We have demonstrated the basic concept of clustering and segmentation by way of example. In this analysis we have considered only Income and Spend. Depending on the situation, we can include a variety of attributes e.g. age, sex, type of food, etc. Including more variables will improve the quality of the segmentation outcome, however this also increases the complexity of clustering and dimensionality reduction should be applied at that point.
Other potential applications
Clustering and customer segmentation can be applied in various fields such as marketing, retail, finance, banking, image classification.
What are your next steps?
Do some research and consider undertaking a clustering and segmentation exercise in your company.
Start small with a limited investment and then scale it out after you have proved it works.
Feel free to contact Lucid Insights if you need assistance with implementing customer clustering and segmentation analytics in your organisation.
To view the full source code, check on GitHub.