Summary of Web Appendices
Web Appendices 1: Stimuli
Page
1.1
1.2
1.3
2-7
8-20
21-25
100 articles 2004-05 used as a holdout to calibrate marketing science
Main questionnaires used in the survey
Bibliography of articles used to identify 20 high practice papers
Web Appendices 2: Results
2.1
2.2
2.3
Transition matrices: Articles Tools, ToolsDecisions, Articles  Decisions
Articles sorted by practice/academic quadrant
Qualitative comments by respondents to main survey
26-32
33-35
36-38
Web Appendices 3: Information on recent trends
3.1
3.2
3.3
3.4
Study of contents of Kotler/Kotler and Keller Marketing Management 1984-2012
MSI Research Priorities 2004-2012
American Marketing Association Advanced Research Techniques Forum 2002-2013
Marketing Science articles referenced in patent applications
39-41
42-44
45-48
49-52
1
Web Appendix 1.1
Bibliography of 100 articles 2004-05 used as a holdout to calibrate marketing science
Acquisti, A; Varian, HR 2005. Conditioning prices on purchase history Marketing Science. 24 (3), 367-381.
Ailawadi, KL; Kopalle, PK; Neslin, SA 2005. Predicting competitive response to a major policy
change: Combining game-theoretic and empirical analyses Marketing Science. 24 ( 1 ), 12-24.
Akcura, MT; Srinivasan, K 2005. Research note: Customer intimacy and cross-selling strategy
Management Science. 51 ( 6 ), 1007-1012.
Amaldoss, W; Jain, S 2005. Conspicuous consumption and sophisticated thinking Management Science.51 (
10 ), 1449-1466.
Anand, BN; Shachar, R 2004. Brands as beacons: A new source of loyalty to multiproduct firms
Journal of Marketing Research. 41 ( 2 ), 135-150.
Arentze, TA; Oppewal, H; Timmermans, HJP 2005. A multipurpose shopping trip model to assess retail
agglomeration effects Journal of Marketing Research. 42 ( 1 ), 109-115.
Arora, A; Fosfuri, A 2005. Pricing diagnostic information. Management Science. 51 ( 7 ), 1092-1100.
Atuahene-Gima, K 2005. Resolving the capability-rigidity paradox in new product innovation Journal of
Marketing. 69 ( 4 ), 61-83.
Balasubramanian, S; Bhardwaj, P 2004. When not all conflict is bad: Manufacturing-marketing
conflict and strategic incentive design. Management Science. 50 ( 4 ), 489-502.
Barone, MJ; Manning, KC; Miniard, PW 2004. Consumer response to retailers' use of partially
comparative pricing Journal of Marketing. 68 ( 3 ), 37-47.
Bart, Y; Shankar, V; Sultan, F; Urban, GL 2005. Are the drivers and role of online trust the same for all
Web sites and consumers? A large-scale exploratory empirical study Journal of Marketing. 69 ( 4 ), 133-152.
Besanko, D; Dube, JP; Gupta, S 2005. Own-brand and cross-brand retail pass-through. Marketing Science.
24 ( 1 ), 123-137.
Bhaskaran, SR; Gilbert, SM 2005. Selling and leasing strategies for durable goods with
complementary products Management Science. 51 ( 8 ), 1278-1290.
Biyalogorsky, E; Gerstner, E 2004. Contingent pricing to reduce price risks Marketing Science. 23 ( 1 ), 146155.
Bowman, D; Heilman, CM; Seetharaman, PB 2004. Determinants of product-use compliance
behavior Journal of Marketing Research. 41 ( 3 ), 324-338.
Bowman, D; Narayandas, D 2004. Linking customer management effort to customer profitability in
business markets. Journal of Marketing Research. 41 ( 4 ), 433-447.
Cao, Y; Gruca, TS 2005. Reducing adverse selection through customer relationship management
Journal of Marketing. 69 ( 4 ), 219-229.
Chandon, P; Morwitz, VG; Reinartz, WJ 2005. Do intentions really predict behavior? Self-generated
validity effects in survey research. Journal of Marketing. 69 ( 2 ), 1-14.
2
Chen, YX; Moorthy, S; Zhang, ZJ 2005. Research note-Price discrimination after the purchase:
Rebates as state-dependent discounts Management Science. 51 ( 7 ), 1131-1140.
Chintagunta, P; Dube, JP; Goh, Y 2005. Beyond the endogeneity bias: The effect of unmeasured
brand characteristics on household-level brand choice models. Management Science. 51 ( 5 ),
832-849.
Choudhary, V; Ghose, A; Mukhopadhyay, T; Rajan, U 2005. Personalized pricing and quality
differentiation Management Science. 51 ( 7 ), 1120-1130.
Christen, M 2005. Research note-Cost uncertainty is bliss: The effect of competition on the
acquisition of cost information for pricing new products. Management Science. 51 ( 4 ), 668676.
Danaher, PJ; Wilson, IW; Davis, RA 2003. A comparison of Online and offline consumer brand
loyalty Marketing Science. 22 ( 4 ), 461-476.
Debruyne, M; Reibstein, DJ 2005. Competitor see, competitor do: Incumbent entry in new market niches
Marketing Science. 24 ( 1 ), 55-66.
Dellaert, BGC; Stremersch, S 2005. Marketing mass-customized products: Striking a balance
between utility and complexity. Journal of Marketing Research. 42 ( 2 ), 219-227.
Dhar, R; Nowlis, SM 2004. To buy or not to buy: Response mode effects on consumer choice
Journal of Marketing Research. 41 ( 4 ), 423-432.
Dholakia, UM; Simonson, I 2005. The effect of explicit reference points on consumer choice and online
bidding behavior. Marketing Science. 24 ( 2 ), 206-217.
Draganska, M; Jain, D 2004. A likelihood approach to estimating market equilibrium models
Management Science. 50 ( 5 ), 605-616.
Fader, PS; Hardie, BGS; Lee, KL 2005. Counting your customers the easy way: An alternative to the
Pareto/NBD model. Marketing Science. 24 ( 2 ), 275-284.
Fay, S 2004. Partial-repeat-bidding in the name-your-own-price channel. Marketing Science. 23 ( 3 ), 407418.
Fitzsimons, GJ; Lehmann, DR 2004. Reactance to recommendations: When unsolicited advice yields
contrary responses Marketing Science. 23 ( 1 ), 82-94.
Fox, EJ; Hoch, SJ 2005. Cherry-picking Journal of Marketing. 69 ( 1 ), 46-62.
Franses, PH 2005. On the use of econometric models for policy simulation in marketing. Journal of
Marketing Research. 42 ( 1 ), 4-14.
Ghosh, M; John, G 2005. Strategic fit in industrial alliances: An empirical test of governance value
analysis Journal of Marketing Research. 42 ( 3 ), 346-357.
Godes, D; Mayzlin, D 2004. Using online conversations to study word-of-mouth communication
Marketing Science. 23 ( 4 ), 545-560.
Gruca, TS; Rego, LL 2005. Customer satisfaction, cash flow, and shareholder value. Journal of
Marketing. 69 ( 3 ), 115-130.
3
Hauser, JMR; Toubia, O 2005. The impact of utility balance and endogeneity in conjoint analysis
Marketing Science. 24 ( 3 ), 498-507.
Hess, JD; Lucas, MT 2004. Doing the right thing or doing the thing right: Allocating resources
between marketing research and manufacturing Management Science. 50 ( 4 ), 521-526.
Hitt, LM; Chen, PY 2005. Bundling with customer self-selection: A simple approach to bundling
low-marginal-cost goods Management Science. 51 ( 10 ), 1481-1493.
Homburg, C; Bucerius, M 2005. A marketing perspective on mergers and acquisitions: How
marketing integration affects postmerger performance. Journal of Marketing. 69 ( 1 ), 95-113.
Homburg, C; Koschate, N; Hoyer, WD 2005. Do satisfied customers really pay more? A study of the
relationship between customer satisfaction and willingness to pay Journal of Marketing. 69 (2 ), 84-96.
Hui, KL 2004. Product variety under brand influence: An empirical investigation of personal
computer demand Management Science. 50 ( 5 ), 686-700.
Iyer, G; Soberman, D; Villas-Boas, JM 2005. The targeting of advertising Marketing Science. 24 ( 3 ), 461476.
Jain, SP; Posavac, SS 2004. Valenced comparisons Journal of Marketing Research. 41 ( 1 ), 46-58.
Jarrar, R; Martin-Herran, G; Zaccour, G 2004. Markov perfect equilibrium advertising strategies of
Lanchester duopoly model: A technical note. Management Science. 50 ( 7 ), 995-1000.
Jayachandran, S; Sharma, S; Kaufman, P; Raman, P 2005. The role of relational information
processes and technology use in customer relationship management Journal of Marketing. 69 (
4 ), 177-192.
Johnson, EJ; Moe, WW; Fader, PS; Bellman, S; Lohse, GL 2004. On the depth and dynamics of
online search behavior Management Science. 50 ( 3 ), 299-308.
Kalnins, A 2004. An empirical analysis of territorial encroachment within franchised and company owned
branded chains Marketing Science. 23 ( 4 ), 476-489.
Kim, Y; Street, WN; Russell, GJ; Menczer, F 2005. Customer targeting: A neural network approach
guided by genetic algorithms. Management Science. 51 ( 2 ), 264-276.
Kirca, AH; Jayachandran, S; Bearden, WO 2005. Market orientation: A meta-analytic review and
assessment of its antecedents and impact on performance Journal of Marketing. 69 ( 2 ), 24-41.
Klein, JG; Ahluwalia, R 2005. Negativity in the evaluation of political candidates Journal of Marketing. 69 ( 1
), 131-142.
Klein, JG; Smith, NC; John, A 2004. Why we boycott: Consumer motivations for boycott
participation. Journal of Marketing. 68 ( 3 ), 92-109.
Kumar, P 2005. Brand counterextensions: The impact of brand extension success versus failure.
Journal of Marketing Research. 42 ( 2 ), 183-194.
Kumar, P 2005. The impact of cobranding on customer evaluation of brand counterextensions.
Journal of Marketing. 69 ( 3 ), 1-18.
Lewis, M 2005. Incorporating strategic consumer behavior into customer valuation. Journal of
Marketing. 69 ( 4 ), 230-238.
4
Lewis, M 2005. Research note: A dynamic programming approach to customer relationship pricing
Management Science. 51 ( 6 ), 986-994.
Manchanda, P; Rossi, PE; Chintagunta, PK 2004. Response modeling with nonrandom marketing mix
variables. Journal of Marketing Research. 41 ( 4 ), 467-478.
Markovitch, DG; Steckel, JH; Yeung, B 2005. Using capital markets as market intelligence: Evidence from
the pharmaceutical industry. Management Science. 51 ( 10 ), 1467-1480.
Moe, WW; Fader, PS 2004. Dynamic conversion behavior at e-commerce site's Management Science. 50 ( 3 ),
326-335.
Montgomery, AL; Hosanagar, K; Krishnan, R; Clay, KB 2004. Designing a better shopbot
Management Science. 50 ( 2 ), 189-206.
Narayanan, S; Manchanda, P; Chintagunta, PK 2005. Temporal differences in the role of marketing
communication in new product categories Journal of Marketing Research. 42 ( 3 ), 278-290.
Nowlis, SM; Shiv, B 2005. The influence of consumer distractions on the effectiveness of food sampling
programs Journal of Marketing Research. 42 ( 2 ), 157-168.
Nunes, JC; Boatwright, P 2004. Incidental prices and their effect on willingness to pay Journal of Marketing
Research. 41 ( 4 ), 457-466.
Okada, EM 2005. Justification effects on consumer choice of hedonic and utifitarian goods Journal of
Marketing Research. 42 ( 1 ), 43-53.
Park, YH; Bradlow, ET 2005. An integrated model for bidding Behavior in Internet auctions:
Whether, who, when, and how much Journal of Marketing Research. 42 ( 4 ), 470-482.
Park, YH; Fader, PS 2004. Modeling browsing behavior at multiple websites. Marketing Science. 23 ( 3 ),
280-303.
Pauwels, K 2004. How dynamic consumer response, competitor response, company support, and
company inertia shape long-term marketing effectiveness Marketing Science. 23 ( 4 ), 596-610.
Pauwels, K; Silva-Risso, J; Srinivasan, S; Hanssens, DM 2004. New products, sales promotions, and firm
value: The case of the automobile industry Journal of Marketing. 68 ( 4 ), 142-156.
Payan, JM; McFarland, RG 2005. Decomposing influence strategies: Argument structure and
dependence as determinants of the effectiveness of influence strategies in gaining channel
member compliance Journal of Marketing. 69 ( 3 ), 66-79.
Pieters, R; Wedel, M 2004. Attention capture and transfer in advertising: Brand, pictorial, and textsize
effects. Journal of Marketing. 68 ( 2 ), 36-50.
Sandor, Z; Wedel, M 2005. Heterogeneous conjoint choice designs Journal of Marketing Research. 42 ( 2 ),
210-218.
Seetharaman, PB 2004. Modeling multiple sources of state dependence in random utility models: A
distributed lag approach Marketing Science. 23 ( 2 ), 263-271.
Shin, J 2005. The role of selling costs in signaling price image. Journal of Marketing Research. 42 ( 3 ), 302312.
5
Shocker, AD; Bayus, BL; Kim, N 2004. Product complements and substitutes in the real world: The
relevance of "other products" Journal of Marketing. 68 ( 1 ), 28-40.
Singh, VP; Hansen, KT; Gupta, S 2005. Modeling preferences for common attributes in
multicategory brand choice Journal of Marketing Research. 42 ( 2 ), 195-209.
Sismeiro, C; Bucklin, RE 2004. Modeling purchase behavior at an E-commerce web site: A task
completion approach. Journal of Marketing Research. 41 ( 3 ), 306-323.
Soberman, DA 2004. Research note: Additional learning and implications on the role of informative
advertising Management Science. 50 ( 12 ), 1744-1750.
Souza, GC; Bayus, BL; Wagner, HM 2004. New-product strategy and industry clockspeed
Management Science. 50 ( 4 ), 537-549.
Srinivasan, R; Moorman, C 2005. Strategic firm commitments and rewards for customer relationship
management in online retailing Journal of Marketing. 69 ( 4 ), 193-200.
Srinivasan, S; Pauwels, K; Hanssens, DM; Dekimpe, MG 2004. Do promotions benefit
manufacturers, retailers, or both? Management Science. 50 ( 5 ), 617-629.
Srinivasan, V; Park, CS; Chang, DR 2005. An approach to the measurement, analysis, and prediction of
brand equity and its sources Management Science. 51 ( 9 ), 1433-1448.
Steenkamp, JBEM; Nijs, VR; Hanssens, DM; Dekimpe, MG 2005. Competitive reactions to
advertising and promotion attacks Marketing Science. 24 ( 1 ), 35-54.
Stock, A; Balachander, S 2005. The making of a "hot product": A signaling explanation of marketers'
scarcity strategy Management Science. 51 ( 8 ), 1181-1192.
Sudhir, K; Chintagunta, PK; Kadiyali, V 2005. Time-varying competition Marketing Science. 24 ( 1 ), 96109.
Swait, J; Andrews, RL 2003. Enriching scanner panel models with choice experiments Marketing
Science. 22 ( 4 ), 442-460.
Thomas, JS; Sullivan, UY 2005. Managing marketing communications with multichannel customers
Journal of Marketing. 69 ( 4 ), 239-251.
Toubia, O; Hauser, JR; Simester, DI 2004. Polyhedral methods for adaptive choice-based conjoint
analysis Journal of Marketing Research. 41 ( 1 ), 116-131.
Tyagi, RK 2004. Technological advances, transaction costs, and consumer welfare Marketing Science. 23 ( 3
), 335-344.
Tyagi, RK 2005. Do strategic conclusions depend on how price is defined in models of distribution
channels? Journal of Marketing Research. 42 ( 2 ), 228-232.
Urban, GL; Hauser, JR 2004. Listening in to find and explore new combinations of customer needs
Journal of Marketing. 68 ( 2 ), 72-87.
Vakratsas, D; Feinberg, FM; Bass, FM; Kalyanaram, G 2004. The shape of advertising response
functions revisited: A model of dynamic probabilistic thresholds. Marketing Science. 23 ( 1 ),
109-119.
6
Van Heerde, HJ; Bijmolt, THA 2005. Decomposing the promotional revenue bump for loyalty
program members versus nonmembers Journal of Marketing Research. 42 ( 4 ), 443-457.
van Heerde, HJ; Leeflang, PSH; Wittink, DR 2004. Decomposing the sales promotion bump with store
data. Marketing Science. 23 ( 3 ), 317-334.
Venkatesan, R; Krishnan, TV; Kumar, V 2004. Evolutionary estimation of macro-level diffusion
models using genetic algorithms: An alternative to nonlinear least squares Marketing Science.
23 ( 3 ), 451-464.
Villas-Boas, JM; Zhao, Y 2005. Retailer, manufacturers, and individual consumers: Modeling the
supply side in the ketchup marketplace Journal of Marketing Research. 42 ( 1 ), 83-95.
Vorhies, DW; Morgan, NA 2005. Benchmarking marketing capabilities for sustainable competitive
advantage Journal of Marketing. 69 ( 1 ), 80-94.
Wathne, KH; Heide, JB 2004. Relationship governance in a supply chain network Journal of
Marketing. 68 ( 1 ), 73-89.
Wuyts, S; Geyskens, I 2005. The formation of buyer-supplier relationships: Detailed contract
drafting and close partner selection Journal of Marketing. 69 ( 4 ), 103-117.
Wuyts, S; Stremersch, S; Van Den Bulte, C; Franses, PH 2004. Vertical marketing systems for
complex products: A triadic perspective Journal of Marketing Research. 41 ( 4 ), 479-487.
Zoltners, AA; Sinha, P 2005. Sales territory design: Thirty years of modeling and implementation
Marketing Science. 24 ( 3 ), 313-331.
7
Web Appendix 1.2
Main questionnaire used in the survey of Academics, Intermediaries, and Managers
Marketing Science Practice Impact (Academics)
We would like to thank you for participating in this survey. The survey was commissioned by the Practice
Committee of the INFORMS Society for Marketing Science, publisher of Marketing Science and sponsor of
the annual Marketing Science Conference. Our objective is to understand the impact that marketing science in
general, and specific articles in particular, have had on the practice of marketing. We are looking at a time
horizon of twenty-five years. While we appreciate that many marketing science advances were made before this
time, in order to make our study manageable we are focusing on that period. Again, in order to minimize the
task that we ask of you, we have focused our attention on just the Journal of Marketing, the Journal of
Marketing Research, Management Science (Marketing Department), and Marketing Science.
The survey should take you approximately 10 minutes.
You have been selected as a leader in our field. While we understand that there are many calls on your time, we
do ask you to spare the few minutes required to assist us in the important task of gaining a representative view
of marketing science research and its impact on practice.
Your answers will be treated as totally confidential. If you would like a copy of our results, please send us an
email at [email].
If you have any questions please do not hesitate to contact one of us. Our contact details are included below.
Thank you for your help!
Sincerely,
[The authors]
1) Influence of articles – I
We are interested in how influential you think that the following ten articles have been in the practice of
marketing. We will be showing another screen with ten other articles - so we are asking you to rate the influence
of 20 articles in total. Articles are presented in random order. Each article in this list was selected because (i) it
was among the top 100 articles in terms of number of citations, and (ii) it was rated among the top 20 articles in
terms of influence on practice in a previous survey of marketing intermediaries.
We are interested in your opinion as to the impact these articles have had on the practice of marketing in
companies. This impact could either be direct (by practitioners reading them and employing their techniques) or
indirect (by others incorporating important elements of them, and the work or methodologies of those others
being used by practising marketers or marketing analysts). For each article could you please express your view
of the total influence that the article has had on marketing practice? If you are unaware of the article or feel
completely unable to judge its influence you should click on the "Not Aware" button.
How much influence has the article had on overall marketing practice in businesses?
Not Aware; No Influence 1; Slightly Influential 2 Somewhat Influential 3;
Very Influential 4; Extremely Influential 5
Parasuraman A, Zeithaml VA, Berry LL (1985), A Conceptual-Model Of Service Quality
And Its Implications For Future Research, Journal of Marketing, 49 (4), 41-50.
Click here for abstract



Green PE, Srinivasan V (1990), Conjoint Analysis In Marketing – New Developments
With Implications For Research, Journal of Marketing, 54 (4), 3-19.
8
Click here for abstract

Day GS (1994), The Capabilities Of Market-Driven Organizations, Journal of
Marketing, 58 (4), 37-52.
Click here for abstract

Fornell C, Johnson MD, Anderson EW, Cha JS, Bryant BE (1996), The American
Customer Satisfaction Index: Nature, Purpose, And Findings, Journal of
Marketing, 60 (4), 7-18.
Click here for abstract

Louviere JJ, Woodworth G (1983), Design And Analysis Of Simulated Consumer
Choice Or Allocation Experiments - An Approach Based On Aggregate Data, Journal of
Marketing Research, 20 (4), 350-367.
Click here for abstract

Griffin A, Hauser JR (1993), The Voice Of The Customer, Marketing Science, 12 (1), 1-27.
Click here for abstract

Fornell C (1992), A National Customer Satisfaction Barometer - The Swedish Experience,
Journal of Marketing, 56 (1), 6-21.
Click here for abstract

Vanheerde HJ, Gupta S, Wittink DR (2003), Is 75% Of The Sales Promotion Bump Due To
Brand Switching? No, Only 33% Is, Journal of Marketing Research, 40 (4), 481-491.
Click here for abstract

Keller KL (1993), Conceptualizing, Measuring, And Managing Customer- Based Brand
Equity, Journal of Marketing, 57 (1), 1-22.
Click here for abstract

Hunt SD, Morgan RM (1995), The Comparative Advantage Theory Of Competition,
Journal of Marketing, 59 (2), 1-15.
Click here for abstract

Influence of articles – II (Second batch)
9
Rust RT, Zahorik AJ, Keiningham TL (1995), Return On Quality (Roq) – Making Service Quality
Financially Accountable, Journal of Marketing, 59 (2), 58-70.
Click here for abstract

Cattin P, Wittink DR (1982), Commercial Use Of Conjoint- Analysis – A Survey, Journal of
Marketing, 46 (3), 44-53.
Click here for abstract

Guadagni, P, Little, JDC (1983), A Logit Model On Brand Choice Calibrated On Scanner Data,
Marketing Science, 2 (3), 203-238.
Click here for abstract

Punj G, Stewart DW (1983), Cluster- Analysis In Marketing- Research - Review And
Suggestions For Application, Journal of Marketing Research, 20 (2), 134-148.
Click here for abstract

Anderson EW, Fornell C, Lehmann DR (1994), Customer Satisfaction, Market Share, And
Profitability: Findings From Sweden, Journal of Marketing, 58 (3), 53-66.
Click here for abstract

Simonson I, Tversky A (1992), Choice In Context – Tradeoff Contrast And Extremeness
Aversion, Journal of Marketing Research, 29 (3), 281-295.
Click here for abstract

Boulding W, Kalra A, Staelin R, Zeithaml VA (1993), A Dynamic Process Model Of Service
Quality – From Expectations To Behavioral Intentions, Journal of Marketing Research, 30 (1),
7-27.
Click here for abstract

Aaker DA, Keller KL (1990), Consumer Evaluations Of Brand Extensions, Journal of Marketing,
54 (1), 27-41.
Click here for abstract

Hauser, JR, Shugan, S (1983), Defensive Marketing Strategies, Marketing Science, 2 (4), 319360.
Click here for abstract

10
Mahajan V, Muller E, Bass FM (1990), New Product Diffusion-Models In Marketing – A Review
And Directions For Research, Journal of Marketing, 54 (1), 1-26.
Click here for abstract

IMPACT OF MARKETING SCIENCE APPROACHES
We are interested in your view as to the impact that specific quantitative marketing techniques and
tools (or "marketing science approaches") have had on the overall practice of marketing in
businesses. Below we include a set of tools, techniques and approaches that we have distilled from
discussions with selected marketing science academics and practitioners. For each, we would be
grateful for your opinion as to the degree to which these approaches have affected overall marketing
practice over the past twenty-five years. If you are unaware of the technique or its influence you
should mark “Not Aware.”
How much of an influence has this marketing science approach had on overall marketing practice in
businesses in the past 25 years?
Not Aware; No Influence; Slightly Influential; Somewhat Influential; Very Influential Extremely
Influential
Marketing metrics

Customer life time value models

Game theory models

Customer satisfaction models

Sales force allocation models

Aggregate marketing mix models

New product models

Pre-test market models

Panel-based choice models

Survey-based choice models

Perceptual mapping

Segmentation tools

IMPACT ON AREAS OF DECISION MAKING
We are now interested in the impact that quantitative marketing techniques have had on practice in
different areas of marketing decision making. Marketing science’s influence on decisions in
specific areas is a function of its ability to add insight to a management problem or decision in
that area and also how important that area is to the objectives of the organization. Below is a
list of the different types of activities that a marketing manager might perform. For each, we
would be grateful for your opinion as to the degree to which these activities have been influenced by
marketing science (quantitative marketing techniques) over the past twenty-five years.
How much of an influence has marketing science had on this area of marketing decision making in the past
25 years?
Not Aware; No Influence; Slightly Influential; Somewhat Influential; Very Influential; Extremely
Influential
Service/product quality management

Customer
11
Insight Management

Relationship management

Customer/market selection

Channel management

Salesforce management

Pricing management

Promotion management

Advertising management

Product portfolio management

New Product/Service management

Brand management

BACKGROUND DATA
By way of background, we would be grateful if you could tell us a few things about
yourself.
How many years have you worked in industry?
None
Less than 2 years
Between 2 and 5 years
More than 5 years
What is the location of your university?
USA
Europe
Asia
Other (please specify)
If you selected other please specify:_________________________________________
What is your position in the organization?
Full professor
Associate professor
Assistant Professor
Other (please specify)
If you selected other please specify: ________________________________________________
What age category do you fall into?
30 or less
31-40
41-50
>50
Comments: We would be grateful for any comments that you have on the impact of quantitative
marketing techniques not covered by this survey.
____________________________________________________________________________
_____________________________________________________________________________
___________________________________________________________________
Thank you very much for taking the time to complete this questionnaire. We will keep you posted on the results.
If you'd like a copy of the results, please send an email to [email].
12
Marketing Science Practice Impact (Intermediaries)
We would like to thank you for participating in this survey. The survey was commissioned by the
Practice Committee of the INFORMS Society for Marketing Science, publisher of Marketing Science
and sponsor of the annual Marketing Science Conference. Our objective is to understand the impact
that marketing science in general has had on the practice of marketing. We are looking at a time
horizon of twenty-five years. While we appreciate that many marketing science advances were made
before this time, we are focusing on that period in order to make our study manageable.
The survey should take approximately 12 minutes.
You have been selected as a leader in our field, and your organization as one of the leading
companies in marketing science practice. While we understand that there are many calls on your
time, we do ask you to spare the few minutes required to assist us in the important task of gaining a
representative view of our research and its impact. Your answers will be treated as totally confidential.
If you would like a copy of our results, please email us at [email].
If you have any questions please do not hesitate to contact one of us.
Thank you for your help!
Sincerely,
[The authors]
1) Influence of articles – I
We are interested in how influential you think that the following ten articles have been in the practice of
marketing. We will be showing another screen with ten other articles - so we are asking you to rate the influence
of 20 articles in total. Articles are presented in random order. Each article in this list was selected because (i) it
was among the top 100 articles in terms of number of citations, and (ii) it was rated among the top 20 articles in
terms of influence on practice in a previous survey of marketing intermediaries.
We are interested in your opinion as to the impact these articles have had on the practice of marketing in
companies. This impact could either be direct (by practitioners reading them and employing their techniques) or
indirect (by others incorporating important elements of them, and the work or methodologies of those others
being used by practising marketers or marketing analysts). For each article could you please express your view
of the total influence that the article has had on marketing practice? If you are unaware of the article or feel
completely unable to judge its influence you should click on the "Not Aware" button.
How much influence has the article had on overall marketing practice in businesses?
Not Aware ; No Influence 1; Slightly Influential 2 Somewhat Influential 3;
Very Influential 4; Extremely Influential 5
Parasuraman A, Zeithaml VA, Berry LL (1985), A Conceptual-Model Of Service Quality
And Its Implications For Future Research, Journal of Marketing, 49 (4), 41-50.
Click here for abstract



Green PE, Srinivasan V (1990), Conjoint Analysis In Marketing – New Developments
With Implications For Research, Journal of Marketing, 54 (4), 3-19.
Click here for abstract

Day GS (1994), The Capabilities Of Market-Driven Organizations, Journal of
Marketing, 58 (4), 37-52.
Click here for abstract

13
Fornell C, Johnson MD, Anderson EW, Cha JS, Bryant BE (1996), The American
Customer Satisfaction Index: Nature, Purpose, And Findings, Journal of
Marketing, 60 (4), 7-18.
Click here for abstract

Louviere JJ, Woodworth G (1983), Design And Analysis Of Simulated Consumer
Choice Or Allocation Experiments - An Approach Based On Aggregate Data, Journal of
Marketing Research, 20 (4), 350-367.
Click here for abstract

Griffin A, Hauser JR (1993), The Voice Of The Customer, Marketing Science, 12 (1), 1-27.
Click here for abstract

Fornell C (1992), A National Customer Satisfaction Barometer - The Swedish Experience,
Journal of Marketing, 56 (1), 6-21.
Click here for abstract

Vanheerde HJ, Gupta S, Wittink DR (2003), Is 75% Of The Sales Promotion Bump Due To
Brand Switching? No, Only 33% Is, Journal of Marketing Research, 40 (4), 481-491.
Click here for abstract

Keller KL (1993), Conceptualizing, Measuring, And Managing Customer- Based Brand
Equity, Journal of Marketing, 57 (1), 1-22.
Click here for abstract

Hunt SD, Morgan RM (1995), The Comparative Advantage Theory Of Competition,
Journal of Marketing, 59 (2), 1-15.
Click here for abstract

Influence of articles – II (Second batch)
Rust RT, Zahorik AJ, Keiningham TL (1995), Return On Quality (Roq) – Making Service Quality
Financially Accountable, Journal of Marketing, 59 (2), 58-70.
Click here for abstract

14
Cattin P, Wittink DR (1982), Commercial Use Of Conjoint- Analysis – A Survey, Journal of
Marketing, 46 (3), 44-53.
Click here for abstract

Guadagni, P, Little, JDC (1983), A Logit Model On Brand Choice Calibrated On Scanner Data,
Marketing Science, 2 (3), 203-238.
Click here for abstract

Punj G, Stewart DW (1983), Cluster- Analysis In Marketing- Research - Review And
Suggestions For Application, Journal of Marketing Research, 20 (2), 134-148.
Click here for abstract

Anderson EW, Fornell C, Lehmann DR (1994), Customer Satisfaction, Market Share, And
Profitability: Findings From Sweden, Journal of Marketing, 58 (3), 53-66.
Click here for abstract

Simonson I, Tversky A (1992), Choice In Context – Tradeoff Contrast And Extremeness
Aversion, Journal of Marketing Research, 29 (3), 281-295.
Click here for abstract

Boulding W, Kalra A, Staelin R, Zeithaml VA (1993), A Dynamic Process Model Of Service
Quality – From Expectations To Behavioral Intentions, Journal of Marketing Research, 30 (1),
7-27.
Click here for abstract

Aaker DA, Keller KL (1990), Consumer Evaluations Of Brand Extensions, Journal of Marketing,
54 (1), 27-41.
Click here for abstract

Hauser, JR, Shugan, S (1983), Defensive Marketing Strategies, Marketing Science, 2 (4), 319360.
Click here for abstract

Mahajan V, Muller E, Bass FM (1990), New Product Diffusion-Models In Marketing – A Review
And Directions For Research, Journal of Marketing, 54 (1), 1-26.
Click here for abstract

15
IMPACT OF MARKETING SCIENCE APPROACHES
We are interested in your view as to the impact that specific quantitative marketing techniques and
tools (or "marketing science approaches") have had on the overall practice of marketing in
businesses. Below we include a set of tools, techniques and approaches that we have distilled from
discussions with selected marketing science academics and practitioners. For each, we would be
grateful for your opinion as to the degree to which these approaches have affected overall marketing
practice over the past twenty-five years. If you are unaware of the technique or its influence you
should mark “Not Aware.”
How much of an influence has this marketing science approach had on overall marketing practice in
businesses in the past 25 years?
Not Aware; No Influence; Slightly Influential; Somewhat Influential; Very Influential Extremely
Influential
Marketing metrics

Customer life time value models

Game theory models

Customer satisfaction models

Sales force allocation models

Aggregate marketing mix models

New product models

Pre-test market models

Panel-based choice models

Survey-based choice models

Perceptual mapping

Segmentation tools

IMPACT ON AREAS OF DECISION MAKING
We are now interested in the impact that quantitative marketing techniques have had on practice in
different areas of marketing decision making. Marketing science’s influence on decisions in
specific areas is a function of its ability to add insight to a management problem or decision in
that area and also how important that area is to the objectives of the organization. Below is a
list of the different types of activities that a marketing manager might perform. For each, we
would be grateful for your opinion as to the degree to which these activities have been influenced by
marketing science (quantitative marketing techniques) over the past twenty-five years.
How much of an influence has marketing science had on this area of marketing decision making in the past
25 years?
Not Aware; No Influence; Slightly Influential; Somewhat Influential; Very Influential; Extremely
Influential
Service/product quality management

Customer
Insight Management

Relationship management

Customer/market selection

Channel management

16
Salesforce management

Pricing management

Promotion management

Advertising management

Product portfolio management

New Product/Service management

Brand management

BACKGROUND DATA
By way of background, we would be grateful if you could tell us something about
yourself.
What percentage of your firm's business comes from (the numbers that you enter
must add up to 100):
Data Collection ______________
Statistical Analysis ______________________
Strategic Advice _____________
Other ________________________________
What percentage of your firm's business comes from (the numbers that you enter
must add up to 100):
Services related to marketing activity
________________
Services related to other business disciplines
________________
How many employees does your organization have on a global basis?
1-50
51-200
201-500
>500
What is the location of your organization's headquarters?
USA
Europe
Asia
Other
What is your position in the organization?
Lower Management
Other
Middle Management
What age category do you fall into?
1-30
31-40
41-50
Higher Management
>50
What is the highest educational qualification you have attained?
High school
Bachelors Masters
PhD
Have you ever been a faculty member at a university?
Yes
No
Comments: We would be grateful for any comments that you have on the impact of quantitative
marketing techniques not covered by this survey.
____________________________________________________________________________
_____________________________________________________________________________
___________________________________________________________________
Thank you very much for taking the time to complete this questionnaire. We
will keep you posted on the results. If you would like a copy of the results,
please email us at [email].
17
Marketing Science Practice Impact (Managers)
We would like to thank you for participating in this survey. The survey was commissioned by the
Practice Committee of the INFORMS Society for Marketing Science, publisher of Marketing Science
and sponsor of the annual Marketing Science Conference. Our objective is to understand the impact
that marketing science in general has had on the practice of marketing. By marketing science, we
refer to quantitative approaches to understanding marketplace behavior and the effect of marketing
activity upon it. We are looking at a time horizon of twenty-five years. While we appreciate that many
marketing science advances were made before this time, we are focusing on that period in order to
make our study manageable.
The survey should take you approximately 8 minutes.
You have been selected as a leader in our field, and your organization as one of the leading
companies in marketing practice. While we understand that there are many calls on your time, we do
ask you to spare the few minutes required to assist us in the important task of gaining a
representative view of our research and its impact.
Your answers will be treated as totally confidential. If you would like a copy of our results, please
email us at [email].
If you have any questions please do not hesitate to contact one of us.
Thank you for your help!
Sincerely,
[The authors]
IMPACT OF MARKETING SCIENCE APPROACHES
We are interested in your view as to the impact that specific quantitative marketing techniques and
tools (or "marketing science approaches") have had on the overall practice of marketing in
businesses. Below we include a set of tools, techniques and approaches that we have distilled from
discussions with selected marketing science academics and practitioners. For each, we would be
grateful for your opinion as to the degree to which these approaches have affected overall marketing
practice over the past twenty-five years. If you are unaware of the technique or its influence you
should mark “Not Aware.”
How much of an influence has this marketing science approach had on overall marketing practice in
businesses in the past 25 years?
Not Aware; No Influence; Slightly Influential; Somewhat Influential; Very Influential Extremely
Influential
Segmentation tools

Perceptual mapping

Survey-based choice models

Panel-based choice models

Pre-test market models

New product models

Aggregate marketing mix models

Sales force allocation models

Customer satisfaction models

18
Game theory models

Customer life time value models

Marketing metrics

IMPACT ON AREAS OF DECISION MAKING
We are now interested in the impact that quantitative marketing techniques have had on practice in
different areas of marketing decision making. Marketing science’s influence on decisions in
specific areas is a function of its ability to add insight to a management problem or decision in
that area and also how important that area is to the objectives of the organization. Below is a
list of the different types of activities that a marketing manager might perform. For each, we
would be grateful for your opinion as to the degree to which these activities have been influenced by
marketing science (quantitative marketing techniques) over the past twenty-five years.
How much of an influence has marketing science had on this area of marketing decision making in the past
25 years?
Not Aware; No Influence; Slightly Influential; Somewhat Influential; Very Influential; Extremely
Influential
Brand management

New Product/Service management

Product portfolio management

Advertising management

Promotion management

Pricing management

Salesforce management

Channel management

Customer/market selection

Relationship management

Customer Insight Management

Service/product quality management 
IMPORTANCE OF DECISION AREAS
We are now interested in your opinion of the importance of different areas of decision making to your
company as a whole. Below is the list of the different types of activities that marketing managers
perform. For each, we would be grateful for your opinion as to the degree to which the area is
important.
How important is this area of marketing decision making to your company?
Not Important; Slightly Important; Somewhat Important; Very Important; Extremely Important
Brand management

New Product/Service management

Product portfolio management

Advertising management

Promotion management

Pricing management

19
Salesforce management

Channel management

Customer/market selection

Relationship management

Customer Insight Management

Service/product quality management 
BACKGROUND DATA
By way of background, we would be grateful if you could tell us some background
about yourself.
Is your company:
Primarily service based Primarily product based Both service and product based
Is your company:

Primarily B2B
Primarily B2C
Both B2B and B2C
Is your company a supplier of marketing research and/or a consultancy?

Yes
No
How many employees does your company have on a global basis?

1-999
1,000-10,000
10,001-50,000
>50,000
What is the location of your company's headquarters?

USA
Europe
Asia
Other (please specify):___________________
What is your position in the company?

Product Manager
Marketing Manager
CMO/SVP
Marketing researcher/analyst
Marketing Director/VP
Other (please specify)
What age category do you fall into?
1-30
31-40
41-50
>50
What is the highest educational qualification you have attained?

High school
Bachelors Masters PhD
How many years of marketing management experience do you have?

0-5 years 5-10 years 11-20 years More than 20 years Not applicable
13) Comments: We would be grateful for any comments that you have on the impact of quantitative
marketing techniques not covered by this survey.
____________________________________________________________________________
_____________________________________________________________________________
___________________________________________________________________
Thank you very much for taking the time to complete this questionnaire. Please
email us at [email] if you would like a copy of our results.
20
Web Appendix 1.3
Bibliography of all articles used in the pre-calibration phase
Aaker DA, Keller KL (1990), Consumer Evaluations Of Brand Extensions, Journal of Marketing,
54 (1), 27-41.
Alba J, Lynch J, Weitz B, Janiszewski C, Lutz R, Sawyer A, Wood S (1997), Interactive Home
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21
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22
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Gutman J (1982), A Means-End Chain Model Based On Consumer Categorization Processes,
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Hunt SD, Morgan RM (1995), The Comparative Advantage Theory Of Competition, Journal of
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Jaworski BJ, Kohli AK (1993), Market Orientation - Antecedents And Consequences, Journal of
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Jeuland, A Shugan, S (1983), Managing Channel Profits, Marketing Science, 2 (3), 239-272.
Joreskog KG, Sorbom D (1982), Recent Developments In Structural Equation Modeling, Journal
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Kalwani MU, Narayandas N (1995), Long-Term Manufacturer Supplier Relationships - Do They
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Kamakura WA, Russell GJ (1989), A Probabilistic Choice Model For Market-Segmentation And
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Keller KL (1993), Conceptualizing, Measuring, And Managing Customer-Based Brand Equity,
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23
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24
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25
Web Appendix 2.1
Transition Table 1a: Impact of articles on marketing science approaches -
(Article
Anderson, Fornell, & Lehmann (1994)
Punj and Stewart (1983)
Guadagni & Little (1983)
Cattin & Wittink (1982)
Rust, Zahorik, & Keiningham (1995)
Mahajan, Mueller, and Bass (1990)
Hauser and Shugan (1983)
Aaker and Keller (1990)
Boulding et al. (1993)
Simonson and Tversky (1992)
Louviere and Woodworth (1983)
Fornell et al. (1996)
Day (1994)
Green and Srinivasan (1990)
Parasuraman, Zeithaml, & Berry (1985)
Hunt & Morgan (1995)
Keller (1993)
Vanheerde, Gupta, & Wittink (2003)
Fornell (1992)
Griffin and Hauser (1993)
Segmen
tation
tools
Percept
ual
mapping
0
0.75
0.5
0.25
0
0
0
0
0
0
0.25
0
0
0.25
0
0
0.25
0
0
0
0
0
0
0.25
0
0
0.25
0.25
0
0
0.25
0.25
0
0.25
0
0
0.25
0
0
0
Surveybased
choice
models
0.25
0.5
0
0.5
0.25
0
0
0.25
0.5
0.5
0.5
0.25
0
0.5
0
0
0
0
0
0
Panelbased
choice
models
0
0
0.75
0
0
0
0
0
0
0.25
0.5
0
0
0
0
0
0
0.5
0
0
Pre-test
market
models
0
0
0.25
0
0
0.5
0
0
0
0
0.25
0
0
0.25
0
0
0
0
0
0
Academics (N=4, PRL Reliability 0.94)
New
product
models
0
0
0
0.5
0
0.75
0.5
0
0
0
0
0
0
0.5
0
0
0
0
0
0.5
Aggregate
marketing
mix
models
0
0
0.5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.75
0
0
Sales
force
allocation
models
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.25
0
0
Customer
satisfaction
models
0.75
0
0
0
0.5
0
0
0.25
0.25
0.25
0
0.75
0
0
0.5
0
0
0
0.5
0
Game
theory
models
0
0
0
0
0
0
0.5
0
0
0
0
0
0
0
0
0.25
0
0
0
0
Customer
life time
value
models
Marketing
metrics
0
0
0
0
0.5
0
0
0
0.25
0
0
0.25
0
0
0
0
0.25
0
0
0
0.5
0
0
0.25
0.75
0
0
0.5
0.5
0
0.25
0.5
0.25
0.25
0.5
0.25
1
0.25
0.25
0.25
(Numbers represent the proportion of academics who say that the article has been influential on the approach)
26
Intermediaries (N=4, PRL Reliability 0.95)
Transition Table 1b: Impact of articles on marketing science approaches -
(Article
Anderson, Fornell, & Lehmann (1994)
Punj and Stewart (1983)
Guadagni & Little (1983)
Cattin & Wittink (1982)
Rust, Zahorik, & Keiningham (1995)
Mahajan, Mueller, and Bass (1990)
Hauser and Shugan (1983)
Aaker and Keller (1990)
Boulding et al. (1993)
Simonson and Tversky (1992)
Louviere and Woodworth (1983)
Fornell et al. (1996)
Day (1994)
Green and Srinivasan (1990)
Parasuraman, Zeithaml, & Berry (1985)
Hunt & Morgan (1995)
Keller (1993)
Vanheerde, Gupta, & Wittink (2003)
Fornell (1992)
Griffin and Hauser (1993)
Segmen
tation
tools
Percept
ual
mapping
0
0.6
0
0.4
0.2
0.2
0.2
0
0
0
0.2
0.2
0.2
0.4
0
0.2
0
0
0
0.2
0
0.2
0
0
0.2
0
0.6
0.2
0.2
0
0
0
0
0
0
0
0.2
0
0
0
Surveybased
choice
models
0
0
0
0.6
0
0
0.4
0.2
0
0.8
0.8
0
0
0.6
0.2
0.2
0.2
0.2
0.2
0.2
Panelbased
choice
models
0
0
0.6
0
0
0
0
0
0
0.4
0.2
0
0
0.2
0
0.2
0.2
0.4
0
0
Pre-test
market
models
0
0
0.2
0.2
0
0.6
0.6
0.2
0
0
0.4
0.2
0.2
0.2
0.2
0
0
0
0
0
New
product
models
0
0
0.4
0.6
0
1
0.6
0.4
0
0.2
0.8
0.2
0.2
0.8
0.2
0
0
0
0
0.2
Aggregate
marketing
mix
models
0
0
0.4
0
0.2
0
0.2
0
0
0
0
0
0
0
0
0
0.2
0.6
0
0
Sales
force
allocation
models
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Customer
satisfaction
models
0.6
0
0
0.2
0.6
0.2
0.2
0.2
0.8
0.2
0.2
0.8
0.2
0.2
1
0.2
0.2
0.2
0.6
0.4
Game
theory
models
0
0
0.2
0.2
0
0.2
0
0
0
0.2
0
0
0
0.2
0
0.2
0
0
0
0
Customer
life time
value
models
Marketing
metrics
0.4
0
0
0
0.6
0
0
0
0.2
0
0
0.2
0
0
0.4
0
0.2
0
0.2
0
0
0
0.4
0.2
0.6
0.2
0.2
0.2
0.4
0.2
0.2
0.6
0.4
0.2
0.4
0.2
0.6
0.4
0.4
0.2
(Numbers represent the proportion of intermediaries who say that the article has been influential on the approach)
27
Transition Table 2a: Impact of articles on marketing science approaches -
Brand
manage
ment
(Article
Anderson, Fornell, & Lehmann (1994)
Punj and Stewart (1983)
Guadagni & Little (1983)
Cattin & Wittink (1982)
Rust, Zahorik, & Keiningham (1995)
Mahajan, Mueller, and Bass (1990)
Hauser and Shugan (1983)
Aaker and Keller (1990)
Boulding et al. (1993)
Simonson and Tversky (1992)
Louviere and Woodworth (1983)
Fornell et al. (1996)
Day (1994)
Green and Srinivasan (1990)
Parasuraman, Zeithaml, & Berry (1985)
Hunt & Morgan (1995)
Keller (1993)
Vanheerde, Gupta, & Wittink (2003)
Fornell (1992)
Griffin and Hauser (1993)
0.25
0.25
0.5
0.25
0.5
0
0.75
1
0.5
0.25
0.25
0.5
0.25
0.5
0.25
0
1
0.5
0
0.5
New
Product/
Service
manage
ment
0
0.5
0
0.75
0
0.5
0.75
0.5
0.5
0.25
0.5
0.25
0
0.75
0.25
0.25
0.25
0
0
0.5
Product
portfolio
manage
ment
0.25
0
0
0.25
0.25
0.25
0.25
0.25
0.25
0
0.25
0.25
0.5
0.25
0.25
0.5
0.5
0
0
0.25
Advertisi
ng
manage
ment
0
0.25
0
0
0
0
0.25
0
0
0
0
0
0
0
0
0
0
0.25
0
0
Promoti
on
manage
ment
0
0.25
1
0
0
0
0
0
0
0
0.25
0
0
0
0
0.25
0
0.75
0
0
Academics (N=4, PRL Reliability 0.94)
Pricing
manage
ment
0
0.25
0.5
0.5
0
0.25
0.25
0
0
0
0.25
0
0
0.25
0
0.25
0
0.25
0
0
Salesforce
managem
ent
Channel
managem
ent
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Customer/
market
selection
0
0.5
0
0.25
0.25
0.25
0
0.25
0
0.25
0.25
0.25
0.25
0.25
0
0
0.25
0
0
0
Relation
ship
manage
ment
Customer
Insight
Managem
ent
Service/p
roduct
quality
manage
ment
0.5
0.25
0
0
0.5
0
0
0
0
0
0
0.5
0
0
0.25
0
0
0
0
0
0.5
0.5
0
0.5
0.5
0
0
0.5
0.5
0.25
0.25
0.5
0
0.5
0.75
0
0.5
0
0
0.5
0.25
0
0
0.25
0.5
0
0
0.5
0.5
0
0.25
0.75
0.5
0.25
1
0
0.5
0.25
0
0.5
(Numbers represent the proportion of academics who say that the article has been influential on the decision making area)
28
Intermediaries (N=5 PRL Reliability 0.95)
Transition Table 2b: Impact of articles on marketing science approaches -
Brand
manage
ment
(Article
Anderson, Fornell, & Lehmann (1994)
Punj and Stewart (1983)
Guadagni & Little (1983)
Cattin & Wittink (1982)
Rust, Zahorik, & Keiningham (1995)
Mahajan, Mueller, and Bass (1990)
Hauser and Shugan (1983)
Aaker and Keller (1990)
Boulding et al. (1993)
Simonson and Tversky (1992)
Louviere and Woodworth (1983)
Fornell et al. (1996)
Day (1994)
Green and Srinivasan (1990)
Parasuraman, Zeithaml, & Berry (1985)
Hunt & Morgan (1995)
Keller (1993)
Vanheerde, Gupta, & Wittink (2003)
Fornell (1992)
Griffin and Hauser (1993)
0.2
0.4
0.6
0.4
0.2
0.2
0.6
0.6
0
0.2
0.4
0.2
0.2
0.4
0.4
0.2
0.8
0.2
0
0.2
New
Product/
Service
manage
ment
0
0.4
0.2
0.8
0.2
1
0.6
0.6
0
0.6
0.8
0.2
0.4
0.8
0.2
0.2
0.2
0
0
0.2
Product
portfolio
manage
ment
0
0.2
0.4
0.6
0.2
0.6
0.6
0.6
0
0.4
0.6
0.2
0.2
0.8
0.2
0.2
0.4
0.4
0
0.4
Advertisi
ng
manage
ment
0
0
0.4
0
0
0.2
0.2
0
0.2
0.2
0
0
0.2
0
0
0.2
0.2
0.4
0
0
Promoti
on
manage
ment
0
0
0.6
0.2
0
0.2
0
0
0
0
0
0
0.2
0.2
0
0.2
0.2
0.6
0
0
Pricing
manage
ment
0
0
0.6
0.6
0
0
0.4
0
0
0.4
0.8
0.2
0.2
0.6
0
0.2
0.2
0.6
0
0
Salesforce
managem
ent
Channel
managem
ent
0
0
0.2
0
0
0
0
0
0
0
0
0
0.2
0
0
0.2
0
0
0
0
0
0
0.4
0
0
0
0
0
0
0
0
0
0.2
0
0
0.2
0
0
0
0
Customer/
market
selection
0
0.6
0
0.4
0.4
0.6
0
0.2
0.2
0
0.6
0.4
0.2
0.6
0.4
0.2
0.2
0.2
0
0.4
Relation
ship
manage
ment
Customer
Insight
Managem
ent
Service/p
roduct
quality
manage
ment
0.4
0
0.2
0.2
0.8
0
0.2
0
0.6
0
0
0.6
0.4
0.2
1
0.2
0.2
0
0
0
0.2
0.6
0.2
0.4
0.4
0.2
0.2
0.2
0.4
0.2
0.6
0.2
0.2
0.2
0.4
0.2
0.2
0.2
0
0.6
0.6
0
0
0.2
0.6
0
0.2
0
0.8
0.2
0.2
0.8
0.4
0.2
1
0.2
0.2
0
0.6
0.4
(Numbers represent the proportion of intermediaries who say that the article has been influential on the decision making area)
29
Transition Table 3a: Impact of marketing science approaches on decisions
Brand
manage
ment
(Approach)
Segmentation tools
Perceptual mapping
Survey-based choice models
Panel-based choice models
Pre-test market models
New product models
Aggregate marketing mix models
Sales force allocation models
Customer satisfaction models
Game theory models
Customer life time value models
Marketing metrics
0.75
1
0.5
0.75
0.25
0.25
0.5
0
0.25
0.5
0.5
0.75
New
Product/
Service
manage
ment
0.5
0.75
0.5
0.25
1
0.75
0
0
0.25
0.5
0
0.5
Product
portfolio
manage
ment
0.25
0.5
0.25
0
0.25
0.5
0
0
0.25
0.5
0
0.25
Advertisi
ng
manage
ment
0
0.25
0.25
0.5
0
0
1
0
0
0.25
0.25
0.5
Promoti
on
manage
ment
0.5
0
0.25
0.75
0
0
1
0
0
0.25
0.25
0.5
Academics (N=4 PRL Reliability 0.84)
Pricing
manage
ment
0.5
0
0.25
0.5
0.5
0
1
0
0
0.25
0.25
0.25
Salesforce
managem
ent
Channel
managem
ent
0.25
0
0
0
0
0
0.5
1
0.25
0.25
0.25
0.5
0.25
0
0
0.25
0
0
0.25
0.25
0
0.25
0
0.25
Customer/
market
selection
0.75
0.5
0
0
0.25
0.25
0
0.25
0.25
0.5
0.75
0.25
Relation
ship
manage
ment
Customer
Insight
Managem
ent
Service/p
roduct
quality
manage
ment
0
0
0
0
0
0
0
0.25
0.75
0
0.25
0.25
0.75
0.25
0.5
0.25
0
0
0.25
0
0.75
0
0.75
0.25
0
0.25
0.5
0.5
0.25
0.25
0
0
1
0
0.25
0.5
(Numbers represent the proportion of academics who say that the approach has been influential on the decision making area)
30
Transition Table 3b: Impact of marketing science approaches on decisions
Brand
manage
ment
(Approach)
Segmentation tools
Perceptual mapping
Survey-based choice models
Panel-based choice models
Pre-test market models
New product models
Aggregate marketing mix models
Sales force allocation models
Customer satisfaction models
Game theory models
Customer life time value models
Marketing metrics
1
1
0.4
0.4
0.6
0.2
0.4
0.2
0.4
0.2
0.2
0.4
New
Product/
Service
manage
ment
0.8
0.6
1
0.6
0.8
1
0
0
0.2
0.2
0
0
Product
portfolio
manage
ment
0.8
0.6
0.8
0.6
0.6
0.6
0.4
0.2
0.2
0.4
0.2
0.2
Advertisi
ng
manage
ment
0.2
0.6
0.2
0.4
0.2
0.2
1
0
0.2
0
0
0.4
Promoti
on
manage
ment
0
0
0.2
0.8
0.2
0
1
0.2
0
0
0
0
Intermediaries (N=5 PRL Reliability 0.88)
Pricing
manage
ment
0.6
0.4
1
1
0.6
0.6
1
0.2
0.2
0.6
0.4
0.4
Salesforce
managem
ent
Channel
managem
ent
0
0
0
0
0
0
0.4
0.8
0
0.2
0
0.2
0.2
0
0
0.2
0.2
0.2
0.6
0.2
0
0.2
0.2
0.2
Customer/
market
selection
1
0
0.8
0.4
0.2
0.4
0.2
0.2
0.4
0
1
0.4
Relation
ship
manage
ment
Customer
Insight
Managem
ent
Service/p
roduct
quality
manage
ment
0.4
0
0.6
0
0
0
0
0
0.8
0.2
0.4
0.2
0.8
1
0.6
0.4
0.2
0.4
0.4
0
0.2
0
0.2
0.4
0
0.2
0.6
0.2
0.2
0.2
0.2
0.2
1
0.2
0.2
0.4
(Numbers represent the proportion of intermediaries who say that the approach has been influential on the decision making area)
31
Transition Table 3c: Impact of marketing science approaches on decisions
Brand
manage
ment
(Approach)
Segmentation tools
Perceptual mapping
Survey-based choice models
Panel-based choice models
Pre-test market models
New product models
Aggregate marketing mix models
Sales force allocation models
Customer satisfaction models
Game theory models
Customer life time value models
Marketing metrics
1
1
0.75
0.75
0.5
0.5
0.25
0
0.5
0
0.25
1
New
Product/
Service
manage
ment
1
1
0.75
0.75
1
1
0
0
0.75
0.25
0.75
0
Product
portfolio
manage
ment
1
0.25
0.75
1
0.5
1
0.75
0
0.25
0
0.5
0.25
Advertisi
ng
manage
ment
0.75
1
0
0.25
0.5
0
0.75
0
0
0
0
0.5
Promoti
on
manage
ment
0.75
0
0.25
0.75
0.75
0.25
1
0
0
0.25
0
0.75
Managers (N=4 PRL Reliability 0.89)
Pricing
manage
ment
0
0
1
1
1
0
1
0
0
0
0
0
Salesforce
managem
ent
Channel
managem
ent
0.25
0
0
0
0
0
0.75
1
0
0
0
0.5
0
0.5
0
0.5
0
0.25
1
0.75
0
0.25
0
0.25
Customer/
market
selection
1
0.25
0.25
0
0.5
0.25
0.75
0.5
0.75
0.25
1
0.5
Relation
ship
manage
ment
Customer
Insight
Managem
ent
Service/p
roduct
quality
manage
ment
0.25
0.25
0.25
0
0
0
0
0.5
0.75
0
1
0.25
0.75
0.75
1
0.25
1
0.25
0.25
0
0.75
0
0.75
0
0
0
0
0
0.25
0.25
0
0
0.75
0
0
0.25
(Numbers represent the proportion of intermediaries who say that the approach has been influential on the decision making area)
32
Web Appendix 2.2
100 articles sorted by quadrants in Figure 4
INTIMPACT
rank
Authors, Publication Year
INTIMPACT
MKSIMPACT
Above median
INTIMPACT
Above median
MKSIMPACT
1
Green & Srinivasan (1990)
4.22
2.04
Yes
Yes
2
Louviere & Woodworth (1983)
3.56
2.35
Yes
Yes
5
Guadagni & Little (1983)
3.22
5.94
Yes
Yes
6
Mahajan, Muller & Bass (1990)
3.11
3.31
Yes
Yes
7
Rust, Zahorik & Keiningham (1995)
3.00
2.22
Yes
Yes
8
Hauser & Shugan (1983)
3.00
2.04
Yes
Yes
9
Fornell, Johnson, Anderson, Cha & Bryant (1996)
3.00
1.48
Yes
Yes
11
Day (1994)
2.67
2.98
Yes
Yes
12
Punj & Stewart (1983)
2.67
2.07
Yes
Yes
13
Fornell (1992)
2.67
2.04
Yes
Yes
14
Vanheerde, Gupta & Wittink (2003)
2.63
1.45
Yes
Yes
16
Anderson, Fornell & Lehmann (1994)
2.44
2.73
Yes
Yes
17
Simonson & Tversky (1992)
2.38
1.80
Yes
Yes
18
Boulding, Kalra, Staelin & Zeithaml (1993)
2.38
1.79
Yes
Yes
19
Parasuraman, Zeithaml & Berry (1985)
2.25
5.44
Yes
Yes
20
Keller (1993)
2.25
1.90
Yes
Yes
21
Yu & Cooper (1983)
2.25
1.47
Yes
Yes
27
Joreskog & Sorbom (1982)
2.11
1.71
Yes
Yes
29
Thaler (1985)
2.00
4.43
Yes
Yes
30
Kamakura & Russell (1989)
2.00
2.81
Yes
Yes
31
Zeithaml (1988)
2.00
2.54
Yes
Yes
32
Bolton (1998)
2.00
1.59
Yes
Yes
33
Tversky & Simonson (1993)
2.00
1.49
Yes
Yes
35
Fornell & Bookstein (1982)
1.89
1.57
Yes
Yes
36
Mittal & Kamakura (2001)
1.89
1.56
Yes
Yes
39
Gupta (1988)
1.75
2.01
Yes
Yes
40
Teas (1993)
1.75
1.42
Yes
Yes
41
Anderson & Sullivan (1993)
1.67
2.18
Yes
Yes
3
Aaker & Keller (1990)
3.50
1.00
Yes
No
4
Cattin & Wittink (1982)
3.25
1.12
Yes
No
10
Griffin & Hauser (1993)
2.89
1.23
Yes
No
15
Hunt & Morgan (1995)
2.63
1.33
Yes
No
22
Urban, Carter, Gaskin & Mucha (1986)
2.25
1.19
Yes
No
23
Carpenter & Nakamoto (1989)
2.22
1.09
Yes
No
24
Zeithaml, Berry & Parasuraman (1996)
2.13
1.19
Yes
No
25
Dickson & Sawyer (1990)
2.13
0.94
Yes
No
26
Zeithaml, Parasuraman & Berry (1985)
2.13
0.76
Yes
No
28
Day & Wensley (1988)
2.11
1.41
Yes
No
34
Churchill & Surprenant (1982)
2.00
0.60
Yes
No
37
Srivastava, Shervani & Fahey (1998)
1.88
1.15
Yes
No
38
Churchill, Ford, Hartley & Walker (1985)
1.88
0.96
Yes
No
33
INTIMPACT
rank
Authors, Publication Year
INTIMPACT
MKSIMPACT
Above median
INTIMPACT
Above median
MKSIMPACT
42
Gutman (1982)
1.67
1.19
Yes
No
43
Jaworski & Kohli (1993)
1.63
4.07
No
Yes
44
Slater & Narver (1994)
1.63
2.35
No
Yes
45
Mcguire, TW & Staelin (1983)
1.63
1.98
No
Yes
46
Parasuraman, Zeithaml & Berry (1994)
1.63
1.80
No
Yes
52
1.50
1.93
No
Yes
56
Bitner (1990)
Alba, Lynch, Weitz, Janiszewski, Lutz, Sawyer &
Wood (1997)
1.38
2.32
No
Yes
57
Webster (1992)
1.38
2.06
No
Yes
61
Dwyer & Schurr & Oh (1987)
1.25
4.29
No
Yes
62
Lynch & Ariely (2000)
1.25
1.58
No
Yes
64
Bitner (1992)
1.22
1.82
No
Yes
65
Cronin & Taylor (1992)
1.22
1.45
No
Yes
74
Morgan & Hunt (1994)
1.00
6.54
No
Yes
75
Bakos (1997)
1.00
4.04
No
Yes
76
Narver & Slater (1990)
1.00
3.08
No
Yes
79
Kohli & Jaworski (1990)
0.89
3.03
No
Yes
80
Jeuland & Shugan (1983)
0.89
2.76
No
Yes
93
Hirschman & Holbrook (1982)
0.56
1.86
No
Yes
97
Hoffman & Novak (1996)
0.50
3.29
No
Yes
98
Slater & Narver (1995)
0.44
1.59
No
Yes
99
Ferrell & Gresham (1985)
0.33
1.92
No
Yes
47
Mackenzie & Lutz (1989)
1.63
1.33
No
No
48
Robinson & Fornell (1985)
1.63
1.29
No
No
49
Bitner, Booms & Tetreault (1990)
1.63
1.24
No
No
50
Bolton & Lemon (1999)
1.63
1.23
No
No
51
Henard & Szymanski (2001)
1.63
1.02
No
No
53
Perreault & Leigh (1989)
1.50
1.39
No
No
54
Ruekert & Walker (1987)
1.50
1.08
No
No
55
Mackenzie, Lutz & Belch (1986)
1.50
0.95
No
No
58
Haubl & Trifts (2000)
1.38
1.11
No
No
59
Bearden, Sharma & Teel (1982)
1.38
0.85
No
No
60
Han, Kim & Srivastava (1998)
1.33
0.51
No
No
63
Pollay (1986)
1.25
0.89
No
No
66
Oliver (1999)
1.22
1.19
No
No
67
Garbarino & Johnson (1999)
1.22
0.54
No
No
68
Crosby, Evans & Cowles (1990)
1.22
0.49
No
No
69
Cronin & Taylor (1994)
1.13
1.18
No
No
70
Rindfleisch & Heide (1997)
1.13
1.09
No
No
71
Kalwani & Narayandas (1995)
1.13
0.96
No
No
72
Ganesan (1994)
1.13
0.80
No
No
73
Doney & Cannon (1997)
1.13
0.79
No
No
77
Anderson, Hakansson & Johanson (1994)
1.00
1.15
No
No
78
Deshpande, Farley & Webster (1993)
1.00
0.87
No
No
34
INTIMPACT
rank
Authors, Publication Year
INTIMPACT
MKSIMPACT
Above median
INTIMPACT
Above median
MKSIMPACT
81
Gorn (1982)
0.89
1.35
No
No
82
Anderson & Coughlan (1987)
0.89
1.30
No
No
83
Phillips, Chang & Buzzell (1983)
0.89
1.16
No
No
84
Gaski (1984)
0.89
1.03
No
No
85
Novak, Hoffman & Yung (2000)
0.89
0.50
No
No
86
Lovelock (1983)
0.78
1.34
No
No
87
Solomon, Surprenant, Czepiel & Gutman (1985)
0.75
0.96
No
No
88
Anderson & Narus (1990)
0.75
0.88
No
No
89
Zirger & Maidique (1990)
0.67
0.87
No
No
90
Deshpande & Zaltman (1982)
0.67
0.55
No
No
91
Sinkula (1994)
0.63
1.17
No
No
92
Anderson & Weitz (1989)
0.63
1.08
No
No
94
Huber & McCann (1982)
0.56
1.41
No
No
95
Tse & Wilton (1988)
0.56
1.04
No
No
96
Anderson & Weitz (1992)
0.56
0.73
No
No
100
Gerbing & Anderson (1988)
0.33
1.41
No
No
35
Web Appendix 2.3
Open ended comments from Managers, Intermediaries and Academics
Response to the question:
Comments: We would be grateful for any comments that you have on the impact of quantitative
marketing techniques not covered by this survey.
Managers

I am a huge proponent of market research, but I have found working for a sales driven
company that there is not as much excitement about this as other companies I have worked
for/with.

I think that development of data integration technologies such as Access, Cognos, BEX
SAP, Hyperion and others are essential to consider - and key reasons why marketing
metrics are less departmental every day and more the metrics of the enterprise. The
marketing metrics must tie out in aggregate, and in the microcosms of business - such as
regions of sales people; else the credulity and usefulness is lost, except in a management
decision-making session...

Most of our emphasis is on understanding the value proposition for larger customers. Focus
on customer centricity, quantitative pricing models and strategic planning in building strong
relationships in commodity business has and continues to be the key attributes of a winning
strategy. Part of this is knowing the market of your product(s) but it has a lot to do with
understanding the marketing direction on the part of your key customers and then changing
either your products and or services to meet these new needs. We manage change in a
forever changing global market.

I believe many of these tools are used in some form and to at least a small degree in every
aspect of marketing. In other words, there is no corner of marketing that hasn't been
impacted.

The impact of the items mentioned in the survey have been quite limited in the high
technology industry, with the exception of some of the largest companies. This is changing,
and I do believe marketing science will continue to increase its influence and importance in
high tech going forward.

Six Sigma and Lean are significantly increasing the use of quantitative marketing
techniques rather than formal Marketing Science.

The impact is pervasive in a company that has a data-based decision making culture.
Survey research, syndicated market data and the accompanying models direct decisions as
products move through development, initiatives are qualified for market and packaging and
marketing communications are developed. The overall strategy for brands is heavily
researched. However strategic management of whole categories, new market selection and
macro forces affecting risk and opportunity are less well supported by quantitative
marketing science.

You should profile whether the respondent is responding from a consumer marketing or
business marketing perspective and the industry should be identified. My answers reflect
Marketing's low support role in high tech. I would bet a respondent from say a Proctor and
Gamble would be much more positive in their responses with respect to Marketing's
contribution to the firm.

B2B Marketing research departments typically not current on new or better research
methodologies (primarily use surveys and focus groups for decision making)

Disciplines have to relate to business challenges and issues. Theory is nice. but the real
question is what stands between you and success.

This largely seems to be about best practices for well defined areas. It assumes that how
we think about certain areas (e.g., new product success) are correct. I doubt the adequacy
36
of current paradigms in general, which makes discussions about "marketing science" a bit
off point.

For consumer package goods companies, product assortment models are becoming very
important. They can guide both customer and portfolio strategy for manufacturers and
retailers. I would also think that simulation and optimization technologies are also
becoming important for forecasting and strategy testing. Perhaps these technologies are
imbedded in the already mentioned subject areas however
Intermediaries

I'm surprised that the seminal Bayes/MCMC papers didn't appear. For instance, choice
models are all HB these days. Also find the absence of Ehrenberg interesting (esp. among
CPG folks). Does the sample have a customer sat orientation?

While implied in the topic areas you covered, it might be worth calling out more attention on
loyalty, bundling and price line management -- Mktg Sc has made important contributions in
these specific areas.

I have a sense that the industry is moving in a direction not conducive to sophisticated
research tools. My sense is that market research is becoming less of a craft and more of a
dumbed-down commodity--or maybe I am just becoming cynical in my old age!

Textual analysis of data; Semiotics; Shopper Insights

The technique is covered but my additional comment is that impact is more determined by
ease of use than inherent value of the technique. We see this with Bayesian methods for
instance which are now easier to implement than in past years. Choice analysis as a whole
has benefitted from advances in computing power. Perceptual mapping has been influential
but less so than choice because management still interprets maps incorrectly but embraces
utility functions.

Modeling techniques, such as MNL, HBA, etc.

I think this might have been more useful when thinking about specific organizations rather
than overall impact. I think there is a great deal of variation in how some of these techniques
and approaches have impacted different businesses and I don't think this is adequately
captured in this survey
Academics

Forecasting is a key area of application missing in the survey.

The rating of influence of journal articles are highly correlated with familiarity of them.

Experimental design in general, while not specifically a MARKETING technique, has
increased its value tremendously to marketers in the past thirty years or so, and is now very
influential in marketing decision making, since its use allows the study of many more factors
simultaneously. Perhaps, we would label the influence "indirect," but it is nevertheless strong.
I refer to principles that often go beyond the traditional training folks get in conjoint analysis.

Throw-away comment: glad you're doing this! Substantive comment: it might be nice to see
the influence of particular models (e.g., logit, Bass) or techniques (e.g., MDS, Conjoint) on
marketing practice as well.

Will the results be published? Aggregate data would be very useful for teaching MBAs, etc.

time series modelling

Resource allocation and Planning models should be included. Some key papers on customer
37
Loyalty/customer management have been omitted that are more relevant to the business
practice.

I find that simple and robust is better than complex and elegant if we want the techniques and
methods we develop to be used in practice.

When evaluating articles, it is difficult to separate influence of a particular article from the
authors' overall work in that area.

Most of marketing science has had influence on market research companies not on marketing
management or top management. Luckily we are happy talking to other digit heads like us.

Not clear how you selected the papers for assessment.

I have worked extensively with litigation support yet that area seems to be ignored as an
application area. Also, I am unsure whether years worked in industry should be full time or
part-time self-employed.

The major problem is that MBA programs provide inadequate training for the world's future
executives in quantitative areas. There is a huge gap between what their level of comfort is
quantitatively and the level of sophistication that appears in the leading Marketing journals.
This structural problem will always limit the influence of scholarly research in this area since
people do not typically utilize what they cannot understand!

Current research (due to the nature of the review process) focuses too much on methodology
rather than substantive problems.

I found it rather difficult to assess the impact of articles on practice. Actually, I am a bit sceptic
about this. But, let's see what the results are. Good luck with this good initiative.

Please send me the results of your study. I am sure there will be some surprising insights...

Optimization techniques; Optimal Control techniques; Dynamic games

data mining? crm models?

Sales response function modeling use in practice has advanced somewhat. One should look
at the job descriptions posted for positions in marketing analytical divisions of companies and
marketing research companies to get an idea of where marketing science has advanced in
practice.
38
Web Appendix 3.1
Evolution of subject headings in Kotler/Kotler and Keller Marketing Management
MARKETING
MANAGEMENT, 4/E
1980
PHILIP KOTLER
MARKETING
MANAGEMENT, 6/E
1988
PHILIP KOTLER
Part 1: Understanding
Marketing
Management
1. The Role of
Marketing in Today’s
Organizations
Part 1: Understanding
Marketing
Management
1. Understanding the
Critical Role of
Marketing in
Organizations and
Society
2. Tasks and
Philosophies of
Marketing Management
3. The Marketing
System
4. The Strategic
Management and
Marketing Process
Part 2: Analyzing
Marketing
Opportunities
5. The Marketing
Environment
23. Marketing
Research and the
Marketing Information
System
6. Consumer Markets
and Buying Behavior
7. Organizational
Markets and Buying
Behavior
8. Market
Segmentation and
Targeting
9. Market Measurement
and Forecasting
2. Laying the
Groundwork through
Strategic Planning
3. The Marketing
Management Process
and Marketing Planning
Part 2: Analyzing
Marketing
Opportunities
Part 3: Researching
and Selecting Target
Markets
5. Analyzing the
Marketing Environment
MARKETING
MANAGEMENT, 11/E
2003
PHILIP KOTLER
1. Defining Marketing
for the 21st Century.
2. Adapting Marketing
to the New Economy.
4. Winning Markets
through MarketOriented Strategic
Planning.
MARKETING
MANAGEMENT, 14/E
2012
PHILIP KOTLER &
KEVIN KELLER
Part 1: Understanding
Marketing
Management
Chapter 1. Defining
Marketing for the 21st
Century
Chapter 2. Developing
Marketing Strategies
and Plans
Part 2: Capturing
Marketing Insights
6. Scanning the
Marketing Environment.
Chapter 3. Gathering
Information and
Scanning the
Environment
Chapter 4. Conducting
Marketing Research and
Forecasting on Demand
6. Analyzing Consumer
Markets and Buying
Behavior
7. Analyzing
Organizational Markets
and Buying Behavior
7. Analyzing Consumer
Markets and Buyer
Behavior.
8. Analyzing Business
Markets and Business
Buying Behavior.
Chapter 6. Analyzing
Consumer Markets
10. Identifying Market
Segments, Selecting
Target Markets and
Developing Market
Positions
9. Measuring and
Forecasting Markets
10. Identifying Market
Segments and
Selecting Target
Markets.
Chapter 8. Identifying
Market Segments and
Targets
4. Marketing
Information Systems
and Market Research
Chapter 7. Analyzing
Business Markets
5. Gathering Information
and Measuring Market
Demand.
Part 3: Connecting
with Customers
39
3. Building Customer
Satisfaction, Value, and
Retention.
14. Setting the Product
and Branding Strategy.
Part 3: Planning
Marketing Strategy
10. Market Planning
11. Competitive
Marketing Strategy
12. Product Life Cycle
Strategy
13. New Product
Development Strategy
Part 4: Assembling
the Marketing Mix
14. Product Decisions
15. Price Decisions
16. Marketing Channels
Decisions
17. Physical
Distribution Decisions
18. Marketing
Communications
Decisions
19. Advertising
Decisions
20. Sales Promotion
and Publicity Decisions
Chapter 5. Creating
Long-term Loyalty
Relationships
Part 4: Building Strong
Brands
Chapter 9. Creating
Brand Equity
Chapter 10. Crafting the
Brand Position
Part 4: Designing
Marketing Strategies
8. Analyzing
Competitors
12 Marketing
Strategies for Different
Stages of the Product
Life Cycle
11. Marketing
Strategies for Market
Leaders, Challengers,
Followers and Nichers
Part 5: Planning
Marketing Programs
15. Managing
Products, Product
Lines and Brands
14. Developing, Testing
and Launching New
Products and Services
16. Managing Services
17. Designing Pricing
Strategies and
Programs
18. Selecting and
Managing Marketing
Channels
19. Managing Retailing,
Wholesaling and
Physical Distribution
Systems
20. Designing
Communication and
Promotion Mix
Strategies
21. Designing Effective
Advertising Programs
22. Designing Sales
Promotion and Public
Relations Programs
9. Dealing with the
Competition.
11. Positioning and
Differentiating the
Market Offering through
the Product Life Cycle.
Chapter 11. Competitive
Dynamics
Part 5: Shaping the
Market
Chapter 15. Designing
and Managing
Integrated Marketing
Chapter 12. Setting
Product Strategy
12. Developing New
Market Offerings.
15. Designing and
Managing Services.
16. Developing Price
Strategies and
Programs.
17. Designing and
Managing Value
Networks and
Marketing Channels.
18. Managing Retailing,
Wholesaling, and
Market Logistics.
Chapter 20. Introducing
New Marketing
Offerings
Chapter 13. Designing
and Managing Services
Chapter 14. Developing
Pricing Strategies and
Programs
Part 6: Delivering
Value
Chapter 16. Managing
Retailing, Wholesaling,
and Logistics
19. Managing
Integrated Marketing
Communications.
Part 7:
Communicating Value
20. Managing
Advertising, Sales
Promotion, Public
Relations, and Direct
Marketing.
Chapter 17. Designing
and Managing
Integrated Marketing
Communications
Chapter 18. Managing
Mass Communications:
Advertising, Sales
40
21. Salesforce
Decisions
23. Managing the
Salesforce
21. Managing the Sales
Force.
Part 5: Administering
the Marketing
Program
Part 6: Organizing,
Implementing and
Controlling Marketing
Effort
24. Organizing and
Implementing
Marketing Programs
25. Evaluating and
Controlling Marketing
Performance
22. Managing the Total
Marketing Effort.
22. Marketing
Organization
24. Marketing Control
Part 6: Special
Marketing Topics
25. International
Marketing
13. Marketing
Strategies for the
Global Marketplace
13. Designing Global
Market Offerings.
Promotions, Events and
Experiences, and Public
Relations
Chapter 19. Managing
Personal
Communications: Direct
and Interactive
Marketing, Word of
Mouth, and Personal
Selling
Chapter 22. Managing a
Holistic Marketing
Organization
Part 8: Creating
Successful Long-Term
Growth
Chapter 21. Tapping
into Global Markets
26. Non Business
Marketing
27. Marketing in the
Contemporary
Environment
41
Web Appendix 3.2
Evolution of MSI Research Priorities
1998-2000
2000-02
2002-04
2004-06
2006-08
2008-10
1. Marketing Metrics
and Performance
Measures
2. Metrics/
Measuring
Marketing
Performance
1. Assessing
Marketing
Productivity
(Return on
Marketing)
and Marketing
Metrics
5. Understanding
Customers
4. Metrics
(Communities of
Interest:
Productivity
. Marketing)
3. Marketing Metrics
1. Accountability
and ROI of
Marketing
Expenditures
3. Managing
Customers
(Communities of
Interest:
. Customer
Management)
5. Managing
Customers
3. Brand Equity
4. Customer management
2. Growth
Connecting Innovation with
Growth
2. Understanding the
Customer Experience
3. Marketing and the
Internet
4. Relationship
Marketing
1. E-Business/ECommerce/Impact
of Internet
4. Managing
Customer
Relationships
5. Managing Brands:
Brand Equity, Product
Management
3. Branding
2. Brands and
Branding
6. Marketing
Innovation: Creating
Customers, Creating
Really New Products
6. New Product/
Innovation
4. Growth,
Innovation, and
New Products
(Communities of
Interest:
. Customer Insight )
2. Understanding
Consumer/
Customer
Behavior
3. New
Approaches to
Generating
Customer Insights
5. Brand equity
4. Innovation
2010-12
2012-14
2. Understanding
Customer
Experience and
Behavior
1. Insight into people in
their roles as consumers
2. Rethinking the
journey to purchase and
beyond, whether
conceptualized as a
funnel or a more
iterative process
6. Managing
Brands in a
Transformed
Marketplace
1. Using Market
Information to
Identify
Opportunities for
Profitable Growth
4. Identifying and
Realizing
Innovation
Opportunities
3. Designing
experiences, not
products. What accounts
for experiences that are
remembered, interesting,
repeated, and valued?
42
7. Market Knowledge
Management
8. Marketing Across
Cultures and Countries
9. Marketing
Communications and
Media
10. Marketing
Organizations:
Structures, Processes,
Capabilities
12. Pricing and
Promotion
13. Distribution
Channels, Strategic
Alliances, and Supply
Chain Management
14. Managing Market
Orientation
15. Environmental and
Social Contexts
Affecting the Future of
Marketing
5. Collecting and
Using Marketing
Knowledge
7. Collecting,
Interpreting, and
Using Information
7. Research Tools
7. Market Research tools
7.
Communications
8. Leveraging
Research Tools
and New Sources
of Data
6. New Media
8. Organizational
Processes and
Structure
Connecting Customers with
the Company
10. Alliances/
Relationships/
Partnering/
Channels
11. Customer
Orientation
5. Delivering
Value Through
Enhanced Media
and Channels
3. Developing
Marketing
Capabilities for a
Customer-focused
Organization
7. Marketing
organizations and
capabilities
5. Delivering
Value Through
Enhanced Media
and Channels
9. Strategy/
Competing
Connecting Metrics with
Marketing Strategy
2. Marketing Strategy
6. The Role of
Marketing
5. Marketing
Strategy
6. Role of
Marketing
(Communities of
Interest:
. Marketing
Excellence)
6. Marketing
Implementation
7. Allocating
Resources to
Marketing
Activities
43
4. Mobile platforms and
their impact on how
people live
their lives and the
operation of markets
5. Trust between people
and their institutions and
in social networks
6. Big data
44
Web Appendix 3.3
ART Forum Sessions and Key Papers
ART Forum 2002
Tutorials
Bayesian models
Data mining
Questionnaire design
Design of choice experiments
Discrete choice advances
Applied probability models
Latent class and segmentation
Sessions
Choice based conjoint
Data integration and fusion
Loyalty and retention rates
Mapping language fragments
Bundling and portfolio choice
Bayesian models and methods
Psych aspects of measurement
Ratings in CBVA
Analysing pick k of n data
Probability trees
Stochastic gradient boosting
ART Forum 2003
Tutorials
Practical pricing research
Bayesian models
Choice model implementation
Discrete choice advances
Applied probability models
Latent class and segmentation
Sessions
Conjoint mixture models
Consideration sets in conjoint
Loyalty and retention rates
Multimarket data analysis
Packaging & line optimization
Bayesian models and methods
Text mining
Hierarchical Bayes
Brand credibility/consideration
Customer base analysis
Prospect theory
Metasampling
Validating buyer models
Modeling heterogeneity
Sessions
Genetic algorithms
Useful segmentation
Text mining
Conjoint adaptive ranking
HB on sparse data
Preference changes in conjoint
Customer profitability response
Flexible substitution patterns
CBCA Dominance/compromise
Forward looking CLV
Split sample optimal design
Conjoint/scanner elasticities
Cross-category non stationarity
Game theoretic optimization
HB for bundle pricing
Volumetric forecasting
Innovation effect on structure
Very large scale simulations
ART Forum 2004
Tutorials
10 Data mining mistakes
Bayesian models
Design of choice experiments
Advanced research techniques
Applied probability models
Latent class and segmentation
ART Forum 2005
Tutorials
Advanced research techniques
Bayesian models
Discrete Choice modeling
Agent based models
Applied probability models
Latent class and segmentation
Eye tracking
Issues in measurement
Sessions
Bayesian information processing
Aggregation issues
Shelf vs grid stimuli
Analysing response latencies
Heterogeneous variable selection
Out of sample forecasting
Using second choice data
Attitude scaling techniques
Check all that apply data
Item response theory models
Validity of intent measures
Mapping opportunity
Modeling similarity
Response function dynamics
Cross category resource alloc
Customer base analysis
Heterogeneous learning
Channel migration
ART Forum 2006
Tutorials
Sessions
45
HB modelling with R
Bayesian models
Discrete choice models
Applied probability models
Latent class segmentation
Agent based modeling
Validity of on line surveys
Shopping path research
Consumer tracking
Embedded premiums
Modeling marketing effects
Preference self explication
Pricing theory and biases
Expenditure allocation tasks
Clusterwise spatial analysis
External effects in simulators
Health care simulations
External effects in simulators
Agent based modeling
Random forest visualization
Parallel Bayesian computation
Reducing mode effects
Cross national scale usage
Neural nets/Decision trees
Ensemble techniques
Temporal stability of segments
Using Isovalue curves
Sessions
Segmentation
Data augmentation
Brand equity modeling
Non compensatory models
Complementary product choice
Bayesian models and methods
Hierarchical memory models
Menu based choice
Wavelets in time series anal.
NPD using ideas markets
Store shopping models
Modeling word of mouth
Trust in on line communities
ART Forum 2007
Tutorials
PLS/Thurstonian scaling
Bayesian models
Customer base analysis
Discrete choice intro/advances
Applied probability models
Market segmentation
Agent based modeling
ART Forum 2008
Tutorials
Partial least squares
Intro to Bayesian statistics
Discrete choice intro/advances
Applied probability models
Market segmentation
Agent based modeling
Sessions
Scaling
Maximizing asthetic preference
Bayesian networks
Adaptive based conjoint
Random regret minimization
Loss functions in CBCA
Pricing techniques
Bi plot perceptual maps
Cluster ensemble analysis
Market segmentation
Advertising and consideration
Inter brand competition
Agent based modeling
Auxiliary data in discrete choice
Sessions
Customer base analytics/CLV
Modeling customer attrition
B2B recommendation models
Identifying bad respondents
Virtual reality shopping research
Visualization technologies
Predicting joint choice
Dynamic multi-stakeholders
Probit with similarities
Pricing models
Website morphing
Large scale social networks
Optimal advertising
Recommendation systems
ART Forum 2009
Tutorials
Dynamic marketing models
Intro to Bayesian statistics
Discrete choice models
Applied probability models
Market segmentation
46
ART Forum 2010
Tutorials
R-code for marketing research
Intro to Bayesian statistics
Discrete choice models
Applied probability models
Market segmentation
Sessions
Dynamics in social media
Social media effects on sales
Advertising effects on choice
Nested logit model estimation
Simulating product choices
Volumetric models
Fusing choice and rating data
Stated and derived importance
Gibbs sampling
Effect of context on preference
Probit product offering model
Advances in CRM models
Analysing dynamic data
Compensatory and not rules
Sessions
Viral marketing strategies
Random graph models
Multichannel media use
Bootstrap/ensemble methods
Bivariate attrition model
Modeling customer churn
Conjoint with allocation data
Nonverbal stimuli in choice
Bayesian mixture models
Dynamic hierarchy of effects
Quantitative trend spotting
Optimal product lines
Firm’s brand communities
Social media, beliefs & choice
Identifying unmet demand
Covariates in choice models
Non compensatory satisfaction
Sessions
Text mining
Brand sentiment & social media
Agent based simulations
Adaptive best worst conjoint
Choice effects of visualization
Bayesian needs meta analysis
Heterogeneity in mkt models
User generated content
Gibbs sampling
Interdependence in networks
Open versus closed measures
Perceived value analysis
CLV modelling (BG/NBD)
Order of entry models
Support vector machine model
Product line optimization
Sessions
Exploration/exploitation
Loyalty program incentives
Brand health tracking
Small sample sizes
HB/Respondent heterogeneity
Dynamic menu choice
Dynamic optimization models
Conjoint and shopping models
Models of price promotion
Individual level choice
High value network consumers
Markov models of networks
Latent class conjoint
Latent class/Non linear optim.
MaxDiff applications
Insights from big data
ART Forum 2011
Tutorials
R-code for marketing research
Intro to Bayesian statistics
Discrete choice models
Text mining and sentiments
Analysing network data
Structural choice modeling
ART Forum 2012
Tutorials
R-code for marketing research
Hierachical Bayes models
Controlled web experiments
Applied probability models
Market segmentation
Text mining and classification
Adv computer simulations
ART Forum 2013
Tutorials
R-code for marketing research
Online/field experiment ABCs
MaxDiff: Opportunities
Applied probability models
Summits of marketing research
47
Summary of trends
New methods




Data mining/data fusion/ big data (2002; 2008, 2013) Data mining/data fusion/ big data
(2002; 2008, 2013)
Agent based modelling (2003, 2008, 2012)
Text mining (2012)
Social media and network analysis (2010; Viral 2011; 2012; 2013; Recommendation systems
2009; User generated content 2012)
New techniques within existing methods



Customer management (Retention rates 2002; CLV 2011, 2012)
Choice modelling ((Bayesian models (2002; Hierarchical Bayes 2003, 2012; Bayesian
networks 2008; Gibbs samplers 2010); 2007 Data augmentation/Bootstrap 2011; 2007
menu based choice; Adaptive conjoint 2008; Converging category choice, dynamic models;
Non compensatory 2012; Hierarchical choice 2012; MaxDiff 2013)
Market response models (2007 Wavelet)
48
Web appendix 3.4
The Impact of Marketing Science on Practice, as measured by patent citations
Table W3.4.1: Number of patent citations to marketing science papers by patent application year
and journal.
International
Journal of
Journal of
Research in Journal of Marketing Management Marketing
Patent application Date Marketing Marketing Research
Science
Science
a
1979
NA
0
1
0
0
1988
0
1
3
1
0
1990
0
0
1
0
0
1995
0
0
1
1
0
1996
0
1
2
1
2
1997
0
4
1
0
0
1998
0
10
2
0
6
1999
0
18
5
11
7
2000
0
25
44
8
68
2001
0
34
87
13
147
2002
4
18
46
12
63
2003
0
8
14
8
14
2004
4
10
14
9
37
2005
2
13
25
14
30
2006
4
34
32
20
29
2007
3
45
30
8
51
2008
1
11
12
20
15
2009
1
13
28
9
20
2010
1
12
14
5
18
2011
3
4
5
4
12
2012
0
1
0
0
2
Total citations
23
262
367
144
521
Note: aTo be interepreted as “Of all issued patents that have an application date of 1979, only
one cited a marketing science paper published in the Journal of Marketing Research.”
49
Table W3.4.2: Number of marketing science papers cited by patents by paper publication year
Paper
Publication
Year
1940
1947
1955
1956
1963
1964
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
International
Journal of
Research in
Marketing
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
2
0
0
0
4
2
2
6
0
Journal of
Marketing
2a
2
1
9
1
2
3
0
5
0
0
2
1
3
0
1
0
0
0
2
0
1
10
5
0
1
28
26
0
5
10
24
4
4
39
25
10
16
2
10
Journal of
Marketing Management Marketing
Research
Science
Science
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
3
0
1
19
0
3
14
0
2
0
0
0
0
0
6
0
0
19
1
0
8
2
0
1
1
0
4
0
0
0
1
0
35
4
0
3
0
0
9
3
0
2
4
0
0
2
4
5
0
26
1
10
20
0
2
27
8
0
4
6
3
31
2
0
10
2
0
4
1
8
4
3
0
5
25
1
22
24
3
89
41
5
12
27
1
72
51
13
30
13
3
23
1
22
26
25
6
53
50
PubYear
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2011
Total
Citations
International
Journal of
Research in
Marketing
3
0
0
0
1
1
0
0
0
0
0
Journal of
Marketing
2
0
1
1
2
0
0
2
0
0
0
23
262
Journal of
Marketing Management Marketing
Research
Science
Science
20
1
22
5
1
7
3
2
3
2
2
6
0
0
8
1
3
6
0
0
2
1
3
1
2
1
3
0
0
1
1
0
0
367
144
521
Note: aTo be interpreted as “Papers published in the year 1940 in the Journal of Marketing received
two citations from patents issued since then.”
51
Table W3.4.3: Marketing science papers published since 2004 that are cited by issued patents.
Paper title
A social influence model of consumer participation in network- and smallgroup-based virtual communities
Advance-selling as a competitive marketing tool
A Customer Lifetime Value Framework for Customer Selection and Resource
Allocation Strategy
The Power of Stars: Do Star Actors Drive the Success of Movies?
Decomposing Promotional Effects with a Dynamic Structural Model of
Flexible Consumption
Estimating Promotion Response When Competitive Promotions Are
Unobservable
Modeling Multiple Relationships in Social Networks
Placebo Effects of Marketing Actions: Consumers May Get What They Pay
For
Recommendation Systems with Purchase Data
Category Management and Coordination in Retail Assortment Planning in the
Presence of Basket Shopping Consumers
Customer Targeting: A Neural Network Approach Guided by Genetic
Algorithms
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Integrating the Number and Location of Retail Outlets on a Line with
Replenishment Decisions
Pricing and Allocation for Quality-Differentiated Online Services
A Price Discrimination Model of Trade Promotions
CHAN4CAST: A Multichannel, Multiregion Sales Forecasting Model and
Decision Support System for Consumer Packaged Goods
Contingent Pricing to Reduce Price Risks
Decomposing the Sales Promotion Bump with Store Data
Modeling Browsing Behavior at Multiple Websites
Modeling Movie Life Cycles and Market Share
Modeling Online Browsing and Path Analysis Using Clickstream Data
Performance Regimes and Marketing Policy Shifts
Probabilistic Goods: A Creative Way of Selling Products and Services
Research on Innovation: A Review and Agenda for Marketing Science
The Impact of Advancing Technology on Marketing and Academic Research
The Impact of Endogeneity and Utility Balance in Conjoint Analysis
Website Morphing
Year
Journal
Patent
Citations
2004
2005
IJRM
IJRM
1
1
2004
2007
JM
JM
2
2
2008
JM
1
2007
2011
JM
JM
1
1
2005
2008
JM
JM
1
1
2007
MGS
1
2005
2007
MGS
MGS
1
2
2008
2005
2008
MGS
MGS
MKS
1
2
2
2005
2004
2004
2004
2005
2004
2007
2008
2006
2004
2005
2009
MKS
MKS
MKS
MKS
MKS
MKS
MKS
MKS
MKS
MKS
MKS
MKS
2
2
2
1
2
2
1
1
2
1
2
1
52