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Smarter Customer Targeting with Time-Based RFM

Introducing Time-Sensitive RFM Segmentation in Datma

Mihai Iova avatar
Written by Mihai Iova
Updated today

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to your customer analysis empowering you to target the right people at the right time, based on real behavior, not outdated assumptions.


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.

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