The Value Of RFM Analysis In Ecommerce Customer Segmentation

My Favorite Analyses: the Recency-Frequency matrix. This simple yet powerful framework goes beyond traditional segmentation to provide actionable insights into customer behavior. By focusing on how recently and how often customers engage with your brand, you can tailor your strategies to maximize lifetime value. Why it works: - Recency: Customers who have purchased recently are more likely to purchase again. It's a strong indicator of engagement and future behavior. - Frequency: Customers who purchase more often demonstrate loyalty and satisfaction, leading to a higher customer value. Recency and Frequency are the most important indicators of customer value, exhibiting more correlation to CLV than Monetary Value which is the third component in traditional RFM analyses. The Recency-Frequency matrix helps you categorize your customers into segments based on behaviors instead of factors like demographics or psychographics that imply actions. The analysis reveals distinct customer segments that require unique marketing strategies, including your Champions, the customers who Need Attention, and those who have Already Churned. Implementing the Matrix: Depending on the size of your customer dataset, the Recency-Frequency matrix can be built in a spreadsheet or a more hefty tool like SQL or R. - Excel/Google Sheets: Use `MAXIFS`, `COUNT`, `PERCENTRANK`, and a pivot table to build the Recency-Frequency matrix, but watch out for row limits. - SQL: Leverage functions like `DATEDIFF` and `COUNT` to calculate metrics, and segment with `NTILE`. - R: The `RFM` package handles large datasets with ease, offering advanced segmentation and visualization. This approach isn’t just theory — it’s a data-backed method for ensuring your marketing dollars are spent where they’ll make the most impact. DM me if you'd like to learn more, including the marketing strategies that I most commonly recommend for each Recency-Frequency matrix customer segment. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #MyFavoriteAnalyses #ROI #MROI

As promised, I’m sharing the first project from my series on the Top 5 Data Science Projects that every data scientist should tackle to gain a deeper understanding of the retail industry: Customer Segmentation using the K-means approach 🛒 Before diving into my approach, let’s first explore why customer segmentation is so important for retail businesses. Customer segmentation empowers businesses to better understand their customers, personalize experiences, and optimize strategies. For retail, this is key as it allows companies to: a. Target Specific Customer Groups: Customize marketing campaigns, products, and promotions based on customer preferences and behavior. Improve Retention: Identify loyal or high-value customers and offer personalized rewards to encourage repeat purchases. b. Optimize Resources: Adjust inventory, staffing, and other resources based on the distribution of customer segments. c. Create Personalized Experiences = Higher Conversions: Tailor experiences for different customer groups, leading to increased sales and customer satisfaction. The Approach In this project, I used Machine Learning to segment customers based on their purchasing behavior with the KMeans clustering algorithm. The goal was to identify patterns in customer data and classify them into meaningful segments for targeted strategies. Here’s a quick breakdown of the approach I followed: 1. Data Collection: I used retail transaction data from the UCI Machine Learning Repository. 2. Preprocessing: Cleaned and transformed the data, handled missing values, and dealt with outliers. After that, I standardized the features through scaling. 3. Clustering: I applied the KMeans clustering algorithm to group customers into 3 distinct segments, based on their RFM model (Recency, Frequency, and Monetary value): Recency: How recent was the customer’s last transaction? Frequency: How often does the customer make a purchase? Monetary value: How much has the customer spent in total? 4. Visualization: I visualized the segmentation using box plots, elbow curves, and cluster snapshots to better interpret the patterns within each group. Explore the Full Project Check out the full project on GitHub, where I’ve shared the code and detailed steps for replicating the analysis: Link to GitHub Project- https://lnkd.in/gKFXkFN2 Also sharing some cluster visualizations snaps below to see the results of the segmentation! ✨ Stay tuned for the next project in this series! I’ll be diving deeper into more advanced data science techniques that drive success in the retail industry. Don't miss out—follow me to get notified! #CustomerSegmentation #DataScience #MachineLearning #KMeansClustering #RetailAnalytics #DataScienceInRetail #CustomerInsights #DataAnalysis #RFMModel #MarketingOptimization #RetailStrategy

Don't underestimate the power of old-school data analysis techniques. For example, my clients love RFM analysis: (R)ecency (F)requency (M)onetary Here's how RFM analysis works. (R)ecency is the time since a customer's last purchase. Customers in the top 10% of recent purchases receive a score of 9. Customers in the bottom 10% receive a score of 0. Each customer gets a recency score from 0 to 9. (F)requency is the number of customer orders over some time (e.g., the last year). Customers in the top 10% of frequency receive a score of 9. Customers in the bottom 10% receive a score of 0. Each customer gets a frequency score from 0 to 9. (M)onetary is the total lifetime purchases of your customers. Customers in the top 10% of lifetime purchases receive a score of 9. Customers in the bottom 10% receive a score of 0. Each customer gets a monetary score from 0 to 9. Your absolute best customers will have a score of 999. The next tier will have scores of 998, 989, and 899. And so on. Here's the thing, though. You can customize the ideas behind RFM to fit your situation. Here's a real-life example. I performed a marketing analysis of US geographies. The goal was to find the optimal geographies for digital ads. I joined internal data with free data from the US Census Bureau. I then performed an RFM-style analysis of the data. I scored each geography with four characteristics. For example, the count of high-income households. The best geographies scored 9999. Using this analysis, I identified US geographies with opportunities for digital ads.

That big customer account at the top of your sales report? It might be masking a dangerous trend. We’re conditioned to equate high revenue with high value. But this single metric often hides the real story: the slowly decreasing purchase frequency, the reliance on deep discounts, or the rising cost-to-serve that erodes your margin. By the time these 'star accounts' go dormant, the damage is already done. This is the risk of navigating with an incomplete map. The shift from reactive firefighting (which we too often see in businesses) to proactive growth begins with a foundational segmentation technique called “RFM Analysis”. It’s a super simple, but powerful customer analytics framework that helps you optimize your sales and marketing efforts. Instead of just looking at total sales, RFM evaluates the three main areas of customer behavior: Recency: How recently did they buy? This is the strongest predictor of engagement. Frequency: How often do they purchase? This is the true measure of loyalty. Monetary: How profitable are they, after discounts and support costs? (firms can also measure “total revenues” as part of this category) Analyzing these behaviors shows you precisely who to nurture, who to re-engage, and where your greatest profit opportunities truly lie. It's how you protect your most valuable partners and attempt to reactivate valuable, but currently dormant accounts. In our latest article, we deconstruct this simple concept and present some actionable strategies. Read the full article below. #revenue_growth_analytics #CustomerRetention #RFMAnalysis #Profitability #DataDrivenStrategy #CustomerValue #SalesStrategy