Traffic sources

Your page views will often look different, and that's expected. Here's why:

All views vs. unique views

Google Analytics shows every page view, including when the same person views your listing multiple times. Mantle focuses on unique views. For example, if someone views your listing 80 times but only 50 are unique visitors, we'll show 50 in the Viewed listing count.

But wait, GA also shows unique views that don't match

Even when you look at Google Analytics' unique view count, it often differs from Mantle. This is because Shopify controls the tracking in your Google Analytics property, not us. Shopify can send page view events for many different URLs and activities - not just your app listing page.

We only track your app listing URL for the page views metric

Mantle takes the data from your GA4/BigQuery connection and filters it to only look at your specific app listing URL: apps.shopify.com/your-app-name

We ignore everything else, including:

• Built for Shopify collection pages (apps.shopify.com/built-for-shopify)

• Category pages

• Search results pages

• Any other Shopify App Store URLs

This gives you the most accurate count of how many people actually viewed your app's listing page specifically.

Why GA numbers are usually higher

Google Analytics typically shows inflated numbers because it's tracking all of these URLs and events together. Mantle gives you a focused, accurate view of just your app listing traffic.

To see specific campaign names from ad platforms, you need to add UTM parameters to your ad destination URLs. Add utm_campaign=your_campaign_name to your landing page URLs in your ad platform.

For example: https://apps.shopify.com/your-app?utm_source=google&utm_medium=cpc&utm_campaign=spring_sale_2024

Without UTM parameters, Mantle can identify that traffic came from Google or Facebook, but won't be able to distinguish between different campaigns.

The Source medium dimension tells you how traffic arrived from a source:

• organic — Natural search or browsing (e.g., merchant searched on Google or browsed the App Store)
• cpc — Paid advertising clicks (e.g., Google Ads, Facebook Ads, Shopify search ads)
• referral — Links from other websites (e.g., blog posts, partner sites)
• cpa — Cost-per-action or affiliate campaigns
• (none) — No medium could be determined (often paired with (direct) as the source)

If you're running paid campaigns, make sure you're using UTM parameters (utm_medium=cpc) so traffic is properly categorized.

Currently, the "Exclude from reports" customer setting doesn't filter out test stores from traffic source reporting. This is a known limitation.

To work around this, you can use customer segments to exclude test stores when analyzing your data. Traffic source data is aggregated before customer filtering is applied, which is why test stores may still appear in the totals.

There are a few common causes:

1. App Store slug not configured — Go to Your App → Settings and make sure the app store slug is set. This should match the last part of your Shopify App Store URL (e.g., your-app-name from apps.shopify.com/your-app-name).

2. Google Cloud account inactive — If your GCP billing account is suspended or inactive, BigQuery will stop streaming data. Check your Google Cloud Console to verify your account status.

3. Date range is before integration — Data is only available from when you connected BigQuery. Select a date range after your integration was set up.

This report connects your Google Analytics 4 traffic data with customer value data from Mantle. BigQuery acts as the bridge that allows us to match traffic sources with actual customer behavior and revenue. Without it, we can't connect the dots between where visitors come from and how much value they bring.

Data availability depends on when you connected your BigQuery integration. Google Cloud/BigQuery doesn't backfill historical data, so we can only access traffic data from the date your GA4 to BigQuery connection was established.

Shopify tracks app listing data through Google Analytics, but this tracking isn't always 100% accurate. Ad blockers, privacy tools, and browser settings can prevent some visitor activity from being recorded. This means you might see fewer visits in this report than actual traffic to your listing.

The traffic source report only shows traffic and installs that originated from your App Store listing page, not from all possible sources. Other reports (like activity reports) may show all installs regardless of where they came from. This focused approach helps you understand specifically how your App Store listing is performing as an acquisition channel.

The Traffic Source and Funnel reports use different data aggregation methods, which can result in different numbers for the same events.

The Traffic Source report shows all visitor activity and focuses on traffic attribution. It includes every visitor who viewed your listing, clicked buttons, or installed your app, regardless of their complete journey. This gives you a comprehensive view of how different traffic sources perform in driving awareness and initial actions.

The Funnel report focuses on installed customers and their complete lifecycle journey. It starts with customers who actually installed your app and traces their path backward and forward through all events. It uses a more complex aggregation that separates users with installations from anonymous visitors.

Think of it this way: Traffic Source helps you optimize your marketing and acquisition channels, while Funnel helps you understand customer lifecycle and conversion patterns. Both views are essential for different business decisions.

Numbers only reflect events that happened within your selected time frame and can be affected by ad or traffic blockers. For example, when viewing the App Store listing → Install funnel, you see merchants who both viewed your listing AND installed within that timeframe.

However, if you look at just the Install event alone, you might see additional installs from merchants whose listing views were blocked by privacy tools or occurred outside your date range - such as a customer who viewed your listing on June 8th (outside your selected report period) but installed on June 15th (within your date range). This makes the standalone install number appear higher than the funnel conversion.

The funnel events show a cohorted customer journey. For example, if the report shows 1,000 app listing views and 200 installs, this means that 20% of the merchants who viewed your listing continued on to install your app. This true conversion tracking helps you identify exactly where merchants drop off in your acquisition funnel and which traffic sources deliver the best conversion rates through each stage.

You can control how often the data updates by going to Settings → Integrations → BigQuery and setting the polling frequency. The more frequently you poll BigQuery, the more up-to-date your data will be, but this may increase your BigQuery costs. That said, BigQuery offers a generous free plan that covers most typical usage.