Customer segmentation is a widely used strategy in the world of Data Science and Artificial Intelligence and is essential in marketing areas. It involves dividing customers into groups based on common characteristics, such as age, location, interests, purchasing habits, etc. This helps companies better understand their customers’ needs and offer them more appropriate and personalized products and services.
For example, a fashion company could use customer segmentation to better understand its customers and offer them more suitable products and promotions. Instead of treating all their customers as a homogeneous group, the company could divide them into segments based on their preferences and behaviors. The most immediate application would be to offer specific promotions for different segments, such as offers for customers who buy party clothes, discounts for customers who buy sportswear, and special promotions for customers who purchase home decor items. However, once we increase our knowledge about the customer with data-based segmentation, the possibilities for activation are much broader. Other examples of less direct use could be identifying higher margins in a customer segment and creating campaigns to enhance it or detecting correlations between segments and making cross-selling offers.
A key difference between customer segmentation done by humans and clustering using AI is the amount of data that can be used, and the ability of AI to identify patterns and behavioral trends. People can divide customers into groups based on their perceptions and intuitions, but they cannot always detect subtle patterns or trends within the data. In contrast, AI can analyze large volumes of data and detect patterns that are not evident to humans, which can help identify new customer segments and improve existing segmentation.
In summary, customer segmentation is a powerful tool for companies looking to improve their knowledge about their customers and thereby offer more suitable products and services.
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