Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Kaya, Tolga; Aktaş, Emel; Topçu, Y. İlker; Ülengin, Burç

doi:10.2991/ijcis.2010.3.5.15

Cited by 3 publications

(1 citation statement)

References 46 publications

(30 reference statements)

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“…This prediction helps the retail industry to maximize profit. It is a nonlinear statistical model created for the human brain and can be programmed and trained to identify data patterns (Kaya et al, 2010).…”

Section: Artificial Neural Network and Brand Choicementioning

confidence: 99%

The Impact of Artificial Intelligence on Branding

Varsha¹,

Akter

Kumar

et al. 2021

Journal of Global Information Management

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Understanding the growth paths of artificial intelligence (AI) and its impact on branding is extremely pertinent of technology-driven marketing. This explorative research covers a complete bibliometric analysis of the impact of AI on branding. The sample for this research included all 117 articles from the period of 1982-2019 in the Scopus database. A bibliometric study was conducted using co-occurrence, citation analysis and co-citation analysis. The empirical analysis investigates the value propositions of AI on branding. The study revealed the nine clusters of co-occurrence: Social Media Analytics and Brand Equity; Neural Networks and Brand Choice; Chat Bots-Brand Intimacy; Twitter, Facebook, Instagram-Luxury Brands; Interactive Agent-Brand Love and User Choice; Algorithm Recommendations and E-Brand Experience; User-Generated Content-Brand Sustainability; Brand Intelligence Analytics; and Digital Innovations and Brand Excellence. The findings also identify four clusters of citation analysis—Social Media Analysis and Brand Photos, Network Analysis and E-Commerce, Hybrid Simulating Modelling, and Real-time Knowledge-Based Systems—and four clusters of co-citation analysis: B2B Technology Brands, AI Fostered E-Brands, Information Cascades and Online Brand Ratings, and Voice Assistants-Brand Eureka Moments. Overall, the study presents the patterns of convergence and divergence of themes, narrowing to the specific topic, and multidisciplinary engagement in research, thus offering the recent insights in the field of AI on branding.

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Section: Artificial Neural Network and Brand Choicementioning

confidence: 99%

The Impact of Artificial Intelligence on Branding

Varsha¹,

Akter

Kumar

et al. 2021

Journal of Global Information Management

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The Digital Transformation

Yadav,

Seranmadevi

2024

Advances in Web Technologies and Engineering

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In the business world, the digital transformation has ushered in an era of unprecedented change. The explosive growth of digital technology over the past three decades has profoundly changed how businesses operate, compete, and engage with their customers. Businesses across industries have been forced to navigate this constantly changing environment due to the adoption of cloud computing, data analytics, the growth of e-commerce, and the rise of artificial intelligence. Organizations are forced to reconsider their strategies, operations, and consumer engagement models as a result of this significant instability. This chapter discusses the role of artificial intelligence in aiding customised and personalized marketing strategies, acknowledging the diverse preferences and behaviour of consumers. It shares insights on branding and customer management in an AI-driven environment. It also emphasises on online and technology-mediated customer engagement.

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Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

Shahrabi¹,

Khameneh²

2016

Industrial Engineering and Management Systems

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Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

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Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Cited by 3 publications

References 46 publications

The Impact of Artificial Intelligence on Branding

The Impact of Artificial Intelligence on Branding

The Digital Transformation

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

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