AI-Driven Marketing Research and Data Analytics, 2024
The AI voice assistant mobile application was developed to aid drivers in operating their mobile ... more The AI voice assistant mobile application was developed to aid drivers in operating their mobile phones while driving without touching their phones. The literature review examines multiple innovative artificial technologies involved in applications with voice assistants in natural language processing (NLP) techniques. The methodology used involved a qualitative approach, and the design science paradigm was used for the development of the voice assistant for smartphones with NLP techniques. NLP techniques that were applied in the development of the AI voice assistant are smart synthesis, data flow sequence, core and interface accessing, part of speech tagging, named entity recognition, conference resolution, and porter stemming. Some of the operations that are achieved by the application include arithmetic calculations based on voice commands and returning the computer result via voice, searching the internet based on user voice input, and providing a response via voice assistance.
This chapter introduces a methodological approach to implementing supervised machine learning alg... more This chapter introduces a methodological approach to implementing supervised machine learning algorithms and neuromarketing techniques for predicting customer churn. It addresses the challenge of customer attrition faced by businesses and explores how the combination of neuromarketing strategies with machine learning algorithms can enhance churn forecast accuracy. By conducting a comparative study, the chapter assesses the performance of different algorithms when integrated with various neuromarketing approaches, such as biometric, neuroimaging, eye tracking, neurophysiological and facial expression analysis data. It emphasises the importance of understanding the advantages and disadvantages of different algorithms to select the most suitable methods for churn prediction. The chapter provides an overview of customer churn prediction in the context of neuromarketing, highlighting the connection between customer attrition and neuromarketing. The chapter discusses studies that explore customer relationship characteristics, neuroscience methods for understanding consumer behavior and the significance of emotions in churn prediction. Various neuromarketing techniques, including neuroimaging and physiological measurements are examined for their relevance in predicting churn by uncovering emotional and cognitive processes underlying consumer behavior. Through leveraging neuromarketing insights, marketers can develop predictive models using supervised machine learning algorithms that effectively utilise customer data to accurately predict churn and develop targeted retention strategies. The chapter also explores commonly employed machine learning algorithms such as logistic regression, decision trees, random forests, support vector machines, gradient boosting methods and multilayer perceptron designs, in the context of churn prediction using neuromarketing data. Lastly, the chapter emphasises the importance of evaluation metrics in assessing the performance of predictive models in neuromarketing for customer churn prediction.
Agritourism is a dynamic industry poised for growth, with the global market share projected to re... more Agritourism is a dynamic industry poised for growth, with the global market share projected to reach 117.37 billion by 2027. Despite its potential to foster economic growth and human development in Africa, the industry grapples with several obstacles including market inefficiencies, environmental sustainability concerns, and limited access to information. The application of AI in the context of sustainable agritourism remains largely uncharted territory in Africa. Therefore, there is a pressing need to investigate how AI can be effectively harnessed to address these challenges and promote sustainable agritourism, thereby facilitating human development across the African continent. This chapter delves into the potential of artificial intelligence (AI) to facilitate sustainable agritourism in Africa. It begins by providing an overview of the current state of agritourism in Africa, highlighting the challenges it encounters.
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Books by MARTIN MUDUVA