Scores in Brevo help you analyze, segment, and target your contacts based on their purchasing behavior. They allow you to understand when customers tend to purchase, how valuable they are, and how likely they are to buy again so you can deliver more relevant and timely marketing.
🎯 What are scores?
Scores are automatically generated contact attributes that summarize your contacts’ purchase behavior. They are recomputed automatically on a regular basis to stay up to date and use past orders to reveal patterns to help you understand and act on insights such as:
- How frequently your contacts place orders.
- Which events or holidays drive purchases.
- Who your most valuable or at-risk customers are.
Each score appears as a contact attribute with the prefix SCORE_ and can be used in segmentations, automations, and campaigns just like any other attribute.
Access Attributes visible on contact details pages to view your available scores and drag and drop the ones you want to show on your contacts' details page:
✨ Example of how to use scores
Imagine a customer, Sofia, who makes her first purchase during Black Friday, then buys again at Christmas. Brevo automatically updates her scores: her Key shopping periods now include both events, her Time between orders shows her buying rhythm, her CLV and RFM improve as she spends more, and her Ordering behavior moves from Ordered once to Ordered recently. After several quiet months, her behavior shifts to Very late order, triggering your win-back automation. When she returns and purchases again, all her scores update automatically, helping you continuously understand her value, predict her next purchase, and personalize your engagement.
▶️ Discover the default scores
Brevo automatically calculates five key scores that help you understand how your contacts buy, how often they purchase, and how valuable they are over time:
🕞 Time between orders
Measures the average number of days between two orders placed by a contact. It requires at least two orders to be calculated.
Output |
Unit |
Example |
|---|---|---|
| Numeric value (1 decimal) | Days | 3.5 days, 187 days, 190.2 days |
❓How to use this score in my marketing strategy?
Analyze this score to understand purchasing cadence and identify customers with unusually short or long order intervals.
🔑 Key shopping periods
Identifies the special occasions or events during which a contact has made purchases. If a contact bought something during one or more key periods, those events appear as a list (not ranked).
Output |
Example |
|---|---|
| Multiple-choice attribute | Valentine’s Day, Easter, Halloween, Black Friday, Christmas |
❓How to use this score in my marketing strategy?
Re-engage users who purchased during specific seasonal events.
🛒 Ordering behavior
Analyzes your contacts’ ordering patterns to detect trends and predict future purchases. Each contact is categorized according to their buying activity.
Output |
Example |
|---|---|
| Category attribute |
From best to worst: Orders consistently → Ordered recently → About to order → Late order → Very late order → No recent order → Ordered once → Never ordered |
❓How to use this score in my marketing strategy?
Trigger an automated win-back scenario for contacts whose score drops to “Very late order.”
📈 Recency, Frequency, and Monetary (RFM)
Analyzes your contacts based on how recently they purchased (Recency), how often they buy (Frequency), and how much they spend (Monetary). It ranks each contact relative to your entire contact base, grouping them into 6 quantiles (1–6) per dimension.
Output |
Example |
|---|---|
| Category attribute |
From best to worst: Champion → Loyal customer → Potential loyalist → Promising customer → Needs attention → At risk → About to sleep → Lost → Prospect |
RFM is relative which means that a contact’s category depends not only on their own behavior but also on how the rest of your customer base behaves. This means a contact may change category because:
- Their own purchase behavior changed, or
- Your overall customer base shifted, while they did not. For example, if everyone buys more during Black Friday except one contact, that contact’s ranking may drop.
Brevo calculates RFM using quantile scoring (NTILE(6)) on your entire contact base. Each dimension produces a score from 1 to 6, where 1 = best and 6 = lowest.
-
Recency (R): How recently the contact made a purchase.
Contacts are divided into 6 quantiles based on their last order date.
1 = most recent purchasers, 6 = least recent. -
Frequency (F): How often the contact makes purchases.
Contacts are divided into 6 quantiles based on number of orders.
1 = most frequent buyers, 6 = least frequent. -
Monetary (M): How much the contact spends on average.
Contacts are divided into 6 quantiles based on average order amount.
1 = highest spenders, 6 = lowest spenders. -
Frequency–Monetary (FM) interaction: A combined score representing both frequency and monetary value.
Frequency and monetary scores are combined to produce a new value, then split again into 6 quantiles.
❓How to use this score in my marketing strategy?
Try to add personalized messages or exclusive perks in your campaigns for “Champion” or “Loyal” customers.
💰 Customer lifetime value (CLV)
Represents the total revenue generated by a contact over their entire relationship with your company.
It is the sum of all purchases made by that contact.
Output |
Unit |
|---|---|
| Numeric value | Monetary (no specific currency) |
❓How to use this score in my marketing strategy?
Segment customers who have spent more than €2000 and send them VIP or loyalty campaign offers.
📝 Use scores in Brevo
Scores behave like standard attributes, which means you can:
- Segment contacts based on score values (e.g., contacts with “Key shopping period = Black Friday”)
- Trigger automations when a score changes (e.g., send a reactivation email when “Ordering behaviour = Very late order”)
- Personalize campaign content based on scores (e.g., highlight premium products for high-CLV contacts)
⏭️ What's next?
- Create a segment to filter your contacts
- Send an email from an automation
- Personalize your emails using contact attributes
- Explore and analyze your data with Analytics
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