Over recent years, hyper-dependency on data has become a challenge for digital businesses. Every decision to be made and every change to be implemented depends entirely on digital analytics. And that's why it's so important to guarantee that your data is reliable.
When a business faces discrepancies in its analytics data, a major dilemma arises: which information should you trust when making a move? If metrics don't match across different platforms, it's difficult (or practically impossible) to know which is the right path.
In this article we explain why the lack of consistency and coherence in your analytics matters so much for your business and what steps you should take to avoid discrepancies and improve your decision-making.
In addition to the challenge of depending on data, there's a second problem for digital businesses: depending on too many data sources. In an increasingly digitalised environment, more and more tools and services are being used to run marketing campaigns, optimise processes and even carry out quality control.
Every time we add a new digital tool or subscription to our business, we add a new source of information. The data we start collecting follows the logic and criteria of that platform and often doesn't align with our own analytics.
Analytics data discrepancies arising from different collection, analysis and visualisation models are a very common problem. A problem that leads to real headaches when trying to get the digital analytics to take all the information into account and integrate it correctly.
Such a common problem as discrepancies between your analytics data has implications for your business that often go beyond what you might imagine. Although its impact affects many areas of your business, there's one big victim of inconsistent data: your marketing decisions.
For your business to grow in the digital environment, you depend on many external tools and software. Google Ads, Meta, CRM tools… Without them, there's no growth. But each of them measures data in its own way. The consequence: you can't always make the right decision.
Deciding which platform to invest in, which campaign to pause or which message is working best depends on your data. And if the data from one tool doesn't match that from another, it's practically impossible to make the right call.
Analytics with consistency issues can stem from many factors (both internal and external). Although the list of causes ranges from synchronisation issues to a lack of ownership, there are a number of causes that recur very frequently. Here they are:
Every digital tool or software is a world unto itself. And so is the way they measure and analyse metrics. Many analytics data discrepancies come precisely from these different measurement models.
Some are based on clicks (the user's last interaction), while others track behaviour in much more detail (measuring actual conversion). This results in data that doesn't always represent reality: the user may have clicked, but that doesn't always mean a real purchase occurred.
Measuring the impact of a marketing campaign over a week is very different from measuring it over a whole month. The conversion windows each tool considers vary considerably and may not align with your own timelines.
The time range each tool takes into account varies and they can't be compared directly. This can lead to an overrepresentation of results from some of them, or the opposite effect: something that's working well going unnoticed.
Hyper-dependency on data means hyper-dependency on cookies. While server-side tracking is a more secure and reliable data collection model, many tools and businesses still depend on browsers to measure their impact.
Cookie-based measurement has two major risks: data being more diffuse due to the scope of measurement, and privacy tools used by users themselves creating obstacles to analytics. In short: the data ends up not reflecting reality.
What Google Ads considers a conversion may not align with how Hubspot measures the same metric. This lack of coherence is often due to incorrect event configuration.
Before starting to use a tool (and trusting its analytics), every digital business must ensure that metrics, tags and events match its own analytics setup. Otherwise, very significant "errors" can occur.
The whole world is hyperconnected. If something fails on AWS servers in the United States, the rest of the world feels the effects. And that same effect plays out in the digital analytics of many digital businesses.
Depending on tools located in different time zones or that update their data at a different frequency from your business is also reflected in analytics data discrepancies. And it often makes it impossible to know what's really happening in your business.
The first and most important thing when tracking down analytics data discrepancies is identifying where the problem lies. Before redesigning all your digital analytics, it's advisable to analyse whether this is a specific, isolated issue.
Discrepancies can come from specific channels (your CRM, for example), a specific campaign (with an unusual configuration) or even a generic information source (cookie configuration). Depending on where it originates, you can assess the scope of the inconsistency.
With the source of the problem identified, it's time to analyse the discrepancy. In many cases it's simply a configuration issue in that campaign or tool. Perhaps a human error or a technical problem.
It's important to pay close attention to how each conversion and important event is being measured and analysed. We often trust our tools and assume that their understanding of metrics is exactly the same as ours (and that's not always the case).
In addition to event and metric configuration, it's essential to make sure parameters and tags are configured in the same way across tools. In other words, the technical side must also be consistent.
If UTMs are built and applied differently across your information sources, in addition to having discrepancies you'll also spend a great deal of time trying to find connections between each parameter or tag and resolving a completely incoherent configuration.
Finally, don't forget to account for each tool's timing. Many analytics data discrepancies come exclusively from completely different time windows.
Review how often the data from each tool is updated and how that can cause your digital analytics to update automatically and without warning, changing your business results overnight.
While each digital business's discrepancies need to be addressed with a specific action plan designed for their particular case, there are some general solutions that can help ensure coherence in analytics.
Start by mapping the different attribution models of the tools you use, identifying the metrics and events that truly add value to your business and standardising them across all of them. In general, tools will allow you to follow your own model that genuinely responds to your company's needs.
The more technical configuration of your digital analytics will also require a plan that unifies how you collect and record information coming from each of those tools. Review that all campaign URLs are correctly tagged with UTM parameters following a standardised format, to ensure that traffic data is recorded accurately, uniformly and in a way that's useful for analysis.
Nothing beats ensuring your data passes through a data hub that controls, unifies and verifies that information from different sources meets your criteria and is correctly aligned. Opting for data integration tools will help you ensure coherence and rely exclusively on a single source of truth.
To avoid discrepancies in your analytics data, you'll also need to verify that the timing of those tools aligns, or at least that your digital analytics takes into account possible discrepancies. If conversion windows differ, you should design models that update correctly so those deviations are accounted for.
Discrepancies can appear at any time. Good digital analytics doesn't only depend on a one-off good configuration. It requires constant review and improvement work to ensure that coherence is maintained over time. For this, nothing beats a recurring digital analytics audit.
Discover our specialised digital analytics action plan →
Two of the most common tools in digital businesses are Google Analytics 4 and Meta Ads. Both enable these businesses to grow and improve their results, but the way they measure that impact varies considerably.
It's common to find inconsistencies between the two and, generally, Meta Ads campaigns show better results due to attribution that doesn't always align with GA4. To avoid problems, it's necessary to align criteria between both.
On many occasions, tools serve different objectives for a company: acquisition, activation, retention… And the fact that each is configured differently often leads to data being duplicated and counted more than once.
Advertising platforms may consider a conversion complete when, for the CRM tool, it's only a lead. This generates many discrepancies when trying to measure those users who have actually converted or not.
Your data is your most valuable ally for making the right decisions in your business and achieving better results. That's why you should make sure to treat it well and be able to trust it completely. Avoiding analytics data discrepancies is key to laying the foundations of good digital analytics. At Boost we help you carry out an audit to identify possible inconsistencies and design a specific measurement plan for your business. All you need to do is reach out to arrange a free audit session.
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