Standard pre-computed attributes list
The Pre-computed attributes are indicators you can use as conditions in Custom Audience Filters, in your content personalization, in your Business Intelligence tool, and some are useful for Splio dashboards. They are based on the data imported into Splio. They are also called aggregates.
Standard pre-computed attributes
Name | Definition | Example of use case(s) |
Share of discounted orders | Percentage of orders made with at least one product with a discount | Easily identify contacts who are interested solely in discounted items |
RFM segmentation | Current status on the RFM Segmentation. To know more, here is a dedicated article about RFM Segmentation. | Customize campaign content for different segments (Ex: VIP) |
Previous RFM Segmentation | Previous status on the RFM Segmentation. | Automatically trigger scenarios based on RFM segment changes to address inactive clients |
RFM update date | Date of change on the RFM Segmentation | Automatically trigger scenarios based on RFM segment changes to address inactive clients |
Date of first order | YYYY-MM-DD | Welcome pack (for customers) |
Date of last order | YYYY-MM-DD | Create a scenario following the last orders |
Channel of first order | First channel used to order - Web or Store | Customize the post-first-purchase journey. Ex: Survey on in-store experience, Message encouraging cross-channel engagement |
Tag - Web customer | True if at least 1 web order (based on available purchase data) | Promote offline stores to increase omnichannels customers |
Tag - Shop customer | True if at least 1 order in store (based on available purchase data) | Promote online store to increase omnichannels customers |
Tag - Mixed customer | True if Web customer AND Store customer | Identify the mixed customer for cross-channels operations |
Preferred Channel | Web or Store. If 50/50, the most recent store. | Personalize a template with a banner showcasing the benefits of the preferred channel: Free shipping for orders over X for the web, Click and Collect for stores |
Preferred store | Store in which the customer made the most purchases (and last store of purchase if no purchase were done). | Personalize templates with information related to the preferred store: Addresses / Opening Hours |
Channel of last order | Channel on which the last order was done | Post-purchase scenario per channel to enhance cross-channel engagement (Web or Store Benefits) |
Last page view date | Date and time of the user's most recent page view | Useful for identifying active users or triggering re-engagement campaigns |
Last product view date | Date and time of the user's last product view. | Automate post-visit product campaign thanks to web tracking feature |
Last products viewed in X days | List of products external ID viewed by the user in the last X days | Enable personalized recommendations or retargeting. |
Last products viewed | List of products external ID viewed by the user (not limited to 7 days). | Can be used for building user profiles or dynamic content. |
Top product viewed | The product most frequently viewed by the user. | Indicates strong interest; useful for targeting contacts interested in specific products. |
Top product viewed in X days | Most viewed product by the user in the last X days. | Short-term interest tracking for time-sensitive campaigns. |
Top brand viewed | Brand most frequently viewed by the user. | Helps in brand affinity analysis and segmentation. |
Top brand viewed in X days | Most viewed brand in the last X days. | Useful for recent brand engagement tracking. |
Top category viewed | Category most frequently viewed by the user. | Supports category-level personalization and targeting. |
Top category viewed in X days | Most viewed category in the last X days. | Enables short-term category interest analysis. |