Of course, it could be that a connection/integration or a data model has a fundamental issue that needs debugging especially when discrepancies are significant. If so, then let us know!
But, most of the times smaller discrepancies come down to how the data is modeled or interpreted. Therefore, datapoints can show mismatches between platforms but still be accurate.
Common Questions
My revenue is wrong or off - different revenue in shopify or an ad platform compared to adtribute
Too many or little orders - there is a different order count in my shopsystem compared to adtribute
Too many or little leads - there is different number of leads in my CRM or ad platform compared to adtribute
My ad spend is wrong or off - there is a different amount of ad spend in the ad platform compared to adtribute
My campaign or ad performance seems bad or wrong - Why are there campaigns without spend get revenue attributed?
Tracking and attribution seems off - can you check?
Reasons for data mismatches
One of the following reasons might be cause for data mismatches. While data is not the same, this does not mean that it is wrong. But rather, that the data is modeled and interpreted in different way between the adtribute and other platforms or systems and usually more accurate in adtribute.
Tracking & Attribution
This results in revenue mismatches.
First of, it is important to understand that our revenue for channels will not be the same as you see in your respective platforms - and it shouldn't, as this is what we solve: Better data through better attribution.
We attribute conversion value e.g. revenue to channels, campaigns and ads based on different attribution models more accurately than the platforms. Therefore, you will always see different numbers as in your ad platforms when it comes to revenue.
If you think tracking is off, we can of course check your tracking setup and see if the script is not firing for certain pages. Almost always there is no actual technical tracking issue but our data collection is more thorough and for longer time frames and if you look at an unlimited attribution window it will lead to large differences between ad platforms and adtribute.
Here is a common example that leads to different data between the ad accounts and adtribute data:
Scenario:
You've been collecting data for a long time with us
You're using a "First Touch" model with an unlimited attribution window and conversion date attribution
Today, someone makes a purchase
The result: Your reports might show sales credited to old campaigns or ads that:
Aren't running anymore (no current spend)
Haven't been active for weeks or months
But were the "first touch" in the past that eventually led to today's purchase
In simple terms: You'll see orders and revenue attributed to campaigns you're not spending money on anymore, because they were the first interaction that started the customer's journey - even if that was a long time ago. This can make your current campaign performance look confusing if you don't understand that some of your attributed revenue is coming from old, inactive campaigns.
If you switch to an attribution model with a short window, the numbers will change drastically.
Always make sure that you understand the context of your attribution model. For information read Understand Attribution Models.
Refund logic
This results in revenue mismatches.
When a refund occurs, we subtract the refund amount on the day the original order was placed, while Shopify (and other shop systems) subtract it on the day the refund was processed.
For example, if an order was placed on February 12th and refunded on the 25th of March, the refund would be:
adtribute: subtracted from February 12th's revenue
Shopify: subtracted from March 25th's revenue
This approach was chosen because from a marketing perspective, attributing refunds to the original order date provides cleaner data for analysis.
This difference in refund handling can lead to slight variances between adtribute and Shopify revenue for specific timeframes, though the overall data picture remains accurate.
Timezone differences
This results in revenue, order, spend, lead mismatches.
Timezone differences can cause discrepancies in several ways. For example, an order placed at 23:06 CEST might be recorded on a different day in EET (Eastern European Time) since it would be after midnight there.
This can result in:
Orders (or Leads for that matter) appearing on different days between systems
Different daily revenue totals for days
The same goes for spend that was around midnight.
This is why you might see slight discrepancies in daily numbers between different systems and timeframes, though the data is accurate.
Therefore, the total data picture shows no or only very little mismatches across longer time periods.
Note: We can adjust the timezone for adtribute. If you want to change it, just let us know
Currency exchange
This results in mismatches for revenues that are not your default currency.
We utilize exchangeratesapi.io for daily exchange rates. When calculating revenue, we use the specific exchange rate for each day within the selected timeframe.
If you calculate total revenue for a bigger timeframe based on the exchange rate of the day you are reviewing the revenue, discrepancies may arise compared to our data as exchange fluctuate on daily.
Lead deletion & duplication
This results in mismatches for lead count.
When you delete a lead in your CRM this will not reflect in adtribute as leads remain in adtribute.
For some CRMs, when an already signed up lead signs up again, this is counted as a new lead.
If you need changes in lead handling, let us know!