• RFM analysis is a statistical analysis method used to determine basic customer segments by measuring customers’ purchasing habits. RFM stands for Recency, Frequency, and Monetary. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases through a scoring model.

• Recency-Frequency-Monetary (RFM) Analysis is a marketing analysis tool used to define customers according to the nature of their purchasing habits. With RFM analysis, customers are evaluated by scoring them in three main categories: how recently they purchased, how often they purchased, and the size of their purchases.

• As a result of the RFM analysis, each customer is ranked numerically on a simple scale in each of these three categorical values. According to the ranking made, the “Best” customer will be the customer who receives the highest score in each category. Although this ranking is usually done between 1 and 5, it can also be done with letters (A,B,C, …), special name tags (Platinum, Gold, Classic etc.) or adjective definitions (Champions, Soon to Escape, Loyal Customers etc.).

• With RFM analysis, customers can be segmented and sales and marketing tactics and practices can be developed specifically for each segment. This helps companies reasonably estimate which customer segment is more likely to repurchase products, how much revenue/profit is generated from new customers, and how to convert relatively infrequent shoppers into more frequent shoppers.

• 📌 Dataset ; https://www.kaggle.com/nathaniel/uci-online-retail-ii-data-set

• There are two sheets in the dataset covering the years 2009 - 2010 and 2010 - 2011. While performing my analysis, I utilized the first sheet that encompasses the years 2009 - 2010.

• 📌 In this section, I will take 2 groups for evaluation and determine their RFM scores and the action decisions that can be taken regarding these groups.

🔒 About to Sleep :

• The average time since purchase in this group is 53 days.
• There are a total of 457 people in this group.
• This group shopped on average 1 time.
• They spent an average of £523.

🗝️ Suggestions :

• The "About to Sleep" segment includes customers whose last purchase was some time ago, with low shopping frequency but high spending.
• The problem with this group of customers is their shopping frequency and forgetting to shop. The most important action is to remind this group of ourselves frequently and make personalized product recommendations. In addition to the products they buy, different discounts can be applied for the low number of purchases.

🔒 New Customers :

• The average time since purchase in this group is 8 days.
• There are a total of 89 people in this group.
• This group shopped on average 1 time.
• They spent an average of £337.

🗝️ Suggestions :

• It is too early to take action in this group, but looking at the groups as a whole, new customers constitute almost the smallest cluster. The first thing that comes to mind in this case may be the inadequacy of advertising and marketing content. Attracting and winning customers is as difficult as retaining them.
• Therefore, it seems to be very important to offer personalized product recommendations based on data and to have an advantage over other companies in advertising campaigns.