IDEAS home Printed from https://ideas.repec.org/a/ibf/ijmmre/v10y2017i1p69-78.html
   My bibliography  Save this article

Using Big Data To Predict Consumer Responses To Promotional Discounts As Part Of Sales & Operations Planning

Author

Listed:
  • Andrew Manikas
  • Michael Godfrey
  • Ryan Skiver

Abstract

Price promotions (discounts) are a well-known means by which a supply chain can stimulate demand for a product. These promotions could affect demand for a product in three ways by: 1) increasing the overall market growth, 2) stealing market share from competitors, and/or 3) increasing the amount of consumer forward buying. Supply chain members must be able to estimate these effects on demand and the corresponding effects on both revenues and costs when conducting sales and operations planning. We analyzed the effects on demand using a big data approach on promotional data made publicly available by Grupo Bimbo (a multinational bakery product manufacturing company headquartered in Mexico City). This company offered promotional coupons to customers for particular items. Bimbo captured sales history for each customer on how often they shopped, what they bought, and the amount that they spent. Bimbo then tracked how many times during the next year that customers returned to buy the promoted items at full price. Using this data set, we assessed which types of offers were more effective at achieving the goal of increasing repeat purchases at full price. Whether the offer was for a weekend or weekday had no significant effect. However, we found that a larger discount percent was associated with fewer repeat purchases at full price. Further, customers who tended to spend more, on average, per trip had a higher number of repeat purchases for an item

Suggested Citation

  • Andrew Manikas & Michael Godfrey & Ryan Skiver, 2017. "Using Big Data To Predict Consumer Responses To Promotional Discounts As Part Of Sales & Operations Planning," International Journal of Management and Marketing Research, The Institute for Business and Finance Research, vol. 10(1), pages 69-78.
    • Handle: RePEc:ibf:ijmmre:v:10:y:2017:i:1:p:69-78
    as

    Download full text from publisher

    File URL: http://www.theibfr2.com/RePEc/ibf/ijmmre/ijmmr-v10n1-2017/IJMMR-V10N1-2017-5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marta Arce-Urriza & Javier Cebollada & María Fernanda Tarira, 2017. "The effect of price promotions on consumer shopping behavior across online and offline channels: differences between frequent and non-frequent shoppers," Information Systems and e-Business Management, Springer, vol. 15(1), pages 69-87, February.
    2. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    3. Eric T. Anderson & Duncan I. Simester, 2004. "Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies," Marketing Science, INFORMS, vol. 23(1), pages 4-20, February.
    4. Rajiv Lal & David Bell, 2003. "The Impact of Frequent Shopper Programs in Grocery Retailing," Quantitative Marketing and Economics (QME), Springer, vol. 1(2), pages 179-202, June.
    5. Su, Meng & Zheng, Xiaona & Sun, Luping, 2014. "Coupon Trading and its Impacts on Consumer Purchase and Firm Profits," Journal of Retailing, Elsevier, vol. 90(1), pages 40-61.
    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. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
    2. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    3. Arun Gopalakrishnan & Young-Hoon Park, 2021. "The Impact of Coupons on the Visit-to-Purchase Funnel," Marketing Science, INFORMS, vol. 40(1), pages 48-61, January.
    4. Dorotic, Matilda & Verhoef, Peter C. & Fok, Dennis & Bijmolt, Tammo H.A., 2014. "Reward redemption effects in a loyalty program when customers choose how much and when to redeem," International Journal of Research in Marketing, Elsevier, vol. 31(4), pages 339-355.
    5. Uncles, Mark D. & East, Robert & Lomax, Wendy, 2013. "Good customers: The value of customers by mode of acquisition," Australasian marketing journal, Elsevier, vol. 21(2), pages 119-125.
    6. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    7. Andrés Elberg & Pedro M. Gardete & Rosario Macera & Carlos Noton, 2019. "Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 1-58, March.
    8. Van Droogenbroeck, Ellen & Van Hove, Leo, 2020. "Intra-household task allocation in online grocery shopping: Together alone," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    9. Christiaan Behrens & Nathalie McCaughey, 2015. "Loyalty Programs and Consumer Behaviour: The Impact of FFPs on Consumer Surplus," Tinbergen Institute Discussion Papers 15-048/VIII, Tinbergen Institute.
    10. Blattberg, Robert C. & Malthouse, Edward C. & Neslin, Scott A., 2009. "Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 157-168.
    11. Arun Gopalakrishnan & Zhenling Jiang & Yulia Nevskaya & Raphael Thomadsen, 2021. "Can Non-tiered Customer Loyalty Programs Be Profitable?," Marketing Science, INFORMS, vol. 40(3), pages 508-526, May.
    12. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    13. Faramarzi, Ashkan & Bhattacharya, Abhi, 2021. "The economic worth of loyalty programs: An event study analysis," Journal of Business Research, Elsevier, vol. 123(C), pages 313-323.
    14. Kim, Jungkeun, 2019. "The impact of different price promotions on customer retention," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 95-102.
    15. Byun, Kyung-Ah (Kay) & Duhan, Dale F. & Dass, Mayukh, 2020. "The preservation of loyalty halo effects: An investigation of the post-product-recall behavior of loyal customers," Journal of Business Research, Elsevier, vol. 116(C), pages 163-175.
    16. Federico Rossi & Pradeep K. Chintagunta, 2023. "Consumer Loyalty Programs and Retail Prices: Evidence from Gasoline Markets," Marketing Science, INFORMS, vol. 42(4), pages 794-818, July.
    17. Chen, Kee-Kuo & Ho, Hui-Ping & Chang, Ching-Ter, 2015. "Estimating attributes importance for container shipping industry by closing the listening gap with maximum convergent validity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 145-163.
    18. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    19. Ron Berman & Ayelet Israeli, 2022. "The Value of Descriptive Analytics: Evidence from Online Retailers," Marketing Science, INFORMS, vol. 41(6), pages 1074-1096, November.
    20. Christiane Lehrer & Manuel Trenz, 2022. "Omnichannel Business," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 687-699, June.

    More about this item

    Keywords

    Big Data; Data Analytics; Multiple Regression; Promotions; Sales and Operations Planning;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

    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:ibf:ijmmre:v:10:y:2017:i:1:p:69-78. 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: Mercedes Jalbert (email available below). General contact details of provider: .

    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.