The identification of digital market segments to make value-creating propositions is a major challenge for entrepreneurs and marketing managers. New technologies and the Internet have made it possible to collect huge volumes of data that are difficult to analyse using traditional techniques. The purpose of this research is to address this challenge by proposing the use of AI algorithms to cluster customers. Specifically, the proposal is to compare the suitability of supervised algorithms, XGBoost, versus unsupervised algorithms, K-means, for segmenting the digital market. To do so, both algorithms have been applied to a sample of 5 million Spanish users captured between 2010 and 2022 by a lead generation start-up. The results show that supervised learning with this type of data is more useful for segmenting markets than unsupervised learning, as it provides solutions that are better suited to entrepreneurs’ commercial objectives.

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.

The proliferation of reviews evaluating different services on social networks and online platforms and their importance in consumer decision‐making has led some unscrupulous individuals to take advantage of the anonymity offered by the Internet to manipulate these reviews and influence customers' decisions. The main objectives of this study are: (1) to test whether spammers usually perform their misdemeanors from the same IP address; (2) to explore whether there are differences between stated sexes in this regard; (3) to detect the main motivations for posting fraudulent reviews; and (4) to determine the motivations for doing so from the same IP address. These objectives were achieved by means of a quasi‐experiment with a sample of 7,192,487 users, and a qualitative investigation in which 37 users who had falsified information were interviewed. The results show that spammers who tend to fake their identity do so from the same IP address and that they tend to be male. Four types of motivation are presented: revenge, entertainment, opportunity for profit, and self‐esteem; as well as a further three to explain the use of the same IP: convenience, limited resources, and complacency.

This work studies the feasibility of applying quantum kernel methods to a real consumer classification task in the NISQ regime. We present a hybrid pipeline that combines a quantum-kernel Support Vector Machine (Q-SVM) with a quantum feature extraction module (QFE), and benchmark it against classical and quantum baselines in simulation (hardware validation remains future work). Hyperparameters were selected via nested cross-validation on the training partition and then fixed for test evaluation; under these settings, the proposed Q-SVM attains 0.7790 accuracy, 0.7647 precision, 0.8609 recall, 0.8100 F1, and 0.83 ROC AUC, exhibiting higher sensitivity while maintaining competitive precision relative to classical SVM. All headline metrics are obtained via high-fidelity simulation. We interpret these results as an initial indicator and a concrete starting point for NISQ-era workflows and hardware integration, rather than a definitive benchmark. Methodologically, our design aligns with recent work that formalizes quantum–classical separations and verifies resources via XEB-style (Cross-Entropy Benchmarking) approaches, motivating shallow yet expressive quantum embeddings to achieve robust separability despite hardware noise constraints.