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首页> 外文期刊>International Journal of Pharmaceutical and Healthcare Marketing >Modeling the effects of physician-directed promotion using genetic algorithm-partial least squares
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Modeling the effects of physician-directed promotion using genetic algorithm-partial least squares

机译：使用遗传算法-偏最小二乘模型对医师指导的促销进行建模

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Purpose – The aim is to explore the potential of a hybrid genetic algorithm-partial least squares (GA-PLS) modeling approach to understand the important promotional spending variables that influence physicians' prescribing habits and to help leverage managers' insight to plan better spend on their promotional activities. Design/methodology/approach – A GA was used as a variable-selecting tool, and PLS analysis was employed for correlating these variables with the observed variation in the volume of prescriptions. This approach is illustrated using database from a marketing consultant on four major brands in the US antibiotic universe. Findings – Good statistical models were derived that permit simpler and faster computational prediction of the effects of physician-directed promotion. All final models established had r2 values ranging from 0.835 to 0.922 and cross-validated r2 (q2) values ranging from 0.791 to 0.911 whereas the mean absolute percentage error (MAPE) values were confined within 5 percent range on averaging all brand models. Further, thorough statistical analyses revealed the usefulness of promotional spending as a variable and the robustness of GA-PLS as a correlation tool. Research limitations/implications – Modeling frame was comprised of only antibiotic category, which may limit its general utility. Practical implications – Managers can become more adept at interpreting the effects of promotion on prescribing behaviors of physicians and are able to build predictive models that would help identify where and how their curious blend of promotional cocktail would yield the highest future returns. Moreover, if the impact of individual promotional spending element can be measured, then this is perhaps a testament to the way the efficacy of interventions to reduce the harmful consequences of pharmaceutical marketing could be validated given a growing number of public beliefs that physician-directed promotion has grown too heavy-handed and is undermining medical professionalism. Originality/value – This area of research has not received much attention in the pharmaceutical marketing literature until recent years, and hopefully this study will stimulate some interest.
机译：目的–目的是探索混合遗传算法-偏最小二乘（GA-PLS）建模方法的潜力，以了解影响医生开药习惯的重要促销支出变量，并帮助利用管理者的见识来计划更好的支出他们的促销活动。设计/方法/方法–使用GA作为变量选择工具，并使用PLS分析将这些变量与所观察到的处方量变化相关联。使用来自美国抗生素界四个主要品牌的市场顾问的数据库对这种方法进行了说明。研究结果–得出了良好的统计模型，可以对医师指导的促进作用进行更简单，更快速的计算预测。建立的所有最终模型的r2值范围为0.835至0.922，交叉验证的r2（q2）值范围为0.791至0.911，而平均绝对百分比误差（MAPE）值平均所有品牌模型均限制在5％范围内。此外，详尽的统计分析显示促销支出是变量的有用性，而GA-PLS作为相关工具的有效性也很强。研究的局限性/意义–建模框架仅包含抗生素类别，这可能会限制其一般用途。实际意义–管理人员可以更熟练地解释促销对医生开处方行为的影响，并能够建立预测模型，以帮助确定他们好奇地混合促销鸡尾酒将在何处以及如何产生最大的未来回报。此外，如果可以衡量个人促销支出因素的影响，那么这可能证明，鉴于越来越多的公众相信医师指导的促销活动，可以验证干预措施降低药物营销有害后果的有效性的方式已经变得过于束手无策，并且正在损害医学专业水平。独创性/价值–直到最近几年，该领域的研究才在药物营销文献中受到关注，希望该研究会引起一定的兴趣。

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