Python_Cohort_Analysis

I. Introduction

1. Business question

Using Python to analyze transaction data from KPMG to evaluate user engagement from their first transaction

2. Dataset

Dataset KPMG.xlsx includes one table containing transaction information of customers purchasing products in the year 2017

• There are many missing values (in online_order, brand,.. columns) but it does not affect the cohort analysis result => temporarily ignore
• Values in 'transaction_id' column are unique => there are no duplicate records in the dataset
• We need to filter order_status = ‘Approved’ before cohort analysis

3. Method: Cohort analysis

Cohort analysis definition

• Cohort analysis is a type of behavioral analytics in which you take a group of users, and analyze their usage patterns based on their shared traits to better track and understand their actions.
• A cohort is simply a group of people with shared characteristics

Three major types of Cohort

• Time cohorts: customers who signed up for a product or service during a particular time frame.
• Behavior cohorts: customers who purchased a product or subscribed to service in the past.
• Size cohorts: refer to the various sizes of customers who purchase the company’s products or services.

Cohort type of this project

• This project focuses on performing Cohort Analysis based on Time
• Customers will be divided into acquisition cohorts depending on the month of their first purchase (cohort month)
• The cohort index would then be assigned to each of the customer’s purchases, which will represent the number of months since the first transaction

II. Data Visualization with Python

III. Insights

• In general, the retention rate at KPMG is quite stable, mostly maintained in the range of 31% to 45%. However, the number of customers who made the first purchase decreased sharply over the months, from 1347 customers (Jan 2017) to 4 customers (Dec 2017)
• Customers who placed their first order between April and July 2017 tend to have higher retention rates than the other groups: 48.1% in the fifth month of July 2017, 45.1% in the fourth month of April 2017.
• Retention rates of customers purchasing their first products in January, February, and March 2017 were relatively stable (about 33% to 43%). Meanwhile, these rates in August, September, and October 2017 are more fluctuating, especially in August: 43.1% in the 3rd month but 25.5% in the 5th month.

IV. Recommendations

• KPMG did a good job of maintaining stable retention rates over the user's lifetime, but the number of customers who made the first purchase declines over time → We need to gather more information about the context in the past (promotions, seasonal trends, product quality changes, etc.) to analyze the cause and evaluate the effectiveness of marketing campaigns
• The mid-year months (Apr - Jul) have higher retention rates than other groups -> focus on finding out the characteristics of these groups to design special offers or new marketing campaigns for other months of the year.