IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v37y2021i1p302-321.html
   My bibliography  Save this article

Forecasting week-to-week television ratings using reduced-form and structural dynamic models

Author

Listed:
  • Song, Lianlian
  • Shi, Yang
  • Tso, Geoffrey Kwok Fai
  • Lo, Hing Po

Abstract

Rather than being sold several months before a program is aired, more than 20% of TV advertising slots are retained for sale weekly near the program’s broadcast time. Distinct from the literature that mainly focuses on the forecasting of program ratings for advanced sales of advertising slots, we explore approaches that can provide more accurate forecasts for near real-time ratings. We propose two dynamic models that mainly employ individual viewing records for past episodes to forecast viewers’ decisions on episodes in the coming week, and therefore the ratings for these episodes. One is a reduced-form dynamic model that measures the influence of past watching experience by the weighted average of the viewers’ choices of past episodes. The other is a structural dynamic model that goes deeper in its use of previous viewing information by modeling the underlying process of this influence based on the Bayesian updating theory. Using data from the Hong Kong TV industry, we test and compare the two models. Results show that the reduced-form model generally performs better when the variance of ratings across episodes is small, while the structural model generates more accurate forecasts in other cases.

Suggested Citation

  • Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:1:p:302-321
    DOI: 10.1016/j.ijforecast.2020.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207020300893
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2020.06.002?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. Jeffrey H. Horen, 1980. "Scheduling of Network Television Programs," Management Science, INFORMS, vol. 26(4), pages 354-370, April.
    2. Nickolay V. Moshkin & Ron Shachar, 2002. "The Asymmetric Information Model of State Dependence," Marketing Science, INFORMS, vol. 21(4), pages 435-454, August.
    3. Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
    4. C Wu & H-L Chen, 2000. "A consumer purchasing model with learning and departure behaviour," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 583-591, May.
    5. Ching, Andrew T., 2010. "Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 619-638, November.
    6. John H. Roberts & Glen L. Urban, 1988. "Modeling Multiattribute Utility, Risk, and Belief Dynamics for New Consumer Durable Brand Choice," Management Science, INFORMS, vol. 34(2), pages 167-185, February.
    7. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, vol. 15(4), pages 359-378.
    8. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    9. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    10. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    11. Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
    12. Danaher, Peter & Dagger, Tracey, 2012. "Using a nested logit model to forecast television ratings," International Journal of Forecasting, Elsevier, vol. 28(3), pages 607-622.
    13. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    14. M. Tolga Akçura & Füsun F. Gönül & Elina Petrova, 2004. "Consumer Learning and Brand Valuation: An Application on Over-the-Counter Drugs," Marketing Science, INFORMS, vol. 23(1), pages 156-169, April.
    15. James H. Pedrick & Fred S. Zufryden, 1991. "Evaluating the Impact of Advertising Media Plans: A Model of Consumer Purchase Dynamics Using Single-Source Data," Marketing Science, INFORMS, vol. 10(2), pages 111-130.
    16. Robert J. Meyer & Arvind Sathi, 1985. "A Multiattribute Model of Consumer Choice During Product Learning," Marketing Science, INFORMS, vol. 4(1), pages 41-61.
    17. Purushottam Papatla & Lakshman Krishnamurthi, 1992. "A Probit Model of Choice Dynamics," Marketing Science, INFORMS, vol. 11(2), pages 189-206.
    18. Tao Chen & Baohong Sun & Vishal Singh, 2009. "An Empirical Investigation of the Dynamic Effect of Marlboro's Permanent Pricing Shift," Marketing Science, INFORMS, vol. 28(4), pages 740-758, 07-08.
    19. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    Full references (including those not matched with items on IDEAS)

    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. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    2. Szymanowski, M.G., 2009. "Consumption-based learning about brand quality : Essays on how private labels share and borrow reputation," Other publications TiSEM b12825d8-5e21-4437-adda-b, Tilburg University, School of Economics and Management.
    3. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    4. Nobuhiko Terui & Masataka Ban & Greg M. Allenby, 2011. "The Effect of Media Advertising on Brand Consideration and Choice," Marketing Science, INFORMS, vol. 30(1), pages 74-91, 01-02.
    5. SATO Masahiro & OTA Rui & ITO Arata & YANO Makoto, 2020. "Three Minds Equal Manjushari's Wisdom: An Anatomy of Informal Social Learning with Heterogenous Agents by the Hierarchical Bayesian Approach," Discussion papers 20092, Research Institute of Economy, Trade and Industry (RIETI).
    6. Michael P. Keane, 2013. "Panel data discrete choice models of consumer demand," Economics Papers 2013-W08, Economics Group, Nuffield College, University of Oxford.
    7. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    8. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    9. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    10. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    11. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    12. Jinyang Zheng & Fei Ren & Yong Tan & Xi Chen, 2020. "Optimizing Two-Sided Promotion for Transportation Network Companies: A Structural Model with Conditional Bayesian Learning," Information Systems Research, INFORMS, vol. 31(3), pages 692-714, September.
    13. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    14. Haiyan Liu, 2016. "A Structural Model of Advertising Signaling and Social Learning: The Case of the Motion Picture Industry," Working Papers 0216, University of South Florida, Department of Economics.
    15. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    16. Li, Dong & Nagurney, Anna & Yu, Min, 2018. "Consumer learning of product quality with time delay: Insights from spatial price equilibrium models with differentiated products," Omega, Elsevier, vol. 81(C), pages 150-168.
    17. Arjen van Lin & Els Gijsbrechts, 2019. "“Hello Jumbo!” The Spatio-Temporal Rollout and Traffic to a New Grocery Chain After Acquisition," Management Science, INFORMS, vol. 67(5), pages 2388-2411, May.
    18. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    19. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    20. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.

    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:eee:intfor:v:37:y:2021:i:1:p:302-321. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.