TMCnet Feature
September 23, 2020

Web analytics tips and tricks: What are you missing out on?

As connected, digital devices are increasingly used in every aspect of people's lives around the world, analytics and data have become hot topics, and rightly so.

From Fortune 100 tech companies to a single food truck accepting digital payments, leveraging data about both the business and customers to improve performance is more than important, it is the new standard for how to run a company. (Sure, there are still businesses that don't collect or use any data, but they are becoming increasingly rare.)

It's important to remember, though, that simply "using analytics" isn't a cure-all. Getting clean and accurate reporting is hard and the difficulty increases with the size of the company.

It's no surprise, then, that even tech-savvy companies can make simple mistakes and miss out on critical insights that could increase performance. 

## Inaccurate website data

Most businesses rely on their website and landing pages in some way or another. Websites are the lifeblood of eCommerce businesses, and B2B companies use their website to build their brand, educate visitors and drive interest from potential customers.

There are multiple analytics tools out there, but we'll focus on Google (News - Alert) Analytics in this article because it is the most widely used (in large part because it's both free and a really great product).

Any analytics tool, though, is only as good as the data in it. Getting clean data isn't as easy as a simple install. Here are a few of the top issues we run across with Google Analytics (and other web analytics providers).

Let's look at a few of the most common mistakes that cause businesses to miss out on the power of clean web analytics.

### Basic filtering and settings

As simple as it sounds, you and your company can inadvertently  skew your website data by visiting your own site.

A classic example is the dynamic of sales people visiting the site to look at product information while on a sales call. The information is helpful, but the visit from the sales person is collected in your analytics. There are a few problems here.

First, their time on site (session duration) is probably significantly higher than the average visitor. Second, the content they view and how they navigate to that content isn't reflective of how average visitors actually use your site.

In bad cases, internal traffic can make it incredibly difficult to determine how potential customers are using the site, which makes it impossible to optimize the site for those customers. Worse yet, analytics data is almost always immutable, meaning you can't go back and change it.

Even highly tech-savvy companies can forget to filter properly, or fail to maintain a rigorous process for new employees, locations, etc. Strong data processes are even more difficult in the age of COVID-19 remote work, but they are more critical (location-based data from remote employees can skew analytics as well).

So, what do you do? Well, like any great product, Google Analytics (and others) has several solutions: views, filters and  a few other settings. Let's quickly take a look at each.

#### Views

We recommend having multiple 'views' within Google Analytics. Specifically, we recommend a 'raw data' view that has no filtering, a 'master' view that filters internal traffic, bots, etc. and a 'testing' view that developers and others can use when they are rolling out new features, making updates, testing tracking and working on other internal website functions.

Your technical team should be able to set up views without any problem. (You can also learn more in this help article from Google)

#### Filters

As you might have guessed, filters 'act' on views. The most commonly used filter for removing internal traffic is the IP filter. The basic process is simple: collect IP addresses from all of your locations and all of the locations of your employees (don't forget about VPN as well!), then add them in as filters.

Your IT team should be able to easily put together a plan for ensuring as much internal traffic is blocked as possible.

A few more filters we recommend (your IT team can implement):

- Lowercase filters - these will lowercase components of incoming traffic, links, tags, etc. so that your data is uniform (things like capitalized letters are treated as distinct). We recommend lower-casing hostname, page title, as well as campaign and ad group details

- Remove trailing index - Some sites may have the index.html or index.php file at the end of some pages. This can divide data of one page into two in some instances. To prevent that, the index file can be removed from Google Analytics.

### Data skewed by ad blockers

Ad-blockers have come a long way from being primarily used by geeks and privacy advocates to becoming mainstream now. The percentage of users deploying ad blockers in the US is close to 30% (some studies peg the number at ~45%).

While most people know about ad blockers, not many growth leaders understand how they can negatively impact web analytics. Let's dig in.

Ad-blockers can create major issues when it comes to tracking your users, especially in tools like Google Analytics. Why?

Quite simply, ad blockers are designed to detect and block 3rd-party tracking scripts, which means that the Google Analytics tracking script, and any others you might be using, are highly likely to be blocked.

Think about the implications: if ad blocker usage is at 30% and you have a high-traffic website, you could be losing visibility into almost 1/3 of your website traffic! What's worse is that this is not a 'random' 30% of traffic. Users with ad blockers are much more likely to be young and tech-savvy, which means you are under-representing the behavior of an entire cohort in your analytics.

Even if this isn't a problem for you now based on your audience, it will impact you in the future as ad blockers become more prominent and browsers and devices begin implementing system-wide blocks against some types of tracking.

Unfortunately, the web analytics tools themselves don't have a great solution to this problem because they are, by nature, 3rd-party tools.

The best solution is to make a strategic commitment to collecting your data *yourself*, instead of relying on a 3rd party. Not only does this solve the ad-blocker problem, but is a big win in terms of privacy as well, especially as user tracking regulations become more strict around the world.

There are a few options as far as collecting data yourself. For web analytics only, tools like Matamo  aim to replace Google Analytics entirely.

For companies pursuing a comprehensive data-driven approach, tools like RudderStack ( allow you to collect website data (or app data), then send it to both web analytics tools and other important pieces of the tech stack, like data warehouses, marketing and sales software, and more.

### Lack of proper tagging

Last, but certainly not least, poor tagging is a pervasive issue in web analytics.

The classic example is sending an email campaign to a large group of users, but not tagging the links that point back to your website. (Some email providers auto-tag (News - Alert) links, but even then you can often override those settings when creating new email templates.)

If you forget to define the source, medium, campaign, etc. in the parameters of the URL (i.e., utm_source=email), that traffic will show up in your web analytics as direct traffic, making it impossible for you to track the effectiveness of campaigns.

We recommend a comprehensive link-tagging strategy across teams (marketing, sales, lifecycle, social, etc.) so that any link pointing to your website is properly tagged.

With comprehensive tagging, you can unlock the true power of analytics, which is the ability to see what's working and what's not.

## Conclusion

Analytics can certainly become very advanced, but many companies miss out on a wealth of rich insights because they haven't mastered the basics.

Get with your IT and analytics teams today to confirm that you're filtering internal traffic and tagging all links, then discuss a strategy for implementing tools like Motamo or RudderStack to ensure you're future-proofing your analytics setup with first-party data collection.

About the Author:

Eric Dodds, Head of customer Success,

Eric was formerly CMO of The Iron Yard, which at its peak was the largest coding school in the world. There he grew the business 10x in less than 2 years by building out a data-driven acquisition practice and full-funnel attribution models across a dozen software systems. He joined RudderStack as the Director of Customer Success after several years of highly technical consulting, helping companies build data-driven growth through modern data layers and tech stacks.

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