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Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods

机译:品牌资产的预测建模:基于多元Logistic回归和后向逐步模型选择方法的分析

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Brands play a significant role at the point of consumer purchases decisions. Brand managers make all the efforts to induce consumers to purchase their brands and increase eventual brand associations for long-term profits. This paper focuses on how different generations, especially the Millennial and the Baby boomers, behave towards brands based on organizations' brand building efforts to create Brand Equity (BE) using a predictive model. Prior research has not been successful to provide a detailed understanding of generations and their potential brand behavior in a predictive perspective. In this article, author used a predictive model of the brand behavior of different generations using a Multiple Logistic Regression (MLR) method. In addition, it is determined how the predictor variables (awareness, recall, relate, purchase, knowledge, trials, association, recommendations, salience, imagery, performance, feelings, judgement, and resonance) influence the response variable, brand equity, to predict brand equity in these two audiences. In this study, the author administered an online survey using Survey Monkey to reach local (US) and international college/university respondents (n=267) age 18 years and above. The survey was administered using a questionnaire (46 data points). In the analysis process, the author developed a Multiple Logistic Regression (MLR) model, tested the model error, predicted the brand equity of generations, and determined the best model with parsimonious number of predictor variables using the Backward Stepwise Method (AIC). The analysis suggested the model to be reliable model with a 100% prediction of the brand equity (BE) with a mean value of 1. Given the predictors, the model correctly predicted 63% respondents, millennial and baby boomers, to be associated with brand equity and 35% respondents to be otherwise, while the Best Model based on the Backward Stepwise Selection Method (BSSM) using Step AIC function, suggested thirteen out of fourteen predetermined predictors included in the model to predict Brand Equity (BE). In the results generated, the AIC value indicated was 106.
机译:品牌在消费者购买决策方面起着重要作用。品牌经理竭尽全力诱使消费者购买其品牌并增加最终的品牌联想以获得长期利润。本文着眼于不同年龄段的人,特别是千禧一代和婴儿潮一代,如何根据组织的品牌建立努力使用预测模型来建立品牌资产(BE),对品牌表现如何。先前的研究未能成功地从预测角度提供对各代及其潜在品牌行为的详细了解。在本文中,作者使用多元逻辑回归(MLR)方法使用了不同代的品牌行为的预测模型。另外,确定预测变量(认知度,召回率,关联性,购买,知识,试验,联想,推荐,显着性,意象,表现,感觉,判断和共鸣)如何影响响​​应变量,品牌资产以进行预测这两个受众群体的品牌资产。在这项研究中,作者使用Survey Monkey进行了在线调查,以覆盖18岁及18岁以上的本地(美国)和国际大学/大学受访者(n = 267)。使用问卷调查进行了调查(46个数据点)。在分析过程中,作者开发了多元逻辑回归(MLR)模型,测试了模型误差,预测了几代人的品牌资产,并使用后向逐步方法(AIC)确定了预测变量数量少的最佳模型。分析表明,该模型是可靠的模型,对品牌资产(BE)的预测为100%,平均值为1。给定这些预测因素,该模型正确地预测了63%的受访者,千禧一代和婴儿潮一代与品牌相关资产净值和35%的受访者则相反,而基于使用逐步AIC函数的向后逐步选择法(BSSM)的最佳模型则建议在该模型中包含的14个预定预测器中,有13个预测品牌资产(BE)。在生成的结果中,指示的AIC值为106。

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