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The Analytics Missteps Too Many Companies Make
The Analytics Missteps Too Many Companies Make
by John McDonough

One of the fun (albeit challenging) aspects of analytics consulting is that you get to work with a lot of clients in a number of roles. Because of the nature of the work, you’re typically juggling a few different clients at any given time, and because of the way many clients treat analytics work (we’ll come to this later), you’re often cycling onto new ones pretty regularly as well.

While this can certainly introduce some challenges, it has its advantages, too. For one, it’s a fabulous way to learn. When you’re not facing new, unique problems, you’re fine-tuning the solution you developed to the last one. And the learning isn’t restricted to the work. When you work with twenty or more clients over the course of the year, you pick up a lot about the business. You find out what practices different organizations do and don’t follow, what tools are in fashion, and the problems the industry is facing as a whole.

As you can tell from the title of this piece, we want to share some of what we’ve seen over the years. Because yes, there are a number of mistakes we constantly see businesses making when it comes to data collection. And I’m not just talking about nitty gritty configuration errors, I’m talking about the entire way companies approach analytics.

By avoiding these errors, you can run your analytics practice more effectively, more efficiently, and with a greater benefit to your business writ large.

So let’s jump into it.

Right Sizing the Solution

To steal a metaphor from a fairy tale, data collection is a bit like porridge, and unfortunately, most companies never taste the bowl that’s just right.

On the one hand, you have your companies who aren’t heating their porridge up enough. They’re collecting data, but doing the bare minimum. They’re collecting pageviews, and maybe they have a couple of destination goals set up in Google Analytics. No events, no eCommerce data. Another common version of this character type is the business that’s trying to collect the metrics they need, but relying on a one-size-fits-all tool to manage data collection for them—a plugin, perhaps, or trusting the built-in feature of their web platform.

The problem, of course, is that if you want useful, valuable insights, you need good data. And unfortunately, high-quality data is rarely so easy to acquire, especially when it comes to eCommerce. Of the dozens of clients we’ve worked with who trusted an out-of-the-box tool to implement web tracking for them, I can count on a single hand the number who didn’t face significant data issues. Common errors we see include:

  • Under and over-counting of key metrics due to site customizations or features

  • Lack of support for key eCommerce actions, such as product impressions or clicks

  • Incomplete product or transaction data

  • Lack of support for key non-eCommerce site actions, such as form fills, video plays, and more

  • Lack of support for GA4

With marketing and custom acquisition success now so reliant on the performance of algorithmic MarTech tools such as Google Ads and Meta Ads, the importance of complete, correct data can’t be overstated. That’s why we recommend a customized data layer-based analytics implementation for all clients. The slight increase in upfront development costs is paid back in spades.

On the other hand, you have your companies with porridge that’s too hot to eat. These businesses understand the value of data, but let their ambition get the best of them. They know they’re not collecting what they should be, and their proposed solution is to collect data on everything they possibly can, using the latest, most cutting-edge tech, ideally implemented tomorrow.

It can seem counter-intuitive to discourage a client from collecting as much data as they can, but the problem here isn’t the desire, it’s the process. You need to learn to walk before you try to run, and that applies here. Cutting-edge analytics solutions are time-consuming and expensive to implement, and may be designed to support use cases and features you don’t have a need for. How much value does your data have if no one in your organization knows how to access or use it?

We prefer a more methodical approach. Get the basics in place, optimize your practices with what you have, and learn where you can improve. Make enhancements, rinse and repeat. You’ll get better value for your investment, and have a solution that actually fits your needs, rather than what you think they might be three years from now.


Analytics Implementation as a Process

As you can tell from the introduction to this post, I make a lot of temporary friends. The way it tends to work is this: a business realizes they have a hole in their analytics setup, they come to us for help fixing it, we do so, then they lose our number. It’s no hard feelings; I get it! I think we all wish that finding a solution to a problem meant the problem was gone forever.

But this is the web, and being an analytics consultant is more like being a car mechanic than a TV installer. When you finish building your website, you don’t let your development team go, because you know it’s going to require bug fixes, enhancements, and general maintenance to keep operating at the highest level.

Analytics is the same way. Those enhancements you’re making to your site? You probably didn’t plan for them when you implemented your tracking plan. If a problem with data collection arises, you want to catch it before it’s too late. New opportunities for marketing optimization will continue to arise, and your analytics practice will need to be able to support them.

For our clients, we strongly recommend treating the relationship like a long-term partnership, rather than a short-term consultancy. That doesn’t mean you have an ongoing analytics implementation project operating in perpetuity, but it does mean that with a small bank of hours and regular status meetings (once or twice a month), your analytics program won’t just remain reliable, but will continue to grow as your website does. Take ad spend, for example: research has shown that implementing server side data collection can have a significant impact on ROAS. This work pays for itself via valuable insights and the reliable, high-quality decision making our work enables.


We’re closing in on 1,000 words, and we haven’t even touched on the way that clients actually use their data. Needless to say, we see some problems there as well. We’ll be talking more about these issues, and how you can get the most out of the data you are collecting, in an upcoming post.

But remember, there’s no point in talking about how to use data if you can’t trust the data you have. Building a quality web analytics practice starts with a foundation of good processes and the right mindset.

If you’d like to talk more about how Avatria can help you build the analytics team you’ve been dreaming of, give us a shout at the link below.

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