IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v20y2021i3d10.1057_s41272-021-00310-5.html
   My bibliography  Save this article

Decoupling the individual effects of multiple marketing channels with state space models

Author

Listed:
  • Melvin Woodley

    (Sabre Research)

Abstract

An important problem in marketing is understanding the impact of various marketing efforts on sales and revenue. If specific measures have not been taken before hand to distinguish responders across the various marketing initiatives, it becomes increasingly difficult to assess the effectiveness of investment across individual marketing channels. We present State Space model to estimate the individual channel effects using aggregate sales or response data across all channels. The proposed framework allows for varying carry over effects across marketing channels. Also, the proposed framework allows for differing rates of decay across marketing channels. We demonstrate its use when data on sales due to individual marketing channels is not available and only aggregated sales data are available. The proposed State Space modeling approach offers the advantage of: (1) allowing for varying rates of decay across marketing channels, and (2) allowing for a natural way of modeling time series dynamics. The approach also opens the way for more comprehensive marketing-mix optimization by allowing varying rates of decay.

Suggested Citation

  • Melvin Woodley, 2021. "Decoupling the individual effects of multiple marketing channels with state space models," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 248-255, June.
    • Handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00310-5
    DOI: 10.1057/s41272-021-00310-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00310-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-021-00310-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
    2. Leeflang, P.S.H. & Wittink, Dick R., 2000. "Building models for marketing decisions: past, present and future," Research Report 00F20, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    3. repec:dgr:rugsom:00f20 is not listed on IDEAS
    4. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    5. Franses, Ph.H.B.F. & van Oest, R.D., 2004. "On the econometrics of the Koyck model," Econometric Institute Research Papers EI 2004-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    7. David B. Montgomery & Alvin J. Silk, 1972. "Estimating Dynamic Effects of Market Communications Expenditures," Management Science, INFORMS, vol. 18(10), pages 485-501, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hrabec, Dušan & Kučera, Jiří & Martinek, Pavel, 2023. "Marketing effort within the newsvendor problem framework: A systematic review and extensions of demand-effort and cost-effort formulations," International Journal of Production Economics, Elsevier, vol. 257(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kremer, Sara T.M. & Bijmolt, Tammo H.A. & Leeflang, Peter S.H. & Wieringa, Jaap E., 2008. "Generalizations on the effectiveness of pharmaceutical promotional expenditures," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 234-246.
    2. Leeflang, Peter S.H. & Bijmolt, Tammo H.A. & van Doorn, Jenny & Hanssens, Dominique M. & van Heerde, Harald J. & Verhoef, Peter C. & Wieringa, Jaap E., 2009. "Creating lift versus building the base: Current trends in marketing dynamics," International Journal of Research in Marketing, Elsevier, vol. 26(1), pages 13-20.
    3. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    4. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    5. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    6. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    7. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    8. Sridhar, Shrihari & Naik, Prasad A. & Kelkar, Ajay, 2017. "Metrics unreliability and marketing overspending," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 761-779.
    9. Beltran-Royo, C. & Zhang, H. & Blanco, L.A. & Almagro, J., 2013. "Multistage multiproduct advertising budgeting," European Journal of Operational Research, Elsevier, vol. 225(1), pages 179-188.
    10. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    11. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    12. Franses, Philip Hans, 2006. "Forecasting in Marketing," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 18, pages 983-1012, Elsevier.
    13. Josh Stillwagon & Peter Sullivan, 2020. "Markov switching in exchange rate models: will more regimes help?," Empirical Economics, Springer, vol. 59(1), pages 413-436, July.
    14. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    15. Hui Li & Qiaowei Shen & Yakov Bart, 2018. "Local Market Characteristics and Online-to-Offline Commerce: An Empirical Analysis of Groupon," Management Science, INFORMS, vol. 64(4), pages 1860-1878, April.
    16. Fatemeh Salimi Namin, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," AMSE Working Papers 2037, Aix-Marseille School of Economics, France.
    17. Adina Bărbulescu Robinson & Kapil R. Tuli & Ajay K. Kohli, 2015. "Does Brand Licensing Increase a Licensor's Shareholder Value?," Management Science, INFORMS, vol. 61(6), pages 1436-1455, June.
    18. Wiesel, Thorsten & Skiera, Bernd & Villanueva, Julian, 2011. "Customer Lifetime Value and Customer Equity Models Using Company-reported Summary Data," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 20-22.
    19. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    20. Arindam Banerjee & Tanushri Banerjee, 2017. "Determinants of Analytics Process Adoption in Emerging Economies: Perspectives from the Marketing Domain in India," Vikalpa: The Journal for Decision Makers, , vol. 42(2), pages 95-110, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00310-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.