Welcome! In this video, we’ll take a deep dive into the data foundation behind Optimove's powerful capabilities and show you how your raw data is transformed into actionable insights.

Your raw data is provided to Optimove via a batch data process and real-time event streaming (SDK or server side). This raw data is mapped to various data structures within Optimove, serving as the foundation for its advanced segmentation, analytics, and personalization capabilities.

1. Single Customer View

The first data structure is the Single Customer View (also known as Customer 360), which represents an aggregated profile of each customer, encompassing demographic, behavioral, and predictive attributes. These customer attributes range from gender, age, and registration date to the number of purchases, average purchase amount, favorite brand, risk of churn, and predicted future value.

Let’s look at the data flow:

This is raw data from an eCommerce health & beauty brand, specifically their Customers table. It includes a unique Customer ID, along with static demographic information and general attributes related to their loyalty program details. This structure is already flattened and aggregated at the customer level, and it is ingested into Optimove as-is.

This data is available for you in various reports in Optimove. You can leverage these attributes when building your audience using the Customer Attribute Selection Criteria.

Additionally, these attributes can be used as personalization tags in your email or push messaging.

Another type of customer attribute includes generated attributes derived from your raw data. These can be behavioral and predictive attributes, ranging from simple aggregations to more advanced metrics such as preferences, percentages, ratios, predictive scores, and much more. Some will be in our standard schema and others will be custom for you.

Here is an example from our Health & Beauty brand. This dataset includes fields such as Customer ID, date, Order ID, and Purchase Amount.

From this table, we can calculate attributes like the number of Orders, days since the last order, number of items purchased (over the last year, week, etc.), favorite brand, and much more. These behavioral attributes will also appear in your customer attributes list and can be used for segmentation, targeting, analytics, and personalization.

Note that you can update attributes in real-time by using SDK events or server-side events via the data studio.

2. Activity History

The second data structure is called 'Activity History,' and it aggregates data by activity, per day, for each customer. An activity can be defined as a specific metric, such as purchase amount, number of items purchased, or number of logins. Optimove transforms your data to create a table in which each record in this structure summarizes the daily activity by selected metrics.

The Activity History data structure offers similar insights but with greater granularity and flexibility. It aggregates data not only at the customer level but also by specific dates, providing various time frame options, such as the first of each month, the last calendar month, and more. You can apply functions like average, maximum, and total. Using this table, you can build an audience of customers who placed more than one order this month or those who spent over $500 this year, for example.

3. Purchase History

This structure tracks customers' historical interactions with your products by aggregating daily engagement per product.

When combined with your Product Catalog, it opens up opportunities for personalization based on customer historical purchases.

Targeting cross-sell, up-sell offers, and recommendations based on historical purchase patterns has consistently proven to increase customer spending and loyalty.

Consider the following use cases:

• Customers who purchased L’Oreal items over the past year with a total spend over $200.
• Customers who purchased at least one item from the Hair Care category in the last six months.

These are just a few examples of the endless creative strategies you can gain from this dataset.

4. Web & App Activity

This is an event-based dataset tracking user interactions on your website or app, such as page visits or specific pixel triggers (e.g., adding items to a cart).

The data is ingested into Optimove via SDK or server-side events. In the settings area, you can view and manage the events configured in your instance.

The event names you see here correspond to the activity types used in the Web & App Activity selection criteria for segmentation. Optimove aggregates your raw event data by customer, activity type, and date, providing a structured dataset that enables precise segmentation based on user engagement with your website or app.

This adds an entirely new dimension by including browsing activity, unlocking a wide range of personalization options, including the ability to target visitors based on their behavior.

For example:

• Customers who viewed a specific product category but never made a purchase.
• Inactive customers who visited your site.

We hope this gives you a clearer understanding of how your raw data is mapped into Optimove, and the wide opportunities it opens for tailored segmentation and targeting. Thank you for watching!

Welcome! In this video, we’ll take a deep dive into the data foundation behind Optimove's powerful capabilities and show you how your raw data is transformed into actionable insights.

Your raw data is provided to Optimove via a batch data process and real-time event streaming (SDK or server-side). This raw data is mapped to various data structures within Optimove, serving as the foundation for its advanced segmentation, analytics, and personalization capabilities.

1. Single Customer View

The first data structure is the Single Customer View (also known as Customer 360), which represents an aggregated profile of each customer, encompassing demographic, behavioral, and predictive attributes. These customer attributes range from gender, age, and registration date to the number of deposits, average deposit amount, favorite game, risk of churn, and predicted future value.

Let’s look at the data flow:

This is raw data from a gaming operator brand, specifically their customers' table. It includes a unique Player ID, along with static demographic information and general attributes and contact information. This structure is already flattened and aggregated at the customer level, and it is ingested into Optimove as-is.

This data is available for you in various reports in Optimove. You can leverage these attributes when building your audience using the Customer Attribute Selection Criteria.

Additionally, these attributes can be used as personalization tags in your email or push messaging.

Another type of customer attribute includes generated attributes derived from your raw data. These can be behavioral and predictive attributes, ranging from simple aggregations to more advanced metrics such as preferences, percentages, ratios, predictive scores, and much more. Some will be in our standard schema and others will be custom for you.

Here is an example from our Gaming Operator brand. This dataset includes fields such as Player ID, date, Platform, Game ID, Bet Amount, and Net Gaming Revenue.

From this table, we can calculate attributes like the number of bets, time since the first deposit, total bets (over the last year, week, etc.), favorite platform, and much more. These behavioral attributes will also appear in your customer attributes list and can be used for segmentation, targeting, analytics, and personalization.

Note that you can update attributes in real-time by using SDK events or server-side events via the data studio.

2. Activity History

The second data structure is called 'Activity History,' and it aggregates data by activity, per day, for each customer. An activity can be defined as a specific metric, such as bet amount, number of bets placed, or net revenue. Optimove transforms your data from this to this and creates a table in which each record in this structure summarizes the daily activity by selected metrics.

The Activity History data structure offers similar insights to attributes but with greater granularity and flexibility. It aggregates data not only at the player level but also by specific dates, providing various time frame options, such as the first of each month, the last calendar month, and more. You can apply functions like average, maximum, and total. Using this table, you can build an audience of players who placed more than five sports bets this month or those who placed a total of Sport Revenue over 500 EUR this year, for example.

3. Game History

This structure tracks customers' historical interactions with your products by aggregating daily engagement per game.

When combined with your Games Catalog, it opens up opportunities for personalization based on players’ historical engagement.

Targeting cross-sell, up-sell offers, and recommendations based on historical game patterns has consistently proven to increase customer spending and loyalty.

Consider the following use cases:

1. Players who placed a bet on Poker within the last 90 days.

2. Players who placed casino bets in Roulette during the last month.

These are just a few examples of the endless creative strategies you can gain from this data set.

4. Web & App Activity

This is an event-based dataset tracking user interactions on your website or app, such as page visits or specific pixel triggers (e.g., adding items to a cart).

The data is ingested into Optimove via SDK or server-side events. In the settings area, you can view and manage the events configured in your instance.

The event names you see here correspond to the activity types used in the Web & App Activity selection criteria for segmentation. Optimove aggregates your raw event data by player, activity type, and date, providing a structured dataset that enables precise segmentation based on user engagement with your website or app.

This adds an entirely new dimension by including browsing activity, unlocking a wide range of personalization options, including the ability to target visitors based on their behavior.

As you can see on the right side, when we click on an item on the website, it triggers an event set, displaying all the relevant information right there within the event itself. This data is then sent to Optimove directly from the website, enabling various use cases.

For example:

1. Players who viewed a specific live game page but never placed a bet (by adding Game History-based criteria).

2. Reactivate customers who visited your site (by adding criteria where Lifecycle Stage = Reactive).

We hope this gives you a clearer understanding of how your raw data is mapped into Optimove, and the wide opportunities it opens for tailored segmentation and targeting. Thank you for watching!