In the context of customer segmentation, customer clustering analysis involves using mathematical modelling to discover groups of similar customers based on finding the smallest variations between customers within each group. These homogeneous groups are called “customer archetypes” or “personas”.
The goal of cluster analysis in marketing is Customer Segmentation to achieve more effective customer marketing through personalization. A popular cluster analysis method is a mathematical algorithm known as k-means cluster analysis, sometimes referred to as scientific segmentation. The resulting clusters help improve customer modelling and predictive analytics, and are also used to target customers with personalised offers and offers based on wants, needs, and interests there.
While much of today's media portrays artificial intelligence (AI) as creating robots that will take over the world, in reality, AI is a powerful tool that can help complete tasks ranging from the simplest problems to complex problems that no human can solve. Essentially, a subset of AI, machine learning, is the development and creation of algorithms that take historical data and use mathematical intelligence to make predictions about the data without the need for direct human intervention.
Machine learning can be leveraged to sift data per ton and manipulated to provide valuable insights into your customer behaviour. This information can then be used to inform data-driven decisions, go-to-market strategies, real-time Audience Targeting, and countless other applications.
