Pre-computed attributes categories
The Pre-computed attributes are indicators you can use as conditions in Custom Audience Filters. They are based on the data imported into Splio. They are also called aggregates.
They are deterministic, based on your customer's past behavior, or predictive, which are probabilities of future behaviors.
We have sorted the attributes into categories. You can see an overview of the categories in the image. The other pages of this guide list all the segments, most of the time with a definition or a calculation formula, the ones without a definition are self-explanatory. We also provide some examples of values you will be able to use in Custom Audience Filters.
Orders
By default:
Date of first order - YYYY-MM-DD
Date of last order - YYYY-MM-DD
Date of last online order - YYYY-MM-DD
Date of last offline order - YYYY-MM-DD
Average frequency of order: the average difference between orders expressed in a number of days. Empty if < 2 orders.
Tag - Multi buyer: true if the number of orders > 1
Tag - New customer: true if the number of orders = 1
On-demand:
Total spent
Total spent during the current year
Total spent during the last 6 or 12 months
Total spent online
Total spent in physical stores
Total orders online
Total store orders
Total number of orders
Total number of orders over the last 6 or 12 months
Average basket: total spent/number of orders. This segment is also available online/offline and over the last 12 months.
Tag - High spending potential: true if purchase of at least one item at X€ or more (X to be defined during the setup)
Total spent over N Months
Number of orders over the last N months
Average basket (custom): Average basket calculated with a specific definition
Tag - Regular customer: true if the number of orders >= 2 over the last N months
Purchase periods
By default:
Preferred shopping day: the day of the week with the most purchases. If equality between two days, the most recent is chosen - Monday, Tuesday...
Preferred shopping month: the month with the most purchases - January, February...
On-demand:
Preferred shopping N days: the days of the week with the most purchases (N to be defined during the setup).
Preferred shopping N months: the months with the most purchases (N to be defined during the setup).
Discounts
On-demand:
Average discount rate: percentage of discounts obtained on the total spent
Number of discounted orders: number of orders including at least one product with a discount
Share of discounted orders: percentage of orders made with at least one product with a discount
Products
On-demand:
Orders by category: number of products purchased on up to 5 categories (the categories need to be defined during setup)
Total product quantity: number of products purchased in total/over 6/12 months. The same product can be counted several times.
Total product quantity in the last 6 or 12 months
Average number of products per order
List of categories: list of distinct product categories with at least one order (30 categories maximum) - shoes, pants...
Top 5 product categories: list of top 5 product categories where an order has been made
Revenue per category: total revenue in a specific product category
Tag - Multi categories: orders in more than 1 product category
Last purchase date on a specific category: last purchase date on a specific product category
Large size buyer: if the customer has purchased at least X products with a large size tag (X to be defined during setup)
Stores
By default:
Preferred Channel: Web or Store. If 50/50, the most recent store.
Channel of first order: first channel used to order - Web or Store
Tag - Web customer: true if at least 1 web order
Tag - Shop customer: true if at least 1 order in store
Tag - Mixed customer: true if Web customer AND Store customer
On-demand:
Preferred store: store where the customer bought most often during the last 12 months. If 50/50, the most recent store
Tag - Single store: true if store number = 1
Tag - Multi-stores: true if store number > 1. True if one Web purchase + 1 Store purchase.
Store of first order
Store of last order
Channel of last order: last channel used to order - Web or Store - Web if 2 purchases the same day
Vendor last order
Store list: list of (distinct) stores with at least one purchase, including Web, 10 stores maximum
Engagement
By default:
RFM segmentation: this segmentation splits your customers into categories depending on their purchase recency, frequency, and the amount (monetary). It is the current status for the client.
Previous RFM Segment
RFM Date of the status change
On-demand:
Tag - Engaged Contact: true if the number of clicks over N months >= X (X to be defined during setup)
Historical RFM 3 months: RFM Segmentation 3 months ago
IFR: Individualized Frequency Recency: This segmentation defines the customer in terms of their individualized purchase rhythm (on time, late...).
Historical RFM N months: RFM Segmentation N months ago
Predictive
By default:
Customer Lifetime Value
Propensity to Buy