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

Gehan Shanmuganathan Dhameeth

doi:doi:10.11648/j.ijdsa.20190504.13

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

Gehan Shanmuganathan Dhameeth

Published in International Journal of Data Science and Analysis (Volume 5, Issue 4)

Received: 31 July 2019 Accepted: 26 August 2019 Published: 10 September 2019

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Abstract

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.

Published in	International Journal of Data Science and Analysis (Volume 5, Issue 4)
DOI	10.11648/j.ijdsa.20190504.13
Page(s)	67-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Brand Equity, Millennials, Multiple Logistic Regression, Best Model Selection

References

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APA Style

Gehan Shanmuganathan Dhameeth. (2019). Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods. International Journal of Data Science and Analysis, 5(4), 67-72. https://doi.org/10.11648/j.ijdsa.20190504.13

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ACS Style

Gehan Shanmuganathan Dhameeth. Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods. Int. J. Data Sci. Anal. 2019, 5(4), 67-72. doi: 10.11648/j.ijdsa.20190504.13

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AMA Style

Gehan Shanmuganathan Dhameeth. Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods. Int J Data Sci Anal. 2019;5(4):67-72. doi: 10.11648/j.ijdsa.20190504.13

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@article{10.11648/j.ijdsa.20190504.13,
  author = {Gehan Shanmuganathan Dhameeth},
  title = {Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {4},
  pages = {67-72},
  doi = {10.11648/j.ijdsa.20190504.13},
  url = {https://doi.org/10.11648/j.ijdsa.20190504.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190504.13},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods
AU  - Gehan Shanmuganathan Dhameeth
Y1  - 2019/09/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190504.13
DO  - 10.11648/j.ijdsa.20190504.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 67
EP  - 72
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190504.13
AB  - 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.
VL  - 5
IS  - 4
ER  -

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Author Information

Gehan Shanmuganathan Dhameeth

Business and Economics Department, Wells College, Aurora, New York, USA

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Gehan Shanmuganathan Dhameeth. (2019). Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods. International Journal of Data Science and Analysis, 5(4), 67-72. https://doi.org/10.11648/j.ijdsa.20190504.13

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ACS Style

Gehan Shanmuganathan Dhameeth. Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods. Int. J. Data Sci. Anal. 2019, 5(4), 67-72. doi: 10.11648/j.ijdsa.20190504.13

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Gehan Shanmuganathan Dhameeth. Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods. Int J Data Sci Anal. 2019;5(4):67-72. doi: 10.11648/j.ijdsa.20190504.13

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@article{10.11648/j.ijdsa.20190504.13,
  author = {Gehan Shanmuganathan Dhameeth},
  title = {Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods},
  journal = {International Journal of Data Science and Analysis},
  volume = {5},
  number = {4},
  pages = {67-72},
  doi = {10.11648/j.ijdsa.20190504.13},
  url = {https://doi.org/10.11648/j.ijdsa.20190504.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190504.13},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Predictive Modeling of the Brand Equity: Analysis Based on Multiple Logistic Regression and Backward Stepwise Model Selection Methods
AU  - Gehan Shanmuganathan Dhameeth
Y1  - 2019/09/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdsa.20190504.13
DO  - 10.11648/j.ijdsa.20190504.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 67
EP  - 72
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20190504.13
AB  - 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.
VL  - 5
IS  - 4
ER  -

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