Predicting the effects of physician-directed promotion on prescription yield and sales uptake using neural networks

Lim, Chee Wooi; Kirikoshi, Toru

doi:10.1057/palgrave.jt.5740140

Cited by 21 publications

(8 citation statements)

References 14 publications

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“…1 Despite this fact, little detailed material is available regarding the methodological aspects of predicting the effects of promotion. 2 Thus, it becomes imperative for the marketers to study the perception of physicians towards the promotion tools being used by the pharmaceutical companies.…”

Section: Introductionmentioning

confidence: 99%

Perception of physicians towards pharmaceutical promotion in India

Handa

Vohra

Srivastava

2013

Journal of Medical Marketing

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Pharmaceutical firms spend a significant amount of their budget on promotions. Thus, it becomes imperative to study the perception of physicians, at whom a major share of these promotional efforts is targeted. Despite this, there exists very little published research examining customer response at the perceptual level. Thus, the authors have attempted to study the importance attached to promotion tools as information sources and the extent to which these promotion tools influence prescription behavior as perceived by physicians. Primary data for the study was collected through a survey of physicians. A total of 115 physicians participated in the study. The study indicates that physicians perceive conferences/symposia to be the most credible information source. An examination of the relationship between perceptions regarding various promotion tools and demographic variables indicates a significant relationship between gender and type of hospital and credibility attached to the various promotional tools. The study indicates a positive correlation between credibility of promotion tools and the extent to which it influences prescription behavior.

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Section: Introductionmentioning

confidence: 99%

Perception of physicians towards pharmaceutical promotion in India

Handa

Vohra

Srivastava

2013

Journal of Medical Marketing

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“…The testing data set should be independent of the training set and is used to assess the classification accuracy of the MLP after training. Following Lim and Kirikoshi (2005), an error back-propagation algorithm with weight updates occurring after each epoch was used for MLP training. The learning rate was set at 0.1.…”

Section: Nn Analysismentioning

confidence: 99%

Oil price forecasting using gene expression programming and artificial neural networks

2016

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This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice.

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“…Specifically, a good understanding of the outcomes associated with a wide range of spending levels would clearly be valuable in helping to reduce unnecessary expenditure, as well as in identifying promotional expenditure that needs augmentation. While there had previously been extensive study conducted to determine the effectiveness of common promotion modes like media spending, sampling as well as detailing ( [1][2][3][4][5][6][7][8][9] and related references therein), the list of research on promotional response in the prescription marketplace is still relatively short [10][11][12][13]. This is partly due, perhaps, to difficulties related to accurate and comprehensive data collection, confidentiality issues, and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…A more managerially useful approach is therefore to embrace a less stressful tool that could predict observed variation in response variable(s) without knowledge about the nature of the relationship between the response and predictor variables; i.e., data go in and a prediction comes out. Of particular interest is the neural network approach put forth by Lim and Kirikoshi for measuring the impact of physician-directed promotion on prescription yield [12,13]. However, neural networks are often affected by an effect called overfitting when the network size becomes too bulky with many predictor variables.…”

Section: Introductionmentioning

confidence: 99%

Understanding the effects of pharmaceutical promotion: a neural network approach guided by genetic algorithm-partial least squares

Lim

Kirikoshi

2008

Health Care Manage Sci

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With escalating healthcare costs and increasing concerns about optimizing use of medicine, there is an unresolved debate over years around the potential impact of pharmaceutical promotion on physicians' prescribing behaviors. What should be the appropriate balance of promotion dollars to physicians? We use three major brands in the US antibiotic universe to explore this issue, presenting a theoretical framework for better understanding the cause-and-effect relationship between common promotional spending and prescription responsiveness. Using simulations we demonstrate that neural networks guided by genetic algorithm-partial least squares is able to provide managers with better understanding of physicians' prescribing activities without an appreciably lower predictive accuracy when compared to that obtained by a standalone neural network modeling.

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Predicting the effects of physician-directed promotion on prescription yield and sales uptake using neural networks

Cited by 21 publications

References 14 publications

Perception of physicians towards pharmaceutical promotion in India

Perception of physicians towards pharmaceutical promotion in India

Oil price forecasting using gene expression programming and artificial neural networks

Understanding the effects of pharmaceutical promotion: a neural network approach guided by genetic algorithm-partial least squares

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