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Ecommerce Analytics vs Google Analytics

Ecommerce Analytics and Google Analytics can help you grow your business in different ways. However, you may see discrepancies when looking at data collected by both systems (even if you were to compare the same metrics). Each system uses its own technique and methodology for collecting data, identifying and counting users, and calculating metrics. Below we'll cover some of the differences between the two analytic tools and why some of the metrics are reported differently.


How They Compare

We recommend using both Ecommerce Analytics and Google Analytics. However, there are key differences in the type of information each tool provides, how the information is presented, and the level of detail to which merchants can drill down.

Although Google Analytics can measure and help you improve your search engine ranking and connect with Google Search Console, it does not provide the necessary insights needed for scaling businesses to compete with big box brands and marketplaces like Amazon.

A big limitation of Google Analytics is that you cannot track individual customer behavior by name or email address. The Ecommerce Analytics Customer Report helps you understand how your customers are behaving on your site in near real-time. You can also use this report to determine whether you are doing a good or bad job converting new customers to repeat customers.

Example Customer Summary Report

Ecommerce Analytics will empower you to track behavior back to revenue and see where each customer ranks in terms of spend — which Google also doesn’t do. Successful merchants want to measure how much of their revenue comes from existing customers. Great businesses like Amazon generate two-thirds of their revenue this way, and we’re glad to provide our merchants with the same suite of capabilities.


Why They Report Differently

The first thing to note is that these tools are not cut from the same cloth. Each reporting tool was built by different organizations, each with different goals, criteria, and terminology. Although these tools report similar success metrics, there will be discrepancies when making comparisons. Additionally, Ecommerce Analytics does not pull any data from Google Analytics. We use our own custom javascript to capture tracker and visit attribution data.

Here are a few reasons as to why your reporting may look different when comparing data between your in-store analytics and Google Analytics:


Ghost Referrals

Ghost Referrals are something you will want to account for when using Google Analytics. Ghost Referrals are typically the reason behind data inconsistencies between Google Analytics and Ecommerce Analytics. The vast majority of spam is of this type and is commonly referred to as “ghost” because there is no interaction with your store at all.

Spammers use Measurement Protocol, which allows for them to send data directly to Google Analytics' servers. Using this method, and likely some randomly generated tracking codes, the spammers leave a "visit" with fake data, without even knowing what site they are hitting.

Of course, you can ignore this data, but the fake trail that the spam leaves behind pollutes your reports. It might have a greater or lesser impact depending on the amount of site traffic to your online store (the less traffic you have, the more significant the effect will likely be), but everyone is susceptible to spam.

Before we dive in, here are some tactics for managing spam you should avoid:

  • DO NOT assume your site has been hacked. The page that the spam shows on the reports doesn't exist, and if you try to open it, you will get a 404 page. Your site hasn't been compromised.

  • DO NOT use the referral exclusion list to stop spam. The name may confuse you, but this list is not intended to exclude referrals in the way we want to for the spam. It has other purposes.

  • DO NOT worry that bounce rate change will impact your search engine rankings. With or without spam, Google doesn't consider Google Analytics’ metrics as a ranking factor. Here is an explanation about this from Matt Cutts, the former head of Google's web spam team.

To cope, we recommended adding the referral to an exclusion filter after it is spotted. Although this is useful for quick action against the spam, it is time-consuming and not foolproof as spammers use direct visits along with referrals.

However, you will find that most of your spam is ghost spam, which means it’s hitting Google Analytics's random tracking-ID (meaning the offender doesn't know who the target is), and for that reason either the hostname is not set, or it uses a fake one. You will find strange hostname conventions or that none were set when spam is present.

Example of ghost referrals

Valid traffic will always use a real hostname. In most instances, the hostname will be a domain. However, these can also result from paid services, translation services, or any other place where you've inserted the Google Analytics tracking code.

Example of valid referrals

This information will allow you to create a filter in Google Analytics that only includes real hostnames. By creating a filter, you will begin to automatically exclude all hits from ghost spam, whether it shows up as a referral, keyword, or page view; or even as a direct visit.


Session Timeouts

Another item for you to be aware of is that Google Analytics logs a “session timeout” after 30 minutes of inactivity. This means that after 30 mins of inactivity on your online store, a shopper is then considered a new visitor.

Similarly, Ecommerce Analytics assigns a user two IDs (a visitor_id and a visit_id.). If a user (unique visitor_id) has been inactive for 30 minutes, we will assign the visitor a new visit_id. Because your in-store analytics visit count is based on visit_ids, the number is not unique for visitors but rather a sum of all the visit ids.

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