Our Vanilla Out-of-the-Box solution delivers a consistent data structure tailored for E-commerce operations. Designed for marketers, it enables easy segmentation and personalized campaigns. Below, you will find detailed information on the output attributes for the E-commerce vertical.

What Data Should You Send?

To unlock powerful segmentation, predictive insights, and personalized campaigns for your shoppers, Optimove requires specific data about your customers and their activities. Providing accurate and complete data ensures you can leverage the full range of attributes—like demographic details, purchasing behaviors, and predictive scores—described in this article. The following data types are essential:

• Customers: Information about your shoppers, such as age, country, and contact preferences, used to build personalized profiles and target specific audiences.
• Orders: High-level information for each transaction, including the order date, total amount, and customer ID, which is critical for tracking purchase history.
• Orders Items: Details of the specific products included in each order (line items), such as the item ID and price, which are used to analyze product-level behavior.
• Items Catalog: Information about all the products you sell, including brand, category, and name, enabling personalized product recommendations and promotions.

Properly preparing these data sets allows Optimove to deliver the actionable insights needed to convert New shoppers, retain Active customers, and reactivate Churned shoppers effectively. See Prepare your data for Optimove for more information.

What You Will See in Your Instance

Our solution provides a rich set of attributes to help you understand and engage your customers effectively. Below, each category of attributes is detailed with practical use cases and comprehensive tables.

Demographic Attributes

Demographic attributes provide essential information about your customers’ identities and locations, enabling personalized marketing and segmentation. For example:

• An online retailer uses the Country attribute to segment customers by location, allowing them to tailor marketing campaigns with region-specific promotions and shipping offers.

Demographic Attributes

Attribute Name	Description	Source Table
Customer ID	A unique identifier for each customer.	Customers
Email	The customer's email address.	Customers
First Name	The customer's first name.	Customers
Last Name	The customer's last name.	Customers
Mobile Number	The customer's mobile phone number.	Customers
Date of Birth	The customer's date of birth.	Customers
Age	The customer's age, calculated from their date of birth.	Customers
Gender	The customer's gender.	Customers
Country	The country where the customer is located.	Customers
City	The city where the customer is located.	Customers
Address	The customer's physical address.	Customers
Language	The customer's preferred language.	Customers
Registration Date	The date the customer first registered an account.	Customers
Currency	The currency used for the customer's transactions.	Customers
Registered Platform	The platform or device used by the customer to register (e.g., iOS, Android, Web).	Customers

Consent Attributes

The consent-related attributes track a customer's communication preferences and verification statuses, ensuring compliance and effective campaign delivery. For example:

• A company utilizes the ALLOW_EMAIL attribute to determine which customers have opted in for email communication, allowing them to send personalized marketing emails only to customers who have explicitly consented, ensuring compliance with privacy regulations and improving email campaign effectiveness.

Consent Attributes

Attribute Name	Description	Source Table
Allow Email	Indicates if the customer has consented to receive marketing communications via email.	Customers
Allow Push	Indicates if the customer has consented to receive push notifications.	Customers
Allow SMS	Indicates if the customer has consented to receive marketing communications via SMS.	Customers
Is Blocked*	Indicates if the customer's account is blocked or suspended.	Customers
Is Email Verified	Indicates if the customer has verified their email address.	Customers
Is Optin	A general indicator that the customer has opted into at least one marketing channel.	Customers
Is SMS Verified	Indicates if the customer has verified their mobile number.	Customers
Is Test*	Indicates if the customer account is a test account used for internal purposes.	Customers

Please Note: The Is_OptIn attribute acts as a general consent indicator for receiving marketing messages. It is used to query or segment customers who have opted into at least one communication channel, enabling flexible targeting of those with active marketing consent. For example, if a customer opts into email but not push notifications, their attributes would show Allow_Push = 'No', Allow_Email = 'Yes', and Is_OptIn = 'Yes'.

Attributes marked with * are not specific to channel consent but can be used to exclude customers ineligible for marketing communications.

Please Note: As consent-related attributes are provided alongside other customer data, you are responsible for managing the associated logic, rules, and maintenance.

Calculated Attributes

Calculated attributes track customers’ purchasing behaviors, providing insights for targeting high-value shoppers and optimizing campaigns. These attributes are calculated over the following snapshot periods:

• Lifetime: Daily aggregated data over the entire customer history.
• 2 Weeks: Daily aggregated data calculated based on the last two weeks' data.
• 1 Month: Daily aggregated data calculated based on the last month’s data.
• 3 Months: Daily aggregated data calculated based on the last three months' data.
• 1 Year: Daily aggregated data calculated based on the last year’s data.

For example:

• A company uses the AVERAGE_ORDER_AMOUNT to analyze customer spending behavior, helping them identify high-value customers and tailor VIP offers to improve retention and encourage more frequent purchases.

Frequency Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Number of Discounted Items, Lifetime*	The total count of individual items a customer has purchased at a discounted price.	Order_Items	Lifetime
Number of Discounted Orders, Lifetime*	The total number of orders that contained at least one discounted item.	Orders, Order_Items	1 Month, 6 Months, 1 Year
Number of Items, Lifetime*	The total number of individual items purchased by the customer.	Orders, Order_Items	1 Month, 6 Months, 1 Year
Number of Order Days, Lifetime*	The number of distinct days on which the customer placed an order.	Orders	1 Month, 6 Months, 1 Year
Number of Orders, Lifetime*	The total number of orders placed by the customer.	Orders	1 Month, 6 Months, 1 Year
Number of Refunded Orders, Lifetime*	The total number of orders that have been refunded.	Orders, Order_Items	1 Month, 6 Months, 1 Year
Number of Returned Items, Lifetime*	The total number of individual items returned by the customer.	Orders, Order_Items	1 Year
Number of Store Orders, Lifetime*	The total number of orders placed via a physical store.	Orders	Lifetime
Number of Times Churned, Lifetime	The number of times the customer has entered a 'Churn' lifecycle stage.	Internal Tables	Lifetime
Number of Web Orders, Lifetime*	The total number of orders placed online.	Customers, Orders	Lifetime
Order Activity	A categorical representation of the customer's order frequency.	Internal Tables	1 Year
Orders With Discounts	A flag indicating if the customer has ever used a discount.	Orders	6 Months, 1 Year
Orders With Returns	A flag indicating if the customer has ever returned an order.	Orders	6 Months, 1 Year
Return Activity	A categorical representation of the customer's return frequency.	Internal Tables	1 Year
Discount Activity, One Year	A categorical representation of the customer's discount usage over the past year.	Internal Tables	Lifetime
Frequency, One Year	The average number of days between orders over the past year.	Internal Tables	6 Months, 1 Year

Monetary Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Average Item Amount, Lifetime	The average monetary value per item a customer has purchased.	Orders	6 Months, 1 Year
Average Number of Items Per Order, Lifetime	The average quantity of items included in each of the customer's orders.	Orders, Order_Items	6 Months, 1 Year
Average Order Amount, Lifetime	The average monetary value of a customer's orders.	Orders	6 Months, 1 Year
Discount Ratio, Lifetime	The percentage of a customer's total spending that was covered by discounts.	Order_Items	6 Months, 1 Year
Return Ratio, Lifetime	The percentage of a customer's purchased items that have been returned.	Order_Items	6 Months, 1 Year
Total Discount Amount, Lifetime*	The total monetary value of all discounts applied to the customer's orders.	Orders	1 Month, 6 Months, 1 Year
Total Gross Sales Amount, Lifetime*	The total revenue from a customer's orders before discounts and returns.	Orders	1 Month
Total Net Sales Amount, Lifetime*	The total revenue from a customer's orders after accounting for discounts and returns.	Orders	1 Month
Total Order Amount, Lifetime*	The total monetary value of all orders placed by the customer.	Orders	1 Month, 6 Months, 1 Year
Total Refund Amount, Lifetime*	The total monetary value of all refunds issued to the customer.	Orders	1 Month, 6 Months, 1 Year
Favorite Product Category	The product category the customer has spent the most money on.	Internal Tables	Lifetime

Recency Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Days Since Registration	The number of days that have passed since the customer registered.	Customers	Lifetime
Days Since Last Login	The number of days that have passed since the customer last logged in.	Customers	Lifetime
Days In Churn, Lifetime	The total number of days the customer has spent in the 'Churn' lifecycle stage.	Internal Tables	Lifetime
Days Since First Order	The number of days that have passed since the customer's first order.	Order_Items	Lifetime
Days Since Last Item Return	The number of days that have passed since the customer last returned an item.	Order_Items	Lifetime
Days Since Last Order	The number of days that have passed since the customer's last order.	Orders	Lifetime
First Order Date	The date of the customer's first order.	Orders	Lifetime
Last Order Date	The date of the customer's most recent order.	Orders	Lifetime
Last Login Date	The date of the customer's most recent login.	Customers	Lifetime
Time Since Registration	A category representing the time since the customer registered (e.g., 'Up to 1 Month').	Customers	Lifetime

Note: Attributes marked with * are part of the Activity History feature.

Lifecycle Stage (LCS) Attributes

Lifecycle Stage attributes track the progression of customers through different phases of their journey with your brand, enabling tailored retention and engagement strategies. For example:

• An e-commerce company uses the Previous Lifecycle Stage attribute to identify customers who have moved from "Active" to "Churn" and targets them with a personalized "We Miss You" campaign featuring products related to their past purchases.

Lifecycle Stage (LCS) Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Dormant Type	Indicates if a dormant customer was previously a purchaser or only registered.	Internal Tables	Lifetime
Order Activity Percentile By Lcs	The customer's rank (percentile) based on their number of orders, compared only to other customers in the same lifecycle stage.	Internal Tables	1 Year
Order Amount Percentile By Lifecyclestage	The customer's rank (percentile) based on their total order amount, compared only to other customers in the same lifecycle stage.	Internal Tables	6 Months, 1 Year
Previous Lifecycle Stage	The lifecycle stage the customer was in immediately before their current one.	Internal Tables	6 Months
Lifecycle Stage	The customer's current lifecycle stage (e.g., New, Active, Churn).	Internal Tables	Lifetime

Predictive Model Attributes

Predictive model attributes forecast customer behaviors, helping you prioritize retention and conversion efforts for maximum impact. For example:

• A company uses the CHURN_PROBABILITY_SCORE to identify active customers who are at high risk of churning and proactively targets them with a special discount to encourage their next purchase.

Predictive Model Attributes

Attribute Name	Description	Source Table
Churn Probability Score	The probability that this customer will move into a 'Churn' lifecycle stage within the next two periods.	Internal Tables
Rank in Churn Probability	The customer's churn probability score ranked against all other customers, on a scale of 1 (lowest risk) to 100 (highest risk).	Internal Tables
Rank in LCS - Churn Probability	The customer's churn probability score ranked against only those customers in the same lifecycle stage.	Internal Tables
Conversion Probability Score	The probability that a non-purchasing customer will make their first order (convert) within the next two periods.	Internal Tables
Rank in Conversion Probability	The customer's conversion probability score ranked against all other non-purchasing customers, on a scale of 1 (lowest probability) to 100 (highest probability).	Internal Tables
Is Top Spender?	A flag (Yes/No) indicating if the customer is currently considered a top spender based on their order activity.	Internal Tables
Becoming Top Spender Score	The probability that this customer will become a top spender within the next six periods.	Internal Tables
Random Customer Percentage	A randomly assigned percentile (1-100) for each customer, used for creating random control groups for A/B testing campaigns.	Internal Tables
Reactivation Probability Score	The probability that a churned customer will become active again within the next two periods.	Internal Tables
Rank in Reactivation Probability	The customer's reactivation score ranked against all other churned customers, on a scale of 1 (lowest probability) to 100 (highest probability).	Internal Tables
Rank in Becoming Top Spender	The customer's 'Becoming Top Spender' score ranked against all other customers, on a scale of 1 (lowest probability) to 100 (highest probability).	Internal Tables
Rank by LCS Becoming Top Spender	The customer's 'Becoming Top Spender' score ranked against only those customers in the same lifecycle stage.	Internal Tables

Product Attributes – Purchase History

Product attributes detail customers’ purchasing preferences, allowing for tailored cross-sell and up-sell promotions based on their favorite products. For example:

• A company uses the Category attribute to identify customers who frequently purchase running shoes and targets them with a campaign for new arrivals in that category.

Product Attributes – Purchase History

Attribute Name	Description	Source Table
Brand	The brand name of the purchased item.	Product History
Category	The category of the purchased item.	Product History
Department	The department of the purchased item.	Product History
Item_Name	The name of the purchased item.	Product History

Out-of-the-box Campaign KPIs

Out-of-the-box (OOTB) Campaign KPIs measure the success of your marketing campaigns by comparing the performance of targeted customer groups against a control group. These metrics help you evaluate how campaigns drive orders and revenue, enabling data-driven optimization.

• Total Order Amount: The total monetary value of all orders, measuring the direct revenue impact of a campaign.
• Number of Order Days: The number of distinct days on which customers placed orders, indicating a campaign's effect on purchase frequency.
• Number of Orders: The total count of all orders placed, showing a campaign's ability to drive transactions.
• Total Net Sales Amount: The total revenue from sales after accounting for discounts and returns, highlighting a campaign's true profitability.

Understanding Your Customers: Grouping and Predicting Behavior (Segmentation Model)

To help you connect with your customers in the best way, we organize them into groups based on their actions—like whether they’ve ever placed an order, how often they purchase, or when they were last active on your site. This process is called Segmentation. We also use these groups to predict what customers might do next, like if they are likely to make their first purchase or stop buying from you. Let’s break it down into Customer Groups and Behavior Predictions.

Customer Groups: Organizing Shoppers by Their Journey

Imagine you’re running an online store. You’d want to group your shoppers based on whether they're just browsing, new buyers, or loyal, repeat customers. We do the same with your customers, organizing them into groups based on where they are in their journey with your brand. We call these groups Lifecycle Stages (LCS), and we look at different details for each stage to understand them better.

Here’s how we group customers at each stage of their journey:

1. Registered Only

These are users who have created an account but have not yet made their first purchase. We group them by how long it's been since they registered.

Example: A customer who registered a week ago but hasn't purchased anything could receive an automated welcome email with a 10% discount to encourage their first order.

2. New

These are recent first-time buyers. We group them by their order activity and value to encourage them to make a second purchase and become loyal customers.

Example: A new customer who made their first purchase last week could receive a follow-up email showcasing products related to their first order, along with a "free shipping on your next purchase" offer.

3. Active

These are repeat customers who have purchased recently. We group them by their long-term value and recent activity to keep them engaged and maximize their lifetime value.

Example: An active customer in the top 10% for order value who hasn't purchased in over a month could receive an exclusive "VIP early access" notification for an upcoming sale.

4. Churn

These are customers who have not purchased in a while and are at risk of becoming permanently inactive. We group them by their past value and time since their last order to target them with reactivation campaigns.

Example: A churned customer who was previously a high-value shopper could receive a "We've missed you!" email with a significant, limited-time discount (e.g., 25% off) to entice them back.

5. Dormant

These are customers who have been inactive for a very long time (over a year for purchasers, or over 6 months for registered-only users).

Example: A dormant customer who used to purchase might be targeted in a major seasonal campaign (e.g., Black Friday) with a high-value offer to try and win them back.

Please note that the out-of-the-box customer model excludes customers who haven’t been active or registered for over 3 years.

Why This Matters for the E-commerce Vertical

By grouping your customers and predicting their behavior, you can create personalized experiences that make them feel valued. Whether it’s offering a first-purchase discount to a new user, rewarding a loyal customer with VIP perks, or winning back someone who hasn’t ordered in a while, these tools help you connect with your customers in the right way at the right time—driving both revenue and brand loyalty.

Want to learn more? Check out this guide: The Optimove Model.