‘Who said making sense of marketing campaign data was easy? Here are 5 rookie data analysis mistakes to avoid. ‘
To say that today’s marketers have their plates full when it comes to data collection and analysis is an understatement. From the many channels used to engage customers, to the number of ways audiences interact with a brand; finding and deciphering the right data to help you identify actionable insights can feel like searching for a needle in a haystack.
If you want to understand and make the most of your data without getting lost in the numbers, here are 5 rookie mistakes you’ll want to avoid. Keep these in mind, and you’ll be better able to identify new opportunities, adjust your practices where necessary, keep your credibility in check, and make your stakeholders happy!
Being too Broad
Marketing data should never be used to paint a pretty picture or write a fairytale about a given campaign. Though marketers should be great storytellers, this art shouldn’t apply when it comes to analysis. After all, art is subjective, and subjective findings don’t tell stakeholders what’s going on across marketing platforms. What’s more, with so much data at your disposal, drumming up a fairytale from an excel sheet laden with numbers is next to impossible!
You want to narrow down what data to look at, which requires you to ask direct questions that yield specific answers. Rather than asking how your social media presence is doing now that you’ve tied up “campaign X”, you may want to ask, “What are our top-performing Facebook and Instagram posts in terms of reach and engagement?” then, “What are our lowest-performing ones?”. Cross-examination of these will give you insight into what’s working, what isn’t, and why, giving you ample information to refine your social strategy for ongoing and future campaigns.
Mistaking Metric Meanings
Each marketing platform, be it Facebook, Instagram, Google Analytics, MailChimp, or the like, speak a unique language, which begs the question: how knowledgeable are you when it comes to the meaning of marketing metrics?
Are you clear on the difference between an email click-through rate and a conversion rate? What about a hard bounce versus a soft bounce? To truly drill down on the success of an email campaign, you need to know what’s what. Regarding website traffic, do you really know the difference between a visit and a view? How about a bounce rate versus an exit rate? As for social, if you’re focusing on Facebook Likes as a primary indicator of brand engagement, you’re in trouble.
Though many marketers pride themselves on their ability to analyze their efforts, it’s rare to come across someone who hasn’t muddled up a metric or two throughout their career. Before making grand inferences about the success of a campaign element, make sure to brush up on metric meanings to ensure you’re looking at the right KPIs and drawing the right conclusions from them.
Suffering from Correlation/Causation Confusion
Let’s get one thing straight. Correlation and causation are two different things! When two events occur at the same time, you can establish a correlative relationship between them. When one event causes another one to occur, this points to a causal relationship between the two.
Say for example, that you’ve just given your website and social media pages a new look and feel for an upcoming product launch, and you’ve noticed that web traffic is up. Is the new look so fresh that it’s inspired people to visit your homepage (causation)? Or was organic traffic simply up during that same period (correlation)?
It’s our nature as humans to want to connect an event with the most recent (or salient) action we’ve taken. Though it may sometimes turn out to be accurate, more often than not, we’ve mistaken a causal relationship for a correlative one. Though causation is often what we hope to find, it’s important to note that there’s much to be learned from correlations as well.
Becoming Number Obsessed
We’ve been taught that big numbers signal a job well done. From school grades and sports scores to bank account balances, the higher the number, the bigger the win, right? Wrong! Not when it comes to marketing analysis.
A high bounce rate signals a problem on a landing page. A high unsubscribe rate indicates you’re probably missing the mark on some aspect of your email strategy. A high CPA could mean that your marketing strategies aren’t as efficient as they should be. As you can see, a higher number isn’t always an indicator of success. Conversely, smaller numbers don’t necessarily indicate failure.
Furthermore, focusing solely on the numbers — as objectively reassuring as they can be — might lead you to miss out on essential details that can shape the success of your overall marketing strategy. You may have 1.5 million Facebook followers, for example, but how often are they engaging with your posts? Your website may be garnering a lot of organic traffic, but how many of your colleagues are currently on the site at that time?
Failing to Generate Actionable Insights
No matter how awesome your campaigns are, or how much time you’ve spent analyzing the data they’ve generated; if you can’t identify what’s working, what’s not, and how to do better, the time you spend crunching the numbers will have been for nothing!
Marketing data analysis is not solely about documenting traffic sources, post Likes, or lead captures. It’s about identifying the value your efforts bring to the business as a whole. It’s about deciphering which tactics are working, which ones aren’t, and where to invest more of your time and budget. When done right, it also shows exactly how much a marketing department contributes to the growth of a business.
Depending on the type and size of the business you own or work for, manual data analysis might not be the most beneficial practice for your bottom line. Luckily, there are solutions out there that can help speed up the process and clarify your findings. The best part is? Your journey to actionable insights, meaningful conclusions, and smart recommendations may only require a click of a button.
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