Skip to main content
SpringerLink
Log in
Book cover

Handbook of Marketing Decision Models pp 431–462Cite as

Marketing Models for Internet Advertising

  • Chapter
  • First Online:

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 254))

Abstract

This chapter reviews recent advances in the application of marketing models to improve decision-making in internet advertising. It focuses on models developed for use in display advertising and sponsored search advertising. Together, these media account for the bulk of online advertising spending and most of the recent modeling advances in internet advertising. The chapter describes the challenges that confront modelers in these areas, including data limitations and selection effects due to targeting, and details how recent advances in model formulation and model-enhanced experiments are able to address them. The chapter closes with a look at recent modeling work that incorporates the effects of both online display and sponsored search advertising.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Rich media describes advertisements that contain video, audio, or other content that encourages user interaction with the ad.

  2. 2.

    Annual spending figures here exclude display ad spending targeted for mobile devices.

  3. 3.

    For a detailed discussion of this model, please see Bucklin (2008, pp. 332–334) in the first edition of this volume.

  4. 4.

    This model is also discussed in detail in Bucklin (2008, pp. 334–335).

  5. 5.

    In practice, CPC is generally close to or equal to the bid.

  6. 6.

    Because search ad costs are triggered by customer queries and click-throughs, there is no easy way to use placebo ads as a control condition as is done with display ad tests.

References

  • Abhishek, V., K. Hosanagar, and P.S. Fader. 2015. Aggregation bias in sponsored search data: The curse and the cure. Marketing Science 34 (1): 59–77.

    Article  Google Scholar 

  • Agarwal, A., K. Hosanagar, and M.D. Smith. 2011. Location, location, location: An analysis of profitability in position in online advertising markets. Journal of Marketing Research 48 (6): 1057–1073.

    Article  Google Scholar 

  • Blake, T., C. Nosko, and S. Tadelis. 2015. Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica 83 (1): 155–174.

    Article  Google Scholar 

  • Braun, M., and W. Moe. 2013. Online display advertising: modeling the effects of multiple creatives on individual impression histories. Marketing Science 32 (5): 753–767.

    Article  Google Scholar 

  • Bruell, A. 2014. Digital Ad Spending to Pass TV in U.S. by 2017, New Forecast Says. 8 December 2014. http://adage.com/article/agency-news/digital-ad-spending-pass-tv-2017-magna-global/296090/. Accessed 31 Aug 2015.

  • Bucklin, R. 2008. Marketing models for electronic commerce. In Handbook of marketing decision models, ed. Berend Wierenga, 1st ed., 327–372. Springer.

    Google Scholar 

  • Chan, T., C. Wu, and Y. Xie. 2011. Measuring the lifetime value of customers acquired from Google search advertising. Marketing Science 30 (5): 837–850.

    Article  Google Scholar 

  • Chatterjee, P., D.L. Hoffman, and T.P. Novak. 2003. Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science 22 (4): 520–541.

    Article  Google Scholar 

  • Dinner, I.M., H.J. van Heerde, and S.A. Neslin. 2014. Driving online and offline sales: The cross-channel effects of traditional, online display, and paid search advertising. Journal of Marketing Research 51 (5): 527–545.

    Article  Google Scholar 

  • Drèze, X., and F.X. Hussherr. 2003. Internet advertising: Is anybody watching? Journal of Interactive Marketing 17 (4): 8–23.

    Article  Google Scholar 

  • eMarketer. 2016. More Than Two-Thirds of US Digital Display Ad Spending Is Programmatic. 5 April 2016. http://www.emarketer.com/Article/More-Than-Two-Thirds-of-US-Digital-Display-Ad-Spending-Programmatic/1013789. Accessed 20 June 2016.

  • Friedman, D. 2009. Conversion Rates Don’t Vary Much by Ad Position. http://adwords.blogspot.com/2009/08/conversion-rates-dont-vary-much-with-ad.html. Accessed 16 Oct 2012.

  • Ghose, A., and S. Yang. 2009. An empirical analysis of sponsored search in online advertising. Management Science 55 (10): 1605–1622.

    Article  Google Scholar 

  • Goldfarb, A., and C. Tucker. 2011. Online display advertising: Targeting and obtrusiveness. Marketing Science 30 (3): 389–404.

    Article  Google Scholar 

  • Greene, W. 1999. Econometric Analysis, 4th ed. Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Hoban, P.R., and R.E. Bucklin. 2015. Effects of internet display advertising in the purchase funnel: Model based insights from a randomized field experiment. Journal of Marketing Research 52 (3): 375–393.

    Article  Google Scholar 

  • Johnson, G.A., R.A. Lewis and E.I. Nubbemeyer. 2017. Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness. Journal of Marketing Research forthcoming.

    Google Scholar 

  • Kalyanam, K., J. McAteer, J. Marek, J. Hodges and L. Lin. 2015. Cross Channel Effects of Search Engine Advertising on Brick and Mortar Retail Sales: Meta-Analysis of Large Scale Field Experiments on Google.com, working paper, Santa Clara University, September.

    Google Scholar 

  • Lambrecht, A., and C. Tucker. 2013. When does retargeting work? Information specificity in online advertising. Journal of Marketing Research 50 (5): 561–576.

    Article  Google Scholar 

  • Lewis, R.A., J.M. Rao, and D.H. Reiley. 2011. Here, There, and Everywhere: Correlated Online Behaviors. In Proceedings of world wide web conference 2011 research papers, 157–166. Hyderabad, India: ACM.

    Google Scholar 

  • Manchanda, P., J.P. Dube, K.Y. Goy, and P. Chintagunta. 2006. The effect of banner advertising on internet purchasing. Journal of Marketing Research 43 (1): 98–108.

    Article  Google Scholar 

  • Narayanan, S., and K. Kalyanam. 2015. Position effects in search advertising and their moderators: A regression discontinuity approach. Marketing Science 34 (3): 388–407.

    Article  Google Scholar 

  • Nerlove, N., and K. Arrow. 1962. Optimal advertising policy under dynamic conditions. Economica 29 (114): 129–142.

    Article  Google Scholar 

  • PricewaterhouseCoopers. 2016a. IAB Internet Advertising Revenue Report 2015 Full Year Results, New York: Interactive Advertising Bureau, 21 April. http://www.iab.net/media/file/IAB_Internet_Advertising_Revenue_FY_2014.pdf. Accessed 20 June 2016.

  • PricewaterhouseCoopers. 2016b. IAB Internet Advertising Revenue Full Year 2015, Interactive Advertising Bureau, 21 April. http://www.iab.com/wp-content/uploads/2016/04/FY2015-PwC-Matt-Hobbs-Presentation-Global-Trends.pdf. Accessed 21 June 2016.

  • Rutz, O.J., and R.E. Bucklin. 2011. From generic to branded: A model of spillover in paid search advertising. Journal of Marketing Research 48 (1): 87–102.

    Article  Google Scholar 

  • Rutz, O.J., and R.E. Bucklin. 2012. Does banner advertising affect browsing for brands? Clickstream model says yes, for some. Quantitative Marketing and Economics 10 (2): 231–257.

    Article  Google Scholar 

  • Rutz, O., and R.E. Bucklin. 2013. Paid search advertising. In Advanced database marketing: Innovative methodologies & applications for managing customer relationships, ed. K. Coussement, K.W. De Bock and Scott A. Neslin, 229–243. Gower.

    Google Scholar 

  • Rutz, O.J., and M. Trusov. 2011. Zooming in on paid search ads—an individual-level model calibrated on aggregated data. Marketing Science 30 (5): 789–800.

    Article  Google Scholar 

  • Rutz, O.J., M. Trusov, and R.E. Bucklin. 2011. Modeling indirect effects of paid search advertising: which keywords lead to more future visits? Marketing Science 30 (4): 646–665.

    Article  Google Scholar 

  • Rutz, O.J., R.E. Bucklin, and G.P. Sonnier. 2012. A latent instrumental variables approach to modeling keyword conversion in paid search advertising. Journal of Marketing Research 49 (3): 306–319.

    Article  Google Scholar 

  • Sahni, N. 2015. Effect of temporal spacing between advertising exposures: evidence from an online field experiment. Quantitative Marketing and Economics 13 (3): 203–247.

    Article  Google Scholar 

  • Simonov, A., C. Nosko, and J. Rao. 2015. Competition and Crowd-out for Brand Keywords in Sponsored Search, working paper, University of Chicago (Booth), September 30.

    Google Scholar 

  • Skiera, B., and N. Abou Nabout. 2013. Practice prize paper—PROSAD: A bidding decision support system for profit optimizing search engine advertising. Marketing Science 32 (2): 213–220.

    Article  Google Scholar 

  • Yao, S., and C. Mela. 2009. Sponsored search auctions: Research opportunities in marketing. Foundations and Trends in Marketing 3 (2): 75–126.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UCLA Anderson School of Management, Los Angeles, USA

    Randolph E. Bucklin

  2. University of Wisconsin-Madison, Madison, USA

    Paul R. Hoban

Authors
  1. Randolph E. Bucklin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul R. Hoban
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Randolph E. Bucklin .

Editor information

Editors and Affiliations

  1. Rotterdam School of Management, Erasmus University, Rotterdam, South Holland, The Netherlands

    Berend Wierenga

  2. Department of Marketing, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Ralf van der Lans

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Bucklin, R.E., Hoban, P.R. (2017). Marketing Models for Internet Advertising. In: Wierenga, B., van der Lans, R. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-56941-3_14

Download citation

Publish with us

Policies and ethics

Search

Navigation