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

What Data Should You Send?

To unlock powerful segmentation, predictive insights, and personalized campaigns for your traders, 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, trading behaviors, and predictive scores—described in this article. The following data types are essential:

• Customers: Information about your traders, such as age, country, and contact preferences, used to build personalized profiles and target specific audiences.
• Transactions: Records of deposits, withdrawals, and bonuses, critical for calculating financial metrics like average deposit amounts and identifying high-value traders.
• Assets: Details about the financial instruments available for trading, such as currency pairs or commodities, enabling promotions based on traders' preferences.
• Positions: Data on traders' trading activity, including invested amounts and leverage, which drives insights into trading patterns and engagement levels.

Properly preparing these data sets allows Optimove to deliver the actionable insights needed to engage New Traders, retain Active Traders, and reactivate Churned Traders 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 traders effectively. Below, each category of attributes is detailed with practical use cases and comprehensive tables.

Demographic Attributes

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

• A trading company uses the AGE attribute from the Customers table to segment traders into different age groups and tailor targeted educational content that increases engagement and retention.

Demographic Attributes

Attribute Name	Description	Source Table
Address	Address	Customers
Age	Calculated by The DATEOFBIRTH column from TRADERS table	Customers
City	The CITY column from TRADERS table	Customers
Country	The COUNTRY column from TRADERS table	Customers
Currency	The CURRENCY column from TRADERS table	Customers
Date of Birth	The DATEOFBIRTH column from TRADERS table	Customers
Email	The EMAIL column from TRADERS table	Customers
First Name	The FIRSTNAME column from TRADERS table	Customers
Gender	The GENDER column from TRADERS table	Customers
Language	The LANGUAGE column from TRADERS table	Customers
Last Name	The LASTNAME column from TRADERS table	Customers
Mobile Number	The MOBILENUMBER column from TRADERS table	Customers
Referral Type	The INITCAP(REFERRALTYPE) column from TRADERS table	Customers
Registered Platform	Registered platform	Customers
Registration Date	The REGISTEREDDATE column from TRADERS table	Customers

Consent Attributes

The consent-related attributes track a customer's communication preferences and verification statuses across channels, including email, SMS, and push, as well as flags for opt-in status, test accounts, and general eligibility—all sourced from the Customers table. A kickoff meeting will be held for each channel to provide specific guidance and ensure proper setup based on the channels in use. 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	The ISOPTINEMAIL column from TRADERS table	Customers
Allow Push	The ISOPTINPUSH column from TRADERS table	Customers
Allow SMS	The ISOPTINSMS column from TRADERS table	Customers
Is Blocked*	The ISBLOCKED column from TRADERS table	Customers
Is Email Verified	The ISEMAILVERIFIED column from TRADERS table	Customers
Is Optin	The ISOPTIN column from TRADERS table	Customers
Is SMS Verified	The ISMOBILEVERIFIED column from TRADERS table	Customers
Is Test*	The ISTEST column from TRADERS table	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 traders’ financial and trading behaviors, providing insights for targeting high-value customers and optimizing campaigns. These attributes are categorized into Frequency Attributes, Monetary Attributes, and Recency Attributes, 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_DEPOSIT_AMOUNT to analyze customer deposit behavior over a lifetime, helping them identify high-value customers who consistently deposit large amounts, allowing them to tailor personalized offers, reward programs, or exclusive services to improve customer retention and encourage more frequent deposits.

Frequency Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Number of Activity Days, Lifetime*	Number of activity days, lifetime	Transactions,Positions	3 Months
Number of Closed Positions, Lifetime*	The total number of trading positions a customer has closed over their lifetime.	Positions	Lifetime
Number of Open Positions, Lifetime*	The total number of trading positions a customer currently has open.	Positions	Lifetime
Number of Positions Opened, Lifetime*	Number of records From POSITIONS table	Positions	2 Weeks, 3 Months
Number of Times Churned, Lifetime	Number of times which the customer has Churned	Internal Tables	Lifetime
Number of Trade Days, Lifetime*	Number of trade days, lifetime	Internal Tables	2 Weeks, 3 Months
Number of Bonus Granted, Lifetime*	Number of records From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Bonus'	Draws	1 Month
Number of Deposit Days, Lifetime*	Number of distinct TRANSACTIONDATE records From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Transactions	2 Weeks, 3 Months
Number of Deposits, Lifetime*	Number of records From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Transactions	2 Weeks, 3 Months
Number of Withdrawal Days, Lifetime*	Number of distinct TRANSACTIONDATE records From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Withdrawal'	Transactions	Lifetime
Number of Withdrawals, Lifetime*	Number of records From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Withdrawal'	Transactions	Lifetime
Frequency, Last Three Months	Days difference between first and last activity divided by (number of activity days minus 1) in the last three months	Internal Tables	3 Months
Deposit Days Since Reactivated	Deposit days since reactivated	Internal Tables	Lifetime
Deposit Activity Categorical, Last Two Weeks	Deposit activity categorical, last two weeks	Internal Tables	2 Weeks
Frequency Categorical, Last Three Months	Frequency categorical, last three months	Internal Tables	3 Months

Monetary Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Average Deposit Amount, Lifetime	The Total Deposit Amount, Lifetime divided by the Number of Deposits, Lifetime	Transactions	1 Month, 3 Months
Average Monthly Deposit Amount, Lifetime	Average monthly deposit amount, lifetime	Transactions	Lifetime
Bonus Deposit Ratio, Lifetime	The Total Bonus Granted Amount, Lifetime divided by the Total Deposit Amount, Lifetime	Transactions	1 Month, 3 Months
Cashout Ratio, Lifetime	The Total Withdrawal Amount, Lifetime divided by the Total Deposit Amount, Lifetime	Internal Tables	1 Month, 3 Months
Deposit Amount Monthly Change, Lifetime	Deposit amount monthly change, lifetime	Internal Tables	Lifetime
Average Daily Volume, Lifetime	The Total Volume, Lifetime divided by the Number of Trade Days, Lifetime	Internal Tables	2 Weeks, 3 Months
Average Leverage, Lifetime	The Total Leverage, Lifetime divided by the Number of Positions Opened, Lifetime	Internal Tables	2 Weeks, 3 Months
Customer Profit, Lifetime*	The Sum of CUSTOMERPROFIT From POSITIONS table	Internal Tables	Lifetime
Net Revenue, Lifetime*	The Sum of NETREVENUE From POSITIONS table	Positions	2 Weeks, 3 Months
Total Leverage, Lifetime*	The Sum of LEVERAGE From POSITIONS table	Positions	2 Weeks, 3 Months
Total Volume, Lifetime*	The Sum of INVESTEDAMOUNT From POSITIONS table	Internal Tables	2 Weeks, 3 Months
Volume Percentile 05, Lifetime	Volume percentile 05, lifetime	Internal Tables	Lifetime
Net Cash, Lifetime*	Total Deposit Amount, Lifetime Minus Total Withdrawal Amount, Lifetime	Internal Tables	2 Weeks
Total Bonus Granted Amount, Lifetime*	The Sum of TRANSACTIONAMOUNT From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Bonus'	Positions	3 Months
Total Deposit Amount, Lifetime*	The Sum of TRANSACTIONAMOUNT From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Transactions	2 Weeks, 3 Months
Total Withdrawal Amount, Lifetime*	The Sum of TRANSACTIONAMOUNT From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Withdrawal'	Transactions	2 Weeks, 3 Months
Deposit Amount Since Reactivated	Deposit amount since reactivated	Internal Tables	Lifetime
First Deposit Amount, Lifetime	The first TRANSACTIONAMOUNT From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Transactions	Lifetime
Favorite Asset Category	The favorite ASSETCATEGORY From POSITIONS based on The Sum of INVESTEDAMOUNT	Internal Tables	3 Months
Favorite Asset Name	The favorite ASSETNAME From POSITIONS based on The Sum of INVESTEDAMOUNT	Internal Tables	Lifetime
Last Deposit Amount, Lifetime	The last TRANSACTIONAMOUNT From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Transactions	Lifetime

Recency Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Days Since First Deposit	Days since first deposit	Transactions	Lifetime
Days Since Last Deposit	Days since last deposit	Transactions	Lifetime
Days Since Last Login	Days since last login	Customers	Lifetime
Days Since Last Withdrawal	Days since last withdrawal	Transactions	Lifetime
First Deposit Date	First TRANSACTIONDATE column From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Customers	Lifetime
Time Since First Deposit	Categorical attribute that distinguish between the following values: ('1 Day', '2 to 7 Days', 'More than 7 Days', 'No Deposits') based on Days Since First Deposit	Internal Tables	Lifetime
Days Since First Position Close Date	Days since first position close date	Positions	Lifetime
Days Since First Position Open Date	Days since first position open date	Positions	Lifetime
Days Since Last Activity	Days since last activity	Positions	Lifetime
Days Since Last Position Close Date	Days since last position close date	Positions	Lifetime
Days Since Last Position Open Date	Days since last position open date	Positions	Lifetime
Days Since Last Reactivation	Days since last reactivation	Internal Tables	Lifetime
Days Since Lifecycle Stage Change	Days since the customer migrated to the current Lifecycle Stage	Internal Tables	Lifetime
Last Activity Date	Last activity date	Positions, Transactions	Lifetime
Time Since Last Activity	Categorical attribute that distinguish between the following values: ('1 Day', '2 to 7 Days', 'More than 7 Days') based on Days Since Last Activity	Internal Tables	Lifetime
Last Deposit Date	Last TRANSACTIONDATE column From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Deposit'	Transactions	Lifetime
Last Login Date	The LASTLOGINDATE column from TRADERS table	Customers	Lifetime
Last Withdrawal Date	Last TRANSACTIONDATE column From TRANSACTIONS table Where STATUS is equal to 'Approved' and TRANSACTIONTYPE equal to 'Withdrawal'	Transactions	Lifetime
First Activity Date, Last Three Months	First activity date, last three months	Internal Tables	3 Months

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

Lifecycle Stage (LCS) Attributes

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

• A company uses the Lifecycle Stage Before Churn attribute to analyze behavior patterns of previously valuable traders, enabling targeted reactivation campaigns with compelling offers to bring them back.

Lifecycle Stage (LCS) Attributes

Attribute Name	Description	Source Table	Snapshot Periods
Days Since Registration	Days since registration	Customers	Lifetime
Deposit Percentile By Lifecycle Stage	Categorical attribute that divides the customers to percentiles within their Lifecycle Stage based on deposit amount	Internal Tables	2 Weeks, 3 Months
Deposits Since Reactivated	Deposits since reactivated	Internal Tables	Lifetime
Days In Churn, Lifetime	Number of days which the customer is in Churn	Internal Tables	Lifetime
Days In Reactivated, Lifetime	Number of days which the customer is in Reactivated	Internal Tables	Lifetime
Dormant Type	Categorical attribute that distinguishes between depositors and non-depositors customers	Internal Tables	Lifetime
Lifecycle Stage Before Churn	The Lifecycle Stage which the customer was before churned	Internal Tables	Lifetime
Number of Times Churned Categorical	Categorical attribute based on the number of times which the customer has Churned	Internal Tables	Lifetime
Previous Lifecycle stage	The Lifecycle Stage which the customer was before migrating to the current one	Internal Tables	Lifetime
Reactivation Action	The activity which reactivated the customer after being churned	Internal Tables	Lifetime
Time Since Last Activity Active	Categorical attribute that distinguish between the following values: ('1 Day', '2 to 7 Days', '8 to 14 Days', 'More than 14 Days') based on Days Since Last Activity	Internal Tables	Lifetime
Time Since Last Activity Churn	Categorical attribute that distinguish between the following values: ('Up to 1 Month', '1 to 3 Months', '3 to 6 Months', 'More than 6 Months') based on Days Since Last Activity	Internal Tables	Lifetime
Time Since Registration	Categorical attribute that distinguish between the following values: ('1 Day', '2 to 7 Days', '1 Week to 1 Month', '1 to 3 Months', 'More than 3 Months') based on Days Since Registration	Customers	Lifetime
Volume Percentile By Lifecycle Stage	Categorical attribute that divides the customers to percentiles within their Lifecycle Stage based on trade amount	Internal Tables	2 Weeks, 3 Months
Lifecycle Stage	The current Lifecycle Stage	Internal Tables	Lifetime
Months Since Registration	Months since registration	Internal Tables	Lifetime
Deposit Activity Reactivated, Last Two Weeks	Deposit activity reactivated, last two weeks	Internal Tables	2 Weeks

Predictive Model Attributes

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

• A company uses the CHURN_PROBABILITY_SCORE attribute to prioritize high-risk Active traders for retention campaigns, like offering bonus leverage or educational webinars, to prevent churn.

Predictive Model Attributes

Attribute Name	Description	Source Table
Churn Probability Score	Probability of this customer to migrate into Churn within the next 2 periods.	Internal Tables
Rank in Churn Probability	Rank among all live customers by Churn probability. On a scale of 1 (lowest) to 100 (highest).	Internal Tables
Rank in LCS - Churn Probability	Rank among same lifecycle stage customers by Churn probability.	Internal Tables
Conversion Probability Score	Probability of this customer to convert within the next 2 periods.	Internal Tables
Rank in Conversion Probability	Rank among all not-converted by Conversion Probability Score. On a scale of 1 (lowest) to 100 (highest).	Internal Tables
Is Top Spender?	Indicates a customer being currently a top spender.	Internal Tables
Becoming Top Spender Score	Probability of this customer to become a top spender within the next 6 periods.	Internal Tables
Random Customer Percentage	Random Customer Percentage	Internal Tables
Reactivation Probability Score	Probability of this customer to be reactivated within the next 2 periods.	Internal Tables
Rank in Reactivation Probability	Rank among all churned customers by Reactivation Probability Score. On a scale of 1 (lowest) to 100 (highest).	Internal Tables
Rank in Becoming Top Spender	Rank among all customers by Becoming Top Spender Score. On a scale of 1 (lowest) to 100 (highest).	Internal Tables
Rank by LCS Becoming Top Spender	Rank among same lifecycle stage by Becoming Top Spender Score. On a scale of 1 (lowest) to 100 (highest).	Internal Tables
Imported Customer Upload Date	Lead Upload Date	Internal Tables
Imported Customer File Name	Imported Customer File Name	Internal Tables

For more information regarding predictive metrics available within the Optimove instance, see the following articles:

• Top Spenders Predictive Model
• Segmenting Customers by Future Value

Product Attributes – Purchase History

Product attributes detail traders' preferences for specific assets, allowing for tailored promotions and content based on their trading history. For example:

• The brokerage uses the ASSET_CATEGORY attribute to identify traders who prefer trading cryptocurrencies and sends them targeted analysis and news on that market, increasing trading volume.

Product Attributes – Purchase History

Attribute Name	Description	Source Table
Asset Category	Asset Category from ASSETS table	Product History
Asset Name	Asset Name from ASSETS table	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 trader groups against a control group. These metrics help you evaluate how campaigns drive engagement, deposits, and revenue, enabling data-driven optimization.

• Number of Activity Days: Counts days with trader activity (e.g., deposits or trades), showing campaign impact on engagement.
• Number of Deposits: Tracks deposit frequency, indicating campaign success in encouraging financial commitment.
• Total Volume: Measures the total invested amount, reflecting campaign influence on trading volume.
• Total Deposit Amount: Sums all deposits made by traders, reflecting campaign effectiveness in driving financial activity.
• Net Revenue: Calculates net revenue from trading activities, highlighting campaign profitability.

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

To help you connect with your traders in the best way, we organize them into groups based on their actions—like how long it’s been since they last traded, how much they deposit, or their total trading volume. This process is called Segmentation. We also use these groups to predict what traders might do next, like whether they’ll keep trading or become inactive. Let’s break it down into two simple ideas: Trader Groups and Behavior Predictions.

Trader Groups: Organizing Traders by Their Journey

Imagine you’re running an exclusive trading club. You’d want to group your members based on whether they've ever deposited funds, how actively they trade, or how recently they last opened a position. We do the same with your traders, organizing them into groups based on where they are in their journey with your platform. We call these stages Lifecycle Stages (LCS), and we look at different details for each stage to understand them better.

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

1. Non-Depositors: Traders Who Registered but Haven’t Deposited

These traders have signed up for your platform but haven’t added money to their account yet. We group them by how long it’s been since they joined.

Example: A trader who registered yesterday but hasn't deposited could receive a "Get Started Trading" email offering a guide to the platform to encourage their first deposit.

2. New: Just Started Depositing and Trading

These are traders who recently added money to their account and started trading. We group them by their trading volume, deposit activity, and time since their first deposit and last activity.

Example: A new trader in the bottom 50% for trade volume might receive a push notification with a link to a webinar on basic trading strategies to build their confidence.

3. Active: Regular Traders

These are traders who regularly log in and trade on your platform. We group them by their recent activity, deposit and trading volume in the last 3 months, and how often they trade.

Example: An active trader who is in the top 10% for deposits might get an email inviting them to an exclusive Q&A with a market analyst to reward their loyalty.

4. Churn: Stopped Trading

These are traders who haven’t logged in or traded in a while. We group them by how long it’s been since their last activity, their past deposit and trade volumes, and what stage they were in before they stopped.

Example: A churned trader who was previously in the top tier for deposits—and who hasn’t been active in two months—may receive a “Market Opportunities You're Missing” email with a bonus on their next deposit.

5. Reactivated: Came Back After a Break

These are traders who stopped trading for a while but recently returned. We group them by their recent activity, deposits, and trade volume since coming back.

Example: A reactivated trader who made a deposit three days ago might get a push notification with a “Welcome Back” bonus to encourage their next trade.

6. Dormant: Inactive for a Long Time

These are traders who haven’t been active in a very long time, whether they’ve deposited before or just signed up. We group them by whether they have ever deposited.

Example: A dormant trader who previously deposited might get an email about a major market event, like an interest rate decision, with a special offer to get them trading again.

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

Why This Matters for the Forex Vertical

By grouping your traders and predicting their behavior, you can create personalized experiences that make them feel valued. Whether it’s offering a trading guide to a new customer, rewarding a loyal trader with exclusive market insights, or winning back someone who has been inactive, these tools help you connect with your traders in the right way at the right time—all through your platform.

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