Marketing teams are constantly being bombarded with new ways to measure the performance of their tactics. Most marketing tactics can be broadly divided into two parts – Execution and Measurement.
And while both are equally important, it’s crucial for marketing teams to ensure they have access to accurate, timely, and meaningful analytics. But with so many analytics tools requiring manual (and often times complex) data management, analytics can be prone to significant user error.
“CMOs say analytics are actually helping them less now than they were in 2012.” – Source
So how can marketers ensure data integrity and simplify their analytics needs? And what should they be on the lookout for when managing their analytics?
To answer these questions and more, we’ve tried to identify the most common mistakes that are made during the measurement phase.
8 Mistakes You’re Making With Analytics
1) Not knowing where to start or not starting at all.
This one’s pretty straightforward, but it’s an important point to note. When launching any marketing campaign, you need to have an idea of what metrics you need to measure and just as importantly, what success looks like.
How do you know if a marketing effort is underperforming? What metrics are you looking at and why? What numbers indicate a successful execution of a campaign?
These are important questions you need to ask yourself prior to launching a campaign. Here are some other questions to help get you started:
2) Thinking that low numbers are a bad thing.
One of the more common misconceptions when it comes to analytics measurement is the assumption that low numbers are a bad thing.
Before you judge any of your metrics, be sure to fully understand the context of what you’re measuring. What does the metric truly measure? What does this mean in relation to the marketing tactic you’re launching? (Hint: Check out Metrics that Matter series for more insights)
For example, let’s say your website analytics shows a low number for the exit rate on a page and for visit duration. These two metrics mean two completely different things.
On one hand a low exit rate indicates high retention, which is a good thing (in most cases), whereas low visit duration indicates that visitors to your site are not spending too much time on a particular page.
3) Assuming that more time equals more engagement.
Just as it’s wrong to assume that low numbers are a bad thing, it’s also a mistake to assume that visitors to your website are engaged because they spend a large amount of time each visit.
For example, let’s say that your website traffic shows a drastic spike in time spent on a blog page and on your help center.
In the case of your blog, this could in fact indicate higher engagement because your visitors are spending more time reading the content you’re publishing. However, more time spent on your help center could mean that your visitors are having trouble finding what they’re looking for.
It’s important to get a complete understanding of your analytics and take into account the context of each metric in relation to the marketing effort being measured. This brings us to the next point which is…
4) Understanding your metrics well and truly knowing what they mean.
Each metric offers a unique piece of the puzzle. But in order to fully understand the performance of your marketing tactics, you need to first understand what each of them measure.
For example, a common mistake is confusing visits with views.
Visits measure when a website visitor comes to your website from some external URL. But a pageview on the other hand, is measured when your page is loaded (or reloaded) by a browser.
So a visitor to your website could have multiple pageviews when they visit your site (each time their browser loads a page), but are only attributed one visit regardless of how many pages they go to.
Similarly, bounce rate is a highly misunderstood metric that is often times misquoted by marketers. But there are plenty of resources that can help you understand what each metric means, like this great infographic from KISSMetrics:
5) Mistaking correlation for causation.
Just because two metrics increase or decrease at the same time, it doesn’t mean they are related.
For example, this post includes a Google Trend chart which measures the number of times the terms “inbound marketing” and “yoga workout” were searched on Google over the past few years.
Notice that both lines are increasing at about the same rate.
Now if you weren’t aware of what these terms meant you might be lead to believe that the increase in both trends is related and causation did in fact play a part. However, this chart is an example of how correlation does not imply causation.
Finding connections between various metrics is a good approach, but you need to be careful to not mistakenly associate when one metric caused an increase or decrease in another.
6) Not visualizing data properly.
Visualizing data helps you capture your results and share them with your audience. And while it’s important to visualize your data, not doing it properly could hurt your efforts even more. You could misrepresent the data or even reduce the message you were trying to convey.
Good data visualization is about presenting information in a way that is easy to understand, and saves the viewer time and effort.
Here’s a great post on common data visualization mistakes.
7) Focusing on short-term fluctuations, rather than long-term trends
Data is somewhat unpredictable at times. You may see spikes in your analytics based on the day of the week, recent events, or variety of different factors.
So if you do monitor your data regularly, don’t get stuck looking at individual data spikes as a way to determine success or failure of your marketing efforts. Instead, focus on the long-term and try to identify any trends in your data.
For example, in Google Analytics you can view your data across custom date ranges. Pick one for the last 30, 60, or 90 days and see if you find any new patterns in the data. Here’s a screenshot of a Google Analytics graph that shows total number of website sessions each day:
8) Not having actionable steps to take from the data
You’ve looked at the data, identified new trends, found an area to target for improvement…now what?
Once you’ve pulled the data you need, it’s time to figure out actionable steps based on that data. For example, if you’re running a marketing campaign and your social media efforts are underperforming compared to your content efforts, you need to understand why and then invest time and effort into making your social efforts successful.
Maybe your content marketing team has additional bandwidth and can help implement better ideas for social sharing. Or perhaps, your lack of social media performance is indicative of a lack of manpower, which can be fixed by hiring another marketer.
Always use your data to formulate actionable steps that improve your marketing efforts.
Marketing analytics is an iterative process.
There is a lot of trial and error involved when you’re analyzing your data. But it’s important that your mistakes don’t damage your attempts to improve your overall marketing efforts.
Simplify your marketing analytics and establish a scalable process that helps you achieve relevant business outcomes. Doing so will enable you to objectively measure results and position yourself for marketing success.