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June 29, 2022
Google Analytics and programmatic reporting tools are both essential for understanding your website’s traffic. However, you may occasionally notice discrepancies between the two.
Additionally, programmatic reporting takes into account ad frequency and viewability, while Google Analytics does not. As a result, discrepancies between the two tools are to be expected.
However, by understanding the differences between them, you can get a more accurate picture of your website’s traffic. Let’s dive into the real reasons why you’re seeing discrepancies and then talk about how to resolve those issues.
One of the main reasons why you’ll see discrepancies between these two types of reporting tools is because Google Analytics uses representative data. Programmatic reporting tools use total population data.
Representative data and total population data are two ways of looking at data that are both helpful and valid. So we’re not saying one is necessarily “better” than the other.
Representative data is a type of data that is based on a sample of a population. You can use it to make educated guesses about a population. But you’re not able to use it to see the specific behavior of each and every visitor.
Total population data is data that includes all members of a population. It is used to describe a population. Representative data is less accurate than total population data. However, it’s definitely easier to collect and analyze.
Ultimately, total population data is more accurate, but it’s more difficult to analyze. This is why most small businesses don’t use it!
Which one is right for you? It’s less about which is “right” and more about being able to understand and implement the information you get from each one.
Representative data is better for making decisions about marketing campaigns. Total population data is better for making decisions about products and services. In this sense, it’s hard to compare the two but easier to understand why you might be seeing some differences.
One barrier to using data effectively is data language barriers. This can make it difficult for people to understand what the data is saying and how to use it effectively.
Namely, the issue here is that with Google Analytics and front-end DSPs, there are different terminologies for different types of events.
DSPs focus primarily on what happens before a user gets to your website. In this context, the data language they’re using is centered around impressions, views, clicks, and view-through rates.
Google Analytics focuses on data that relates to what a user does once they’ve already clicked on your website. This means that a click isn’t the same thing as a user. Therefore, it’s like the two reporting tools are speaking different data languages.
Data language barriers can be overcome by using data reporting tools that are designed to be accessible to a wide audience, and by using a common language when communicating about data.
Define what a user is and what other metrics mean for your business. Then, use the reporting tool that most closely provides data related to your definition of that metric.
Often, when it comes to Google Analytics vs. programmatic reporting tools in this case, it’s like comparing apples to oranges.
If you’ve ever looked at your Google Analytics and compared it to your programmatic reporting, you may have noticed some discrepancies. For example, Google Analytics might show more clicks than your programmatic platform does. So what’s going on?
One possibility is that the way each platform counts clicks is different. Google Analytics counts clicks when a user clicks on an ad and is taken to the landing page.
Programmatic platforms, on the other hand, only count clicks when a user actually engages with the ad. In other words, when they click on something within the ad itself.
This means that if someone sees an ad but doesn’t click on anything within it, that won’t be counted as a click by the programmatic platform.
Of course, this isn’t the only reason for discrepancies between platforms. There could also be technical issues or differences in how users are tracked.
But if you’re seeing a significant difference in click numbers, it’s worth considering that the platforms might be counting clicks differently.
Discrepancies between Google Analytics and programmatic reporting can cause issues with accurately matching cookies. Google Analytics uses first-party cookies, which are set by the website that is being visited.
Programmatic reporting uses third-party cookies, which are set by a domain other than the website that is being visited. These cookies can be set for a variety of reasons, such as to track conversions or serve targeted ads. 5r90
However, because they are set by different domains, they aren’t always accurately matched. This can lead to discrepancies in the data that is reported by Google Analytics and programmatic reporting tools.
As a result, it is important to be aware of these discrepancies when interpreting data from both sources.
Are there other reasons why you might be seeing differences in your data? Sure! There can actually be quite a few if you take into account all of the technical glitches that can occur.
However, here are a couple more that we’ve seen that could potentially be leading to skewed data.
Definitely don’t forget about geographic inaccuracies and attribution inaccuracies.
Geographic inaccuracies occur when visitors to a website are incorrectly assigned to a particular geographic location. This can happen for a variety of reasons, but the most common cause is IP geolocation errors.
If a visitor’s IP address is incorrectly identified, they will be incorrectly assigned to the wrong geographic location. This can lead to discrepancies between Google Analytics and programmatic reporting, as the two rely on different methods of determining geographic location.
Attribution inaccuracies occur when credit for a conversion is incorrectly assigned to a particular marketing channel. This can happen when cookies are blocked or deleted, or when users click on multiple links before converting.
Attribution inaccuracies can also occur when users convert on a different device than the one they used to first visit the website.
For example, if a user clicks on an ad on their desktop computer and then converts on their mobile phone, the conversion will be attributed to the desktop ad in Google Analytics but not in programmatic reporting.
As a result, attribution inaccuracies can lead to discrepancies between the two types of reporting.
Discrepancies between Google Analytics and programmatic reporting also exist because ad fraudsters use sophisticated methods to commit fraud.
For example, programmatic ad buying relies on real-time bidding (RTB) in order to purchase ad inventory. Advertisers are then able to target their ads to specific audiences. However, RTB is susceptible to bot traffic because it allows for automated bids.
Bot traffic is generated by software that impersonates a real user in order to generate clicks on ads. This artificially inflates ad impressions and click-through rates. That ultimately leads to higher advertising costs for advertisers.
In addition, domain spoofing is another method that ad fraudsters use to commit fraud.
Domain spoofing is when an advertiser’s domain is imitated in order to send fake traffic to the advertiser’s website. This fraudulent activity results in inflated web traffic statistics and can ultimately lead to higher advertising costs for the advertiser.
Discrepancies between Google Analytics and programmatic reporting are not unique to one platform. In fact, they exist across all demand-side platforms (DSPs). The main reason for this discrepancy is that DSPs measure ad impressions in a different way than Google Analytics.
Discrepancies can also occur due to differences in the way that DSPs and Google Analytics track mobile devices.
DSPs typically use device IDs to track mobile devices, while Google Analytics uses a combination of cookies and pixels. This can lead to discrepancies in the reported number of mobile devices.
Finally, discrepancies may also occur due to differences in the way that DSPs and Google Analytics track ad blocking.
DSPs typically use ad blockers to track ad blocking, while Google Analytics uses a combination of cookies and pixels. This can lead to discrepancies in the reported number of ad blockers.
Worried about clicks as your main metric of choice?
If you notice that the total number of clicks in your Google Analytics account doesn’t match the number of clicks reported by your ad platform, there are a few potential explanations.
It’s important to keep in mind that Google Analytics tracks clicks on your website. Your ad platform tracks clicks on your ads. As a result, there are a few potential reasons for the discrepancies.
First, let’s walk through why you might be seeing these differences.
This can happen if someone clicks on your ad but then navigates away from your website before the page has a chance to load. In this case, Google Analytics won’t register the click.
If your ad includes multiple links (for example, a clickable image and accompanying text), it’s possible that someone clicked on the image or text without intending to visit your website.
In this case, Google Analytics will register the click as interaction with your ad, but not as a clickthrough.
Google Analytics filters out certain types of invalid clicks, such as accidental clicks or clicks that were generated by bots. As a result, the total number of clicks reported in Google Analytics will be lower than the number reported.
This might sound super basic, but when you start to notice click discrepancies, it’s possible that you’re looking at two different time periods. Make sure that you’re comparing apples to apples by checking the dates in both places.
Second, it is possible that Google Analytics is tracking clicks from bots and other automated systems. To filter out these clicks, go to the “Acquisition” tab and then click on “Source/Medium.” From there, you can click on “Exclude” and then enter the relevant information.
Finally, it’s possible that some of your clicks are being attributed to the wrong campaign or source. If this is the case, you can use Google Analytics’ “Referral Exclusion List” to fix the problem.
Simply go to the Admin tab and then click on “Property Settings.” Scroll down to the bottom of the page and you’ll see the Referral Exclusion List option. Click on it and then add the domains that should be excluded.
By taking these steps, you can ensure that your Google Analytics account is accurately tracking your clicks.
In the digital age, data is everything. Marketers rely on data to determine which ads are working and which aren’t. But what if there was more to measuring success than just ad clicks?
Data reporting has come a long way in recent years. With the advent of real-time data, marketers can now see how their ads are performing in real-time and make changes accordingly.
However, you might still see data variances. This is why it’s important to look at other measures of success when evaluating your marketing data.
For example, consider engagement rates. If people are seeing your ad it’s still successful. This is true even if nobody clicks on it. Or, if you’re seeing a lift in brand awareness or other key metrics, that’s also a sign of success.
So, don’t get too caught up in ad clicks. Measuring success outside of ad clicks can give you a more well-rounded view of your marketing data and help you make better decisions going forward.
We’ve just thrown a lot of technical jargon at you. Whether you’re new to Google Analytics or love the in-depth data that programmatic reporting provides you with, we can help you make sense of any discrepancies you’re seeing.
From traditional ad agencies looking for a digital partner to local businesses that are entirely new to this whole digital thing, we can help you navigate the choppy waters of online advertising.
Get in touch today. We’d love to chat about how we can help you and your business grow. We promise not to leave you punch drunk with technical talk.