基于数据科学技术的医药企业营销组合建模

G. Chornous, Y. Fareniuk
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引用次数: 4

摘要

本文包含数据科学技术应用(包括机器学习和回归分析)的结果,以2015年至2019年期间的历史数据为基础,每周详细描述乌克兰制药公司之一的关键品牌营销活动的结果。研究的主要目的是估计营销组合的关键要素(连锁药店的渗透率、价格政策与主要竞争对手的关系、品牌及其竞争对手在所有传播渠道(电视、数字、广播、户外广告、报刊)上的广告活动)对公司在相关药品细分市场的销售、数量市场份额和价值市场份额的影响。基于所得结果,本文详细解释了渗透率、价格政策和媒体活动对企业竞争力和市场地位的影响。价格政策和渗透率对销售(市场份额)的直接影响,以及对其他因素(包括品牌在电视上的广告活动的有效性)的影响进行估计,并考虑到制定有效的营销策略。在研究的基础上,本文提出了优化营销策略的主要建议,以最大限度地提高公司的销售额,并在货币或实物方面增加市场份额。数据科学技术成为销售管理的工具,因为它创造了量化每个因素对销售的影响的能力,确定它们的最佳组合,以实现业务目标,加强公司在市场中的地位,有效的营销预算分配和情景预测。持续的模型支持允许增加每个因素的回报,提高投资回报,并确保以最有效的方式实现业务目标。数据科学是找到有效的营销解决方案和形成有效的业务发展战略的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Marketing mix modeling for pharmaceutical companies on the basis of data science technologies
The article contains the results of Data Science technologies application (including machine learning and regression analysis) to modelling the results of marketing activities of key brand of one of the Ukrainian pharmaceutical companies on the basis of historical data for the period from 2015 to 2019 in weekly detail. The main goal of research is to estimate the influence of key elements of the marketing mix (penetration of pharmacy chains, price policy vs main competitors, advertising activity of the brand and its competitors in all communication channels (television, Digital, radio, outdoor advertising, press)) on company’s sales, volume market share and value market share in relevant segment of drugs. Based on the results obtained, the article explains in detail the impact of penetration, price policy and media activity on the competitiveness of the enterprise and its position in the market. The influence of the price policy and penetration directly on sales (market share), as well as on other factors (including the effectiveness of the brand's advertising activity on television) is estimated and taken into account for development the effective marketing strategy. Based on the research, the article contains main recommendations for optimizing the marketing strategy to maximize the company's sales and increasing market share in monetary or physical terms. Data Science technologies become a tool for sales management, because it creates the ability to quantify the impact of each factor on sales, determine their optimal combination for achievement of business goals and strengthening the company's position in the market, effective marketing budgets distribution and scenario forecasting. Continuous model support allows to increase the return on each factor, improve return on investment and ensure the achievement of business goals in the most efficient way. Data Science forms the basis for finding effective marketing solutions and forming an effective business development strategy.
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