Tackling the MSI Research Priorities: Which Methods to Use ......Tackling the MSI Research Priorities: Which Methods to Use ? Dominique Hanssens, UCLA ... Capturing Information to

Tackling the MSI Research Priorities: Which Methods to Use ?

Dominique Hanssens, UCLANatalie Mizik, University of Washington

MSI Webinar

May 23, 2018

© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

The 2018-2020 MSI Research Priorities

Cultivating the Customer Asset

The Evolving Landscape of Martech and Advertising

The Rise of Omnichannel Promotion and Distribution

Capturing Information to Fuel Growth

Organizing for Marketing Agility

Which research tools come to the rescue ?


http://www.msi.org/research/2018-2020-research-priorities/cultivating-the-customer-asset/

http://www.msi.org/research/2018-2020-research-priorities/the-evolving-landscape-of-martech-and-advertising/

http://www.msi.org/research/2018-2020-research-priorities/the-rise-of-omnichannel-promotion-and-distribution/

http://www.msi.org/research/2018-2020-research-priorities/capturing-information-to-fuel-growth/

http://www.msi.org/research/2018-2020-research-priorities/organizing-for-marketing-agility/

OVERVIEW OF PRIORITIES

1. CULTIVATING THE CUSTOMER ASSET

1.1. Characterizing the Customer Journey Along the Purchase Funnel and Strategies to

Influence the Journey.

1.2 The Customer Technology Interface

1.3. Macro Trends Influencing Consumer Decision Making

2. THE EVOLVING LANDSCAPE OF MARTECH AND ADVERTISING.

2.1. Defining the Communication Message

2.2. Optimizing Media Strategy

2.3. Setting the Advertising Budget

2.4. Measuring Media Efficacy

3. THE RISE OF OMNICHANNEL PROMOTION AND DISTRIBUTION

3.1. Managing Promotion Across Channels

3.2. Managing Distribution and Demand Across Channels

3.3. Channel Structure

4. CAPTURING INFORMATION TO FUEL GROWTH

4.1. Painting a 360 degree/Holistic View of the Customer

4.2. What Key Performance Indices (KPI’s)/Metrics Should Be Measured and How?

4.3. Assessing Causality

4.4. Approaches to Ingesting and Analyzing Data to Drive Marketing Insights

5. ORGANIZING FOR MARKETING AGILITY

5.1. Internal Organization

5.2. External Organization


Marketing Science has developed a number of practical implementable research tools

https://www.e-elgar.com/shop/handbook-of-marketing-analytics© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

https://www.e-elgar.com/shop/handbook-of-marketing-analytics

64 authors contributed to the Handbook

Angela Y. Lee • Alice M. Tybout • Anja Lambrecht • Catherine Tucker • Olivier Toubia •

Marnik G. Dekimpe • Dominique M. Hanssens • Natalie Mizik • Eugene Pavlov •

Peter E. Rossi • John Roberts • Denzil G. Fiebig • Greg M. Allenby • Pradeep K. Chintagunta •

Dawn Iacobucci • Daria Dzyabura • Hema Yoganarasimhan • Asim Ansari • Yang Li •

Donald R. Lehmann • Murali K. Mantrala • Vamsi K. Kanuri • Vithala R. Rao • Koen Pauwels •

Robert Jacobson • Jorge Silva-Risso • Deirdre Borrego • Irina Ionova • Daniel M. Ringel •

Bernd Skiera • Michael Trusov • Liye Ma • Marc Fischer • Sönke Albers • Klaus Wertenbroch •

Zoë Chance • Ravi Dhar • Michelle Hatzis • Michiel Bakker • Kim Huskey • Lydia Ash •

Noah J. Goldstein • Ashley N. Angulo • Avi Goldfarb • Keiko I. Powers • Paulo Albuquerque •

Bart J. Bronnenberg • Rebecca Kirk Fair • Laura O’Laughlin • Joel H. Steckel •

T. Christopher Borek • Anjali Oza • Alan G. White • Rene Befurt • Sean Iyer •

Michael Akemann • Rebecca Reed-Arthurs • J. Douglas Zona • John R. Howell • Rahul Guha •

Darius Onul • Sally Woodhouse • Vildan Altuglu • Rainer Schwabe© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

I. Experimental Designs 1. Laboratory experiments (Angela Y. Lee & Alice M. Tybout)

2. Field Experiments (Anja Lambrecht & Catherine Tucker)

3. Conjoint analysis (Olivier Toubia)

Applications:

• Mktg: Industry applications (Chapter 15)

• Policy: Consumer (mis)behavior and public policy intervention (Chapter 22)

• Policy: Nudging healthy choices (Chapter 23)

• Policy: Promoting environmentally friendly consumer behavior (Chapter 24)

• Policy: Regulation in Online Advertising Markets (Chapter 25)

• Litigation: Avoiding bias in surveys (Chapter 28)

• Litigation: Experiments in litigation (Chapter 29)

• Litigation: Conjoint analysis in litigation (Chapter 30)

• Litigation: Conjoint applications in antitrust (Chapter 31)

• Litigation: Feature valuation using equilibrium conjoint analysis (Chapter 32)© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

II. Classical Econometrics

4. Time-series models (Marnik G. Dekimpe & Dominique M. Hanssens)

5. Panel data models (Natalie Mizik & Eugene Pavlov)

6. Causality and Endogeneity (Peter E. Rossi)

Applications:

Mktg: Online and offline funnel progression (Chapter 16)

Mktg: Effectiveness of direct-to-physician pharmaceutical marketing (Chapter 17)

Policy: Narcotics use and property crime (Chapter 26)

Litigation: Evaluating harm in a breach of contract (Chapter 33)


III. Discrete Choice Modeling

7. Choice models (John Roberts & Denzil G. Fiebig)

8. Bayesian econometrics (Greg M. Allenby & Peter E. Rossi)

9. Structural Models (Pradeep K. Chintagunta)

Applications

Mktg: Automakers’ pricing and promotion planning (Chapter 18)

Policy: Impact of the “Cash for Clunkers” policy (Chapter 27)

Litigation: Feature valuation using equilibrium conjoint analysis (Chapter 32)


IV. Latent Structure Analysis V. Machine Learning and Big Data

10. Latent structure analysis (Dawn Iacobucci)

11. Machine Learning (Daria Dzyabura & Hema Yoganarasimhan)

12. Big Data (Asim Ansari & Yang Li)

Applications

Mktg: Visualizing competitive market structure (Chapter 19)

Mktg: User profiling in display advertising (Chapter 20)

Policy: …

Litigation: Avoiding bias in surveys (Chapter 28)

Litigation: Surveys in trademark infringement (Chapter 34)

Litigation: Surveys to evaluate a claim (Chapter 35)

Litigation: Machine Learning in litigation (Chapter 36)© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

VI. Generalizations and Optimizations

13. Meta-Analysis (Donald R. Lehmann)

14. Optimization (Murali K. Mantrala & Vamsi K. Kanuri)

Applications

• Mktg: Generalizations in eight marketing areas (Chapter 13)

• Mktg: Online and offline funnel progression (Chapter 16)

• Mktg: Optimization for marketing budget allocation at Bayer (Chapter 21)


Key challenge: connecting the marketing questions with the right data and methods• Data sources: primary or secondary• Which data would you need for

• RP 1.3. Macro Trends Influencing Consumer Decision Making ?• RP 2.3. Setting the Advertising Budget ?• RP 4.1. Painting a 360 degree/Holistic View of the Customer ?

• We pick two scenarios to illustrate • Assessing causality (RP 4.3)

and• Defining the communication message (RP 2.1)• Optimizing Media Strategy (RP 2.2)• Capturing Information to Fuel Growth: Approaches to Ingesting and Analyzing Data

to Drive Marketing Insights (RP 4.4)


Scenario 1: Assessing causality

• Why important ? Because at its core, many marketing decisions involve counterfactual reasoning

• “What if customer X had NOT been exposed to our ad ?”

• “What if the economy hadn’t tanked just as we were introducing our new product?”

• Case study: the (non-)impact of changing ad agencies

• Two different approaches : experiments (A/B testing) and models on historical data


Assessing causality from experiments

• Field experiments vs. lab experiments

• Key advantages: • randomization of treatment • easily understood results communication • easier to implement in the digital world

• Limitations and challenges • Full response function is not revealed • Which marketing activities are experimented on? • Is anyone building a master database of results ?

• A “culture of experimentation” needs to be developed


Assessing causality from models on historical data

• On time-series data, cross-sectional data, panel data

• Key advantages: • Can reveal the entire response function, across the marketing mix • Value increases as data quality and quantity improve • Lends itself to machine learning, AI

• Limitations and challenges • Selection bias, endogeneity • Results are not as easily communicated → choice of response metrics • Scepticism in executive ranks → prove prediction accuracy

• Towards a data-driven culture: a database is a set of answers, waiting for questions to be asked


The power of empirical generalizations

• When a model or an experiment is run, store the response results→develop meta-analyses of marketing impact

• Examples from marketing science: • Price elasticities average -2.6

• Advertising elasticities average 0.11

• We know conditions that either increase or decrease the effect in any given situation

• The meta-database becomes the quantitative repository of the firm’s collective marketing experiences


Eugene Pavlov, Natalie Mizik

16

Increasing Consumer Engagement with

Firm-Generated Social Media Content:

The Role of Images and Words

Scenario 2: New Tools for Capturing Information to Drive Marketing Insights: Defining the Message and

Optimizing Media Strategy


Motivation

• 90% of marketers would like to increase engagement with their content Stelzner’16

• 34% of 2016 annual marketing budgets earmarked for creating, producing, and publishing visual content, up from 26% in 2014, Koshy 2016

• 72% believed that Visual is more effective than Text-based mktg, Gujral 2015

17

Why imagery?

• Distinct brain areas are activated when processing text vs image, Khateb et al. (2002)

• Images are processed quicker and more automatically, Luna and Peracchio 2003

• Images are more effective in eliciting emotional processing, Hsee and Rottenstreich 2004; Lee, Amir, and Ariely 2009; Lieberman et al. 2002


Previous literature: focus on Text

• High emotional activation helps to make NYT articles viral, Berger and Milkman (2012)

• Emotional ads with brand as an integral part of the ad increase both ad virality and sales, Akpinar and Berger (2017)

• Emotional text content, banter, humor, and philanthropy increases reach of ads on FB, Lee, Hosangar, and Nair (2017)

• Outrageousness of ad increases sharing, humor increases both sharing and persuasiveness, Tucker (2014)

• Other Text UGC-focused research: Reviews, Chevalier and Mayzlin 2006; Online chatter, Borah and Tellis 2016, Tirunillai and

Tellis 2014; Forums, Netzer et al. 2012; Blogs, Mayzlin and Yoganarasimhan 2012; Networks, Shriver et al. 2013; Corporate communications, A. Kumar et al. 2016

No research on Visual content • but industry reports suggest it is important

• Image increases retweets by 35%, Video by 28%

18© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

To understand the impact of Text vs. Imagery

1. Assess the value of Visual Rhetoric in social media

2. Examine effectiveness of persuasion of Text vs. Image• Resistance to persuasion: Friestad and Wright’s (1994)

persuasion knowledge model• Campbell and Kirmani’s (2000) tests of the persuasion

knowledge with busy targets


• Develop a tool to predict emotional loading of an image, and lab-validate• Dimensions: sentiment and arousal

• Setup a tool to predict emotional loading of text• Models developed in NLP and computer science

• Apply the two tools to empirical analysis of user engagement with twitter content created by 656 brands over 11 years• Use Double machine learning framework to check robustness


Text of brand tweet

Visual of brand tweet

Outcome engagement metrics

Brands on Social Media


Russell (1980) Model of Affect: 2-D Valence-Arousal map of emotions


Want to place both Text and Image in this space

Tools for text exist.

Extracting emotional content from an image requires a dataset of images that are pre-labeled on Sentiment and Arousal dimensions by human subjects. No such pre-labeled dataset exists.

Solution: develop a model that links image features to emotions the image evokes.


Computer Vision: Image Features

•Digital image = array of numbers•Standard image features (elements of design): (Shapiro and Stockman

2000, Szeliski 2010)

•8 features relevant to analysis of image aesthetics (Datta et al. 2006)

• Color• Texture• Shape• Lines, curves• Corners • Edges• Orientation• Size and aspect ratio


Color features: HSB Hue, Saturation, Brightness

• Each pixel has a value of Hue, Saturation, Brightness

• Count pixels which have low,… medium,...,high amount (20 levels) of Hue to get a histogram of Hue• Repeat for Sat and Brightness

• 20 bins × 3 channels = 60 variables© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

Other Features: edges, corners, lines, texture, orientation

• Number of edges, corners:

marks smoothness of textureLine orientation:horizontal/ vertical, 45/135-degree

Canny edge detector, Harris corner detector Variance of Sobel / Laplacian gradients© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

Data source• Online community, large art display/social networking service since 2000

• 13th largest social network with 3.8 million weekly visits

• Categories include photography, digital art, traditional art, etc.

• 1.5 million comments and 140,000 art submissions daily

Two types of data on the website:1. Images provide visual features2. Text by users (image title, description, tags and comments) provides emotional labels

• Scrape digital images and mine labels for emotions• Use a dictionary of keywords: ANEW data.


Affective Norms for English Words (ANEW) Dictionary. The ANEW provides a set of normative emotional ratings for a large number of words in the English language.

FALSE

absurd

abundance

abuse

acceptance

accident

ace achievement

admired

adultadvantage

adventure

affection

afraid

aggressive

agreement

air

alert

alien alive

alone

ambition

angel

anger

angry

answer

anxious

applause

arm

army

aroused

art

assault

autumn

avenue

baby

bakebar

barrel

basket

bastard

bathbathroom

beach

beautiful

beauty

bed

bees

bench

bird

birthday

blackblind

blond

blue

board

body bold

bomb

book

bored

bottle

bowl

boy

brave

breast

breeze

bridebrightbroken

brother

building

bullet

burial

burn

bus

butter

cabinet

cancer

candy

cane

capable

car

cash

cell

cellar

cemetery

chair

champion

chance

chaos

charm

child

chin

christmas

church

circle

city

cliff

clock

clothing

clouds

coast

cold

color

column

comedy

comfort

computerconcentrate

confidentconfused

contempt

contents

controlling

cook

corner

corridorcottage

couple

cow

crash

crime

criminal

crisis

crown

crude

cruelcurious

custom

cut

damage

dancer

danger

darkdawn

daylight

dead

death

debt

defeated

delayeddelight

dentist

depresseddepression

desire

destroy

destruction

detacheddetail

devil

devoteddinner

dirt

dirty

disaster

discouraged

divorce

doctor dog

dollar

door

dream

dressearth

easy

eateducation

egg

elegant

elevator

employment

engaged

engine

enjoymentevent

evil

excellence

excitement

excuse

execution

exercise

fabric

facefailure

fall

fame

family

famous

fantasy

farm

fat

father

fault

favor

fear

fearful

feverfield

fight

finger

fire

fish

flag

flood

flower

foam

food

foot

fork

free

freedomfriend

friendly

fun

funeral

fur

game

garden

gentle

gift

girlglass

gloom

glory godgold

good

gossip

graduate

grass

grateful

green

grin

guilty

gun

habit

hand

handsome

happy

hard

hat

hate

hatred

hawk

hay

health

heart

heavenhellhelpless hide

highway

history

hit

holiday

home

honest

honey

honor

hope

hopeful

horror

horse

hospital

hostile

hotelhouse

humble

humor

hungry

hurt idea

identity

ignorance

illness

imagine

impressed

improveincentive

indifferent

industry

infant

injury

innocent

insane

insect

inspired

interest

intimate

iron

item

jail

joke

journal

joy

justice

key

kick

kids

killer

kind

king

kiss

knifeknowledge

lakelamp

lantern

laughter

lawn

leader

learn

legendletter

liberty

lie life

lightning

lion

lively

lonely

lost

loveloved

loyal

lucky

luxury

machine

mad

magical

mail

man

manner

marketmaterial

medicine

melody

memories

memory

metal method

mighty

milk

mind

miracle

misery mistake

modestmoldmoment

money

month

moral

mother

mountain

movie

murderer

muscular

museum

music

naked

namenatural

nature

needle

neglect

nervous

news

nicenonsense

nude

nurse

nursery

ocean

oddoffice

opinion

optimism

orchestra

outstanding

pain

paint

palace

panic

paper

paradise

part

party

passage

passion

patent

patient

peace

penalty

pencil

peopleperfection

personphase

pie

pistol

pityplain

plane

plant

pleasure

poetry

poverty

power

powerful

prairie

present

pressureprestige

prettypride

priest

prison

privacy

profit

progress

promotion

protected

proud

punishmentpython

quality

quarrel

queen

quick

quiet

rabbit

radio

rage

rain

razor red

rejected

relaxed

rescue

reserved

respectrestaurant

reunion

revolver

reward

rifle

rigidriverrock

romantic

rough

sadsafe

saint

satisfiedsave

scared

scholar

scream

seatsecure

sentiment

serious

severe

sex

shadow ship

shy

sick

silk

sillysin

sky

slave

sleep

slow

smooth

snake

snow

social

soft

solemn

song

space

sphere

spirit

spray

spring

square

star

startled

statue

stiffstomach

storm

stove

street

stress

strong

stupid

success

sugarsuicide

sun

sunlight

sunset

surprised

suspicious

swift

table

talent

tank

taste

taxi

teacher

tender

tennis

tense

terrible

theorythought

thoughtful

timetobaccotomb

tool

tower

tragedy travel

treat

tree

triumph

trouble

troubled

truck

trust

truth

tumor

ugly

unhappy

unit

upset

useful

useless

vacation

vehicle

victim

victory

vigorous

village

violent

violin

virgin

virtuevision

voyage

wagon

war

warmth

waste watch

water

wealthyweapon

weary

wedding

white

wife

win

window

wine

wise

wish

witwoman

wonder

world

writeryellow

youngyouth

24

68

Aro

usal

, m

ean

=5.1

1, S

D=

1.05

2 4 6 8 10

Valence, mean= 5.14, SD=1.99

1 {positive V, high A} 2 {negative V, high A}

4 {positive V, low A} 3 {negative V, low A}

Data: Bradley & Lang 1999© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

Classification results: Image Sentiment

Arousal

Sen

tim

ent

— +

Drivers of positive visual Sentiment:• Horizontal or 135-degrees orientation• Green, blue, orange• High brightness• Color variety• Less smooth texture

Drivers of negative visual Sentiment :• Vertical or 45-degrees orientation• Red• Presence of dominant color• Low brightness and/or saturation• Concentrated brightness and/or

saturation© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

Classification results: Image Arousal

Drivers of HIGH visual arousal:• Vertical or 45 degree orientation• Many corners• Deep red, yellow, pink

Drivers of low visual arousal :• Blue• Black and white• Many edges• 135 degree orientation

Arousal

Sen

tim

ent

— +


Emotional content of Text

Text Sentiment:• Valence Aware Dictionary for sEntiment Reasoning (VADER) optimized specifically for twitter posts, Hutto and

Gilbert 2014

[-] “Mesothelioma is a rare but fatal form of cancer that is often difficult to diagnose” vs.

[+] “Waffles make people happy. Like, really happy. Like, best way to start your day happy”

Text Arousal:• Usage of punctuation (!, ?) and upper-case, Barbosa & Feng 2010

• Usage of “call-to-action” verbs, Naveed et al. 2011

[High] “ITS NATIONAL CHOCOLATE DAY! DROP EVERYTHING AND EAT CHOCOLATE!“ vs.

[Low] “Beer at the end of a long day makes everything better.”


Emotional Content of TextAnalyzed with VADER (2014) lexicon optimized for twitter

Emotional Content of Image images + video thumbs: analyzed with our model

Outcome engagement metric: Cumulative number of retweets

Brands on Social Media


Promo keywords effect over time (𝛾𝑦𝑒𝑎𝑟)


Text sentiment/arousal effect over time (𝛽𝑦𝑒𝑎𝑟)

• Consumers became significantly more resistant to persuasion through positive, high motivation-to-act text.

• Also became more resistant to text promo keywords which used to work


Relative impact, text vs image

Text effects Image effects


Industry heterogeneity

36Charity / nonprofit Quick-service restaurants Health and beauty© 2018 Dominique M. Hanssens and Natalie Mizik at MSI Webinar “Tackling the MSI Research Priorities: Which Methods to Use? on May 23, 2018. All rights are reserved.

Conclusions

• The marketing analytics discipline has developed a wide array of available research methods

• It is important to match the research method with the marketing question at hand

• Experimental (primary) and historical (secondary) methods have different strengths and weaknesses

• Either way, building a master database of results creates a new strategic asset for the firm

• New tools (machine learning, artificial intelligence) generate knowledge that could not have been developed before


Documents

Tackling the MSI Research Priorities: Which Methods to Use ......Tackling the MSI Research Priorities: Which Methods to Use ? Dominique Hanssens, UCLA ... Capturing Information to