Think about your marketing stack. How many sources of data do you have? Maybe you use Google Analytics, a CRM, a Social Media Manager, a Marketing Automation Software, an SEO tool, and each of these platforms gives you their own detailed data. This is just the beginning of the data you might have to mine — throw in a Point of Sale, Call Center Manager, or Optimization Platform, not to mention purchased data, and I’m dizzy just listing all of that information. How do you verify that data? How do you know that what you’re looking at is accurate? In this blog I’ll walk through some pitfalls of bad data analysis, sort through the information suggesting that less is more or more is more, and how to make sure your data-driven marketing efforts are based on the right stuff.
Is There Such A Thing As Too Much Data?
In a great Forbes interview, CMO of Bazaarvoice Sara Spivey gets to the core of the data saturation problem. She says, “This created a bad practice, as the amount of data proliferated at a rapid scale. Now, the marketer is sitting on top of a mound of data and trying to sift through it to mine for ‘ahas,’ which becomes untenable. The better approach is to start with the business objectives and the key questions you want to answer—and then go seek out the right data.”
In other words, Spivey suggests that with with the amount of data we all have access to, it’s counterproductive to collect everything and then decide what is important to your goals.
Is There Such A Thing As Not Enough Data?
On other side of the “infobesity” coin, when finding a story via data analysis, you really need more than one metric, and you might not know ahead of time what metrics will be the most important.
For example, in a recent SiteSpect case study, a brand revamped its mobile site and A/B tested it. Purchases went down. Focus on that and you’d believe the story that the redesign failed. Look at more data and you’ll see that add to carts increased — something was causing friction in the cart. Everything else had improved.
So, you don’t want too much data and you want too little data. So what do you? When you’re talking web analytics, is your data abundance pulling focus away from your business goals, or are you making ill-founded decisions because you don’t have the whole picture?
How To Find The Whole Story.
You’re going to need to understand how your data gets collected and what it means. What visitor behavior exactly triggers a record, what does not? What do the numbers, the trends, the graphs you see actually mean?
Moz has an article from a while back that holds up about data best practices. Echoing Spivey’s advice, the first must is to separate data from analysis. This means making sure that your data stays constant and you can export and reanalyze it if necessary. This also means, when possible, to make sure you understand the difference between your data and the way that it is reported within your software. For example, if you’re looking at your Twitter engagement rate, you’re not seeing the data but the analysis.
Collect all the data you want, but make sure you know exactly what every number means, and that it’s active. Looking again at Twitter, is your engagement rate determined by likes, retweets, replies, all of the above, or some other combination? When you’re trying to get to the bottom of the story you need some objective record of the facts.
Finding The Balance
When it comes to web optimization or management, the amount of data you have isn’t as important as the quality of data you have. Whatever your method, and whatever side you fall on here (more or less data), you can get the valuable analytics that you need if you see the whole story — from what triggers a record to how you analyze it.