Forecasting with a Repeat Purchase Diffusion Model

A methodology for forecasting the sales of an ethical drug as a function of marketing effort before any sales data are available and for updating the forecast with a few periods of sales data is presented. Physicians' perceptions of the drug on a number of attributes, e.g. effectiveness, range...

Full description

Saved in:
Bibliographic Details
Published in:Management science 1988-06, Vol.34 (6), p.734-752
Main Authors: Rao, Ambar G, Yamada, Masataka
Format: Article
Language:English
Subjects:
buyer behavior
Consumer behavior
Drug design
Estimation methods
Forecasting
forecasting applications
Forecasting models
Management science
Marketing
Mathematical analysis
Modeling
new products
Parametric models
Physicians
Prescription drugs
Product introduction
Sales forecasting
Statistical analysis
Time series forecasting
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A methodology for forecasting the sales of an ethical drug as a function of marketing effort before any sales data are available and for updating the forecast with a few periods of sales data is presented. Physicians' perceptions of the drug on a number of attributes, e.g. effectiveness, range of ailments for which appropriate, frequency of prescriptions, are used to estimate the parameters of a model originally proposed by Lilien, Rao and Kalish (Lilien, G. L., A. G. Rao, S. Kalish. 1981. Bayesian estimation and control of detailing effort in a repeat purchase diffusion environment. Management Sci. 27 (May) 493–506.). This model conceptualizes the drug adoption process as a repeat purchase diffusion model; sales are expressed as a function of a drug's own and competitive marketing efforts and of word of mouth. The model is first validated in this paper via predictive testing on 19 drugs prescribed by three types of physicians. The forecasting methodology is illustrated using physicians' perceptions on these drugs. Forecasts obtained without any sales data, and updated forecasts using seven periods of sales data are presented, and are encouraging.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.34.6.734