Every entrepreneur knows that “you can’t control what you don’t measure.” How do you know if your online strategy is working? That is where web analytics come in.
Obsessively tracking user actions, then converting the raw data into marketing insights, allowed Amazon to quickly shoot from sprout to giant. The current consensus is that any company not embracing data-driven strategies is almost surely in for a thrashing from the more adept competition.
Behind every great insight, though, there’s the potential for a curse. And where data collection and analysis are concerned, we’ve identified three pitfalls that can drive your marketing and sales efforts in a data-driven journey right off the online sales cliff.
Avoiding these traps will help you save time, gain focus, and grow the bottom line.
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3 Web Analytics Mistakes That Can Hurt Your Business
When you’re driving down the highway and see another car pulling out in front of you – that’s data. Slowing down to avoid a collision is utilizing that data.
Your e-commerce website can’t see out the windshield, though. Responsible driving there means relying on data analytics to determine where you are and where you’re headed.
Just as with driving a car, it’s important to know which data is significant and which isn’t. That oak tree in the pasture is a lovely sight, but it’s not likely to jump out in front of you. That child on a bicycle just might. A good driver watches the child, but ignores the tree.
The world is full of data. But how much of it is crucial to your business?
Web Analytics Mistake #1: Focusing on the wrong data
Smart data collection doesn’t begin with setting up a throng of tools and churning out pages and pages of numbers. Smart data managers know the effective use of data begins with a look inside the company.The effective use of data begins with a look inside the company. Click To Tweet
You must first acknowledge and solidify your business objectives: who are you, what do you want to do, how will you do it?
Begin by making sure you are rock solid on these three points:
- What are our objectives? What do we want to see happen as a result of our efforts?
- What are the specific goals we want to achieve (and when) for each of those objectives?
- How will we know whether or not we succeed at each goal? What are the key performance indicators we need to monitor?
By beginning with an internal focus, you’ll automatically rule out most of the data sets available to you. While they may be interesting, if they don’t affect your KPI’s, then why should you exert the effort?
There’s nothing wrong with dabbling in the ocean of information surrounding you. It’s even possible you’ll fish up something of real interest and value there. Effective web analytics, though, are targeted web analytics – aim your primary efforts at your primary needs.Effective web analytics are targeted web analytics. Click To Tweet
The tools and procedures you most need to establish and monitor are those that provide the insight you need to steer your business in the desired direction. If you’re going the wrong way, efficiency and speed aren’t beneficial, they’re detriments.
Web Analytics Mistake #2: Assuming all data is accurate data
Have you heard nightmare stories about what happens when travelers accept data from online maps as infallible? One report described GPS discrepancies that led people on frustrating, even fatal, trips into Death Valley.
The same can happen when the data you rely on to make crucial business decisions is flawed. The errors can lead you astray, maybe even into catastrophe.
A corollary to this principle, though, is to not demand that data be 100% accurate. You’ll want to compare results from several different tools or sources to test for reliability, and you’ll undoubtedly find discrepancies.
That means you’ll have to determine your own acceptable margin of error, and you’ll have to focus on comparing data trends more than on comparing data points.
One reason for erratic results is that measurement criteria and collection methods differ from tool to tool. There can also be filter sets that adjust the numbers. For instance, website traffic stats can drop significantly when bot traffic and internal traffic are filtered out.
To gain confidence in the numbers you’re given, look at the source – but be sure to look at the originating source, not at the discovery source. A poorly executed study is still a poorly executed study – even if it’s reported in a well-respected journal.
Web Analytics Mistake #3: Failing to make effective use of your data
Worse than not having the accurate data you need to guide your business is going to the trouble to collect the data… then not using it effectively.
Data collection and reporting can become an activity that gets plenty of interest, but brings about little change. Those who don’t focus on the right data are especially susceptible to this mistake. It’s tough to make good use of something that doesn’t apply to the work you’re trying to accomplish.
Other reasons companies can drop the ball here include:
- There’s an insidious tendency to make the data fit the plan rather than adjusting the plan to fit the data. No one is invulnerable to this threat.
- It’s easy to stand so close to the forest that you miss it and end up focusing on the trees. Data analysts must discipline themselves to consider data points in relation to the larger picture.
- It’s always easier to take a snapshot than to edit a movie. While agility is a definite attribute in the fast-moving world of e-commerce, there’s still much to say for taking time to make informed decisions.
Experiment with the ways you view data. Turn it into pie charts, scatterplots, heat maps, tree maps… the possibilities are numerous. There are times when a pertinent factor is hidden when viewed in one way, but obvious when looked at from a different perspective.
One more point: remember to segment data according to the criteria applicable to your particular business. Do women interact differently with your website and your products than do men? Is age a factor in the results you seek? How about geography or season?
Segmentation, combined with different viewing mechanisms, can help you make the best use of the information you capture.
Take a look at your current data acquisition and analysis procedures. Is the work aimed at accomplishing specific goals within your stated objectives?
Have you established benchmarks and identified key performance indicators to tell you whether or not you are on track to meet those goals and objectives?
And are you milking every drop of actionable knowledge from your data analytics efforts?
If not, refuse to listen to the next staff member who suggests you invest in a new tool or subscribe to another source of information.
Get the fundamentals in line first. That’s always the best strategy.