Understanding your customers is the cornerstone of smart ecommerce. At Datma, we’re excited to announce a major enhancement to customer segmentation: RFM classification over 1-year, 2-year, and 3-year windows.
These new dimensions bring greater accuracy, recency, and strategic clarity to your customer analysis empowering you to target the right people at the right time, based on real behavior, not outdated assumptions.
When choosing which RFM time frame to analyze—1, 2, or 3 years—it’s important to consider the nature of your shop and how your customers typically buy. For example, high-frequency shops like those in fashion or beauty may benefit most from a 1-year view, while businesses with longer purchase cycles, such as furniture or home improvement, might gain more insight from 2- or 3-year windows. Aligning the time frame with your domain and shopper behavior ensures your segmentation reflects real buying patterns and maximizes relevance.
Check out a report preview here: RFM segments
What is RFM Segmentation?
RFM stands for:
Recency: How recently a customer made a purchase.
Frequency: How often they’ve purchased.
Monetary: How much they’ve spent.
Each customer gets a score from 1 to 5 in each category (higher is better), which is then used to classify them into clear, actionable segments.
Why Use 1Y / 2Y / 3Y RFM Instead of All-Time?
Most RFM tools use all-time data, but that can introduce staleness. In fast-moving environments like online commerce, customer behavior from years ago shouldn’t carry the same weight as more recent activity. Long-past transactions can distort the true picture of current engagement and overshadow meaningful, up-to-date trends that better reflect today’s customer reality.
Datma now gives you three RFM snapshots:
1-Year RFM: Highly relevant and focused on recent activity.
2-Year RFM: Balanced lookback to capture medium-term behavior.
3-Year RFM: A history of recent years for longer-term trend spotting.
This gives you more control over segmentation, with fresher data and flexible horizons for different campaigns.
All RFM Segments Explained
Based on your customers' RFM scores, Datma assigns them into one of the following segments (these segments follow closely those defined by Shopify):
RFM Group & Description | Recency (R) | Avg. of Frequency and Monetary (FM) |
Dormant No recent purchases, infrequent orders, low spend. | R ≤ 2 | FM ≤ 2 |
At Risk No recent purchases, but strong past activity. | R ≤ 2 | 2 < FM ≤ 4 |
Previously Loyal No recent purchases, very strong order/spend history. | R ≤ 2 | FM > 4 |
Needs Attention Recent purchases, some orders, moderate spend. | R = 3 | FM = 3 |
Almost Lost Low recency, few orders, lower spend. | R = 3 | FM ≤ 2 |
Loyal Recent purchases, frequent orders, high spend. | 3 ≤ R ≤ 4 | FM > 3 |
Promising Recent purchases, few orders, low spend. | R = 4 | FM ≤ 1 |
Active Recent purchases, some orders, moderate spend. | R ≥ 4 | 1 < FM ≤ 3 |
New Very recent purchases, few orders, low spend. | R = 5 | FM ≤ 1 |
Champions Very recent purchases, many orders, high spend. | R = 5 | FM > 3 |
Each of these segments is generated independently for 1-year, 2-year, and 3-year periods (overlapping), enabling powerful time-based filtering in your marketing, support, and product decisions.
Some Example Uses for Datma's Time-Based RFM
Target “Champions” (1Y) with exclusive previews or loyalty perks.
Send reactivation emails to “At Risk” (2Y) customers who once loved your brand.
Evaluate long-term lifecycle trends using “Dormant” and “Loyal” (3Y) segments.
Available Now in Datma
These new RFM dimensions are now live and ready to use in your dashboards, reports, and customer filters. Whether you’re personalizing email flows or analyzing customer churn, Datma gives you the lens to act with confidence and context.
Don’t let outdated data drive your decisions. Use Datma’s time-framed RFM segmentation to power precise, effective e-commerce strategy.