The Customer Profiling with RFM Index project aims to conduct a customer profiling study based on the analysis of the RFM (Recency, Frequency, Monetary) index using a dataset containing information on transactions from an online retail company based in the United Kingdom, spanning the period between 01/12/2010 and 09/12/2011. The primary objective of this project is to identify customer habits towards the company in order to develop targeted and personalized advertising.
The RFM index, composed of three key components, provides a measure of the relationship between customers and the company. The three features that make up the RFM index are:
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Recency (R): Represents the number of days since the customer's last transaction. A low value indicates a more recent transaction, while a high value indicates a distant or nonexistent transaction.
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Frequency (F): Indicates the total number of transactions made by a customer during a certain period of time. A high value indicates a higher frequency of purchases, while a low value indicates a lower frequency.
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Monetary (M): Represents the total monetary value of transactions made by a customer during a certain period of time. A high value indicates a higher monetary value generated by the customer, while a low value indicates a lower monetary contribution.
The main objective of the project is to utilize the RFM index to identify customer segments based on their Recency, Frequency, and Monetary characteristics. This will enable a better understanding of customer habits and facilitate the development of targeted marketing strategies for each segment.
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Clone this repository to your computer using the command:
git clone https://github.com/g4lius/rfm-customer-profiling.git -
Install the necessary dependencies required for running the project.
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Execute the customer profiling script using Python or follow any specific instructions provided in the code.
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Analyze the obtained results and utilize them to inform your marketing and advertising strategies.