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Forecasting demand for a newly introduced product using reservation price data and Bayesian updating

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  • Lee, Jongsu
  • Lee, Chul-Yong
  • Lee, Kichun Sky

Abstract

Forecasting demand during the early stages of a product's life cycle is a difficult but essential task for the purposes of marketing and policymaking. This paper introduces a procedure to derive accurate forecasts for newly introduced products for which limited data are available. We begin with the assumption that the consumer reservation price is related to the timing with which the consumer adopts the product. The model is estimated using reservation price data derived through a consumer survey, and the forecast is updated with sales data as they become available using Bayes's rule. The proposed model's forecasting performance is compared with that of benchmark models (i.e., Bass model, logistic growth model, and a Bayesian model based on analogy) using 23 quarters' worth of data on South Korea's broadband Internet services market. The proposed model outperforms all benchmark models in both prelaunch and postlaunch forecasting tests, supporting the thesis that consumer reservation price can be used to forecast demand for a new product before or shortly after product launch.

Suggested Citation

  • Lee, Jongsu & Lee, Chul-Yong & Lee, Kichun Sky, 2012. "Forecasting demand for a newly introduced product using reservation price data and Bayesian updating," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1280-1291.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:7:p:1280-1291
    DOI: 10.1016/j.techfore.2012.04.003
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    Citations

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    Cited by:

    1. Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    2. Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
    3. Weihua Liu & Xinran Shen & Di Wang, 2020. "The impacts of dual overconfidence behavior and demand updating on the decisions of port service supply chain: a real case study from China," Annals of Operations Research, Springer, vol. 291(1), pages 565-604, August.
    4. Jiří Šindelář, 2019. "Sales forecasting in financial distribution: a comparison of quantitative forecasting methods," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(3), pages 69-80, December.
    5. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    6. Wu, Xiang & (Yale) Gong, Yeming & Xu, Haoxuan & Chu, Chengbin & Zhang, Jinlong, 2017. "Dynamic lot-sizing models with pricing for new products," European Journal of Operational Research, Elsevier, vol. 260(1), pages 81-92.

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