TY - JOUR
T1 - Peak sales time prediction in new product sales
T2 - can a product manager rely on it?
AU - Krishnan, Trichy V.
AU - Feng, Shanfei
AU - Jain, Dipak C.
N1 - Funding Information:
We thank China Europe International Business School (CEIBS) for the financial support provided through a research grant. We also thank the editor and anonymous reviewers for their valuable feedback, which greatly contributed to enhancing the quality of this paper.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/10
Y1 - 2023/10
N2 - Managers dealing with new products need to forecast sales growth, especially the time at which the sales would reach the peak, known as the peak sales time (T*). In most cases, they only have a few initial years’ data to predict T*. Although product managers manage to predict T*, there is no method to date that can predict T* accurately. In this paper, we develop a new metric based on the diffusion modeling framework that can help in assessing the prediction accuracy of T*. This metric is built on the premise that observed sales growth is affected both by the force that systematically varies with time and by the non-systematic random forces. We show that the two forces must be carefully combined to assess if a predicted T* is accurate enough. In addition, we empirically prove the efficacy of the proposed metric.
AB - Managers dealing with new products need to forecast sales growth, especially the time at which the sales would reach the peak, known as the peak sales time (T*). In most cases, they only have a few initial years’ data to predict T*. Although product managers manage to predict T*, there is no method to date that can predict T* accurately. In this paper, we develop a new metric based on the diffusion modeling framework that can help in assessing the prediction accuracy of T*. This metric is built on the premise that observed sales growth is affected both by the force that systematically varies with time and by the non-systematic random forces. We show that the two forces must be carefully combined to assess if a predicted T* is accurate enough. In addition, we empirically prove the efficacy of the proposed metric.
KW - New product diffusion
KW - Peak sales time
KW - Prediction accuracy
UR - http://www.scopus.com/inward/record.url?scp=85162794505&partnerID=8YFLogxK
U2 - 10.1016/j.jbusres.2023.114054
DO - 10.1016/j.jbusres.2023.114054
M3 - Article
AN - SCOPUS:85162794505
SN - 0148-2963
VL - 165
JO - Journal of Business Research
JF - Journal of Business Research
M1 - 114054
ER -