View Test Data In GA4

Once you've connected Shoplift to GA4, you can view your test data in two ways: through auto-generated Audiences or through custom Explorations. This guide walks you through both methods.

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Before You Start: Register the Custom Dimension

Required Step: You must register exp_variant_string as a custom dimension before you can use test data in GA4 Explorations. Skip this step and your test variants won't appear as a segmentation option, populating as (not set) values instead.

GA4 requires event parameters to be registered as custom dimensions before they can be used in reports. If you've already done this, you can skip to the next steps.

1. In Google Analytics, click Admin (gear icon in the bottom left)

2. Under your property, click Custom definitions

3. Click Create custom dimension

1. Configure the dimension:

• Dimension name: exp_variant_string

• Scope: Event

• Event parameter: Select exp_variant_string from the dropdown

1. Click Save

Note: If exp_variant_string doesn't appear in the Event parameter dropdown, it means GA4 hasn't received any events with this parameter yet. Launch a test in Shoplift and visit your store as a test participant, then return to this step. It may take up to 24 hours for new parameters to appear.

Once registered, the custom dimension will be available for use in Explorations within 24-48 hours.

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Method 1: Analyzing data with audiences

Shoplift automatically creates two GA4 audiences for each active test—one for the control group and one for the variant. This is the quickest way to see test participation data.

Audiences only gives you high level information. For deeper analysis, use Explorations.

Finding Your Test Audiences

1. In Google Analytics, click Admin

2. Under Data display, click Audiences

3. Look for audiences with the description "Auto-generated audience for a Shoplift test"

For each test, you'll see two audiences following this naming pattern:

• Shoplift Test - [Test Name] - Control

• Shoplift Test - [Test Name] - Variant

What You Can See in Audiences

Click into any audience to view:

• Total users assigned to that test variant

• Device category breakdown (desktop vs. mobile)

• User trends over time

Audiences provide a quick snapshot but have limited analytical capabilities. For deeper analysis—like comparing conversion rates or revenue between variants—use Explorations instead.

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Analyze Data in Explorations

Explorations give you full control over how you analyze test performance, allowing you to compare any GA4 metric across your test variants.

GA4 Explorations are 24–48 hours behind Shoplift. GA4 processes Exploration data on a daily cycle, so data from the current day and previous day may be incomplete or missing entirely. Always compare against dates that are at least 2 days old.

Creating a Test Analysis Exploration

Follow these steps to build an Exploration that compares your control and variant groups:

Step 1: Start a new Exploration

1. In GA4, click Explore in the left navigation

2. Click Blank to create a new exploration

Step 2: Create a segment for your control group

1. In the Variables panel on the left, click the + next to Segments

2. Click Create new segment (top right of the modal)

3. Select User segment

4. Name the segment (e.g., "Shoplift - [Test Name] - Control")

5. Click Add new condition

6. Search for and select exp_variant_string

1. Set the condition to contains and enter the hypothesis ID for your control variant. To find your hypothesis ID, go to your test report in Shoplift and copy the value from the info tooltip next to the title of your control experience.

1. Once entered, the dropdown will populate with the valid string for your control.

1. Click Apply then Save

Step 3: Create a segment for your variant group

Repeat the process above for your variant:

1. Click + next to Segments again

2. Create a new User segment

3. Name it (e.g., "Shoplift - [Test Name] - Variant")

4. Add a condition where exp_variant_string contains your variant's hypothesis ID

5. Click Apply then Save

Step 4: Build your comparison report

1. Drag both segments into the Segment Comparisons area in the Tab Settings panel

2. Add the Dimensions you want to analyze (e.g., Device category, Landing page)

3. Add the Metrics you want to compare (e.g., Sessions, Conversions, Purchase revenue)

4. Your Exploration will now show these metrics side-by-side for control vs. variant

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Understanding GA4 data scopes

GA4 organizes data into four scope levels: User > Session > Event > Item. Understanding how these scopes interact is important for building accurate Explorations, because combining metrics from different scopes can produce unexpected results.

Why scopes matter for test analysis

The exp_variant_string parameter is event-scoped. It is attached only to the experience_impression event and does not carry forward to subsequent events like purchase or add_to_cart. A purchase event from the same visitor will show (not set) for exp_variant_string.

This is exactly why the steps above use User Segments rather than dimension filters. A User Segment identifies visitors who fired experience_impression with a specific variant string, then includes all of that visitor's events (purchases, add-to-carts, page views, etc.) across all their sessions. A dimension filter, by contrast, would only return the experience_impression events themselves — hiding all downstream conversions.

Why adding metrics or dimensions can change your visitor count

When you add a dimension like "Event name" as a row in your Exploration, GA4 breaks the data into one row per event name. The Total users figure in each row shows visitors who fired that specific event within the segment, not the full segment population. A visitor who is in the segment but never fired add_to_cart won't appear in the add_to_cart row.

Similarly, if you apply an event-scoped filter (e.g., Event name = add_to_cart), the displayed visitor count drops to only the subset of segment visitors who fired that event. This is expected GA4 behavior, not a bug — but it is frequently misinterpreted as data loss.

Why item-scoped metrics reduce your visitor count

Some GA4 metrics operate at different scope levels. Item Revenue is item-scoped, while Total users is user-scoped. When you add Item Revenue to an Exploration that shows Total users, GA4 must join user-level data with item-level data. Only visitors who have matching item-level events (i.e., purchasers) produce rows. Visitors who were part of the test but never triggered an ecommerce event simply disappear from the report.

This is why Item Revenue is not reliably cross-combinable with event-scoped metrics like Ecommerce Purchases in the same Exploration. They operate at different scopes and produce misleading aggregations when combined. If you need both metrics, build separate Explorations for each.

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Recommended metrics for comparing with Shoplift

When building GA4 Explorations to validate or compare against Shoplift's test results, use these specific metrics for the most accurate comparison.

Visitors: use Total users

GA4 has two user metrics that are easy to confuse:

• Total users counts anyone who triggered any event (including session_start).

• Active users (the default "Users" metric in standard reports) requires an engaged session (10+ seconds, 2+ page views, or a conversion event).

Always use Total users when comparing against Shoplift's visitor count.

Add to cart: count unique visitors, not events

GA4's add_to_cart event fires once per item addition. If a visitor adds 3 items, 3 events fire. Shoplift's "Added to Cart" metric tracks whether a visitor added anything at all — it's a yes/no per visitor.

To create a comparable metric in GA4:

1. Create a User Segment with the condition event_name = add_to_cart

2. Use Total users as the metric

This gives you the count of unique visitors who performed at least one add-to-cart action, which matches Shoplift's definition. Do not use "Event count" for add_to_cart (that counts total item additions, not unique visitors).

Revenue: use Product revenue, not Item revenue

GA4 offers several revenue metrics, and choosing the wrong one is the most common cause of revenue discrepancies:

• Item Revenue is item-scoped. It reflects gross pre-discount revenue — GA4 does not automatically subtract item.discount from item.price. For stores running frequent promotions, this alone can inflate revenue by 15–30%. Item Revenue is also not reliably cross-combinable with event-scoped metrics like Ecommerce Purchases in the same Exploration.

• Product Revenue is a better comparison point. While GA4 and Shoplift still calculate product revenue totals differently, the gap is smaller and more predictable than with Item Revenue.

Shoplift calculates revenue using the actual amount paid by the customer (post-discount, excluding taxes and shipping), pulled directly from Shopify's order data.

Custom GA events

If you're using your own custom GTM events (e.g., a custom add-to-cart event or engagement event) to compare with Shoplift data, keep two things in mind:

• Make sure your custom events are event-scoped. This is the default for custom events in GA4.

• Always analyze custom events using User Segments, not dimension filters. A dimension filter on exp_variant_string will only return experience_impression events, and your custom events will be filtered out because they don't carry that parameter.

Sampling

If the icon in the top-right corner of your Exploration shows yellow or orange instead of a green checkmark, your data is being sampled. GA4 activates sampling when a query exceeds approximately 10 million events.

Sampled data can randomly inflate or deflate one variant relative to another, which can create the appearance of a meaningful difference where none exists.

The most effective fix is to shorten your date range. Longer date ranges, multiple segments, and high-cardinality dimensions all increase the likelihood of sampling.

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Tips for Effective Test Analysis

Set appropriate date ranges. Only include data from when the test was actively running. Exclude any periods when the test was paused.

Account for statistical significance. GA4 doesn't calculate statistical significance for you. Use Shoplift's built-in reporting for significance calculations, and use GA4 Explorations for deeper behavioral analysis.

Filter by device if needed. If your test targets specific devices, make sure to filter your Exploration accordingly to avoid skewed results.

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Pausing experiments

Google Analytics doesn't support pausing audiences, so Shoplift can't pause audience data collection when you pause a test. If visitors who were previously assigned to a test return to your store while the test is paused, their events will still be sent to GA4.

When analyzing data for a test that was paused:

• Set your date range to only include the period when the test was actively running

• Avoid date ranges that span across pause/resume periods

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Audience durations

Shoplift sets audience membership duration to the maximum of 540 days. This means a visitor who participated in a test will remain in that test's audience for up to 540 days, allowing for long-term analysis of test participants' behavior.