What Is Digital Analysis?

In my last column, I shared a recent experience at the eMetrics Marketing Optimization Summit on the Analytics Symposium. There, the question of the day was, what is analysis? That column included an overview of some definitions of analysis provided by my colleague Chris Kerns’ Web analytics team. Based on his team’s input, I shared some of the top 10 terms that describe analysis.

As promised, this column will go deeper here into what analysis is and what it isn’t. This again will expose why so many companies fail to reach the potential Web analytics has to offer.

Before digging into that topic, let’s consider a question posed by a reader in response to my last column: what type of analytics are we talking about here and how do you use it? It doesn’t matter what type of data you use; the principles of good analysis are the same. But in terms of specific analytics discussed here and at the Analytics Symposium, it’s all about digital analytics — with the goal of using metrics to improve your digital properties’ performance. (Note: I’m wording this carefully to avoid saying “Web site” because that’s only part of the story. Mobile, media, social, partner, offline impact of the site, etc., are part of the story, too.)

The primary type of data and analytics that most people think of in this space is Web analytics, but there are many other data types that can help you understand how your Web channel is performing, including:

• Attitudinal measurement (surveys)
• Satisfaction studies
• Social media measurement
• Media measurement, including display, paid, and organic search
• Competitive data analysis
• Focus groups or heuristic reviews

This is only a partial list and will vary depending on your industry, company size, and other factors that must be balanced against the value of the action you may be able to take from insights you gather. The descriptions outlined in my last column offer a good view into any type of analysis, but I was referring specifically to experiences that came from digital analysis and insight.

But what is analysis and what isn’t it? The following lists include a few important distinctions between the two categories. These lists could go on and on, but these items are the ones we see most companies struggle with.

Analysis is:

• Goal oriented. Too often people start looking at data without understanding the initiative’s specific goals and business drivers. Unfortunately, many companies skip this step assuming it’s obvious, which it never is. The top goal may be obvious, but the waters get muddy quickly after that. The foundation of all great analysis is to ensure that goals are defined and that you’re conducting analysis based on improving the initiatives’ performance against those goals.
• Actionable. Good analysis drives action. It isn’t just looking at how you did last week or last month but drives improvement. Analysis must be delivered in a way that it entices recipients to take action and make changes.
• Insightful. Think differently. Consider different perspectives.
• Impactful. Focus where the biggest potential upside is.
• Change-based. Again, work with the intent to drive changes to an initiative.
• Inclusive. Make sure you consider the big picture, not just one audience segment, one site page, or one type of data.
• Ongoing. Good analysis is conducted on an ongoing basis. As people make changes based on your recommendations, you must continually reanalyze to identify additional opportunities and evaluate the changes made.
• Difficult. Analysis isn’t easy and not every hour you spend pouring through data will deliver a nugget that can change the site. It’s hard to find experienced people who can combine the data and truly uncover insightful recommendations rather than just report the data.

Analysis is not:

• Reporting. By “reporting,” I mean more of the rearview-mirror type of data, such as how you did last week or last month. We’ve all seen these reports; they come out every week or month and include tons of data points and minimal copy describing what’s going on and what the recommendations are.
• Web analytics. Analytics is not analysis; it’s simply one data source that can be used in successful analysis. Too often, marketers think Web analytics is the only source and that they can do everything they need to with just the data in the Web analytics tool. This isn’t the case. Think of Web analytics as a tool in your toolbox, like a hammer you use to help build a home.
• Automated. Reporting can be automated, but the creation of insight through analysis is a human activity based on data, experience, knowledge of the business, goals, and more.
• Siloed. Don’t look at just one audience segment or one data type. Instead, consider the big picture and have the data you need to get a view into that.
• Easy. Again, this stuff isn’t easy. When done correctly, analysis can truly shift the way companies make decisions about their digital channel.

What can you do to transition from a lack of analysis to true insight and action to improve your digital properties? Start by finding a executive or senior sponsor who can get excited about the insight digital analytics can bring to her team; this person will be your ally and help you shift the way people within your organization think about data. Second, create a cross-functional team and make sure the channel goals are clearly defined, agreed on, documented, and shared throughout the organization.

Now comes the hard part: find an important initiative and, using data analyze, have the team analyze how it’s performing and make recommendations to improve it. (Yes, I’m glossing over this, but I’ve covered it in depth in other columns.) Finally, the most important aspect: help the team take action on that insight. This may include A/B or multivariate testing. Until you can get your organization to take action on insight, your analysis is really just good-to-know information and won’t be seen as the truly valuable information.

How else have you moved from reporting to insightful analysis? Please share in the comments section below.

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