FINDING YOUR FOCUS - American Marketing Association

22ND ANNUAL 

ART2011 

Advanced Research Techniques Forum 

FINDING YOUR FOCUS 

June 5–8, 2011 

Palm Desert, CA

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June 5–8, 2011 • Palm Desert, CA 

CONFERENCE COMMITTEE 

Wendy Moe, Chair 

Associate Professor of Marketing 

University of Maryland 

Anocha Aribarg 

Assistant Professor of Marketing 

University of Michigan 

Chris Chapman 

Senior Researcher 

Microsoft Advertising R&D 

Jeff Dotson 

Assistant Professor of Marketing 

Vanderbilt University 

Elea McDonnell Feit 

Research Director 

Wharton Customer Analytics Initiative 

David Lyon 

Principal 

Aurora Market Modeling 

MarketingPower.com/artforum 

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by May 5 

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Dear Attendee: 

22ND ANNUAL 

Advanced Research Techniques Forum ART2011 

The Advanced Research Techniques Forum focuses on the use of sophisticated 

methodologies and quantitative techniques to support strategic and tactical marketing 

decisions. The conference, now in its 22nd year, provides a unique opportunity for 

academics, practitioners and research clients to exchange ideas and solutions. 

The 2011 ART Forum Program Committee has assembled an innovative slate of papers 

and posters featuring methods that address both traditional and emerging research 

challenges. We have brought back the end-of-day roundtables to give attendees and 

speakers a chance to delve deeper into issues raised in the presentations. You may also 

notice that we have scheduled additional networking receptions this year. I hope you take 

advantage of these receptions to spend time with old friends and to meet new attendees 

working on interesting projects. 

Over two and a half days of programming, attendees are encouraged to share in a 

lively and open discussion of the various methods. Additional learning opportunities are 

provided through optional tutorials on a variety of topics. This year, the committee has 

worked with a number of new instructors to provide several new tutorial offerings: 

Â Advanced Computer Simulations for Improved Marketing Decisions 

Â Advanced Theory and Application of Hierarchical Bayes Choice Models 

Â Analyzing Network Data 

Â Introduction to Bayesian Statistics and Marketing 

Â Introduction to Discrete Choice Modeling 

Â Introduction to R for Marketing Research 

Â Modeling Market Dynamics 

Â Structural Modeling of Forward-Looking Consumer Behavior 

Â Introduction to Text Mining and Sentiment Analysis 

Â Introduction to Bayesian Networks: 

Their Applications in the Field of Marketing Science 

We hope you’ll join us for an exciting and informative 2011 ART Forum! 

Wendy Moe 

Committee Chair 

Associate Professor of Marketing 

University of Maryland 

FROM THE CHAIR 


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CONFERENCE PROGRAM 

Sunday, June 5 

7:45–8:15 am Breakfast 

8:15 am–12:15 pm Preconference Tutorials 


(see website for complete descriptions) 

A. Introduction to Bayesian Statistics and Marketing 

Greg M. Allenby, The Ohio State University 

B. Introduction to Discrete Choice Modeling 

Brian Orme, Sawtooth Software 

Jon Pinnell, MarketVision Research 

C. Introduction to R for Marketing Research 

Eric Zivot, University of Washington 

Chris Chapman, Microsoft 

D. Introduction to Text Mining and Sentiment Analysis 

Matt Taddy, University of Chicago 

12:15–1:00 pm Lunch on Your Own 

1:00–5:00 pm Preconference Tutorials 


E. Advanced Computer Simulations for Improved Marketing Decisions 

David G. Bakken, KJT Group 

F. Analyzing Network Data 

Wolfgang Jank, University of Maryland 

G. Structural Modeling of Forward-Looking Consumer Behavior 

Andrew Ching, University of Toronto 

5:00–5:30 pm New Attendee Orientation 

5:30–7:00 pm Welcome Reception 

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by May 5 

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Monday, June 6 

22ND ANNUAL 



8:15 am–12:00 pm Monday Morning Sessions 

SESSION 1: 

EMERGING METHODS 

Evolving Viral Marketing Strategies 

William Rand, University of Maryland 

Forrest Stonedahl, Northwestern University 

Uri WIlensky, Northwestern University 

As the growth of social media accelerates, viral marketing as a mechanism to 

spread an idea or product through online social venues has become increasingly 

important. However, in a complex social network, which consumers should be 

targeted to maximize the reach and increase the speed of diffusion within these 

systems? We explore a way to select consumers on the basis of local network 

properties, and then examine how these network properties affect the ability of 

consumers to influence their social networks. In the end, to provide an answer 

to the question of which consumers to target, we construct a tool that uses 

a combination of social network metrics, agent-based modeling and genetic 

algorithms, to discover a robust solution that we demonstrate on several social 

networks, including Twitter and a college alumni social networking site. 

Exponential Random Graph Models of 

Linked Records in a Consumer Panel 

Mark Kinnucan, The Nielsen Company 

Christina Gutierrez, The Nielsen Company 

Recent concerns about online panel quality have prompted efforts to ensure that 

panels are free of duplicate registrations; these efforts have become a standard 

practice in the market research industry. In the process of identifying duplicates, 

the duplicate records are linked. By studying the networks formed by these links, 

we can increase our understanding of the threat posed by duplicates in a panel. 

Recent advances in estimation, inference and software availability of exponential 

random graph models (ERGMs) have greatly increased their utility for analyzing 

social and other networks. This presentation describes the use of ERGMs (also 

called p* models) to explore the panelist attributes and network microstructures 

that characterize duplication within an active online panel. 


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SESSION 2: 

EXTENDING CHOICE MODELING TO NEW DOMAINS 

Forecasting Multi-Channel Media Consumption 

During the World Cup 

Elea McDonnell Feit, Wharton Customer Analytics Initiative 

Pengyuan Wang, The Wharton School, University of Pennsylvania 

Eric T. Bradlow, Wharton Customer Analytics Initiative 

Peter S. Fader, Wharton Customer Analytics Initiative 

We investigate customer behavior on multiple media platforms around the world’s 

largest sporting event, the 2010 FIFA World Cup, with the aim of understanding 

and projecting multi-platform consumption habits of the United States audience 

across digital properties including websites, streaming video and mobile. The effect 

of the game schedule on each individual’s multi-platform usage is modeled as a 

function of latent teams’ media attractiveness parameters, and allows for us to 

understand the interplay (if it exists at all) between each team’s FIFA world rankings 

(indicating the quality of the team on the field) and a team’s ability to draw viewers 

to the event (its media attractiveness). Through a correlation structure, the model 

also allows us to explore the relationship between channels. The resulting model 

allows us to predict and understand multi-channel reach and frequency around 

this event—commonly used metrics critical to planning and pricing advertising in a 

multi-platform media environment. 

On the Use of Bootstrap and Ensemble Methodology 

for Improving DCM Predictions 

Cazhaow Qazaz, MarketTools Inc. 

Joseph Retzer, MarketTools Inc. 

One of the most important considerations in discrete choice modeling is the size 

of the survey sample. While the issue can be addressed via appropriate statistical 

analysis, reality often dictates that the researcher has little or no control over 

the data size. There are situations, for example, where the size of the population 

is inherently small. Other contributing factors include cost and time limitations 

associated with the data gathering, thus marketers are often faced with an 

insufficient amount of data to do the modeling work. In this paper, we demonstrate 

that DCM ensembles, created via bootstrap samples, can alleviate the worst 

symptoms of the problem and yield improved predictions. 

MarketingPower.com/artforum

22ND ANNUAL 


12:00–1:00 pm Lunch 

1:00–4:30 pm Monday Afternoon Sessions 

SESSION 3: 

MODELING CUSTOMER CHURN 

What Are Your Customers Still Doing? 

A Bivariate Attrition Model 

David A. Schweidel, University of Wisconsin-Madison 

Young-Hoon Park, Cornell University 

Zainab Jamal, HP Laboratories 

While attrition models are commonly used for customer valuation and identifying 

those customers who are still “alive,” extant research has considered only a single 

type of transactional activity. In a number of contexts, customers may engage in 

multiple activities, such as purchasing in multiple product categories and shopping 

across different channels. In this research, we employ a bivariate attrition model 

to examine the way in which customers cease engaging in two non-sequential 

activities—whether all at once or through a tiered attrition process. Leveraging the 

relationship between the two activities, we demonstrate how jointly examining them 

offers a more complete picture of customers’ dynamic behavior than analyzing the 

individual activities in isolation. 

Modeling Customer Lifetimes with 

Multiple Causes of Churn 

Michael Braun, Massachusetts Institute of Technology 

David A. Schweidel, University of Wisconsin-Madison 

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Customer retention and customer churn are key metrics of interest to marketers, 

but little emphasis has been placed on linking the different reasons for which 

customers churn to their value to a contractual service provider. Some of these 

reasons for churn can be influenced by the firm (e.g., service problems or pricevalue 

tradeoffs), but others are uncontrollable (e.g., customer relocation and death). 

We examine how the relative likelihood to end service due to different reasons 

shifts during the course of the customer-firm relationship, and how the effect of a 

firm’s efforts to reduce customer churn for controllable reasons are mitigated by 

the presence of uncontrollable ones. The result is a measure of the “upper bound” 

on the return to retention marketing that a firm can expect to accrue by delaying 

churn for different reasons—for customers with different demographic profiles and 

different elapsed tenures with the firm. 


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SESSION 4: 

REFINEMENTS IN CHOICE MODELING 

A Conjoint Model for Allocation Data 

Jeff Brazell, The Modellers 

Takuya Satomura, Keio University, Japan 


In industry, various research needs lead us to ask allocation questions in conjoint 

studies. For example, in pharmaceutical studies, it is a common industry practice 

to ask physicians to allocate treatments across patients. Similarly, allocation 

questions may be appropriate in situations where respondents purchase products 

across multiple occasions, or as a representative for multiple people (e.g., a parent 

for her family), when studying a category with variety seeking, or in other contexts 

where decision-makers typically order products or services in quantity (e.g., 

shipper selection). Typical approaches to analyzing these data rely on regression 

analysis or assume that respondents are providing independent responses to 

different choice tasks. We propose a new model based on a constrained utility 

maximization. We show in-sample and predictive improvements over the use of 

existing models, particularly in identifying choice options with low demand. 

Controlling for Complex Nonverbal Stimuli in a Choice Model 

Mark A. Beltramo, General Motors 

Jeff P. Dotson, Vanderbilt University 

Elea McDonnell Feit, Wharton Customer Analytics Initiative 

Randall C. Smith, General Motors (Retired) and Oakland University 

It is well established that in many product categories, industrial design has an 

important influence on consumer choice. Consequently, product designers often want 

to understand the effect of design on product choice, but it hasn’t been clear how to 

incorporate such complex, multi-dimensional attributes into consumer choice models. 

Existing approaches either ignore important information about visual design and its 

effect on vehicle choice—particularly the fact that individuals differ in what they find 

appealing—or subject respondents to long and difficult tasks where they rate many 

alternative new designs. Because it is so difficult to measure preferences for visual 

designs, choice modelers often treat a design as equally appealing to all consumers. 

But when the effect of design at the individual level is ignored, the observed individual 

behavior is likely to violate the Independence of Irrelevant Alternatives property, a 

core assumption underlying the vast majority of choice models used in business 

practice, including hierarchical choice models. We propose a new approach that 

controls for the fact that customers who prefer one vehicle’s shape are likely to prefer 

similar-shaped vehicles. Using choice-based conjoint data for crossover vehicles, we 

demonstrate that the proposed model makes better predictions about which products 

will gain or lose share when a new vehicle enters the market. We will discuss how 

such methods can be applied to other complex attributes, such as speed of a 

computer, sound quality of a stereo or taste of a snack food. 


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22ND ANNUAL 


4:30–5:15 pm Speaker Roundtables 

5:15–6:45 pm Networking Reception and 

Poster Sessions 

Tuesday, June 7 


8:15 am–12 pm Monday Morning Sessions 

SESSION 5: 

DYNAMIC MODELS AND ONLINE DATA 

From Online Search to Offline Demand: 

A Dynamic Model of the Hierarchical Effects of Advertising 

Stefan Conrady, Conrady Applied Science, LLC 

Qing Liu, University of Wisconsin-Madison 


Sandeep Chandukala, Indiana University 

Active consumer participation in online product and information markets has 

created a rich source of secondary data. Although the conditions that motivate 

individuals to buy, sell, search and post on the Internet are diverse, the information 

generated as a byproduct of these activities has the potential to help marketers 

develop a better understanding of consumer behavior. In this paper, we collect and 

utilize online product consideration data for luxury automobiles in order to build a 

dynamic model of sales and advertising. Our model allows us to better forecast 

sales while simultaneously providing insights into the role of advertising in the sales 

generation process. 

Quantitative Trendspotting – Identify Common Trends 

Hidden Behind a Large Number of Time Series 

Rex Yuxing Du, University of Houston 

Wagner A. Kamakura, Duke University 

We demonstrate how trendspotting can be performed quantitatively by 

systematically uncovering key common trends hidden behind a large number of 

observed signals over time. By developing a state-of-the-art Structural Dynamic 

Factor-Analytic (SDFA) model and applying it to multitudes of time series, we are 

able to distill a large set of noisy individual signals into a few key smooth latent 

trends that isolate seasonal movements from non-seasonal shifts in consumer 


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interests and behaviors. We demonstrate this novel approach to trendspotting 

with an application in the U.S. automotive industry using online keyword search 

data from Google Insights for Search. Our results show that online consumer 

interest tracking services such as Google Insights can be mined more effectively 

as a powerful source of marketing intelligence in spotting and projecting major 

trends in the marketplace. Of course, our proposed method can also be applied to 

identifying hidden trends from more traditional sources, such as sales data tracked 

by syndicated service providers or customer interaction data from firms’ transaction 

databases. 

SESSION 6: 

PRODUCT LINE OPTIMIZATION 

Enhancing Marketing with Engineering: 

Designing Optimal Product Lines for a Heterogeneous Audience 

Jeremy Michalek, Carnegie Mellon University 

Peter Ebbes, The Ohio State University and Penn State University 

Feray Adiguzel, Vrije Universiteit Amsterdam 

Fred Feinberg, University of Michigan 

Panos Papalambros, University of Michigan 

Successful product line design and development require balancing technical and 

market tradeoffs. “Preference elicitation” methods like conjoint help marketers 

learn which attributes and levels consumers desire, but not with achieving those 

desired attribute levels in designing products, which is critical for products with 

heavy engineering content. This is especially critical for product lines, where 

additional products launched to capture new segments can lead to serious losses 

via cannibalization. 

Bias in Main Effects Models for Line Optimization 

Kevin D. Karty, VP Analytics, Affinnova, Inc. 

This presentation identifies a source of bias in line optimization models that results 

from the use of fractional factorial utility structures in the core model estimation; 

compares and contrasts with full factorial models. Isolates an artificial tendency 

of line optimizations derived from main effects models to include variations along 

multiple dimensions even when the true underlying preference structure does not 

support a need for variation along multiple dimensions. 


22ND ANNUAL 


12:00–1:00 pm Lunch 

1:00–4:30 pm Tuesday Afternoon Sessions 

SESSION 7: 

MODELING THE NEW SOCIAL ENVIRONMENT 

‘Social Dollars’: The Economic Impact of Consumer 

Participation in a Firm-Sponsored Brand Community 

Puneet Manchanda, University of Michigan 

Grant Packard, University of Michigan 

Adithya Pattabhiramaiah, University of Michigan 

Many firms have jumped on the social media/Web 2.0 bandwagon by developing 

brand-specific consumer social networks or communities online in the belief 

that consumers who join the brand’s online social network/community are likely 

to become more engaged with the brand. The end objective of such firms is to 

leverage this engagement to generate what we call the “social dollars.” These 

social dollars represent the incremental revenue from the engaged consumer (as 

a result of joining the brand community). Using a unique dataset from a firm that 

established a brand community in 2007, we show (via a difference-in-differences) 

estimator that social dollars are equivalent to a 19% increase in revenue for the 

firm. We show that this finding is robust to a variety of alternative explanations and 

provide some insights into the behaviors that suggest high levels of engagement. 

Empirically Investigating the Relationship Between 

What Brands Do and What Consumers Say (Social Media), 

Sense (Mindset) and Do (Purchase) 

Manish Tripathi, Emory University 

Douglas Bowman, Emory University 

Larry Friedman, TNS Global 

Melinda Smith de Borrero, TNS Global 

Natasha Stevens, Cymfony 

Market research practitioners are just beginning to understand how to quantitatively 

use social media data. There is very limited work on relating this content to other 

data collected (brand tracking surveys) and disseminated (brand-generated 

content) by a firm. A contribution of this research is to provide both a visual (brand 

positioning maps) and quantitative framework to describe the relationship betweens 

the firm’s message and the customer’s verbal and attitudinal disposition. 


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SESSION 8: 

THE UPPER LEVEL IN HIERARCHICAL CHOICE MODELS 

Covariates in Discrete Choice Models: Are They Worth the Trouble? 

Dimitri Liakhovitski, Ninah Consulting Ltd. 

Faina Shmulyian, MarketTools, Inc. 

Jia Liu, Columbia Business School 

This research compared Hierarchical Bayesian estimation of Discrete Choice 

utilities with and without covariates. The performance of both estimation methods 

was examined under a number of scenarios when the true relationship between 

the covariate and the true utilities was known. Several experimental factors were 

varied and their impact on the estimation quality of both methods considered. The 

relevance of the results to practitioners is discussed and possible “rules of thumb” 

for considering covariates are formulated. 

Identifying Unmet Demand 

Sandeep R. Chandukala, Indiana University 

Yancy D. Edwards, St. Leo University 


Brand preferences and marketplace demand are a reflection of the importance 

of consumer need and the efficacy of product attributes in delivering value. Dog 

owners, for example, may look to dog foods to provide specific benefits for their 

pets (e.g., shiny coats) that may not be available from current offerings. An analysis 

of consumer wants for these consumers would reveal weak demand for product 

attributes due to low efficacy, despite the presence of strong latent interest. The 

challenge in identifying such unmet demand is in distinguishing it from other 

reasons for weak preference, such as general non-interest in the category and 

heterogeneous tastes. We propose a model for separating out these effects within 

the context of conjoint analysis, and demonstrate its value with data from a national 

survey of toothpaste preferences. Implications for product development and 

re-formulation are explored. 

4:30–5:15 pm Speaker Roundtables 

5:15–6:45 pm Networking Reception and 

Poster Sessions 


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22ND ANNUAL 


Wednesday, June 8 

8:15 am–12:00 pm Wednesday Morning Sessions 

SESSION 9: 

KEY DRIVERS ANALYSIS 

Do We Halo or Form? A Bayesian Mixture Model 

for Customer Satisfaction Data 

Joachim Büschken, Catholic University of Eichstätt (Germany) 

Thomas Otter, University of Frankfurt (Germany) 


The analysis of customer satisfaction data to identify the drivers of satisfaction 

assumes that respondents provide independent assessments of the components 

that make up the overall satisfaction score. But what if, instead of forming an 

overall measure from the components, some respondents recalled their overall 

satisfaction, and then assigned component scores that were consistent with the 

aggregated score? Respondent scores would then exhibit a halo effect, and the 

components would be uninformative about the drivers of overall satisfaction. We 

present a new model for identifying haloed responses, and find that respondents 

who are most likely to halo are those most familiar with the offering. Conversely, 

those who do not halo are individuals less likely to have had in-depth experience. 

This presents a dilemma in terms of how and when to collect data and how to 

interpret results. 

Key Drivers Methods in Market Research: 

A Comparative Analysis 

Michael Egner, Ipsos Open Thinking Exchange 

Scott Porter, Ipsos Open Thinking Exchange 

Robert Hart, Jr., Ipsos Open Thinking Exchange 

Although the analysis of key drivers is one of the most common tasks in market 

research, practitioners have not reached a consensus on which techniques are 

best—particularly in the presence of multicollinearity, non-linear effects and other 

data complexities. Over the years, proponents of various statistical techniques 

have claimed that their preferred methods can “fix” or otherwise account for 

such problems when running drivers analysis. To evaluate these claims, we 

simulated data exhibiting these complexities, and evaluated how well a variety of 

techniques—including ridge regression, PLS, Shapley value analysis, Bayesian 

networks, decision tree ensembles, neural nets, and support vector machines 

(SVMs)—captured the true impact of changing a driver on a key outcome. We 


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show that many methods claiming to address multicollinearity are blind to the 

underlying structural relationships that give rise to the high correlations. As a 

result, these methods may give even more misleading measures of impact than 

OLS. Accordingly, we recommend methods that incorporate a search for structure 

among the variables being studied. 

Non-Compensatory Customer Satisfaction Modeling 

Keith Chrzan, Chief Research Officer, Maritz Research 

Michael Kemery, Senior Research Consultant, Maritz Research 

Customer satisfaction studies typically use “derived” importance models that 

assume a linear, compensatory relationship between performance attribute inputs 

and overall satisfaction outputs. However, performance on some attributes might be 

so terrible (or excellent) that they, all by themselves, ruin (or perfect) a customer’s 

experience. Joffre Swait introduced a clever way to sneak non-compensatory 

effects into aggregate MNL conjoint models (Swait 1998, 2001) and to reap 

benefits as a result. Adapting Swait’s non-compensatory approach to customer 

satisfaction analysis may allow customer satisfaction researchers to gain some of 

these benefits as well. Using six empirical studies, we illustrate this method, its 

results and its performance relative to standard customer satisfaction modeling. 

12:45–4:45 pm Postconference Tutorials 



H. Advanced Theory and Application of Hierarchical Bayes Choice Models 


Elea McDonnell Feit, Wharton Interactive Media Initiative 

I. Introduction to R for Marketing Research (Repeat) 

Eric Zivot, University of Washington 

Chris Chapman, Microsoft 

J. Modeling Market Dynamics 

Prasad Naik, University of California-Davis 

K. Introduction to Bayesian Networks: 

Their Applications in the Field of Marketing Science 

Lionel Jouffe, Bayesia SAS 

Stefan Conrady, Conrady Applied Science, LLC

22ND ANNUAL 


POSTER SESSIONS 

Using Memory Persistent Techniques in Classification Algorithms 

Christopher Perkins, Roche Diabetes Care 

In many market research situations, the product/marketing team aims to classify 

observations into one of two groups. Methods for achieving this include logistic 

regression, discriminate analysis, neural nets and support vector machines (SVMs). 

This poster presents a novel approach to solving a problem related to SVMs: when 

training data is supplemented with new information, the methodology does not 

require restarting the “learning process” on the entire training data. It is possible to 

continue the algorithm leveraging the learnings already achieved on the preliminary 

dataset but now including the augmented data. 

Model-Based Estimation of CBC Attribute Impact 

Chris Chapman, Microsoft Advertising R&D 

James Alford, Blink Interactive 

This poster presents a new measure of attribute “impact” to supplement traditional 

choice model measures of attribute importance. The new method is adapted from 

variable importance detection methods used in machine learning and models the 

contribution that an attribute makes to predicting choices at a full model level. The 

poster outlines the approach, compares it to traditional importance measures, and 

offers free code in R to estimate the impact measures for standard CBC studies. 

Applying Visualization and Text Mining Techniques 

to Consumer-Generated Media 

Kurt A. Pflughoeft, Maritz 

Felix Flory, evolve24 

Capturing information from “unstructured” sources such as consumer-generated 

media presents significant challenges in summarizing and displaying the data. The 

use of advanced text mining and visualization techniques provides a powerful way 

for clients to understand this data. A case study is used to show how this process 

works and what type of client deliverables can be constructed. 

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Using Synthetic Data to Understand the Best Approach to 

Accounting for Cannibalization in Market Simulations 

Jeffrey Dumont, Resource Systems Group, Inc. 

Nelson Whipple, Resource Systems Group, Inc. 

Independence from irrelevant alternatives (IIA) is a well-known issue in choice 

modeling. Improper accounting for IIA results in inaccurate cannibalization rates in 

market simulations. By creating a synthetic set of “scanner data,” different modeling 

techniques developed in response to IIA can be compared for their efficacy with 

respect to retrieving the true cannibalization rate in the market. Aggregate MNL, HB 

MNL, Nested Logit, SOV probit, randomized first choice and enhanced randomized 

first choice are tested to understand their overall effectiveness in measuring true 

cannibalization. 

Measuring the Delightfulness Evoked by 

Products through Online Reviews 

Daniel H. Abbott, Blink Interactive 

A new method is described to measure the delight evoked by products through 

online reviews. A large corpus of reviews is assembled, and canonical ratings of 

training cases are used in human subjects. Several methods are then used to 

estimate scores for the rest of the corpus. Face validity is measured, and delight’s 

effect on choice-based purchasing decisions is calculated. Areas of future research 

are discussed. 

Alternative Approaches to Choice-Based Conoint-Analysis: 

Is Best-Worst Better and Do We Need HB? 

Ralph Wirth, GfK Marketing Sciences 

In this presentation, different approaches for estimating part-worth utilities from 

CBC data are compared: Standard CBC, Best-Worst CBC (both estimated using 

HB algorithms) and a purely individual approach suggested by Louviere et al. The 

following are examined in an extensive simulation study: 1) whether HB choice 

models work well, even under very challenging and sparse data conditions, 2) 

whether HB choice algorithms have systematic troubles when individual-level 

errors are not constant across the sample, and 3) whether HB-Best-Worst CBC 

has an advantage over traditional (“best only”) HB-CBC. It will be shown that 

both HB approaches perform very well in parameter recovery and predictive 

validity, even in challenging data situations and when individual error variances 

are not equal. Another observation is that information about worst choices in 

addition to best choice information can significantly improve results. An empirical 

comparison of HB-CBC and HB-Best-Worst-CBC using four different data sets 

confirms this finding. 


22ND ANNUAL 


Identifying Premium Brands – A Comparison of Two Anchored 

Scaled Max-Diff Approaches to a Van Westendorp Model 

Paul Johnson, Opinionology 

Finding accurate pricing points and establishing brand premium metrics is essential 

to successful marketing. A Van Westendrop pricing model is a common way to 

measure brand equity for a single name or product. However, having respondents 

evaluate a long list of names is very fatiguing to them. We propose two alternative 

ways of using anchored scaled Max-Diff methods to identify premium brands in a 

less fatiguing manner for respondents. Practical implications and tradeoffs between 

the methods will be discussed. 

Multilevel Segmentation to Aid Micro-Targeting 

Gayathri Swahar, The Nielsen Company 

N.S. Muthukumaran, The Nielsen Company / BASES 

The market has become more matured, diversified and international in the past 

few decades. Companies often sell products to consumers across many different 

countries or in markets that comprise disparate geographic units. A simple broad 

brush segmentation solution for their entire market will not work. Thus, structuring 

heterogeneity across markets may call for segmenting countries or consumers 

within countries—or both. This paper demonstrates the use of multilevel 

segmentation, which segments consumers and geographies in one step, to come 

up with actionable segment solutions for facilitating micro-targeting. 

Introducing a New Brand/Feature into an Existing Conjoint Study 

N.S. Muthukumaran, The Nielsen Company / BASES 

Vibha Ayyar, The Nielsen Company / BASES 

It’s a well-known fact that a conjoint model can be used to simulate various 

scenarios involving brands, and other attributes which were tested in the study. In 

this case, after we deliver the results, a client gets back to us if we can incorporate 

a new SKU/brand or feature in to an already existing choice model which did not 

have this new SKU/brand or feature it cannot be done. Only way out is to repeat 

the entire exercise with a new design which incorporates the new brand/attribute 

level or an SKU etc collect data afresh and model it. This will be an extremely time 

consuming and expensive affair. 

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Searching for a Single Grain of Sand – How to Find Optimal 

Combinations of Features or Components 

John M. Ennis, The Institute for Perception 

Daniel M. Ennis, The Institute for Perception 

Charles M. Fayle, The Institute for Perception 

Michael A. Nestrud, Cornell University 

The search for optimal combinations is ubiquitous in market research, and the 

extensive use of powerful tools such as adaptive conjoint analysis has demonstrated 

a widespread interest in these problems. Even so, there exists an entire category 

of unsolved problems that are not easily accessed with tools in common use and 

require the innovative application of advancements in discrete mathematics. To 

build successful large combinations out of successful smaller combinations or to 

find a maximally distinct collection of objects, efficient algorithms from the field of 

graph theory, together with the speed advantages offered by modern computers, 

make it possible to find optimal combinations from a potentially astronomical 

number of possibilities. In some examples, the number of combinations can exceed 

the number of grains of sand on all the beaches of the world and we nonetheless 

find an optimal single grain. Two examples of appropriate applications for these 

tools are in the design of cases of high-quality meal rations for the U.S. Armed 

Forces and in the design of a successful pizza franchising menu starting from a 

large number of possible topping choices. In this presentation; we identify problems 

not easily solved using current techniques, review recently developed mathematical 

tools for discrete optimization (including tools from graph theory) and illustrate 

through example the value of these tools. 

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HOTEL 

22ND ANNUAL 


JW Marriott Desert Springs 

Resort & Spa 

74855 Country Club Drive 

Palm Desert, CA 92260 

Phone: 1.760.341.2211 

Toll-free reservations: 1.888.538.9459 

Hotel website: 

www.desertspringsresort.com 

A cool oasis in the desert, JW Marriott 

Desert Springs Resort & Spa offers stylish 

surroundings and stunning mountain and 

golf course views. Guests can enjoy four 

signature restaurants, including a sushi bar 

and Starbucks; exciting nightlife; a resort 

spa; shopping; expansive pools; two 18hole 

golf courses; and a newly remodeled 

lobby complete with a waterway and 

gondolas as well as an elegant bar. 

Rates 

Single/double occupancy: 

$189 per night 

Triple occupancy: 

$209 per night 

Optional resort charge: 

$24 per night* 

* Includes self-parking, high-speed 

Internet access, unlimited local calls, 

evening refresh, bottled water (2), 

spa fitness center, The Greens 

(18-hole putting course, lawn 

games and golf bag storage 

at the resort) 

QUESTIONS? 

Call 800.AMA.1150 

REGISTRATION 

AND PRICING 

Conference Fees 

Early Registration* 

(Payment must be received 

in AMA office by May 5, 2011) 

AMA Member $845 

Non-Member $1,110 

AMA Doctoral Student $345 

Any marketing academician/practitioner 

wishing to expose a doctoral student to the 

ART Forum is encouraged to accompany 

them to the conference; the student will 

receive a special discounted rate. (Please 

note: Registrations and payment must be 

received together to qualify for the discount. 

AMA cannot match up registrations at our 

offices. In the case of online payment, 

please have the student’s member ID and 

contact information ready.) 

* Please add $100 to these prices 

after May 5, 2011 

Tutorial Fees 

Early Registration* 

(Payment must be received 

in AMA office by May 5, 2011) 

Tutorials are OPTIONAL and are held on 

Sunday, June 5 and Wednesday, June 8 

AMA Member $250 

Non-Member $275 

* Please add $25 to these prices 

after May 5, 2011 

Tutorials Only: Add $100 administration fee 

if not attending 2011 ART Forum 


19

Non-Profit Org. 

U.S. Postage 

PAID 

Permit No. 9318 

Chicago, IL 

American Marketing Association 

311 S. Wacker Drive, Suite 5800 | Chicago, IL 60606-2266 

MarketingPower.com 

22ND ANNUAL 

ART2011 

Advanced Research Techniques Forum 


June 5–8, 2011 

Palm Desert, CA 

Register 

by May 5 

and save!

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