Segmenting Customers - The Complete Filter Guide

In Remarkety, customers can be segmented in a variety of different pages - The contacts area, the segments area, and within the setup of each campaign.

This article presents a breakdown of the possible segmentation options.

Overview

A segmentation is built by applying a set of filters to the entire customer base. The segmentation will consists of customers who pass *all* of the filters ("and"). However, within any filter, an "or" is applied to the values. Examples follow :)

The filters are separated into sub-groups for convenience.

Segmentation Filters

Lifetime Purchase History

These are filters that concern the lifetime purchase history of the customer. The purchase history is synched from the website or imported by other methods (such as our POS integration).

Filter Name Explanation Real-Life Example
Average order value This is the average order value, over the customer's entire history, in the store's default currency. Find your highest-value customers by searching for customers with an AOV of over $1,000
Customer has/has no cart Does the customer have a current cart that hasn't yet converted into an order? Send order followups only to a customer which does not have a cart
Customer has/hasn't purchased a certain product Whether the customer has ever/never purchased any of the products listed in the details. Use two filters in tandem to final all the customers who bought the iPhone X but haven't bought the iPhone 11
Customer has/hasn't purchased a product from category Whether the customer has ever/never purchased any product from these specific categories. All customers who've purchased winter coats.
Customer has/hasn't purchased a product from category (X days ago) Whether the customer bought a product from a specific category within the past X days All users who bought T-Shirts in the past 90 days
Customer has/hasn't purchased a product from manufacturer If "manufacturer" data is received for your products, you can filter for customers who've purchased products made by specific manufacturers. All customers who bought Nike shoes
 Customer has/hasn't purchased a product from vendor Similar to "manufacturer" above. Some platforms/catalogs also include vendor information.  
Customer made an order between specific dates Whether a customer made at least one order during a specific date range All customer who bought form us last holiday season
Last order date Whether the customer's last order was before/after X days ago, or alternatively within a specific date range. All customers who's last order was more than 90 days ago
Number of orders (any status) Filter by the total number of orders a customer's ever made All customers who have exactly 1 order, ever
Number of orders (completed status) Similar to above, however look at only orders with a "completed" status. To define which statuses are considered "complete", go to the Settings -> Store Info page. All customers who have 1 completed order
Total spending Filter by the customer's total spend in your business All customers who spent more than $1,000 in total
Total spending (w/o shipping) Same as above, however do not count shipping costs.  

Contact Details

These filters apply to specific contact fields. Field values are received from the eCommerce platform, but they can also be modified by uploading files, using our API, or modified manually in the app.

Filter Name Explanation Real-Life Example
City Filter based on the customer's city (multi-selectable)  
Country Filter based on the customer's country (multi-select)  
Email address contains / does not contain text Filter based on the email address of the customer Find all "@gmail.com" emails
Gender Filter based on customer's gender, if available All male customers
Group Filter based on the customer's shopper group. Groups are synched from the ecommerce platform. Find all "wholesale" customers.
Opt-in status Filter based on the customer "Opt-in to marketing" status. This is usually a field we receive from the eCommerce platform or via API. It is related to but not identical to the "Marketing Allowed" field - please read here for more details. Find all customers which have explicitly opted-in to email marketing
Segment Whether a customer is currently included / not included in a specific persistent segment (Segments are setup via the Segments area in the app) Find all customers in the VIP segment
State region Whether a customer's address is from / not from a specific state or region All customers from Vermont
Street Address Whether the customer's address contains a specific text All customers from "Main St."
Tags Whether the customer has or doesn't have specific tags associated with them.  All customers who have either the "loves-dogs" or "loves-cats" tags
Total reward points

Filter based on the customer's current reward points balance. This field is usually passed into Remarkety from an integration with a specific rewards program, or directly from the eCommerce platform if supported.

Note: Specific integrations (such as Smile and Swell/Yotpo) will create their own custom fields such as loyalty_tier under their own separate subsection.

All users which have more than 100 current reward points

Email Engagement

These filters apply to the email engagement history of your customers.

Filter Name Explanation Real-Life Example
Email engagement (specific campaigns) Whether or not a customer received, opened, or clicked on emails sent by specific campaign(s) All customers who opened but did not click the "summer sale" campaign
Last email interaction (any campaign) Whether a customer has received, opened or clicked any campaign within the past X days Set up a segment called "Inactive on email" which contains all customers who haven't opened any email in the past 90 days
Email suppression date Find all customers which have been suppressed since before or after a specific date  
Email suppression type All customers which are currently on the suppression list, because of specific reasons Find all customers who are currently unsubscribed from email marketing. This is useful for setting up a custom Facebook audience for example.
Last open date All customers who's last open date was before or after a specific date  
Purchased from campaign Whether a customer has converted or hasn't converted from specific campaign(s) All customer who converted from the "Summer Sale" campaign we sent last season

Marketing Status

Filter Name Explanation Real-Life Example
Is marketing allowed?

Whether or not marketing is currently allowed for this customer. This setting depends on whether or not the customer has opted in, and whether they have since been suppressed. Read here for details.

Note: Whenever you send a newsletter or an automation, customers who aren't "Marketing Allowed" are automatically removed. There is no need to add this filter in email campaigns, it is applied automatically.

 

Website Activity

Filter Name Explanation Real-Life Example
Last Website visit

Whether or not marketing is currently allowed for this customer. This setting depends on whether or not the customer has opted in, and whether they have since been suppressed. Read here for details.

Note: Whenever you send a newsletter or an automation, customers who aren't "Marketing Allowed" are automatically removed. There is no need to add this filter in email campaigns, it is applied automatically.

 
Registration date (X days ago)

The customer's registration date.

Note: If we receive this field from the platform or via upload, that will determine the value of this field. Otherwise, this will be the date the customer was added to Remarkety.

All customers who registered exactly 30 days ago
Registration date (between two dates)

Same as above, except that the filter applies to a specific date range instead of a relative time frame.

All customers who registered between Dec 1 and Dec 31 in 2019.

Random Split

These filters are used to randomly split customers for testing purposes. The main difference between using these filters and our A/B testing functionality is that a specific customer will always be in a specific filter group (ie: "First quarter"), no matter where the filter is used. However, in an A/B test, customers are randomly shuffled within each campaign to avoid bias.

Filter Name Explanation Real-Life Example
Split in half

Randomly split the customer base in half. Can be used to A/B test entire multi-campaign strategies, since customers will always be bucketed into the same half, regardless of where and when the filter is applied.

 
Split to quarters

Same as above - split the customer based into distinct and predicatble quarters.

 
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