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Online retailers are increasingly interested in aggregating order data to personalize their customer messaging, enhancing engagement and loyalty. By analyzing order data, retailers can gain valuable insights into customer preferences and buying behaviors. This granular understanding allows them to craft targeted marketing messages that resonate with individual interests and needs.

Personalized messaging driven by detailed order data not only improves the relevance of marketing campaigns but also fosters a deeper connection between the retailer and the customer. It enables the creation of more meaningful interactions, such as recommending complementary products, sending reminders for reorders, or offering personalized discounts. This approach not only enhances the customer experience by making them feel valued but also drives higher conversion rates and customer retention, ultimately contributing to long-term business success.

In this use case, we’ll show you how to make the most of order data to identify and segment customers based on purchase information. We will be walking through how to use the Timeline event rollup feature in BlueConic to aggregate order data. Specifically, we will show you how to identify the number of customers who have ordered shirts in the past 90 days and the total number of shirts ordered.

Configuration steps

1. To access Timeline event rollups, hover over the “More” tab in the main navigation menu and click Timeline events. Once on this page, a submenu appears and you can choose Timeline event rollups.
   
   • Note: If you do not see the option for Timeline event rollups, make sure you have the correct user permissions.
2. Now, to create a new timeline event rollup, click the green Add timeline event rollup button.
3. When the new timeline event rollup page opens, you will want to name your rollup and add a description. Here you can also optionally add labels or mark it as a favorite.
   
4. Once the new timeline event rollup has been created, you can begin configuring it by selecting the timeline event type and any additional conditions for the timeline events that you want to roll up. For this use case, you will want to choose the Timeline event type that stores your order data.
   
5. After choosing the order event type, you can add as many conditions as you would like to narrow down your timeline event selection. For this example, we want to aggregate orders that have been made in the last 90 days that contain a shirt. So, we want to create two conditions by clicking the Add condition button:
   • Event date/time is within last 90 days.
   • The “AND” operator is used here to connect all conditions. This makes it so that both conditions must be true to be included in the timeline event rollup. 
     • Note: You can add as many conditions as you would like to narrow down your timeline event rollup, but all conditions must be true for an event to be included.
   • Product SKU contains any (exact match) values. Here, enter the SKUs that are associated with any shirts you want to include in the rollup data, linked with an “OR” operator.
     
6. Now, a timeline event rollup will be created that will parse through order events looking for matches. So, the next step involves mapping this data to be stored in customer profiles. For this example, we want to store the date and time of any shirt orders placed in the last 90 days, along with the sum of shirts in that order, on the customer profile. To do so, you will need to click the Add mapping button and select the timeline event property values of the timeline events that meet the conditions to save them in profile properties as such:
   
   Note: The operator dropdown options in this step are automatically assigned based on property type (i.e., number, text, date, etc.)
7. Lastly, make sure your configuration is saved and your new timeline event rollup is switched to On to begin aggregating data.
   
   
   • Note: All calculations are executed in the background. To see information about the status of which profiles have been processed and which still have to be processed, expand the sidebar on the right of the screen.
     

Now what?

Now that you have a running Timeline event rollup for shirts ordered in the last 90 days, the possibilities of working with this data are endless. For example, suppose a significant portion of customers prefer a specific type of shirt. In that case, retailers can tailor their communications to highlight similar products, exclusive offers, or new arrivals that match these interests. Segments can also be created using this data to assign customers to various levels of loyalty or brand engagement. Additionally, once your Timeline event rollups populate new profile properties, these properties can be used to generate new insights on a dashboard.

For more resources on working with timeline event rollups, check out these articles:

• Timeline event rollups: CDP use cases
• Build segments with timeline event data
• FAQ: Timeline event rollup queries