Scores are automatically generated contact attributes that summarize your contacts' purchase behavior. They help you understand when customers tend to buy, how valuable they are, and how likely they are to purchase again — so you can deliver more relevant and timely marketing.
🎯 What are scores?
Scores are automatically computed contact attributes with the prefix SCORE_. They are recalculated regularly based on your contacts' order data, and they 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. Drag and drop the ones you want to display 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 includes 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 eight scores based on your contacts' order data. The following sections describe each score: its attribute name, what it measures, how it is calculated, the time window used, and the possible output values.
🕞 Time between orders
| Attribute name | SCORE_TIME_BETWEEN_ORDERS |
|---|---|
| What it measures | The average number of days between two consecutive orders placed by a contact. Requires at least two orders to be calculated. |
| How it is calculated | Average of the number of days between each pair of consecutive orders for the contact. |
| Time window | All time by default. Can be changed in Settings to last 3, 6, 12, or 24 months. |
| Output format | Numeric value (1 decimal place) in days. Example: 3.5, 187, 190.2. |
❓How to use this score
Analyze the purchasing cadence of your contacts and identify customers with unusually short or long order intervals to time your follow-up messages more effectively.
🔑 Key shopping periods
| Attribute name | SCORE_KEY_SHOPPING_PERIODS |
|---|---|
| What it measures | The special occasions or seasonal events during which a contact has made at least one purchase. |
| How it is calculated |
A shopping period is assigned to a contact if they placed at least one order within the corresponding date window. The date windows are:
|
| Time window | All time by default. Can be changed in Settings to last 3, 6, 12, or 24 months. |
| Output format | Multiple-choice attribute. One or several of: Valentine's Day, Easter, Halloween, Black Friday, Christmas. Empty if the contact has never purchased during any of these periods. |
❓How to use this score
Re-engage contacts who purchased during a specific seasonal event by targeting them ahead of the same event the following year.
🛒 Ordering behavior
| Attribute name | SCORE_ORDERING_BEHAVIOUR |
|---|---|
| What it measures | A contact's ordering pattern, used to detect trends and predict future purchases. |
| How it is calculated | Each contact is assigned to a category based on their buying activity over time. |
| Time window | All time by default. Can be changed in Settings to last 3, 6, 12, or 24 months. |
| Output format | Category attribute. From most to least active: Orders consistently → Ordered recently → About to order → Late order → Very late order → No recent order → Ordered once → Never ordered. |
❓How to use this score
Trigger an automated win-back campaign when a contact's score drops to Very late order.
📈 Recency, Frequency, and Monetary (RFM)
| Attribute name | SCORE_RECENCY_FREQUENCY_MONETARY |
|---|---|
| What it measures | A contact's engagement based on how recently they purchased (Recency), how often they buy (Frequency), and how much they spend (Monetary). |
| How it is calculated |
Uses quantile scoring (NTILE(6)) across your entire contact base. Each dimension is scored 1–6 (1 = best, 6 = lowest):
The RFM category is relative: a contact's category can change if their own behavior changes, or if the overall behavior of your customer base shifts. |
| Time window | All time by default, relative to your entire contact base. Can be changed in Settings to last 3, 6, 12, or 24 months. |
| Output format | Category attribute. From best to lowest: Champion → Loyal customer → Potential loyalist → Promising customer → Needs attention → At risk → About to sleep → Lost → Prospect. |
The following table shows the exact Recency (R) and FM quantile combinations that determine each segment. Scores range from 1 (best) to 6 (lowest).
| Segment | Recency (R) | FM combined score |
|---|---|---|
| Champion | 5 – 6 | 5 – 6 |
| Loyal customer | 3 – 6 | 4 – 6 |
| Potential loyalist | 4 – 6 | 2 – 3 |
| Promising customer | 4 – 6 | 1 |
| Needs attention | 3 | 3 |
| About to sleep | 3 | 1 – 2 |
| At risk | 1 – 2 | 3 – 6 |
| Lost | 1 – 2 | 1 – 2 (all other combinations) |
| Prospect | — | No order recorded |
❓How to use this score
Add personalized messages or exclusive perks to campaigns targeting Champion or Loyal customer contacts.
💰 Customer lifetime value (CLV)
| Attribute name | SCORE_CUSTOMER_LIFETIME_VALUE |
|---|---|
| What it measures | The estimated total amount a contact is likely to spend over their entire relationship with your company. |
| How it is calculated |
CLV = contact's average order value × average number of orders per customer (all contacts) × average customer lifespan (all contacts) The average customer lifespan is the average time between first and last order across your entire customer base. Contacts with only one order have a lifespan of 0, which results in a CLV of 0. |
| Time window |
All time by default, relative to your entire contact base. Can be changed in Settings to last 3, 6, 12, or 24 months. All time by default. Global averages are computed across your entire customer base. Can be changed in Settings to last 3, 6, 12, or 24 months. |
| Output format | Numeric value (monetary amount, no specific currency). |
❓How to use this score
Segment contacts who have a high CLV and send them VIP or loyalty campaign offers.
🔮 Discover predictive scores
In addition to the five default scores, Brevo offers three predictive scores that use a statistical model to forecast future purchase behavior. These scores require e-commerce data in your Brevo account and are available on Professional, new Enterprise, and legacy CDP plans.
⛔️ Predictive churn risk
| Attribute name | SCORE_PREDICTIVE_CHURN_RISK |
|---|---|
| What it measures | The likelihood that a contact will stop engaging with your business. Churn is defined broadly as the end of the purchasing relationship, not based on subscription cancellation. |
| How it is calculated |
Churn risk = 1 − probability of the contact being still active, given their recency, frequency and the time since their last purchase The model simultaneously estimates each customer's purchase rate and the probability they have silently churned, based on their order history. |
| Time window | Based on full order history. The score reflects the current probability of churn at the time of computation. |
| Output format | Numeric value between 0 and 1 (probability). Example: 0.85 = high churn risk, 0.12 = low churn risk. |
❓How to use this score
Identify contacts with a high churn risk score and target them with a retention campaign or use it as an automation trigger.
🔢 Predicted number of purchases
| Attribute name | SCORE_PREDICTIVE_PURCHASES_EXPECTED |
|---|---|
| What it measures | The expected number of purchases a contact will make in the next 12 months. |
| How it is calculated | Predicted purchases = expected number of purchases in next 365 days given their purchase recency, frequency, and the time since their last purchase. The model estimates the purchase rate based on the contact's full order history. |
| Time window | Forecast horizon: next 12 months. Based on full order history as input. |
| Output format | Numeric value (decimal). Example: 2.86 = approximately 3 purchases expected in the next 12 months. |
❓How to use this score
Identify contacts with a low predicted purchase count and re-engage them before they go silent.
💸 Predictive CLV
| Attribute name | SCORE_PREDICTIVE_CLV |
|---|---|
| What it measures | The forecasted future monetary value a contact will generate over the next 12 months. |
| How it is calculated |
Predictive CLV = expected average order value × expected number of purchases in next 365 days For contacts with only one order, the observed average order value is used as a fallback instead of a model estimate. |
| Time window | Forecast horizon: next 12 months. Based on full order history as input. |
| Output format | Numeric value (monetary amount, no specific currency). |
❓How to use this score
Prioritize high-value contacts in your campaigns by segmenting on contacts with a predictive CLV above a threshold you define.
📝 Use scores in Brevo
By default, all scores are calculated using your complete order history. You can customize the time window for each score individually from Settings > Contacts > Scores:
The available options are: last 3 months, last 6 months, last 12 months, last 24 months, or all time.
Scores behave like standard contact attributes, which means you can:
- Segment contacts based on score values (e.g., contacts with Key shopping periods = Black Friday).
- Trigger automations when a score changes (e.g., send a reactivation email when Ordering behavior = Very late order).
- Personalize campaign content based on scores (e.g., highlight premium products for contacts with a high CLV).
⏭️ 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
🤔 Have a question?
If you have a question, feel free to contact our support team by creating a ticket from your account. If you don't have an account yet, you can contact us here.
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