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Evaluating promotional pricing effectiveness using convenience store daily sales data

Author

Listed:
  • Naragain Phumchusri

    (Chulalongkorn University)

  • Warot Kosawanitchakarn

    (Chulalongkorn University)

  • Sirawich Chawanapranee

    (Chulalongkorn University)

  • Sirawish Srimook

    (Chulalongkorn University)

Abstract

One of the activities that can grab customers attention and rise sales for convenience stores is promotional pricing strategy. Our study aims to examine the effects of promotional pricing and other factors on sales. Six categories of products with 286 SKUs are explored. Four models are compared, and the results show that autoregressive-distributed lag model provides the lowest mean absolute percentage error (MAPE). This model can also capture the interaction between promotion and non-promotion products. Price elasticity of each product is found to be different, and it results different optimal prices for the maximum profit. Moreover, factors like holidays, the beginning of the month, or weekend, can uplift sales at a specific time. Unlike previous literature, this paper focuses on daily sales and related recent factors such as the number of COVID-19 cases. The methodology presented in this research provides guidelines for retailers to measure their pricing strategy and can be managerial insights for other retailers’ future strategy.

Suggested Citation

  • Naragain Phumchusri & Warot Kosawanitchakarn & Sirawich Chawanapranee & Sirawish Srimook, 2023. "Evaluating promotional pricing effectiveness using convenience store daily sales data," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 362-373, October.
    • Handle: RePEc:pal:jorapm:v:22:y:2023:i:5:d:10.1057_s41272-022-00415-5
    DOI: 10.1057/s41272-022-00415-5
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    References listed on IDEAS

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    1. Harald J. van Heerde & Peter S. H. Leeflang & Dick R. Wittink, 2002. "How Promotions Work: Scan Pro-Based Evolutionary Model Building," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 198-220, July.
    2. 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.
    3. Khouja, Moutaz & Subramaniam, Chandra & Vasudev, Vinay, 2020. "A comparative analysis of marketing promotions and implications for data analytics," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 151-174.
    4. Peter S.H. Leeflang & Harald J. van Heerde & Dick Wittink, 2002. "How Promotions Work: SCAN*PRO-Based Evolutionary Model Building," Yale School of Management Working Papers ysm292, Yale School of Management.
    5. Williams, Brent D. & Waller, Matthew A. & Ahire, Sanjay & Ferrier, Gary D., 2014. "Predicting retailer orders with POS and order data: The inventory balance effect," European Journal of Operational Research, Elsevier, vol. 232(3), pages 593-600.
    6. 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.
    7. Lang, Stefan & Steiner, Winfried J. & Weber, Anett & Wechselberger, Peter, 2015. "Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits," European Journal of Operational Research, Elsevier, vol. 246(1), pages 232-241.
    8. Harald J. van Heerde & Peter S. H. Leeflang & Dick R. Wittink, 2004. "Decomposing the Sales Promotion Bump with Store Data," Marketing Science, INFORMS, vol. 23(3), pages 317-334, December.
    9. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
    10. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    11. Gauri, Dinesh K. & Ratchford, Brian & Pancras, Joseph & Talukdar, Debabrata, 2017. "An Empirical Analysis of the Impact of Promotional Discounts on Store Performance," Journal of Retailing, Elsevier, vol. 93(3), pages 283-303.
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