Abstract
This chapter explores the use of artificial intelligence (AI) in market research and its potential impact on the field. Discuss how AI can be used for data collection, filtering, analysis, and prediction, and how it can help companies develop more accurate predictive models and personalized marketing strategies. Highlight the drawbacks of AI, such as the need to ensure diverse and unbiased data and the importance of monitoring and interpreting results and covers various AI techniques used in market research, including machine learning, natural language processing, computer vision, deep learning, and rule-based systems. The applications of AI in marketing research are also discussed, including sentiment analysis, market segmentation, predictive analytics, and adaptive recommendation engines and personalization systems. The chapter concludes that while AI presents many benefits, it also presents several challenges related to data quality and accuracy, algorithmic biases and fairness issues, as well as ethical considerations that need to be carefully considered.
Top2. The Evolution Of Marketing Research With Ai
Modern market research is an integral part of marketing management and contributes to a greater understanding of the process that runs through the customer journey to experience (Lemon & Verhoef, 2016). Studying the customer journey to experience involves monitoring their multiple touch points about a product or brand, ranging from initial awareness before purchase through the purchase process to post-purchase. The information gathered and analysed can help companies understand how consumers interact with their brand at each customer journey stage and identify possible weak areas where they can improve their experience (Lemon & Verhoef, 2016).
Key Terms in this Chapter
Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
Sentiment Analysis: A technique used to identify and extract subjective information from text data, such as opinions, emotions, and attitudes.
Adaptive Recommendation Engines: AI systems that use data and algorithms to provide personalised recommendations and experiences to individual users based on their preferences and behavior.
Rule-Based Systems (R-BS): A type of AI system that uses a set of rules and facts to achieve a specific goal. These systems are deterministic and operate with a “cause and effect” methodology.
Genetic Algorithms: A type of AI system that uses principles of natural selection and genetics to optimise solutions to complex problems.
Fuzzy Logic Systems: A type of AI system that uses degrees of truth instead of binary values to represent uncertainty and imprecision in data.
Computer Vision: A field of AI that focuses on enabling machines to interpret and understand visual information from the world around them, such as images and videos.
Personalization Systems: AI systems that use data and algorithms to provide personalised recommendations and experiences to individual users based on their preferences and behavior.
Market Segmentation: The process of dividing a market into smaller groups of consumers with similar needs or characteristics.
Decision Trees (DT): An algorithm used in classification and regression models that uses a hierarchical distribution to classify subjects according to some self-reported or self-recorded criteria.
Artificial Intelligence (AI): A branch of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Ethical Considerations: The moral and social implications of using AI in market research, including issues related to privacy, bias, transparency, and accountability.
Deep Learning: A subset of machine learning that uses artificial neural networks with multiple layers to learn and extract features from complex data.
Artificial Neural Networks (ANNs): Algorithms that attempt to reproduce the neural circuitry of the human brain through a system of nodes and connections through which information flows.
Expert Systems: AI algorithms that can provide expert advice or recommendations based on rules and knowledge.