Advertising allocation and impact of advertising on event ticket sales: Which product,where,and when

Abstract

When planning for advertising, an accurate assessment of the impact of advertising is important so that managers can allocate their budgets to maximize the return on each advertising dollar. In particular, the managers need to understand the impact of advertising on different products, media types, and time periods. This study develops a simultaneous model to measure the impact of advertising on product sales using weekly ticket sales data and to capture managers’ decision using the data on advertising budget allocation at a performing arts center. This article provides managerial recommendations for advertising decision in terms of the product, media, and time dimensions.

Keywords

advertising budget advertising decision Bayesian duration model event ticket sales simultaneous model when where which products

Get full access to this article

View all access options for this article.

References

Basu

A. K.

Batra

(1988). ADSPLIT: A multi-brand advertising budget allocation model. Journal of Advertising, 17, 44–51.

Berry

Levinsohn

Pakes

(1995). Automobile prices in market equilibrium. Econometrica, 63, 841–890.

Broadbent

(1984). Modelling with adstock. Journal of the Market Research Society, 26, 295–312.

Bronnenberg

B. J.

Rossi

P. E.

Vilcassim

N. J.

(2005). Structural modeling and policy simulation. Journal of Marketing Research, 42, 22–26.

Chatterjee

(2011). Drivers of new product recommending and referral behaviour on social network sites. International Journal of Advertising, 30, 77–101.

Chib

Greenberg

(1995). Understanding the Metropolis-Hastings algorithm. American Statistician, 49, 327–335.

Chintagunta

P. K.

Erdem

Rossi

P. E.

Wedel

(2006). Structural modeling in marketing: Review and assessment. Marketing Science, 25, 604–616.

Corkindale

Newall

(1978). Advertising thresholds and wearout. European Journal of Marketing, 12, 329–378.

Cox

D. R.

(1972). Regression models and life-tables. Journal of the Royal Statistical Society, 34, 187–220.

10.

Doyle

Saunders

(1990). Multiproduct advertising budgeting. Marketing Science, 9, 97–113.

11.

Franses

P. H.

(2005). Diagnostics, expectations, and endogeneity. Journal of Marketing Research, 42, 27–29.

12.

Greyser

S. A.

(1973). Irritation in advertising. Journal of Advertising Research, 13, 3–10.

13.

Huang

Sarigöllü

(2014). Assessment of brand equity measures. International Journal of Market Research, 56, 783–806.

14.

Kent

(2009). Rethinking data analysis—part two: Some alternatives to frequentist approaches. International Journal of Market Research, 51, 1–16.

15.

Koyck

L. M.

(1954). Distributed lags and investment analysis. Amsterdam, The Netherlands: North-Holland.

16.

Little

J. D. C.

(1970). Models and managers: The concept of a decision calculus. Management Science, 16, 466–485.

17.

Manchanda

Rossi

P. E.

Chintagunta

P. K.

(2004). Response modeling with nonrandom marketing-mix variables. Journal of Marketing Research, 41, 467–478.

18.

Moe

W. M.

Fader

P. S.

(2002). Using advance purchase orders to forecast new product sales. Marketing Science, 21, 347–364.

19.

Montgomery

D. B.

Silk

A. J.

(1972). Estimating dynamic effects of market communications expenditures. Management Science, 18, 485–501.

20.

Morrison

D. G.

Schmittlein

D. C.

(1980). Jobs, strikes and wars: Probability models for duration. Organizational Behavior and Human Performance, 25, 224–251.

21.

Naik

P. A.

Mantrala

M. K.

Sawyer

A. G.

(1998). Planning media schedules in the presence of dynamic advertising quality. Marketing Science, 17, 214–235.

22.

Naik

P. A.

Raman

(2003). Understanding the impact of synergy in multimedia communications. Journal of Marketing Research, 40, 375–388.

23.

Neff

(2017). P and G will cut $2 billion in marketing but spend some back to become irresistible. Adage. Retrieved from http://adage.com/article/cmo-strategy/p-g-cut-2-billion-media-agency-costs/308811

24.

Nerlove

Arrow

K. J.

(1962). Optimal advertising policy under dynamic conditions. Econometrica, 29, 129–142.

25.

Newton

M. A.

Raftery

A. E.

(1994). Approximate Bayesian inference with the weighted likelihood bootstrap. Journal of the Royal Statistical Society, 56, 3–48.

26.

Peng

Cui

(2013). The comparative impact of critics and consumers: Applying the generalisability theory to online movie ratings. International Journal of Market Research, 55, 413–436.

27.

Rennhoff

A. D.

Wilbur

K. C.

(2011). The effectiveness of post-release movie advertising. International Journal of Advertising, 30, 305–328.

28.

Rossi

P. E.

Allenby

G. M.

McCulloch

(2005). Bayesian statistics and marketing. Chichester, UK: John Wiley & Sons.

29.

Schweidel

D. A.

Fader

P. S.

Bradlow

E. T.

(2008). Understanding service retention within and across cohorts using limited information. Journal of Marketing, 72, 82–94.

30.

Schweidel

D. A.

Kent

R. J.

(2010). Predictors of the gap between program and commercial audiences: An investigation using live tuning data. Journal of Marketing, 74, 18–33.

31.

Seetharaman

P. B.

Chintagunta

P. K.

(2003). The proportional hazard model for purchase timing. Journal of Business and Economic Statistics, 21, 368–382.

32.

Sinha

R. K.

Chandrashekaran

(1992). A split hazard model for analyzing the diffusion of innovations. Journal of Marketing Research, 29, 161–164.

33.

Taylor

C. R.

(2010). Integrated marketing communications in 2010 and beyond. International Journal of Advertising, 30, 305–328.

34.

Van Diepen

Donkers

Franses

P. H

. (2009). Dynamic and competitive effects of direct mailings: A charitable giving application. Journal of Marketing Research, 46, 120–133.

35.

Van Heerde

H. J.

Dekimpe

M. G.

Putsis

W. P

. (2005). Marketing models and the Lucas critique. Journal of Marketing Research, 42, 15–21.

36.

Villas-Boas

J. M.

Winer

R. S.

(1999). Endogeneity in brand choice models. Management Science, 45, 1324–1338.

37.

Vranica

Sharma

(2014). Advertisers pulling back on cable TV spending. The Wall Street Journal. Retrieved from https://www.wsj.com/articles/gm-p-g-cutting-back-their-tv-ad-commitments-1405699483

38.

Weilbacher

W. M.

(1970). What happens to advertisements when they grow up? Public Opinion Quarterly, 34, 216–223.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.27 MB