sea0125/rfm-model

This is a set of a code that you can categorize your customers based on the recency, frequency, and monetary value.

1. Why RFM analysis?

RFM analysis allows marketers to target specific clusters of customers with communications that are much more relevant for their particular behavior – and thus generate much higher rates of response, plus increased loyalty and customer lifetime value. Like other segmentation methods, an RFM model is a powerful way to identify groups of customers for special treatment.

2. What do R.F.M. stand for?

Recency: How much time has elapsed since a customer’s last activity or transaction with the brand? Activity is usually a purchase, although variations are sometimes used, e.g., the last visit to a website or use of a mobile app. In most cases, the more recently a customer has interacted or transacted with a brand, the more likely that customer will be responsive to communications from the brand.

Frequency: How often has a customer transacted or interacted with the brand during a particular period of time? Clearly, customers with frequent activities are more engaged, and probably more loyal, than customers who rarely do so. And one-time-only customers are in a class of their own.

Monetary: Also referred to as “monetary value,” this factor reflects how much a customer has spent with the brand during a particular period of time. Big spenders should usually be treated differently than customers who spend little. Looking at monetary divided by frequency indicates the average purchase amount – an important secondary factor to consider when segmenting customers.

3. The suitable business scanarios for implementing RFM analysis

RFM analysis serves as a critical groundwork for various strategic initiatives, including but not limited to:

1. Base on customers purchase behaviors, tailoring communication, refining sales and discount approaches for each individual segments.
2. For the high value customer, crafting targeted loyalty and retention initiatives.
3. Pinpointing customers at risk of churn, and define corresponding response.
4. Assess business potential by forecasting customer lifetime value.
5. Better inbound management by utilizing purchase frequency and timing to guide inventory decisions.

4. The consideration to take in implementing RFM analysis

Data Quality

1. Clean and Accurate Data: Ensure that customer data is clean, accurate, and up-to-date. Incorrect data can lead to faulty analysis and decisions.
2. Comprehensive Data Collection: Collect comprehensive data across all customer touchpoints to get a holistic view of customer behavior.

Segmentation Criteria

1. Defining RFM Parameters: Clearly define what recency, frequency, and monetary value mean within your business context. These definitions can vary depending on the nature of the business and the customer lifecycle.
2. Segmentation Thresholds: Establish meaningful thresholds for each RFM parameter to create relevant segments. These thresholds should be based on historical data analysis and business objectives.

Continuous Improvement

1. Regular Updates and Analysis: RFM segments should be updated regularly to reflect the most current customer behaviors. Regularly review and adjust the segmentation criteria and thresholds based on new data.
2. Testing and Learning: Continuously test the effectiveness of targeted strategies for different RFM segments and learn from the outcomes. Use A/B testing to refine approaches and improve ROI.

5. The explanation of the needed package

1. Pandas (pandas) for the data manipulation and analysis.
2. Plotly Express (plotly.express) for drawing attractive and informative statistical graphics.
3. Plotly Graph Objects (plotly.graph_objects) Offers a more flexible interface for creating custom or more complex figures in Plotly.
4. Plotly IO (plotly.io) provides functions for configuring the behavior of Plotly figures.
5. Datetime (datetime)

6. The explanation of the code sequence

1. Calculate recency (days) per customer from the last purchase dates to today.
2. Calculate frequency by grouping data and counting orders
3. Calculate monetary value by summing transaction amounts by customer
4. Define scoring criteria for each RFM score ( Should be configurated based on needed)
5. Assign RFM scores to each customer based on its RFM value
6. Create Low, Mid, High value customer segments based on their total RFM score
7. Create RFM customer segments: (Finer segmentation within the Low, Mid, High value customer segment)