Assume we have a department store that sells a variety of goods. We must have a comprehensive understanding of our customers in order to be more effective in our business. In today's dynamic environment, this is particularly so. In order for us to be able to respond:
One way to understand our customers is by conducting customer segmentation. Segmentation is a process of categorizing customers into several groups based on common characteristics. We can use many variables to segment our customers. The information such as customer demographic, geographic, psychographic, technographic, and behavioral are often used as a differentiator to segment our customers.
By enabling customer segmentation in the business, we will be able to personalized your strategy to suit each segment’s characteristics. So that customer retention can be maximized, customer experience can be improved, have better ad performance, and marketing costs can be minimized.
So, how can we do this customer segmentation?
We will be applying unsupervised machine learning techniques to make customer segmentation on the retail dataset. We will use Recency, Frequency, and Monetary (RFM) that proven as a useful indicator of customer transaction behaviors.
We will leverage the following products to build this use case:
We will start by preparing our data and then doing model training, followed by creating a pipeline for serving the model.
Building End-to-End Customer Segmentation Solution Alibaba Cloud
How to Build Customer Segmentation Phase I: Data Preparation
How to Build Customer Segmentation Phase II: Model Training
How to Build Customer Segmentation Phase III: Model Serving
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