Marketing Analytics: Best Tools for Customer Segmentation

Great question, Emily. Beyond K-means and RFM, you might want to consider hierarchical clustering which can be useful when you're not sure about the optimal number of segments. For visualization, I recommend looking at t-SNE or UMAP for dimensionality reduction to help visualize high-dimensional customer data.

Regarding tools, Python's scikit-learn is excellent for the analysis, while Tableau or PowerBI can create compelling visualizations for stakeholders. For combining behavioral and psychographic data, factor analysis or principal component analysis can help identify underlying dimensions before clustering.

I worked on a similar project last semester. We found that latent class analysis was particularly effective when dealing with mixed data types (behavioral, demographic, and psychographic). The R package 'poLCA' was helpful for this.

For visualization, we used a combination of radar charts to show the characteristics of each segment and geographic heat maps to show their distribution. This approach was very well-received by our client.

Thank you both for the suggestions! I'll definitely look into hierarchical clustering and latent class analysis. @Dr. Thompson, do you have any specific resources you'd recommend for learning more about applying t-SNE for customer data visualization? And @Michael, I'd love to see examples of those radar charts if you're able to share.

Emily, I'll share some resources in our next class. In the meantime, check out the paper by Maaten and Hinton (2008) on t-SNE. For a more practical approach, there's a great tutorial on distill.pub about how to use and interpret t-SNE visualizations.

I'd also suggest looking at the recent case study we discussed about Starbucks' customer segmentation approach - they used a combination of methods that might be relevant to your retail context.