Market segmentation entails breaking down your target market into distinct, approachable groups. This process involves creating market subsets based on factors such as demographics, needs, priorities, common interests, and other psychographic or behavioral criteria to gain a deeper understanding of the target audience.
RFM analysis is a technique that segments customer behavior using data. RFM stands for recency, frequency, and monetary value.
Code Sources:
https://github.com/HoangNguyen5897/rfm_project
To better understand their customers and craft targeted campaigns, businesses need to divide their customers into different segments. Effective customer segmentation will be based on three key factors: R (recency) - the time since the last purchase, F (frequency) - the number of purchases, and M (monetary) - the total spending.
By combining Recency, Frequency, and Monetary (RFM) values, I will classify all customers into 11 unique groups, each with distinct characteristics. The details are outlined in the table below.
I start by creating tables and import data into PostgreSQL
create table sales_dataset_rfm_prj
(
ordernumber varchar,
quantityordered varchar,
priceeach varchar,
orderlinenumber varchar,
sales varchar,
orderdate varchar,
status varchar,
productline varchar,
msrp varchar,
productcode varchar,
customername varchar,
phone varchar,
addressline1 varchar,
addressline2 varchar,
city varchar,
state varchar,
postalcode varchar,
country varchar,
territory varchar,
contactfullname varchar,
dealsize varchar
)
Once the data is entered, I start adjusting the data types of the table's columns to enhance analytical capabilities.
In PostgreSQL, converting a VARCHAR data type directly to a numeric type isn't feasible. Therefore, I employ the 'ALTER TABLE... ALTER COLUMN... TYPE... USING' syntax to perform an explicit data type cast.