Data Discrepancies in Google Analytics: What Can Go Wrong, Why, & How to Fix It

My Post (3)Google Analytics shows 104 conversions. Your CRM shows 123 new leads. Heap reports 97. And so on.

It’s easy to get frustrated by data discrepancies. Which source do you trust? How much variance is okay? (Dan McGaw suggests 5%.)

For most companies, Google Analytics is a—often the—primary source of analytics data. Getting its numbers aligned with other tools in your martech stack keeps results credible and blood pressure manageable.

This post covers discrepancies between Google Analytics and your:

  1. CRM, CMS, accounting, or other back-end software.
  2. A/B testing tool, personalization tool, or some other analytics tool.
  3. Google Ads.

We’ll show you what causes those discrepancies with Google Analytics data and how to resolve (or, at least, minimize) them.

But before you diagnose a “discrepancy”…

Before you start comparing different data sources and looking for discrepancies:

1. You need to know how each tool operates. That includes how each one defines and measures sessions, users, conversions, etc.

Some tools from the same company, like Google Analytics and Google Optimize, have reporting discrepancies. (Google Analytics and Google Ads also have differences.)

Not surprisingly, differences are greater in tools from different companies. For example, “conversion rate” in Google Analytics is conversions/sessions, while in VWO and Optimizely it’s conversions/unique visitors.

Drilling down further, Google Analytics and VWO limit a conversion to once per session or visitor, while Optimizely allows you to count every conversion.

2. The comparison date range should be long enough to include a decent amount of data, and it shouldn’t be from too far in the past (because something might have changed in the setup).

In general, the previous month or last 30 days is a safe pick.

3. Don’t choose metrics that are similar but not the same. There’s not much point in comparing sessions to users or unique conversions per user to total conversions, etc.

4. When identifying a discrepancy, get as granular as possible. Knowing that you have a 15% difference in overall transactions doesn’t tell you much—knowing that 100% of PayPal transactions are missing is much better.

And before you try to fix one…

After figuring out what could be broken, go through the funnel yourself and make sure that certain events are indeed missing or broken or that something else is off.

In most cases, fixing discrepancies requires some work by developers, analytics implementation specialists, or other experts. Don’t start editing, adding, or removing tags or snippets without proper knowledge of how those tools work.

Otherwise, it’s easy to turn a small discrepancy into a massive issue—one that torches year-over-year data comparisons and just about all of your quarterly (even annual) metrics. – Read more

Assessing Your PPC Data and Analytics Needs

My Post (9).pngIn the world of PPC, data is king. You need it, you use it, and it guides every decision big or small. Yet, wrangling your data is like climbing a mountain. It often requires immense effort, specialized tools, and the grit to see it through. Lacking any of these is a recipe for disaster, but often it’s not until we’re halfway into our climb that decisions made during the planning stages can start to impede our progress.

We’re often in a hurry to get our data into reports and into stakeholder’s hands. Sometimes we lack the technical expertise or money or both for the solutions that would best serve us. Often we simply lack the time to carefully understand our problems and vet possible solutions. This can lead to poor product fit for the problem we have and wasted time.

But, it doesn’t have to be like this. No matter your resources the first step is understanding your needs and taking the time to consider what solution might work best for your situation. Note that I did not say the best technical solution or the most expensive solution. Often we get enamored with tech wizardry and forget to consider our own abilities and resources leading to a fancy solution that doesn’t actually work for us.

So how do we arrive at a plan and figure out possible solutions? It starts with understanding the problem you are trying to solve.

The Problem

Anyone who has worked in PPC long enough is well versed in the amount of data that is present and the struggle to make sense of it. You’re in a constant battle of exporting data from multiple sources (Google, Microsoft, Facebook, LinkedIn, etc.) and trying to make sense of it in some type of reporting format. Often it involves you trying to store it in some place like a spreadsheet or database.

Getting the data, storing the data, and reporting on it are usually constant struggles at all places small and large. Ad networks go down, spreadsheets crash, and reports run slow leading to many headaches all around.

Seeing the frustration and the wasted time that comes from these issues, teams often see a problem and want to find new solutions. This is often a good idea, but a team needs to know its own capabilities, resources, and priorities when figuring out solutions.

In short, they need the proper mindset. – Read more