Due to the increasing competitive environment in these days, there is a possibility that companies may lose their customers every day. For this reason, it is extremely important to be able to satisfy them from the first day of they become customers. In order to do this, we need to know the location of our customers. Are they leaving us? Or do they look like the potential profitable customers?

In this project, we will try to understand where our customers are by applying a rule-based segmentation and try to understand which group are suitable for the new customers.

A game company wants to create using some features of its customers new level-based customer definitions (persona). And also wants to determine the segments according to these new customer definitions. Thus, it will be possible to estimate how much profit can be made per a customer in any segment.

For example, how much profit can be generated from a 33-year-old female user from Turkey who is an Android user.

The Persona.csv dataset contains the prices of the products sold by an international game company and some demographics information of the users who buy these products. The data set consists of records created in each sales transaction. This means that the table is NOT deduplicated. In other words, a user with certain demographic characteristics may have made more than one purchase.

• PRICE – Transaction Amount
• SOURCE – The type of device the customer is connecting to (Android/IOS)
• SEX – Gender of the customer (Female/Male)
• COUNTRY – Country of the customer (USA/Brazil/Turkey/Deuchland/France/Canada)
• AGE – Age of the customer