Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Generative Artificial Intelligence (AI), such as ChatGPT by OpenAI, has revolutionized the business world, with benefits including improved accessibility, efficiency, and cost reduction. This article reviews recent developments of generative AI in business and finance, summarizes its practical applications, provides examples of the latest generative AI tools, and demonstrates that generative AI can revolutionize data analysis in industry and academia. To test the ability of generative AI to support decision-making in financial markets, we use the ChatGPT to capture corporate sentiments towards environmental policy by inputting text extracted from corporate financial statements. Our results demonstrate that the sentiment scores generated by ChatGPT can predict firms' riskmanagement capabilities and stock return performance. This study also highlights the potential challenges and limitations associated with generative AI. Finally, we propose several questions for future research at the intersection of generative AI with business and finance.

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