Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v2y2004i3p195-232.html
   My bibliography  Save this article

Modeling and Forecasting the Sales of Technology Products

Author

Listed:
  • Ramya Neelamegham
  • Pradeep K. Chintagunta
Abstract
Managers in technology product markets require sales response models that provide substantive insights into the effects of marketing activities as well as reliable sales forecasts. Such markets are characterized by frequent introductions and withdrawals of multiple models by different companies. Thus, the data available on the performance of any individual model is scarce. A second characteristic is that the effects of product attributes and marketing activities could change over time as different types of consumers participate in the market at different points in time. Given sparse data, it becomes critical to specify a model that allows pooling of information across brand-models while at the same time providing brand-model specific parameters. We accomplish this via a hierarchical Bayesian model specification. Further, to capture the effects of changing consumer preferences over time, we specify a time varying parameter model. Our modeling framework therefore, integrates a hierarchical Bayesian model within a time varying parameter framework to develop a dynamic hierarchical Bayesian model. We employ data on digital cameras in the U.S. market to estimate the parameters of our proposed model. We use thirty-three months of national level data on the digital camera market with the data series beginning very close to the inception of this product category. We find that while there is little variation in reliance of benefits by early adopters, the second wave of adopters focus on Ease of Use followed by later adopters who rely on Storage and Image Quality. Looking at the elasticities of demand with respect to the various benefits, we find that at around the halfway point of our data series, the industry as a whole would have been better off investing in increasing image quality rather than storage if costs associated with the two are equal. However, at the end of the time horizon both benefits appear to have about equal impact. Further, the relative benefits of improving these attributes vary across brands and points in time. We then generate single period and multiple period ahead sales forecasts. We make different assumptions about information availability and find that the average (across brand-models and time) MAPE ranges from 7.5 to 14.5% for the model. We provide extensive comparisons of our model with 4 potential alternatives and find that our model outperforms these alternatives on the nature of substantive insights obtained as well as in forecasting out-of-sample especially when there is a very short time window of data.

Suggested Citation

  • Ramya Neelamegham & Pradeep K. Chintagunta, 2004. "Modeling and Forecasting the Sales of Technology Products," Quantitative Marketing and Economics (QME), Springer, vol. 2(3), pages 195-232, September.
    • Handle: RePEc:kap:qmktec:v:2:y:2004:i:3:p:195-232
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/1570-7156/contents
    Download Restriction: Access to the full text of the articles in this series is restricted.
    ---><---

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

    Citations

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


    Cited by:

    1. Yuri Peers & Harald J. van Heerde & Marnik G. Dekimpe, 2017. "Marketing Budget Allocation Across Countries: The Role of International Business Cycles," Marketing Science, INFORMS, vol. 36(5), pages 792-809, September.
    2. Marc Fischer & Peter Leeflang & Peter Verhoef, 2010. "Drivers of peak sales for pharmaceutical brands," Quantitative Marketing and Economics (QME), Springer, vol. 8(4), pages 429-460, December.
    3. Norris I. Bruce, 2008. "Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters," Marketing Science, INFORMS, vol. 27(4), pages 659-673, 07-08.
    4. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    5. Joseph Pancras & S. Sriram & V. Kumar, 2012. "Empirical Investigation of Retail Expansion and Cannibalization in a Dynamic Environment," Management Science, INFORMS, vol. 58(11), pages 2001-2018, November.
    6. Arruda-Filho, Emílio J.M. & Cabusas, Julianne A. & Dholakia, Nikhilesh, 2010. "Social behavior and brand devotion among iPhone innovators," International Journal of Information Management, Elsevier, vol. 30(6), pages 475-480.
    7. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    8. Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    9. Zambujal-Oliveira, João & Mouta-Lopes, Manuel & Bangueses, Ricardo, 2021. "Real options appraisal of forestry investments under information scarcity in biomass markets," Resources Policy, Elsevier, vol. 74(C).
    10. van Heerde, H.J. & Dekimpe, M.G. & Putsis, W.P., 2004. "Marketing Models and the Lucas Critique," ERIM Report Series Research in Management ERS-2004-080-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Erjiang E & Ming Yu & Xin Tian & Ye Tao, 2022. "Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    12. Stefan Stremersch & Aurélie Lemmens, 2009. "Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes," Marketing Science, INFORMS, vol. 28(4), pages 690-708, 07-08.
    13. 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.
    14. Ceren Kolsarici & Demetrios Vakratsas, 2015. "Correcting for Misspecification in Parameter Dynamics to Improve Forecast Accuracy with Adaptively Estimated Models," Management Science, INFORMS, vol. 61(10), pages 2495-2513, October.
    15. Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    16. Luo, Anita & Baker, Andrew & Donthu, Naveen, 2019. "Capturing dynamics in the value for brand recommendations from word-of-mouth conversations," Journal of Business Research, Elsevier, vol. 104(C), pages 247-260.
    17. Harald J. van Heerde & Shuba Srinivasan & Marnik G. Dekimpe, 2010. "Estimating Cannibalization Rates for Pioneering Innovations," Marketing Science, INFORMS, vol. 29(6), pages 1024-1039, 11-12.
    18. Stremersch, S. & Lemmens, A., 2008. "Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes," ERIM Report Series Research in Management ERS-2008-026-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    19. Junji Xiao, 2008. "Technological advances in digital cameras: Welfare analysis on easy-to-use characteristics," Marketing Letters, Springer, vol. 19(2), pages 171-181, June.
    20. S. Sriram & Pradeep K. Chintagunta & Ramya Neelamegham, 2006. "Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets," Marketing Science, INFORMS, vol. 25(5), pages 440-456, September.
    21. 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.
    22. Ataman, B.M., 2007. "Managing brands," Other publications TiSEM 462dcbba-2ac1-46d1-a61c-f, Tilburg University, School of Economics and Management.
    23. Duncan Fong & Wayne DeSarbo, 2007. "A Bayesian methodology for simultaneously detecting and estimating regime change points and variable selection in multiple regression models for marketing research," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 427-453, December.

    More about this item

    Statistics

    Access and download statistics

    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:kap:qmktec:v:2:y:2004:i:3:p:195-232. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.springer.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.