Customer Segmentation Analysis

E-Commerce Data

This dataset consists of orders made in different countries from December 2010 to December 2011. The company is a UK-based online retailer that mainly sells unique all-occasion gifts. Many of its customers are wholesalers.

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Data Dictionary

Variable	Explanation
InvoiceNo	A 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c' it indicates a cancellation.
StockCode	A 5-digit integral number uniquely assigned to each distinct product.
Description	Product (item) name
Quantity	The quantities of each product (item) per transaction
InvoiceDate	The day and time when each transaction was generated
UnitPrice	Product price per unit in sterling (pound)
CustomerID	A 5-digit integral number uniquely assigned to each customer
Country	The name of the country where each customer resides

import pandas as pd
import numpy as np
import datetime as dt
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

#Loading Dataset
online = pd.read_csv("online_retail.csv", parse_dates=['InvoiceDate'])

online.head()

online.shape

Assign daily acquisition cohort

# Define a function that will parse the date
def get_day(x): 
    return dt.datetime(x.year, x.month, x.day) 

# Create InvoiceDay column
online['InvoiceDay'] = online['InvoiceDate'].apply(get_day) 

# Group by CustomerID and select the InvoiceDay value
grouping = online.groupby('CustomerID')['InvoiceDay'] 

# Assign a minimum InvoiceDay value to the dataset
online['CohortDay'] = grouping.transform('min')

# View the top 5 rows
print(online.head())

Source of dataset.

Citation: Daqing Chen, Sai Liang Sain, and Kun Guo, Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208, 2012 (Published online before print: 27 August 2012. doi: 10.1057/dbm.2012.17).

Calculate time offsets

def get_date_int(df, column):
    year = df[column].dt.year
    month = df[column].dt.month
    day = df[column].dt.day
    return year, month, day

# Get the integers for date parts from the `InvoiceDay` column
invoice_year, invoice_month, invoice_day = get_date_int(online, 'InvoiceDay')

# Get the integers for date parts from the `CohortDay` column
cohort_year, cohort_month, cohort_day = get_date_int(online, 'CohortDay')

# Calculate difference in years
years_diff = invoice_year - cohort_year

# Calculate difference in months
months_diff = invoice_month - cohort_month

# Calculate difference in days
days_diff = invoice_day - cohort_day

# Extract the difference in days from all previous values
online['CohortIndex'] = years_diff * 365 + months_diff * 30 + days_diff + 1
print(online.head())

Calculate retention rate from scratch

grouping = online.groupby(['CohortDay', 'CohortIndex'])

# Count the number of unique values per customer ID
cohort_data = grouping['CustomerID'].apply(pd.Series.nunique).reset_index()

# Create a pivot 
cohort_counts = cohort_data.pivot_table(index='CohortDay', columns='CohortIndex', values='CustomerID')

# Select the first column and store it to cohort_sizes
cohort_sizes = cohort_counts.iloc[:,0]

# Divide the cohort count by cohort sizes along the rows
retention = cohort_counts.divide(cohort_sizes, axis=0)