rfm

rfm: Python Package for RFM Analysis and Customer Segmentation

Info

rfm is a Python package that provides recency, frequency, monetary analysis results for a certain transactional dataset within a snap. Its flexible structure and multiple automated functionalities provide easy and intuitive approach to RFM Analysis in an automated fashion. It aims to be a ready-made python package with high-level and quick prototyping. On practical hand, real world data is easily suited and adapted by the package. Additionally, it can make colorful, intuitive graphs using a matplotlib backend without breaking a sweat.

Installation

Dependencies

• Python (>=3.7)
• Pandas (>=1.2.4)
• NumPy (>=1.20.1)
• matplotlib (>=3.3.4)

To install the current release (Ubuntu and Windows):

$ pip install rfm

Usage

# predefine a transaction dataset as df

>>> from rfm import RFM

>>> r = RFM(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount')

>>> r.plot_segment_distribution()

License

MIT

Documentation

<-- Temporarily Hosted Here -->

Initialization

Read required dataframe

>>> df = pd.read_csv('~./data.csv')

Import RFM package and start rfm analysis automatically:

>>> from rfm import RFM

>>> r = RFM(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount') 

>>> r.rfm_table

If you want to do rfm analysis manually:

>>> r = RFM(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount', automated=False)

Attributes

RFM.rfm_table

returns resultant rfm table df generated with recency, frequency & monetary values and scores along with segments

>>> r.rfm_table

RFM.segment_table

returns segment table df with 10 unique categories i.e. Champions, Loyal Accounts etc.

>>> r.segment_table

Methods

RFM.plot_rfm_histograms()

Plots recency, frequency and monetary histograms in a single row

>>> r.plot_rfm_histograms()

RFM.plot_rfm_order_distribution()

Plots orders by customer number

>>> r.plot_rfm_order_distribution()

RFM.plot_versace_plot(column1, column2)

Plots scatterplot of two input columns

>>> r.plot_versace_plot(column1='recency',column2='monetary_value')

>>> r.plot_versace_plot(column1='recency',column2='frequency')

>>> r.plot_versace_plot(column1='frequency',column2='monetary_value')

RFM.plot_distribution_by_segment(column, take)

Plots Distribution of input column by segment

>>> r.plot_distribution_by_segment(column='recency',take='median')

>>> r.plot_distribution_by_segment(column='frequency',take='median')

>>> r.plot_distribution_by_segment(column='monetary_value',take='median')

RFM.plot_column_distribution(column)

Plots column distribution of input column

>>> r.plot_column_distribution(column='recency')

>>> r.plot_column_distribution(column='frequency')

>>> r.plot_column_distribution(column='monetary_value')

RFM.plot_segment_distribution()

>>> r.plot_segment_distribution()

Plots Segment Distribution, i.e. Segments vs no. of customers

RFM.find_customers(segment)

returns rfm results df with input category

>>> r.find_customers('Champions')