IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v18y1999i3p301-316.html
   My bibliography  Save this article

PromoCast™: A New Forecasting Method for Promotion Planning

Author

Listed:
  • Lee G. Cooper

    (The Anderson School, 110 Westwood Plaza, University of California at Los Angeles, Los Angeles, California 90095-1481)

  • Penny Baron

    (efficient market services, inc., 106 Wilmot Road, Suite 400, Deerfield, Illinois 60015)

  • Wayne Levy

    (efficient market services, inc., 106 Wilmot Road, Suite 400, Deerfield, Illinois 60015)

  • Michael Swisher

    (efficient market services, inc., 106 Wilmot Road, Suite 400, Deerfield, Illinois 60015)

  • Paris Gogos

    (efficient market services, inc., 106 Wilmot Road, Suite 400, Deerfield, Illinois 60015)

Abstract

This article describes the implementation of a promotion-event forecasting system, PromoCast™, and its performance in several pilot applications and validity studies. Pilot studies involved retail grocery chains with 95 to 185 stores per trading area. The goal was to provide short-term, tactical forecasts useful for planning promotions from a retailer's perspective. Thus, the forecast system must be able to handle any of the over 150,000 UPCs in each store's item master file, and must be scalable to produce approximately 800,000,000 forecasts per year across all the retailers served by (). This is a much different task than one that confronts a manufacturer, even one with a broad product line. Manufacturers can benefit from custom modeling in a product line or category. Retailers need a production system that generates forecasts that help promotion planning. Marketing scientists have typically approached promotion analysis from the manufacturer's perspective. One objective of this article is to encourage marketing scientists to rethink promotion analysis from a different perspective. From the retailer's point of view the “planning unit” is the promotion event. Neither weekly store-tracking data nor shopping-trip data from consumer panels are easily aggregated to reflect total sales during a promotion event. We describe the promotion-event databases and the statistical model developed using these databases. The data are the strategic asset. Our goal is to help retailers use their data to increase the profitability of promotions. We have data on the performance of each UPC in each store under a variety of promotion conditions, on each store's adeptness at executing various styles of promotions, as well as on chain-wide historical performance for each UPC. We use many historical averages from these databases to build a 67-variable, regression-style model. The forecast incorporates a simple bias correction needed when using a log-transformed dependent variable (the natural log of total unit sales). We argue that the historical averages matching the planned ad and display conditions provide a benchmark superior to the widely used “base-times-lift” method. When aggregated into case units (the natural unit for product ordering), 69% of the forecasts in our first validation study were within ± one case compared to 39% within ± one case using the appropriate historical averages. We report the results of two over-time validity studies that reflect the value of our model for retailers. The limitations and implications of this planning tool for managerial decision making concerning stocking levels are discussed. Whenever historical data are the strategic asset we face inherent limitations. Our model does not forecast new products. The forecast error increases when an existing product is promoted in a new way. Over 99.5% of the time, we have full data from which to create a forecast. However, with a database for a typical chain market containing over 20 million promotion events in the 30-month time frame we use, 100,000 events have less than ideal data. The breadth of the database (typically 150,000 UPS) makes it impractical to incorporate data on competitive offerings. We find that regression-style modeling is not adept at incorporating information on the 1,200 subcommodities managed in our pilot stores or the 1,000 manufacturers who supply those stores. Despite these limitations we show the value of using promotion-event data, how tactical forecasts based on these data can directly impact the bottom line of grocery retailers, and how store-by-store forecasts can help retailers with problems of running out of stock or overstocking.

Suggested Citation

  • Lee G. Cooper & Penny Baron & Wayne Levy & Michael Swisher & Paris Gogos, 1999. "PromoCast™: A New Forecasting Method for Promotion Planning," Marketing Science, INFORMS, vol. 18(3), pages 301-316.
  • Handle: RePEc:inm:ormksc:v:18:y:1999:i:3:p:301-316
    DOI: 10.1287/mksc.18.3.301
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.18.3.301
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.18.3.301?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
    ---><---

    References listed on IDEAS

    as
    1. Franses, Philip Hans, 1995. "The effects of seasonally adjusting a periodic autoregressive process," Computational Statistics & Data Analysis, Elsevier, vol. 19(6), pages 683-704, June.
    2. Lee G. Cooper & Giovanni Giuffrida, 2000. "Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results," Management Science, INFORMS, vol. 46(2), pages 249-264, February.
    3. Stephen J. Hoch & David A. Schkade, 1996. "A Psychological Approach to Decision Support Systems," Management Science, INFORMS, vol. 42(1), pages 51-64, January.
    4. Franses, Philip Hans & Hylleberg, Svend & Lee, Hahn S., 1995. "Spurious deterministic seasonality," Economics Letters, Elsevier, vol. 48(3-4), pages 249-256, June.
    5. Eileen Bridges & Chi Kin (Bennett) Yim & Richard A. Briesch, 1995. "A High-Tech Product Market Share Model with Customer Expectations," Marketing Science, INFORMS, vol. 14(1), pages 61-81.
    6. Leonard M. Lodish & Magid M. Abraham & Jeanne Livelsberger & Beth Lubetkin & Bruce Richardson & Mary Ellen Stevens, 1995. "A Summary of Fifty-Five In-Market Experimental Estimates of the Long-Term Effect of TV Advertising," Marketing Science, INFORMS, vol. 14(3_supplem), pages 133-140.
    7. Franses, Philip Hans & Paap, Richard, 1995. "Moving average filters and periodic integration," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 245-249.
    8. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "Empirical Generalizations About Market Evolution and Stationarity," Marketing Science, INFORMS, vol. 14(3_supplem), pages 109-121.
    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. Li, W. & Fok, D. & Franses, Ph.H.B.F., 2019. "Forecasting own brand sales: Does incorporating competition help?," Econometric Institute Research Papers EI2019-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Tong Wang & Cheng He & Fujie Jin & Yu Jeffrey Hu, 2022. "Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model," Information Systems Research, INFORMS, vol. 33(2), pages 659-677, June.
    3. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    4. Fildes, Robert & Goodwin, Paul & Önkal, Dilek, 2019. "Use and misuse of information in supply chain forecasting of promotion effects," International Journal of Forecasting, Elsevier, vol. 35(1), pages 144-156.
    5. Kusum L. Ailawadi & Bari A. Harlam & Jacques César & David Trounce, 2007. "Practice Prize Report—Quantifying and Improving Promotion Effectiveness at CVS," Marketing Science, INFORMS, vol. 26(4), pages 566-575, 07-08.
    6. Lee G. Cooper & Giovanni Giuffrida, 2000. "Turning Datamining into a Management Science Tool: New Algorithms and Empirical Results," Management Science, INFORMS, vol. 46(2), pages 249-264, February.
    7. Shuba Srinivasan & Koen Pauwels & Dominique M. Hanssens & Marnik G. Dekimpe, 2004. "Do Promotions Benefit Manufacturers, Retailers, or Both?," Management Science, INFORMS, vol. 50(5), pages 617-629, May.
    8. Lennart Baardman & Maxime C. Cohen & Kiran Panchamgam & Georgia Perakis & Danny Segev, 2019. "Scheduling Promotion Vehicles to Boost Profits," Management Science, INFORMS, vol. 65(1), pages 50-70, January.
    9. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    10. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    11. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    12. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    13. Hewage, Harsha Chamara & Perera, H. Niles & De Baets, Shari, 2022. "Forecast adjustments during post-promotional periods," European Journal of Operational Research, Elsevier, vol. 300(2), pages 461-472.
    14. Wolters, Jannik & Huchzermeier, Arnd, 2021. "Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment," Journal of Retailing, Elsevier, vol. 97(4), pages 726-745.
    15. Ramanathan, Usha & Muyldermans, Luc, 2010. "Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK," International Journal of Production Economics, Elsevier, vol. 128(2), pages 538-545, December.
    16. van Donselaar, K.H. & Peters, J. & de Jong, A. & Broekmeulen, R.A.C.M., 2016. "Analysis and forecasting of demand during promotions for perishable items," International Journal of Production Economics, Elsevier, vol. 172(C), pages 65-75.
    17. Ramanathan, Usha & Gunasekaran, Angappa, 2014. "Supply chain collaboration: Impact of success in long-term partnerships," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 252-259.
    18. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    19. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    20. Maxime C. Cohen & Ngai-Hang Zachary Leung & Kiran Panchamgam & Georgia Perakis & Anthony Smith, 2017. "The Impact of Linear Optimization on Promotion Planning," Operations Research, INFORMS, vol. 65(2), pages 446-468, April.
    21. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    22. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    23. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.

    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. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    2. Villanueva, Julian & Yoo, Shijin & Hanssens, Dominique M., 2003. "Impact of acquisition channels on customer equity, The," IESE Research Papers D/516, IESE Business School.
    3. Eisend, Martin & Tarrahi, Farid, 2014. "Meta-analysis selection bias in marketing research," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 317-326.
    4. 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.
    5. Franses, Philip Hans, 2013. "Data revisions and periodic properties of macroeconomic data," Economics Letters, Elsevier, vol. 120(2), pages 139-141.
    6. Chadwick J. Miller & Daniel C. Brannon & Jim Salas & Martha Troncoza, 2021. "Advertising, incentives, and the upsell: how advertising differentially moderates customer- vs. retailer-directed price incentives’ impact on consumers’ preferences for premium products," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1043-1064, November.
    7. Fildes, Robert & Goodwin, Paul & Onkal, Dilek, 2015. "Information use in supply chain forecasting," MPRA Paper 66034, University Library of Munich, Germany.
    8. Zhang, Xiaolong & Burke, Gerard J., 2011. "Analysis of compound bullwhip effect causes," European Journal of Operational Research, Elsevier, vol. 210(3), pages 514-526, May.
    9. Namwoon Kim & Jin K. Han & Rajendra K. Srivastava, 2002. "A Dynamic IT Adoption Model for the SOHO Market: PC Generational Decisions with Technological Expectations," Management Science, INFORMS, vol. 48(2), pages 222-240, February.
    10. Gelderman, Maarten, 1997. "Task difficulty, task variability and satisfaction with management support systems: consequences and solutions ˜," Serie Research Memoranda 0053, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    11. da Silva Lopes, Artur C. B., 2001. "The robustness of tests for seasonal differencing to structural breaks," Economics Letters, Elsevier, vol. 71(2), pages 173-179, May.
    12. 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.
    13. 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.
    14. Bayer, Emanuel & Srinivasan, Shuba & Riedl, Edward J. & Skiera, Bernd, 2020. "The impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value," International Journal of Research in Marketing, Elsevier, vol. 37(4), pages 789-804.
    15. Xiao Fang & Olivia R. Liu Sheng & Paulo Goes, 2013. "When Is the Right Time to Refresh Knowledge Discovered from Data?," Operations Research, INFORMS, vol. 61(1), pages 32-44, February.
    16. Bowon Kim & Fouad El Ouardighi & Sangsun Park, 2012. "Optimal dynamics of technology and price in a duopoly market," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1017-1022, July.
    17. Caroline Elliott, 2001. "A Cointegration Analysis of Advertising and Sales Data," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 18(4), pages 417-426, June.
    18. Evren Erdoğan Cosar, 2006. "Seasonal behaviour of the consumer price index of Turkey," Applied Economics Letters, Taylor & Francis Journals, vol. 13(7), pages 449-455.
    19. Yun Kyung Oh & Huseyin Gulen & Jung-Min Kim & William T. Robinson, 2016. "Do stock prices undervalue investments in advertising?," Marketing Letters, Springer, vol. 27(4), pages 611-626, December.
    20. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.

    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:inm:ormksc:v:18:y:1999:i:3:p:301-316. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.