ART 2014 25th SHARING WHAT WORKS

ART2014
Advanced Research Techniques Forum
SHARING WHAT WORKS
AND KNOWING WHY
June 22 – 25, 2014
Santa Fe, NM
25th
Anniversary
Register by
May 23 and save!
www.ama.org/artforum
SHARING WHAT WORKS AND KNOWING WHY | June 22–25, 2014 • Santa Fe, NM
JOIN US IN SANTA FE!
On the 25th anniversary of the AMA’s Advanced Research Techniques (ART) Forum,
we’ve freshened up the program to make it an even better experience for you.
The ART Forum has been called a “journeyman” conference — the place to discover practical
solutions, share methods, opinion and also code. While we have a strong academic presence,
our role is to guide the mid-level solution rather than refinement.
This year, we’re opening up the conference to some new audiences: marketing mix modelers,
statistically oriented data scientists and the legal community. In these fields, new data and
emerging methods demand an understanding not just of how something works but why.
Hence, this year’s conference theme: “Sharing What Works and Knowing Why.”
In addition, we are strengthening areas that are traditional ART Forum topics (e.g., choice and
conjoint, segmentation) and building on recent efforts to expand the conference to topics such
as big data, visualization, text analysis, unstructured data and social networks.
Join your fellow researchers and colleagues from across the industry to find solutions to
marketing problems through the free exchange of ideas and methodologies. The ART Forum
is about science — discovering and sharing what works. You’ll come away with a deeper
appreciation of what models can and cannot do and new connections with peers who share
the same interests and challenges.
We look forward to seeing you in June!
Greg Allenby and Mark Garratt, Chairs
Socia
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CONFERENCE PROGRAM
Sunday, June 22
7:45 am–7:30 pm
Tutorial and Forum Registration
7:30 am–8:00 am
Breakfast (tutorial attendees only)
8:00 am–12:00 pm
Preconference Tutorials (see website for descriptions)
A. Introduction to Interactive Visualization for Practitioners
Sergiy Nesterko, Principal, Theory LLC
B. Marketing Mix
Mark Garratt, Partner and Co-Founder, in4mation insights
Stephen Garry, Director PPS Global Market Analytics, Hewlett-Packard
Sanjib Mohanty, Senior Director Marketing Science, in4mation insights
C. Text Analysis (Latent Topic Modeling)
Joachim Büschken, Professor of Marketing, Catholic University (Germany)
Simon Grammel, Research Assistant, Catholic University (Germany)
D. An Introduction to Probability Models for Marketing Research
Peter S. Fader, Professor of Marketing, The Wharton School of the University of Pennsylvania
Bruce G.S. Hardie, Professor of Marketing, London Business School
12:00 pm–1:00 pm
Lunch on Your Own
1:00 pm–5:00 pm
Preconference Tutorials (see website for descriptions)
E. Probability Models for Customer-Base Analysis
Peter S. Fader, Professor of Marketing, The Wharton School of the University of Pennsylvania
Bruce G.S. Hardie, Professor of Marketing, London Business School
F. Introducing the New SAS® BCHOICE Procedure for Bayesian Choice Models
Amy Shi, Senior Research Statistician Developer, SAS
G. Exploring the Upper Level Model in Hierarchical Bayesian Models
Greg Allenby, Professor of Marketing and Statistics, Ohio State University
Thomas C. Eagle, President, Eagle Analytics of California, Inc.
H.Hierarchical Bayes Methods for Marketing
Jeff Dotson, Associate Professor, Brigham Young University
Elea McDonnell Feit, Executive Director, Wharton Customer Analytics Initiative and Lecturer
in Marketing, The Wharton School
5:30 pm–6:00 pm
New Attendee Orientation
6:00 pm–7:30 pm
Welcome Reception and Poster Sessions
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SHARING WHAT WORKS AND KNOWING WHY | June 22–25, 2014 • Santa Fe, NM
Monday, June 23
8:00 am–6:30 pm
Registration
8:00 am–8:30 am
Breakfast
8:30 am–12:00 pm
Monday Morning Sessions
KEYNOTE (Chris Chapman, Session Leader)
Bayes and Big Data
Steven Scott, Senior Economic Analyst, Google
Big data sometimes poses big problems for statistical theory, depending on just how big the data is and on
the specific research question. Steven Scott of Google will describe computational and statistical trade-offs
associated with various strategies for big data problems. He will then present the “Consensus Monte Carlo”
algorithm, which efficiently scales Bayesian computations by partitioning data across multiple machines
and then combining draws from the multiple posterior distributions into a global posterior.
TEXT ANALYSIS (Joachim Büschken, Session Leader)
Computational Linguistics and Learning from Big Data
Gabriel Doyle, University of California, San Diego
Between social media, blogs and product review sites, the Internet provides marketing researchers with
data of unprecedented size and informativity. But extracting the relevant information from these big data
sources requires careful thinking and new analysis techniques. This talk provides an overview of common
unsupervised learning problems and solutions from computational linguistics, such as parsing, sentiment
analysis, named-entity recognition and information extraction, which can be used to automatically identify
the important information lurking in large unstructured text data sets.
Detecting Customer Experience Web Comments and their Drivers of Satisfaction
Kurt A. Pflughoeft, Director of Marketing Sciences, Maritz Research
Consumer-generated media is often littered with information that is not part of a customer’s experience
with a product or service. Being able to filter out as much irrelevant data as possible is important to make
sure that the true customer feedback is still discernible. Additionally, processing unstructured data must
go beyond simple word clouds and sentiment to understand the drivers of satisfaction. This research
examines several supervised techniques to remove irrelevant data and then applies key driver analysis to
the unstructured data.
Using Text Analysis of Unstructured Data to Navigate a World without Questions
John Fix, Advanced Analytics Innovation, Procter & Gamble
P&G serves almost 5 billion consumers around the world and relies on survey methods to guide its
understanding of its products, markets and communications. This reliance has become a burden on
resources — time and money — to stay in touch with consumers in markets across hundreds of
brands. Text analysis of unstructured data has the potential to sample from an unprecedented base size
of consumers across content that narrates, critiques, advocates and provides measure of consumers’
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ART2014
Advanced Research Techniques Forum
attitudes and behaviors. Sampling from this stream of information provides technical challenges as the data
needs to be filtered for relevancy, analyzed for attributes and themes, and potentially modeled for measures
such as brand equity. It is the intent of the presentation to share the motivation for applying unstructured
data analysis, potential applications and challenges experienced.
12:00 pm–1:30 pm
Lunch and Parlin Award Presentation
1:30 pm–4:30 pm
Monday Afternoon Sessions
MARKETING MIX (Stephen Garry, Session Leader)
Attribution Modeling: State-of-the-Art and Beyond
P. K. Kannan, Professor of Marketing Science, University of Maryland
Presentation will focus on (a) comparing alternative attribution models on dimensions of theoretical basis,
estimation techniques, granularity of data analyzed, ability to estimate spillover and carryover effects, and
suitability for marketing mix implementations, and (b) challenges in dealing with big data, integrating online
and offline channels, incomplete data, and controllable versus uncontrollable elements of the customer
purchase funnel.
Modeling Advertising Effects in a Multi-media Environment —
A Latent Class Latent Markov Chain Approach
Carsten Stig Poulsen, Professor, Aalborg University
A new approach to the measurement of advertising effects in a multi-media environment, based on
consumer panel data, is presented. We formulate a dynamic, individual model that can be used for
descriptive, predictive, segmentation and — potentially — optimization purposes. It may even be utilized
during an ongoing campaign to monitor and eventually correct the media plan. The model is based on
recent advances in latent Markov modeling and views advertising and its effects as interactive stochastic
processes that unfold over time, influenced by the impact of advertising. The model is demonstrated by a
real but disguised application.
How Linking Marketing Mix Modeling and Copy Testing Can Improve
Advertising Performance
Ashish Joshi, Senior Director, Advanced Analytics, The Clorox Company
Stephen Garry, Director, Market Analytics, Hewlett-Packard
With billions of dollars spent on TV advertising every year, a persistent problem has been determining how
much to spend on an ad before it airs. We show how copy test scores can be used to accurately predict inmarket performance of ad executions, making it possible to optimize spending on ads before they enter the
market. This work has caused significant changes in pre-market testing behavior at Clorox and improved
advertising ROI.
Marketing ROI Measurement and Big-ish Data
Ross-boy Link, President, Global MROI Solutions, Nielsen
Is big data big hype? Current developments in marketing ROI measurement are the culmination of 20
years of valiant attempts by our industry at using so-called “single source” data. Big data is truly coming to
marketing measurement, and it’s bringing big changes, so get ready. But don’t throw everything you know
out the window just yet...
5:00 pm–6:30 pm
Networking Reception and Poster Sessions
www.ama.org/artforum • 5
SHARING WHAT WORKS AND KNOWING WHY | June 22–25, 2014 • Santa Fe, NM
Tuesday, June 24
8:00 am–5:00 pm
Registration
8:00 am–8:30 am
Breakfast
8:30 am–12:00 pm
Tuesday Morning Sessions
VISUALIZATION (Chris Chapman, Session Leader)
Visualizations of Model Output via Interactive Dashboards
Sergiy O. Nesterko, Principal, Theory LLC
Mark Garratt, Partner and Co-Founder, in4mation insights
Steve Cohen, Partner and Co-Founder, in4mation insights
With more data and increasingly complex models, the amount of analytic information grows exponentially.
We present two case studies in marketing mix optimization and consumer segmentation where interactive
visualization gives the end-user more control and understanding of the analytic process, and discuss pros
and cons of using interactive visualization in practice.
Visualizing Big Data: Methodology and Applications for Tuneable Maps
Stephen Bell, Senior Partner, Bell Falla and Associates
Tuneable maps are perceptual maps that are optimized in terms of both managerial criteria (e.g.,
comprehensibility) and statistical fit. Briefly, we augment a statistical fitting algorithm with “tuneable”
parameters (e.g., dispersion) that help to define a useful map. A large patient records database is used to
illustrate the methodology.
SOCIAL NETWORKS (Peter Ebbes, Session Leader)
Enterprise Social Network Formation on Yammer
Hema Yoganarasimhan, Assistant Professor of Management, University of California, Davis
Enterprise social networks are networks between employees in a firm or between people who interact
with each other for business activities. Enterprise networks are used for one-to-one as well as one-tomany communications and collaboration. We study factors that affect the formation of enterprise social
networks, using data from the leading enterprise software — Yammer. Specifically, we identify the network
externalities associated with past adoption on current adoption. We quantify how these externalities affect
the speed of network growth and size of the eventual network. We also examine how adoption of the
network by the firm (or the business) affects the overall adoption of the network and the use of the network
by the employees.
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NEW PARTICIPANTS.
NEW SESSIONS.
DON’T MISS IT!
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How the Establishment of Same-Carrier Ego Networks Impacts
Prepaid Mobile Carrier Engagement
Olly Downs, SVP, Data Sciences, Globys
Globys leverages telecom billing data to drive contextual in-base marketing activity for mobile operators.
Uniquely we are able to perform whole-base social network analytics for a carrier based on voice and SMS
interactions, in conjunction with conducting live marketing experiments.
One particular challenge for prepaid mobile operators that SIM activations that do not quickly turn into
revenue-generating usage, often driven by misalignment of dealer incentives and customer benefits with
those of the carrier. It is common, for example, for dealers to be compensated by carriers per activated
SIM, irrespective of whether the SIM becomes a commercially active line. Further, customers will often
acquire SIMs for the mobile carrier networks of their friends and family to exploit unlimited in-network
mobile-to-mobile calling and free inbound calls.
In this work, we have explored the focal and ego-network impacts of an SMS marketing campaign designed
to incent customers with activated SIMs who have not yet added balance to the line to do so and to initiate
commercial activity, testing the hypothesis that it is the development of an ego-network on that carrier that
impacts the propensity for a customer to engage in the offer.
12:00 pm–1:00 pm Lunch and Exhibits
1:00 pm–4:00 pm
Tuesday Afternoon Sessions
UPPER LEVEL MODELING (Thomas C. Eagle, Session Leader)
How to Generalize from a Hierarchical Model?
Max Pachali, Goethe University
Peter Kurz, Head of Research and Development, TNS Infratest
Thomas Otter, Professor of Marketing, Goethe University
When hierarchical models are fit to a sample of observational units — e.g., a representative sample
of consumers in a product category participating in a discrete choice experiment — the relevant
generalization for marketing decisions is to observational units from the same population not included in
the sample. However, current choice simulators aimed at informing decisions that pertain to the entire
population of consumers predict market shares based on simulated decisions of the consumers included in
the sample. We probe into the merits and pitfalls of this practice vis-à-vis approaches that directly rely on
the hierarchical prior using data from several different commercial discrete choice experiments.
Choice & Conjoint: Using the Upper Level of an HB Model
Paul Richard “Dick” McCullough, President, MACRO Consulting, Inc.
This presentation explores the impact of a wide variety of covariates in the upper model of HB estimation
routines. In a single-cell design, numerous potential covariates will be inserted into a questionnaire that also
contains a CBC exercise. Sample will be generated from a panel that contains extensive digital behavior
information on panel members. These data will be appended to the survey data and also examined for
efficacy.
www.ama.org/artforum • 7
SHARING WHAT WORKS AND KNOWING WHY | June 22–25, 2014 • Santa Fe, NM
Integrating Self Stated Data into Choice Models without Bias
Kevin Karty, VP Analytics, Affinnova, Inc.
This session provides a simple mechanism for integrating self-stated data into a hierarchical Bayesian
choice model without suffering from the bias induced by other ad-hoc methods. Specifically, self-stated
data can be integrated as a covariate in the upper level of the choice model simply by defining group
membership based on self-stated responses to questions about feature preference or importance. This
maintains the integrity of the underlying assumptions of the choice model, such as equal error variance in
choices, which are violated by other approaches. The resulting model can strengthen simulated switching
behavior and improve overall accuracy.
Trading Upper Level Model Supervision for Data Augments and
Respondent Level Regression
Kevin Lattery, VP Marketing Sciences, Maritz Research
Respondent level regressions are flexible and give analysts complete control at the respondent level. These
approaches use data augments rather than an upper level model of means and covariances. We discuss
previous research on data augment methods, how the augments can be extended, and introduce a new
source for data augments: latent class ensembles. We compare these data augment methods against each
other and benchmark them against HB.
4:15 pm–5:00 pm
2015 ART Forum Roundtable
Wednesday, June 25
8:00 am–1:00 pm
Registration
8:00 am–8:30 am
Breakfast
8:30 am–12:00 pm
Wednesday Morning Sessions
SEGMENTATION (Jacqueline Dawley, Session Leader)
Monitoring Shifts in Product Feature Importance via Trends in Online Searches
Ye Hu, Associate Professor of Marketing, Bauer College of Business, University of Houston
Sina Damangir, Doctoral Student, University of Houston
Rex Yuxing Du, Marvin Hurley Associate Professor of Marketing, Bauer College of Business,
University of Houston
Various factors beyond the control of marketers can lead to changes in the relative importance of different
product features in shaping consumers’ purchase decisions. Such changes in turn can lead to substantial
shifts in the relative attractiveness of products offering different feature levels. To marketers, the challenge
lies in finding a reliable yet cost-effective way to track the relative weights consumers place on different
product features. In the context of the U.S. automotive market, we explore the potential of using trends
in online searches for feature-related keywords as proxies for trends in the relative importance of five
common features in determining vehicle sales, i.e., fuel economy, acceleration, body type, cost to buy and
cost to operate. By augmenting marketing mix data with feature search data in a sales response model, we
show substantial improvements in both in- and out-of-sample fit. Furthermore, by examining the dynamic
relationship between trends in vehicle feature searches and vehicle sales, we show how the evolution of
feature search intensity can be leveraged to allow marketers to respond more proactively.
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Bayesian Co-Clustering of Dyadic CPG Purchase Data
Ewa Nowakowska, Director Marketing Science, GfK
Joseph Retzer, VP Advanced Analytics, CMI Research
This paper presents an overview of Bayesian co-clustering of dyadic market research data. Specifically,
we show a Bayesian co-clustering of survey based shopper CPG purchase. Clustering is performed
simultaneously on both rows (shoppers) and columns (products) of the data matrix. The approach offers
numerous benefits beyond standard clustering methods that ignore inherent dyadic relationships in
the data. In addition, unlike standard co-clustering methods which are primarily partitional, Bayesian
co-clustering allows for mixed membership of rows/columns in respective clusters, which is a natural
requirement in the context of shopper-product relations.
Paul E. Green Award
This award recognizes the best article in the Journal of Marketing Research that demonstrates the greatest
potential to contribute significantly to the practice of marketing research. It honors Paul E. Green, Professor
Emeritus of Marketing, The Wharton School and S.S. Kresge, Professor Emeritus of Marketing, University of
Pennsylvania.
12:00 pm
Conference Adjourns
1:00 pm–5:00 pm
Post-Conference Tutorials (see website for descriptions)
I. Proving Value: Conjoint for High Stakes Patent Disputes
Peter Rossi, Professor of Marketing, Statistics and Economics, UCLA
Greg Allenby, Professor of Marketing and Statistics, Ohio State University
J. Introduction to R for Marketing Analytics
Chris Chapman, Senior Quantitative Researcher, Google
Elea McDonnell Feit, Executive Director, Wharton Customer Analytics Initiative and Lecturer
in Marketing, The Wharton School
NEW PARTICIPANTS.
NEW SESSIONS.
DON’T MISS IT!
Register by May 23 and save
www.ama.org/artforum
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•9 •9
SHARING WHAT WORKS AND KNOWING WHY | June 22–25, 2014 • Santa Fe, NM
POSTER SESSIONS
Individual-level Models vs. Sample-level Models: Contrasts and Mutual Benefits
Jeffrey Dumont, Senior Consultant, RSG and University of Leeds
Marek Giergiczny, University of Warsaw
Stephane Hess, University of Leeds
Rational Shopper Index: Why Colombian Shoppers are Going Slow After the Party
Camilo Herrera, CEO, RADDAR Consumer Knowledge Group
Mixed Conjoint and Non-Conjoint Design in Discrete Choice Exercise
Jing Jin, Manager, The Nielsen Company
Application of BG/NBD Model to Online Panelists
Edward “Paul” Johnson, Director, Analytics, SSI
RSGHB: A Flexible Approach for Hierarchical Bayesian Model Estimation in R
Jeff Keller, Senior Analyst, RSG
Jeffrey Dumont, Senior Consultant, RSG
Construction of Efficient Heterogeneous Choice Designs: A New Approach
Qing Liu, Assistant Professor of Marketing, University of Wisconsin-Madison
Elina Tang, Assistant Professor of Marketing, University of Illinois at Chicago
Human Benefits of AI Methods to Search for Plausible Models on Large Data Sets
Scott Porter, VP Methods, Added Value
Zoë Dowling, VP R&D & Offer Innovation, Added Value
How Can We Determine Market Success and Effectiveness on Products and
Services Using Social Media Strategies?
Jeff Ritter, Professor, Keiser University
Introducing the New SAS® BCHOICE Procedure for Bayesian Choice Models
Amy Shi, Senior Research Statistician Developer, SAS
Blocks and Version Effects in Discrete Choice Designs
Wei Shi, Senior Specialist – Global Customer Insights Group, Bain & Company, Inc.
Paul Markowitz, Senior Manager – Global Customer Insights Group, Bain & Company, Inc.
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Advanced Research Techniques Forum
GENERAL INFORMATION
Hotel
Registration and Pricing
Eldorado Hotel & Spa
309 W. San Francisco Street
Santa Fe, NM 87501
Phone: 1.505.988.4455
Reservations: 1.800.955.4455
n Conference Fees
Room Rate
The AMA has negotiated a special group rate
for the ART Forum:
Early Registration
(Payment must be received in AMA office by
May 23, 2014)
AMA Member:$950*
Non-Member:$1,225*
AMA Doctoral Student:$525*
*Add $100 to these prices after May 23
$199 single/double occupancy per night
+ applicable state and local taxes,
currently 15.1875%
AMA Doctoral Students must call 800.262.1150,
ext. 9009 and provide their AMA member ID# in
order to register and receive the discount.
(Taxes and fees are subject to change
without notice)
n Tutorial Fees
Rates are available three days before and after
the meeting dates, based on availability. Please
reserve your accommodations early as there are
a limited number of rooms at the special rate.
Call Eldorado Hotel & Spa at 1.800.955.4455
and ask for the AMA ART Forum Conference
room block. The reservation cut-off date is
May 23, 2014.
Questions?
Call 800.AMA.1150 or email info@ama.org
Tutorials are optional and are scheduled for
Sunday, June 22 and Wednesday, June 25.
Early Registration
(Payment must be received in AMA office by
May 23, 2014)
AMA Member:
Non-Member:
$295 per tutorial*
$320 per tutorial*
*Add $25 to each tutorial after May 23
Tutorials only: If you are only attending tutorial(s)
and not the conference, a $100 administration fee
will be assessed in addition to the tutorial fee(s).
Sponsor/Exhibitor Opportunities
Sponsorships and exhibit space are available.
Reserve your space today by contacting Lore Gil at 312.542.9033 or lgil@ama.org.
Conference Committee
Greg Allenby, Chair, Professor of Marketing
and Statistics, Ohio State University
Mark Garratt, Chair, Partner and Co-Founder,
in4mation insights
Joachim Büschken, Professor of Marketing,
Catholic University (Germany)
Chris Chapman, Senior Quantitative
Researcher, Google
Jacqueline Dawley, President, Insight Analysis
Thomas C. Eagle, President, Eagle Analytics
of California, Inc.
Peter Ebbes, Associate Professor of Marketing,
HEC Paris
Stephen Garry, Director PPS Global Market
Analytics, Hewlett-Packard
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May 23
and save!
Anniversary
25th
June 22 – 25, 2014
Santa Fe, NM
SHARING WHAT WORKS
AND KNOWING WHY
Advanced Research Techniques Forum
ART2014
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