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,
Analytics
typically doesn't get its fair share of attention in the online
marketing world. And let's be honest, you never hear of a digital
analyst referred to as a "DA." Without a good TLA (three-letter
acronym), it's not as cool sounding as SEO or PPC.
But digital analytics really drives (or should) the decisions we make. The problem is, people aren't doing it right.
After opening SES Toronto with another great keynote by Avinash, I had many conversations with people who are trying to make their data look as cool as his. If you get back to basics, it's not hard to get on the right path. For me, the day's sessions closed with "Metrics for SEO," where Brent Chaters and I presenting how digital analysts should better represent data up the line to the C-levels.
In his keynote, Avinash displayed an image of a a garbage truck dumping trash into a landfill warning of what he calls "data puking." When you report data in this manner, you're merely telling the boss or the client, here's the data, go find the hidden jewels. That's not our job as digital analysts.
Similarly, in my session, I poked fun at default dashboards like the default dashboard in Google Analytics. While I thank the audience for proving my point by nearly unanimously acknowledging they use similar data dump dashboards, it's not something for which anyone should be happy.
Even if we're reporting "revenue," it's simply a number with a label. Sure, it's more informative than visits or hits, but it's not good enough. It explains a piece of the value, but not the whole picture.
When you merely report metrics, all you're doing is data reporting. You are not truly analyzing. Analysis means coming to a conclusion and making a recommendation.
Revenue generated is a great metric for the bottom line. But it only tells you what you made. It does not tell you where you could be doing better. To make decisions, you have to measure metrics that are more than aggregate pageviews, visits or simple goals.
I suggested creating visitor segments based on visitors who converted each of the micro goals. While they are certainly goals, they are also segments of users.
When you have a long sales cycle, and people aren't converting, you need to still market to them properly and help guide them toward the next step. Look at the pages these visitors are viewing. Are there patterns? Are they viewing pages more than once? Is it possible they're not finding the right call to action?
You can also look at time to conversion. Segment by the conversion visits, see which visit number it was. See which pages or channels led up to the conversion. The answer won't come by dumping numbers on a page and saying "here you go." You need to be pro-active to take the next step. You need to figure out where the hold up is and recommend an action to make it happen.
If your site is a service, look how people are using it. If there is search data, check it and look for patterns. Even if your site isn't a revenue-generator, you can use usage data to make actionable recommendations on which features to add or build up. Your time and money are best spent on features people want, not what you think they want.
,
Analytics
typically doesn't get its fair share of attention in the online
marketing world. And let's be honest, you never hear of a digital
analyst referred to as a "DA." Without a good TLA (three-letter
acronym), it's not as cool sounding as SEO or PPC.But digital analytics really drives (or should) the decisions we make. The problem is, people aren't doing it right.
After opening SES Toronto with another great keynote by Avinash, I had many conversations with people who are trying to make their data look as cool as his. If you get back to basics, it's not hard to get on the right path. For me, the day's sessions closed with "Metrics for SEO," where Brent Chaters and I presenting how digital analysts should better represent data up the line to the C-levels.
Data Dumping
At its core, analytics is an iterative process. You track and measure, analyze the data, make a recommendation and implement it. The most important step is making a recommendation – and it's the one people most often miss.In his keynote, Avinash displayed an image of a a garbage truck dumping trash into a landfill warning of what he calls "data puking." When you report data in this manner, you're merely telling the boss or the client, here's the data, go find the hidden jewels. That's not our job as digital analysts.
Similarly, in my session, I poked fun at default dashboards like the default dashboard in Google Analytics. While I thank the audience for proving my point by nearly unanimously acknowledging they use similar data dump dashboards, it's not something for which anyone should be happy.
Even if we're reporting "revenue," it's simply a number with a label. Sure, it's more informative than visits or hits, but it's not good enough. It explains a piece of the value, but not the whole picture.
Your Data Should Tell a Story
Your story doesn't end with delivering data; it ends by making a recommendation for an action that can be taken to do. Web analytics is not about throwing metrics into a pretty dashboard or a PowerPoint slide!When you merely report metrics, all you're doing is data reporting. You are not truly analyzing. Analysis means coming to a conclusion and making a recommendation.
Revenue generated is a great metric for the bottom line. But it only tells you what you made. It does not tell you where you could be doing better. To make decisions, you have to measure metrics that are more than aggregate pageviews, visits or simple goals.
Getting Back to Basics
After my session, I spoke with someone who mentioned he had a lot of traffic and great search visibility, but he was not converting his sales goals. He understood his sales cycle was long - typically 30 days or more. So he tracked white paper downloads and requests for demos as micro goals. However, the overall purchase goals were down. Reporting revenue data here doesn't solve the problem.I suggested creating visitor segments based on visitors who converted each of the micro goals. While they are certainly goals, they are also segments of users.
When you have a long sales cycle, and people aren't converting, you need to still market to them properly and help guide them toward the next step. Look at the pages these visitors are viewing. Are there patterns? Are they viewing pages more than once? Is it possible they're not finding the right call to action?
You can also look at time to conversion. Segment by the conversion visits, see which visit number it was. See which pages or channels led up to the conversion. The answer won't come by dumping numbers on a page and saying "here you go." You need to be pro-active to take the next step. You need to figure out where the hold up is and recommend an action to make it happen.
If your site is a service, look how people are using it. If there is search data, check it and look for patterns. Even if your site isn't a revenue-generator, you can use usage data to make actionable recommendations on which features to add or build up. Your time and money are best spent on features people want, not what you think they want.
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