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Case problem: Customer segmentation
Contributor IIBest Answer
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Telcontar120
RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 
Unicorn
Segmentation could mean a lot of different things. Can you be more specific about what you are trying to accomplish?
If you are looking for a machine learning driven solution, then you might want to look at some of the modeling operators that use clustering algorithms. Just search for "cluster" in the operator panel and you will see many options come up.
If you want something more user directed, like a RFM scheme, then you will probably do it via data ETL using operators to do binning, date manipulations, etc. There isn't just one or two operators involved, it will depend on what exactly your raw data is and how you want to transform it. I suggest looking at RapidMiner Academy videos on data ETL for some ideas.
5
Answers
Here is my problem statement.
The data set is attached.
Customer segmentation
Problem Statement:
An automobile company has plans to enter new markets with their existing products (P1, P2, P3, P4, and P5). After intensive market research, they’ve deduced that the behavior of the new market is similar to their existing market.
In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D). Then, they performed segmented outreach and communication for different segments of customers. This strategy has worked exceptionally well for them. They plan to use the same strategy in new markets and have identified 2627 new potential customers.
You are required to help the manager to predict the right group of new customers.