Analytics Discrepancies
Elevate's numbers won't match Shopify Analytics or Google Analytics exactly. This is normal — every analytics platform measures things differently. This page explains the specific reasons why numbers vary and how Elevate's approach works.
Why Numbers Differ: The Short Version
Visitor vs. session counting
Elevate counts unique visitors. Shopify often counts sessions. One visitor can have multiple sessions.
Bot filtering
Elevate filters out bots aggressively. Other tools may count some automated traffic.
Ad blockers and privacy tools
Some visitors block tracking entirely. Different platforms are affected differently.
Order attribution
Elevate attributes orders based on which variation the visitor was assigned to. Shopify attributes based on the product purchased.
Page measurement
Elevate counts every visitor who viewed a tested page. Shopify's landing page reports only count visitors who entered through that page.
Consent and privacy
GDPR/cookie consent can block tracking. Different tools handle consent differently.
Timezones
Reports across platforms may use different timezone settings, shifting data between days.
Each of these is explained in detail below.
How Elevate Tracks Visitors
To understand discrepancies, it helps to know how Elevate's tracking works:
First visit: A visitor arrives at your store. The Elevate script assigns them a random, anonymous Visitor ID and a Session ID.
Variation assignment: The script checks which experiments are running and assigns the visitor to a variation based on your traffic allocation. This assignment is stored in a cookie so the visitor always sees the same variation on return visits.
Event tracking: The web pixel fires events and sends them to Elevate's analytics infrastructure with the visitor's ID and variation assignment.
Order attribution: When an order is placed, it's attributed to the experiment variation the visitor was assigned to — regardless of which specific product they purchased.
Visitor Counts: Elevate vs. Shopify Landing Page Reports
This is the most common discrepancy merchants notice.
How Elevate counts visitors
Elevate counts every unique visitor who viewed the tested page at any point during their session. If a visitor enters your store through the homepage, then navigates to the tested product page, that visitor is counted.
How Shopify counts landing page visitors
Shopify's landing page reports only count visitors for whom that page was their entry point — the first page they saw when arriving at your store.
The result
Elevate will almost always show higher visitor numbers than Shopify's landing page reports for the same page. This isn't an error — it's a different measurement. Elevate's approach captures the full set of visitors who were exposed to your experiment, which is what matters for accurate A/B test analysis.
Order Counts: Elevate vs. Shopify Product Reports
How Elevate attributes orders
When a visitor who was assigned to an experiment variation completes a purchase, the entire order is attributed to the experiment — even if the visitor ultimately purchased different products than the one being tested.
This approach captures the downstream behavioral impact of the experiment. A price change on one product can influence overall cart behavior, add-on purchases, and buying confidence. By attributing the full order, Elevate measures the complete effect of the variation on revenue.
Elevate also excludes certain order types that would skew experiment data:
Draft orders (created manually in Shopify admin)
Subscription contract orders (recurring charges, not new purchase decisions)
How Shopify attributes orders
Shopify's product reports typically focus on orders that contain specific products. If a visitor viewed your tested product but ended up buying something else, Shopify's product report won't connect that order to the tested product.
The result
Elevate's order counts for experiments will generally be higher than what you see in Shopify's product-specific reports, because Elevate is measuring the broader influence of the tested experience on purchasing behavior.
Revenue Differences
Elevate reports revenue as subtotal — the order amount excluding shipping and taxes (unless it's a shipping experiment, which includes shipping revenue). Refunds are subtracted from revenue automatically.
If you're comparing to Shopify or another tool that includes taxes or shipping in its revenue figures, the numbers won't match. Make sure you're comparing like-for-like.
Elevate also applies outlier detection — orders with unusually high values (more than 3x the average for that variation) can be capped or excluded to prevent a single large order from skewing your experiment results. This is a feature you can toggle on or off in your report filters.
Bot Traffic
A significant percentage of web traffic comes from automated bots — search engine crawlers, social media scrapers, monitoring tools, and more. Elevate filters these out automatically using known bot patterns (Googlebot, Bingbot, Facebook crawler, etc.) to ensure your experiment data reflects real human behavior.
Other analytics platforms may have different filtering thresholds or may not filter bots at all, leading to higher traffic numbers that include non-human visitors.
Ad Blockers and Privacy Browsers
Modern browsers like Safari, Firefox, and Brave restrict third-party tracking by default. Many visitors also use ad blockers or privacy extensions that can interfere with analytics collection.
Elevate's web pixel runs inside Shopify's sandboxed environment, which makes it more resilient than most third-party tracking scripts. However, some privacy tools can still block it. The impact varies across platforms — Google Analytics is often more heavily blocked than Shopify-native tools.
Cookie Consent and Privacy Regulations
In regions with privacy regulations (GDPR, CCPA, etc.), visitors may decline tracking consent. When this happens, analytics platforms that respect consent won't record those visitors.
Different tools handle consent differently. If your consent banner blocks Elevate's web pixel but not Shopify's native analytics (or vice versa), you'll see discrepancies in visitor counts.
Timezone Differences
An order placed at 11:30 PM in your local timezone might appear in a different day's data depending on the platform. Elevate processes events in UTC and converts to your store's timezone for display. If another analytics tool uses a different timezone, daily totals won't match — even though the overall totals will converge over time.
What To Do About Discrepancies
Small differences between analytics platforms are expected and shouldn't cause concern. Here's what matters:
Don't try to make the numbers match exactly. It's not possible and not necessary. Each platform measures things slightly differently.
Use Elevate for experiment decisions. Elevate's numbers are internally consistent — every variation in your experiment is measured the same way. That's what matters for comparing variations against each other.
Use Shopify Analytics for store-level metrics. For overall store revenue, total orders, and traffic trends, Shopify's native reporting is the source of truth.
Investigate large discrepancies. If the numbers are wildly different (e.g., 50%+ gap), something may be misconfigured. Check that the Liquid Snippet is installed and the Web Pixel is active.
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