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Customer segmentation – combining RFM and predictive algorithms

The Recency-Frequency-Monetary value segmentation has been around for a while now and provides a pretty simple but effective way to segment customers.  An RFM model can be used in conjunction with certain predictive models to gain even further insight into customer behavior.  In this post we’ll discuss three predictive models – K-means clustering, Logistic Regression and …

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Customer segmentation

AzureML model for customer segmentation

As mentioned previously, we are approaching the customer segmentation problem holistically with a view to provide an end to end solution.  This end to end solution comprises of three components. Data preparation and enrichment. Any complex enterprise landscape comprises of multiple systems, each performing a specific function.  There could in fact be more than one system performing the …

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AzureML model

Customer segmentation – RFM technique on big data using Google Cloud Dataflow

We are quickly becoming big fans of Google Cloud Dataflow.  See our previous posts on this topic here and here.  We are excited to continue using this product and creating analytics solutions for our customers.  In this post we are going to describe how RFM technique can be applied on large data sets – think 100’s …

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Google Cloud Dataflow

Customer segmentation

Consumers, customers, clients or users.  Call them what you will, but businesses exist and grow only when they can serve them well and attract more of them.  While there are several business strategies that one employs, one key methodology is to group or segment similar customers based on past purchasing behavior and geodemographic information.  This helps …

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Customer segmentation