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Calculating Lead-Based Returns with Rule-Based Classification using Persona's Dataset

Business Problem

A game company wants to create level-based new customer definitions (persona) by using some features of its customers, and to create segments according to these new customer definitions and to estimate how much the new customers can earn on average according to these segments.

For Example: It is desired to determine how much a 25-year-old male user from Türkiye who is an IOS user can earn on average.

Dataset Story

The persona.csv dataset contains the prices of the products sold by an international game company and some demographic information of the users who buy these products. The dataset 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.

Variables

  • PRICE: Customer spending amount
  • SOURCE: The type of device the customer is connecting with
  • SEX: Gender of the customer
  • COUNTRY: Country of the customer
  • AGE: Age of the customer

Before Application:

  PRICE SOURCE SEX COUNTRY AGE
0 39 android male bra 17
1 39 android male bra 17
2 49 android male bra 17
3 29 android male tur 17
4 49 android male tur 17

Expected Output:

  customers_level_based PRICE SEGMENT
0 BRA_ANDROID_FEMALE_0_18 35.6453 B
1 BRA_ANDROID_FEMALE_19_23 34.0773 C
2 BRA_ANDROID_FEMALE_24_30 33.8639 C
3 BRA_ANDROID_FEMALE_31_40 34.8983 B
4 BRA_ANDROID_FEMALE_41_66 36.7371 A

Requirements

pandas==1.5.1

Author

Oktay Acar

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