By Mercedes on December 15, 2014
As marketers, we live and breath data. We all want to believe the data we use is accurate, but I think we can all readily admit that it might not be as accurate as we would like. It seems like data discrepancies are now just a part of our lives.
Inaccurate data does two things: it causes us to doubt the results and it creates an aversion to rely on it to do our jobs. Most companies will accept a 10% discrepancy in data. In other words, if two disparate systems track the same data and are within 10% of each other, the data is accurate enough. Unfortunately, we aren’t always able to get to a 10% discrepancy.
Let’s take a look at why data is inaccurate. You say tomatoes, I say tomatoes. Most often, data discrepancies rise from two disparate systems counting the seemingly same metric differently. Let’s take clicks for example. Some systems report on unique clicks, while another system might report on sessions. Unless you have their technical definitions, you have no idea if these are the same or completely different metrics.
The second most common discrepancy is due to the tracking methodologies used. Many legacy systems rely solely on cookie-based tracking for conversions. You have probably heard by now that cookies are going away and shouldn’t be used as the sole tracking method. Today, more and more companies are utilizing a multi-layered tracking methodology by combining cookies and device fingerprinting to provide the most accurate data. It is important to know how each system tracks and what methodologies are used so you can sort out any potential issues.
The third most common discrepancy is due to misconfigured systems. This can be due to your tag management system being misconfigured or code your developers implemented not working to spec. Tracking links can be missing query string parameters, causing the data to track incorrectly. Tracking tags can get removed (commented out) after a deployment breaking the tracking chain.
The fourth reason things don’t always add up is missing data. You can’t measure what you don’t have. Many times KPIs and other business metrics aren’t available or aren’t getting included in each system resulting in big data discrepancies or gaping holes in our reporting. This means the most important data you need to do your job isn’t available to you.
The fifth reason for wrong data is caused by latency. The super information highway isn’t as fast as we imagine and things don’t always get where they need to in time to get counted or tracked. Tags can load slow or not at all, preventing the data from getting tracked correctly.
Lastly, just plain old misinterpretation of the facts causes us to get the numbers wrong. I have been given reports that didn’t seem to add up or make sense. After questioning the data, we realized that what I wanted and what I was given were two different things.
Now imagine sprinkling in a little of all of these issues and trying to make sense of your data to figure out what you need to change or fix to get it right. No wonder we never feel 100% about our data. There are a lot of things that can go wrong but thankfully, when you know what to look for, you can work through the potential issues so you can trust your data and make those important business decisions.