RFM analysis is one of the oldest and most effective segmentation techniques. In case you don't know, RFM stands for: 1️⃣ Recency: how long ago? 2️⃣ Frequency: how often? 3️⃣ Monetary value: how much $, or was a given goal achieved? Despite its proven effectiveness, RFM analysis remains under the radar for many professionals who mistakenly believe it’s too complex or requires a team of data scientists to execute. The truth? It’s a straightforward process that can yield incredibly valuable insights with just a few steps. Here’s how I leveraged RFM analysis for a client: • Data cleansing: A client provided a dataset of 65,000 transactions. I ensured all personal information, like customer names and emails, was anonymized, focusing only on transaction data. • Initial analysis: Using ChatGPT, I conducted an RFM analysis on a sample of the data. The output included the RFM values themselves, but also quintiles "bins" (grouped by slices of 20%). • Customized segmentation: I further refined the analysis by creating original segment names tailored to the client’s industry, complete with descriptions and targeted marketing tactics. • Visual enhancements: To make the insights more actionable, I added visualizations directly into the Excel output file, making the data easier to interpret and apply. • Automated efficiency: Finally, I asked ChatGPT to generate the complete Python code for the analysis and applied it to the entire dataset of 65,000 transactions—all in just a few seconds. RFM analysis isn’t just a relic of the past—it’s a practical, powerful tool that can be executed quickly and effectively, and powerful tools like ChatGPT makes it even easier! What could have taken many hours, if not days, was done in about an hour. RFM analysis isn’t limited to sales data—it can also be applied to behavioral data, provided you have a user or customer ID. In the days of Universal Analytics, marketers had easy access to metrics like the number of days since the last visit and visit frequency. With GA4, these insights aren’t as readily available unless you implement custom tracking or utilize BigQuery.