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Web Scraping & Predicting Customer Behaviour

To effectively understand the factors that influence customer buying behavior, one should begin by scraping and analyzing customer data. This involves collecting data from various sources such as online transactions, customer feedback, social media interactions, and more. The data should then be cleaned and organized for analysis. Key metrics such as purchase frequency, product preferences, and customer demographics should be identified. Using statistical and machine learning techniques, a predictive model can be built to identify patterns and trends. This model can highlight significant factors such as price sensitivity, product features, customer service quality, and marketing efforts that drive purchasing decisions. By continuously updating and refining this model with new data, businesses can gain deeper insights into customer behavior, enabling them to tailor their strategies to enhance customer satisfaction and boost sales.

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