In Remarkety, customers can be segmented from 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 segment is built by applying a set of filters to the entire contact base. The segment will consists of all 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 your website or imported by other methods (such as our POS integration).
| Filter Name | Explanation | Real-Life Example |
| Average order value | This is the customer's average order value over the their 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 customers which do 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 find 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. | Find 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. | Find all customers 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 from 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 whose 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. |
| 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 at 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.io 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 that are currently on the suppression list for a specific reason. | 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 / hasn't converted from specific campaign(s). | All customers 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 |
The last time this customer was active on your website was more or less than x days ago. |
Show customers who have been inactive on your website for over 90 days what your newest products are. |
| 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 predictable quarters. |
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