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The process of analysis is arguably the most important element of any marketing campaign. You can gather as much data as you want, but unless you’re analyzing it effectively, it won’t help you form the meaningful conclusions you need, to make changes and design better campaigns in the future. And, of course, if you aren’t analyzing data at all, you won’t have the chance to make improvements to your campaign at all.
Related: Beat the Marketing Competition With Data Analysis
Unfortunately, there are a few traps that many new marketers fall into when it comes to data and campaign analysis; these traps include psychological biases and decisions rooted in misconceptions. If you want to become a more effective — and a more effective — marketer, you’ll need to gain awareness and mastery and avoid all five of the following problems:
1. Not asking questions
First up, your data isn’t there to tell a story. It’s not puzzle pieces, either, which you can pick up and rearrange to form a meaningful picture. Because modern data sets are so comprehensive, gathering meaning from an open spreadsheet or report is next to impossible.
Instead, narrow your focus and specify your intentions by asking questions. For example, instead of looking at your data to see “how the website’s doing,” ask specifically targeted questions like, “Are we earning more social traffic?” or, “Is the new content strategy working?” This will guide you to only the significant data points you need, helping you to then arrive at more meaningful conclusions.
2. Relying on one data set
Most data trackers these days are reliably accurate — to a point. Different analytics platforms and tracking mechanisms have different advantages, and often offer different groups of metrics. If you want the big picture, you can’t pick just one source and be done with it (no matter how tempting it is to rely on Google Analytics for everything).
Besides, if you go with only one set of data, you’ll be limited in the types of questions you can ask. You’ll also want to collect both quantitative and qualitative data — as both are necessary to form a comprehensive picture.
3. Misinterpreting the meaning of a metric.
Online metrics are often labeled ambiguously, and even if they aren’t, it’s still difficult to discern exactly what they mean. Don’t assume you know what a metric means unless you’ve looked it up and verified it for yourself.
For example, do you know the difference between a “visit” and a “view”? Do you know the difference between a “bounce rate” and an “exit rate”? These are similar but distinct metrics, so your conclusions will be skewed if you confuse the two. It’s also common to overestimate or underestimate the value of a metric; for example, many people believe “likes” on Facebook are a direct marker of popularity, when in reality, this number tells you nothing of your audience’s disposition toward your brand.
Related: 4 Marketing Analytics Tools That Are Shaping the Industry
4. Confusing correlation with causation
This is an easy mistake to make since so many different online marketing strategies can influence each other. For example, you might launch a new social media strategy and start seeing an increase in organic traffic. Does this mean that your social strategy is making you rank higher in Google? Not exactly; social media only plays an indirect role when it comes to influencing search ranks. If you take this as a causal link, you’ll be tempted to continue, even if the strategy has only coincidentally or indirectly influenced your stat in question. It’s hard to establish causation, and correlation is often a good thing, but try to keep the two separate in your analysis.
5. Getting wrapped up in the numbers.
For most analysts, numbers are comforting. They’re objective. They’re consistent. They’re crunchable. But unfortunately, when you become too obsessed with the numbers, you tend to lose sight of what’s important in your campaign. For example, it’s good if your organic traffic is up, but what kind of experience do those users have with your site? You have more social media followers, but how actively engaged are they with your brand? Dig a little deeper if you want the whole story.
6. Comparing apples to oranges.
With modern technology and tracking systems, it’s easier than ever to compare identical metrics over differing spans of time, yet so many inexperienced marketers still end up comparing apples to oranges in their analyses. For example, a marketer may compare last month’s bounce rate to this month’s successful conversions; bounce rates and conversions are connected, but it’s hard to make a direct comparison or establish a firm conclusion from this side-by-side glance.
7. Failing to generate actionable conclusions.
Finally, understand that not all conclusions are useful. Instead of just making objective statements about the state of your campaign, go a little deeper and figure out what you can do with those conclusions. Are they telling you to change something? Have they uncovered a successful strategy you’ll need to repeat or grow? Your ultimate goal should consist of more than just realizations: you need actionable takeaways.
Related: 10 Tools Helping Companies Manage Big Marketing Data
These are some of the biggest analysis mistakes a new marketer can make, but they aren’t the only ones. The truth is, even expert analysts sometimes make bad judgments and poor exclusions simply because the data available to marketers today is so rich and multifaceted. Avoid seeking perfectionism (or you’ll end up disappointed), but instead strive to make gradual, regular adjustments to your analytic capabilities as you gain more marketing experience.