KNOW?

I WANT TO BE OPEN, WHAT CAN YOU TELL ME?

LOTS. YOU NEED TO BE MORE SPECIFIC.

OK, WHAT IS OUR CUSTOMERS MOBILE BEHAVIOUR?

DO YOU WANT EVERYTHING?

ERM, YEAH…? OK, THAT’S GOING TO TAKE 6 WEEKS. EXCEL FILE OK?

ARE YOU SERIOUS OR JOKING? I CAN’T TELL. I’M NOT JOKING.

‣ They didn’t know how to get the data they wanted, how it

was stored or how we would be able to use it

‣ The ‘user’ was us…UX & product folks who need to make

informed decisions to prioritise services and features

‣ We’d never built a big data system before, so we had to

learn quickly!

This brief had spiky bits:

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User Experience Architect

Statistician/Data Scientist

Back-End Developer

Front-End Developer

UI Designer

“Unicorn” Developer

Client

Bringer of whisky.

Nobody really knew what they could get. So they didn’t know what they could ask for.

You have to start with qualitative questions you’ve

always wanted to answer.

There will be huge gaps in insight because some

databases don’t play nice.

The Data Protection Act. It sucks.

1. 2. 3.

What the business has always wanted to know, but never knew it could get:

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PERFORMANCE

• App downloads / app usage / app feedback (1view)

• Activity in digital products over time (spot trends, dips and peaks)

• Customer activity (frequency) across channels

• Cross product offering and cross channel purchase behaviour (360

view)

• Touchpoint usage: feature use, feedback on features, drop off

points and completed journeys

• Segments x value earned by channel

EXPERIENCE

• Customer’s comments over a determined period of time

• Behaviour across channels over a period of time Use data to segment users by behaviour, not spend

• Impact of launches or push notifications on behaviour over time

• Movement between transactional segments: crossing demographics

with purchase value/frequency

…..and lots more.

POS

Website Clickstream

On-Site Customer Reviews Loyalty Card

External Product Reviews

Daily Sentiment Analysis

Social Media

Search Data

App Analytics

Postcode Lookup

Dozens of UX Insights

DATA SOURCES

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Red: Data sources we don’t have access to or don’t know how to access, or for which the source itself is unintelligible to us or totally unknown.

Amber: Data sources we have access to but which have problems that severely limit their use.

Green: We have access to these sources today and the data is clean.

53%

10%

37%

30 Data Sources | RealTime | Periodic | Historical

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Their quant stuff we could plug in

Qualitative (Unstructured) Data

The bits we made…all re-usable and open!

ACIXOM

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REAL TIME SALES

SITE CATALYST

HISTORICAL SALES

AXIOM

DEMOGRAPHICS

TWITTER

APP FIGURES

OPINION LABS

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Can I see how many vouchers have been redeemed this week and how that compared to last week?

REAL TIME SALES

SITE CATALYST

HISTORICAL SALES

AXIOM

DEMOGRAPHICS

TWITTER

APP FIGURES

OPINION LABS

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Can I monitor the impact of the app launch on customer’s voucher redemption behaviour?

REAL TIME SALES

SITE CATALYST

HISTORICAL SALES

AXIOM

DEMOGRAPHICS

TWITTER

APP FIGURES

OPINION LABS



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REAL TIME SALES

SITE CATALYST

HISTORICAL SALES

AXIOM

DEMOGRAPHICS

TWITTER

APP FIGURES

OPINION LABS

Can I monitor engagement across different channels in relation to feature releases and I can I overlay that with channel specific sentiment?


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Can I monitor app downloads over time, find out who is using them and what the overall sentiment is about the app?

REAL TIME SALES

SITE CATALYST

HISTORICAL SALES

AXIOM

DEMOGRAPHICS

TWITTER

APP FIGURES

OPINION LABS


Can I monitor engagement across different channels in relation to feature releases and I can I overlay that with channel specific sentiment?


We’re not allowed to know everything we would like to know. Get over it.

It’s really difficult to separate interesting behavioural data from “Personally Identifiable Information”.

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THE DATA ITSELF

• Geography vs location

• Individual & household vs personalisation

• MAC addresses

THE ANALYSIS

• Sentiment + Sales by geography

• Usage + Ratings + Sales by product

….the more you cross and the more ‘accurate’ it is, the closer you get

to effectively breaking the Data Protection Act.

QUANT

QUAL

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“The secret of genius is to carry the spirit of the child into old age, which means never losing your enthusiasm.

Aldous Huxley, Author & Philosopher

@LolaOye

[email protected]

THANK YOU

mailto:[email protected]

quant equals qual

Data & Analytics