Custom segments let you filter the Segments Dashboard to specific groups of subscriptions so you can measure the impact of Skio features, track subscriber behavior, and make data-driven decisions about your retention strategy. This guide covers recommended segment setups, real-world use cases, and tips for getting the most out of your segment data.
Prerequisite: This guide builds on the Segments Dashboard and the Segments page guide. Make sure you're familiar with both before diving in.
Who this is for
This guide is most useful for:
Merchants evaluating retention tactics who want to measure whether Cancel Flow treatments, Surprise & Delight rules, or Quick Actions are actually improving KPIs.
Brands with diverse catalogs looking to understand product-level subscription behavior, like which items get swapped out or which combinations retain best.
Stores that migrated to Skio and need to compare migrated subscriber performance against native Skio subscriptions.
Anyone using the Segments Dashboard who wants to move beyond the default "all subscriptions" view and start segmenting for targeted insights.
How segments and the date selector work together
If there's one thing to take away from this guide, it's how the date selector interacts with your segment.
A custom segment defines which subscriptions you're looking at. The date selector defines the time window for metrics like revenue, orders, and actions. These two controls work independently and combine to give you precise, filterable data.
A few key behaviors to keep in mind:
The date selector applies to all dashboard sections except the Monthly Breakdown. The Monthly Breakdown table filters by segment but always shows full retention history regardless of date range.
The default date range is 30 days. This works fine for action counts, but metrics like AOC (average order count) and ARG (average revenue per customer) will look artificially low. Extend the range to several months or a full year to get a more accurate picture.
A segment filters subscriptions; the date range filters their activity. For example, a segment of "subscriptions containing Product A" combined with a 90-day date range shows you the revenue, actions, and orders from those specific subscriptions during those 90 days.
To target subscriptions created in a specific period, create a segment using the subscription creation date condition. Then use the date range to look at any metric for only those subscriptions. Combining these two is a powerful way to isolate subscription behavior over time.
Don't confuse the date selector with the segment's creation date condition. The date selector filters activity. The creation date condition filters which subscriptions are included.
How to create a segment
From the Segments Dashboard
You can create segments directly from the Segment Dashboard by clicking on the Create segment button beside the date range selector and filters. This opens up a Segment creation modal that mirrors the Segments page in Tools.
From the Segments page
You can also create and manage segments from the Segments page in your Skio Dashboard. In the left-hand menu, go to Tools > Segments, then click Create a segment.
From there, you'll choose your segment type, set your conditions, and save. Once created, your segment becomes available in the All segments filter dropdown on the Segments Dashboard under Analytics > Segments.
For step-by-step instructions on creating and managing segments, see the Segments page guide.
Segment types: static vs. dynamic
When you create a segment, you'll choose between two types:
Dynamic segments sync nightly at 4:00 AM UTC. Skio automatically adds new subscriptions that match your conditions and removes any that no longer apply. Use dynamic segments for ongoing monitoring, like tracking all active subscriptions with a specific product or a high average revenue per customer.
Static segments capture a snapshot based on the conditions at the time you create or sync them. They don't auto-update. Use static segments when the population shouldn't change, like subscriptions created during a specific promotional period or subscriptions migrated from another platform.
Tip: When in doubt, go with dynamic. You get automatic updates without any manual syncing, and you can always create a static segment if you need a fixed snapshot.
Use cases and recommended setups
The examples below include a default condition of subscription status = Active. If you want to include all subscriptions regardless of current status (including cancelled, paused, and failed), remove this condition.
Measure the impact of Surprise & Delight on retention
Scenario: You've set up a Surprise & Delight rule and want to know whether it's actually improving subscriber value and reducing churn.
Recommended setup: Create a dynamic segment with the Surprise & Delight condition, filtering for the specific rule you want to evaluate. If you're running a split test, create two segments: one for the treatment group (received the surprise) and one for the control group (eligible but didn't receive it).
Why this works: By isolating subscribers who received a specific Surprise & Delight rule, you can compare their AOV, ARG, AOC, and retention data against the control group directly on the dashboard. This tells you whether the surprise is driving measurable value or just adding cost.
Tip: Switch between your treatment and control segments in the All segments filter and compare the Monthly Breakdown table for each. If the treatment group retains meaningfully better at months 3-6, the rule is likely worth keeping. If retention percentages are flat across both, consider adjusting the rule or testing a different incentive.
Evaluate Cancel Flow treatment performance
Scenario: You have multiple Cancel Flow treatments and want to understand which ones are producing the highest-value saves.
Recommended setup: Create a dynamic segment using the Cancel Flow condition. Select the specific treatment you want to evaluate. Create separate segments per treatment to compare them.
Note: If you're still on an older version of Cancel Flow (Cancel Flow v2), you'll filter by rebuttal message and whether the subscription was saved. The condition options you see will match whichever version your store is using.
Why this works: Saving a subscription is only valuable if the subscriber continues to order. Filtering the dashboard by Cancel Flow treatment lets you see the post-save ARG, order count, and retention of subscribers who accepted each offer, so you can double down on what's working and retire what isn't.
Tip: Pay close attention to the Audit Log table for Cancel Flow segments. If subscribers who accepted a discount offer are also skipping more frequently afterward, the save may not be as valuable as the save rate suggests.
Segment conditions panel showing Cancel Flow treatment selected.
Track high-value subscribers by LTV
Scenario: You want to understand what your most valuable subscribers have in common, from the products they subscribe to, to the actions they take and how long they retain.
Recommended setup: Create a dynamic segment using the lifetime value condition (e.g., LTV > $200). Adjust the threshold based on your store's average.
Why this works: Filtering the dashboard for high-LTV subscribers reveals patterns you can replicate. Check the Products Breakdown to see what they're subscribed to, the Audit Log to see what actions they take (swaps, add-ons, interval changes), and the Shipping Interval Breakdown to see their preferred delivery frequency.
Segment conditions panel showing lifetime value > $200.
Identify product combinations and product journeys
Scenario: You want to know which products are commonly paired together in subscriptions, or how product composition changes over a subscriber's lifecycle.
Recommended setup (product combinations): Create a dynamic segment filtering for a specific product. The Products Breakdown table will show all other subscription lines paired with it across the segment, revealing natural product bundles.
Recommended setup (product journeys): Create separate dynamic segments for different order counts (e.g., order count = 2, order count = 3, order count = 4). For each, the Products Breakdown table's Gained Quantity and Lost Quantity columns show which products are entering and leaving subscriptions at that stage.
Why this works: Product combinations tell you what to promote together. Product journeys tell you when subscribers change their mix, so you can time interventions like product recommendations, swap prompts, or upsells.
Tip: Add a condition for order count >= 2 to exclude products added at initial checkout. This isolates post-purchase product activity only.
Compare migrated vs. native subscribers
Scenario: You migrated to Skio from another platform and want to track how migrated subscribers are performing compared to those who started on Skio.
Recommended setup: Create a static segment using the migrated from another platform condition. Compare it against a second segment of non-migrated subscribers (or the unfiltered dashboard).
Why this works: Migrated subscribers may behave differently due to prior platform experiences, legacy pricing, or different onboarding. Isolating them helps you set accurate retention benchmarks and decide whether migrated subscribers need targeted outreach.
Tip: Revenue, orders, and actions for migrated subscribers only reflect activity in Skio. Historical data from your prior platform isn't included, so ARG comparisons will only be meaningful after enough time has passed post-migration.
Filter by subscription action
Scenario: You want to understand how a specific subscriber behavior (like updating a payment method, skipping an order, or using a Quick Action) correlates with long-term value.
Recommended setup: Create a dynamic segment using the audit log condition, selecting the specific action you want to evaluate.
Why this works: The Audit Log table splits metrics between subscribers who took the action and those who didn't, giving you a built-in control group. But when you use the action as a segment condition, the entire dashboard filters to only those subscribers, so you can see what other actions they take, what products they have, and how they retain.
Tip: Try filtering for "payment method updated" to see what subscribers do after updating their card. High subsequent order counts and low churn often confirm that payment recovery efforts are working.
Additional segment conditions
The use cases above cover the most common scenarios, but you can also create segments using conditions like:
Customer-based conditions
Customer tag: Filter by whether a customer has or does not have a specified tag.
Credit balance: Filter customers based on their available store or subscription credit balance.
In Tier: Target customers who are currently in specific loyalty tiers.
Distance to tier: Target customers based on how close they are to reaching their next loyalty tier (measured by order count, LTV, or product count) and their current tier level.
Tier at risk: Target customers whose current tier is at risk of being lost due to tier expiry rules.
Subscription-based conditions
Order number: Filter by the current order number (for example, a subscription with 3 orders would have order number = 3).
Total quantity: Filter by total quantity of products in the subscription (for example, 1 × Product A and 1 × Product B = quantity of 2).
Total value: Filter by total dollar value of the subscription (sum of each product's subscription price × quantity).
Subscription creation date: Filter by when the subscription was created.
Day of week: Filter subscriptions with a specific charge day of the week.
Discount code: Filter subscriptions that contain specific discount codes.
Is prepaid renewing: Filter prepaid subscriptions by whether they are set to renew (continue into another term) or end after the current prepaid cycle.
Migrated from another platform: Identify subscriptions migrated from another platform (only captures the migrated subscription, not new subscriptions created by the same customer).
Next billing date: Filter subscriptions where the next billing date falls on, before, or after a selected date.
Shipping interval: Filter by how often the subscription ships (for example, every week, every 2 weeks, monthly).
Subscription country: Filter by shipping address country (supports multiple selections).
Subscription ID: Enter specific subscription IDs to target with the operation.
Subscription note: Filter subscriptions based on keywords or text stored in the internal subscription note.
Subscription status: Filter by status (Active, cancelled, failed, under review, paused).
Product-based conditions
Contains products: Identify subscriptions that include a specific product.
Contains dynamic boxes: Filter subscriptions that include a specific dynamic build-a-box product.
Contains static boxes: Filter subscriptions that include a specific static build-a-box product.
Exclude products: Identify subscriptions that do not include a specific product (helpful when paired with “contains products”).
Other conditions
Audit log: Filter subscriptions based on specific audit log actions.
Cancel Flow: Filter subscriptions that had X rebuttal shown and was saved or not saved.
Quick action: Filter customers or subscriptions based on whether a specific Quick Action link has been actioned, or viewed but not actioned.
Surprise and Delight: Filter customers who were or were not qualified for a surprise and delight gift.
Common mistakes to avoid
Forgetting to adjust the date range for AOC and ARG: The default 30-day window typically shows ~1 order per subscription and compressed revenue. Extend to 6-12 months for a meaningful average order count and average revenue per customer.
Confusing the date selector with a creation date filter: The date selector filters activity (orders, actions, revenue). To filter for subscriptions created in a specific period, add a creation date condition to your segment.
Leaving "subscription status = Active" in when you want all subscriptions: If you're analyzing cancelled subscriber behavior or comparing active vs. cancelled populations, remove the status condition to include everyone.
Creating too many narrow segments: Start broad and narrow down. A segment with five stacked conditions may return too few subscriptions for the data to be meaningful.
Ignoring the Monthly Breakdown table: The Monthly Breakdown doesn't respond to the date range, but it does respond to segment filters. It's one of the most powerful ways to see whether a feature or action is actually reducing churn over time. Use the tabs to switch between Retention, Revenue, LTV, AOC, Orders, and Retention (Orders) views for the same cohorts.
Related resources
Segments Dashboard: Full reference for every metric, table, and chart on the dashboard.
Segments page guide: How to create, update, and manage segments.
Getting started with Analytics: Overview of all seven analytics dashboards and when to use each one.
Getting started with Cancel Flows: Overview and recommendations for building effective Cancel Flows.