Objective: Calculate Recency, Frequency, and Monetary $($RFM$)$ values for

each customer, using total sales as the Monetary measure. This analysis will

help understand customer behavior and identify key customer segments.

Instructions

$1.$ Data Preparation:

$$ Load the provided retail dataset from Week $5$ and $6.$

$$ Ensure the data includes columns such as CustomerID, InvoiceDate,

InvoiceNo, Quantity, and UnitPrice.

$$ Calculate a new column, TotalSales, defined as Quantity$\backslash$times UnitPrice.

$2.$ Calculating RFM Metrics:

$$ Recency: Calculate the number of days since the last purchase for

each customer, based on the last date in the dataset.

$$ Frequency: Count the total number of unique invoices $($or transactions$)$

for each customer.

$$ Monetary: Sum the TotalSales for each customer to get the total

sales as the Monetary value.

$3.$ RFM Scoring:

$$ Assign each customer a score from $1$ to $5$ for Recency, Frequency,

and Monetary, with $5$ being the best. Use quantiles to segment the

scores:

$$ Top $20\%$: Score $=5$

$$ Next $20\%$: Score $=4$

$$ And so on$.$

$$ Create an overall RFM score by concatenating the three scores for

each customer $($e$.$g$.,$ RFM Score: $543).$

$4.$ Customer Segmentation:

$$ Based on RFM scores, segment customers into groups $($e$.$g$.,$ Top

Customers, High$-$Value, Moderate$-$Value, Low$-$Value$).$

$$ Describe each segment in one or two sentences, summarizing characteristics

such as spending habits or engagement.