Big data analytics has permanently altered the marketing landscape – making things a bit more complicated. The data that companies have access to today is like a treasure trove of actionable insights which can be leveraged to improve the way business is done.
Digital marketing thrives in the world of data-born dashboards. And as a result, we follow metrics and traffic influxes like they are a drug.
Let’s be honest, we’re all guilty of obsessing over a blog post that suddenly gains traction. But, the data-driven approach that companies are quickly adopting might not be the promise-land that some analytics companies would have you believe.
Let’s take a look at why.
Mo’ Data, Mo’ Problems
The more sources of big data you begin to access, the deeper your data coffers grow. But, acquiring the data isn’t the hard part, knowing what to do with it is.
Having a lot of big data can be overwhelming, especially for companies that rush into it without asking the right questions.
The more data sources you have, the harder it is to determine exactly what to do with it. You can’t track everything, which means learning to focus on the data sources that hold the most relevant value to your company and its current objectives. Otherwise, it is easy to get distracted from the most relevant and immediate marketing goals.
Companies that adopt a data-driven approach tend to gobble it up like a kid and candy on Halloween. But, not all candy (or data) is created equal.
On the black market for Halloween candy trading, the value of a box of raisins is far less than a full Milky Way bar. The same goes for data; there’s good, valuable data and there’s also bad data that can distract you and even damage the resulting analysis. As you collect data, you’re picking up some Milky Way bars, but you’re also getting some boxes of raisins.
In 2016, Forbes reported that companies would be producing 4,300% more data in 2020 than they are now. That means your big data is growing roughly 1,075% each year. That’s a lot of chocolate and raisins.
No matter how sophisticated your data analytics toolkit is, it’s impossible to utilize every kernel of that incoming data. We’re making data faster than we know where to put it or what to do with it.
As your data coffers grow, the more low-quality data you’re picking up and the more resources and time have to be dedicated towards cleaning, preparing and analyzing the data. This makes it incredibly hard to identify and leverage the good sources of data.
When your halloween candy basket is filled to the brim, it’s harder to find all of the good scores, until you dump the bucket out at the end of the night and take inventory.
Not All Data Tools Are Created The Same
For SEO, there’s a number of data tools available to add to your arsenal. AdWords, Google Analytics, AuthorityLabs, and Rank Tracker are just some of the most popular, in terms of SEO and digital marketing.
But, the list goes on and on, with more and more sophisticated tools popping up like corner coffee shops. Ask any SEO specialist and they’ll rattle off their picks for the best data tools for SEO, but even these “best” tools don’t ensure perfectly accurate data analysis.
In fact, adding more data tools to your starting lineup can muddle analysis just as much as having too much data. Let’s suppose you enlisted the help of Search Console, Google Analytics, AdWords, and Rank Tracking to your data toolkit.
Each add a new dimension, but how helpful is that? Chances are not very, especially when you consider that many rank tracking platforms use different definitions for “clicks” or a unique understanding for how rankings are achieved.
In other words, it is a challenge to get two different reports to “talk” to one another.
The other challenge of data tools, whether they be a search analytics platforms, keyword tracker or another service, is that there’s good tools and bad tools.
More specifically, each tool has its own set of strengths and weaknesses. If you aren’t aware of these, it can make it difficult to use each data-related tool to the best of its ability.
The Data-Driven Approach Has An Inherent Flaw
People are drawn to the data-driven approach because it feels safer. How can data be wrong? While strategies founded on opinions and past experiences aren’t always accurate, data-born tactics seem bulletproof.
The high accuracy of data-driven strategies make them appealing, but that draw comes at a risk. Data-driven decisions are often done with much higher confidence than decisions based on opinion and past experiences.
But, your data isn’t always right and when you formulate strategies based on bad data or incorrect insights, the result can be negatively damaging because of that high confidence placed in the data and subsequent decisions.
Bad data analysis can easily lead a company down the wrong path, especially when the correct questions aren’t being asked. It’s all too easy to make a correlation between data sets that appear to share a relationship.
Yet, their indistinguishable similarities could be a simple coincidence. Alternatively, there could be other factors at play.
For example, it’s not a guarantee that a spike in traffic is a direct result of your analysis of new keywords, but rather could be the result of something else, like a competitor closing business or a seasonal spike in traffic.
Jumping to an incorrect conclusion can easily damage your subsequent marketing tactics if you use this false correlation.
The Data Informed Approach
Data might help you right the course and steer the rudder, but its these non-data opinions that are charting the course. In other words, data should help you make better decisions, but it shouldn’t be the brain behind those decisions.
Data-driven SEO and content strategies are popular because they allow for a tangible way to track the success of your efforts. This is necessary; you can’t survive the SEO or content marketing game without data in today’s world.
But, it’s all-too-easy for businesses to misinterpret what their SEO or content analytics are telling them. And, for the true data-driven loyalists, it’s easy to be misled by above-average metrics.
For example, let’s say this content gets a lot of traffic, like bordering on viral traffic. That’s fantastic and I’ll check in every .032 seconds to see how much more traffic it is getting.
I’ll watch my SEO dashboard like a dog sits by the window watching for her owner to come home. All the data at my disposal will tell me that this content works, so I shift my focus to more conversations about data’s impact on SEO and content marketing.
This means less content that focuses on other non-data aspects, which many long-time readers may find much more valuable than future posts about SEO/content data.
So, by shifting my content strategy to chase high numbers, I could easily disenchant readers that have been following my content for months and even years and thereby damage my brand.
Conclusions
The benefits of analyzing your data and chasing down insights into improving your SEO and content strategies definitely outweigh the potential issues.
That said, you can avoid a lot of these data problems by creating clear goals for your data and asking the right questions. Again, with how much more data we’re creating each year, there’s not a short supply of it.
By having a clear mission, it becomes easier to recognize the data that is going to help support your objectives and the sources that are going to distract from your goals.
It’s okay to explore new data sources and investigate insights that aren’t relevant to your goals, but only sparingly.
Don’t take your data as fact, at least not before you make every effort to ensure its accuracy or think critically about what this potential data-driven decision will have on your brand.
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