Discover Instead of Measure

“Discover instead of measure” was what I kept saying in my last analytics workshop at ClickZ Live Shanghai. In my opinion, analytics is the single most important aspect in digital marketing. Conventional wisdom says if you can’t measure, you can’t respond and take action. I want to push this notion further because measuring is not enough, and as soon as you want to measure, your mind is likely to keep you in a stage of pursuing numbers. In analytics, you need to discover every possibility behind the numbers.

A lot of marketers use analytics as a reporting tool to spontaneously pick a few numbers. This kind of reporting process is partially like Donald Rumsfeld’s famous quote, “There are known knowns.” (It means there are things that we know we know or we know we don’t know.) As the marketers pick numbers for verification, they become more and more number pursuers, and end up falling into the trap of vanity metric. To the readers who don’t know what vanity metric is, it is the measurement that gives you only the aggregated views of hindsight but cannot be actionable or predictable. For instance, how many pageviews you have over the last 90 days, how many fans you have on a social media account, etc. Most of the time, vanity metric only tells the story at the surface or even worse, gives you a false satisfaction.

The first thing I told the audience in my analytics workshop was to avoid vainglory. During the session, I made a comparison between Google Analytics and Baidu Tongji as well as Tencent Analytics. The result shows that unlike Google Analytics, which gives you the capability to develop insight through combining different dimensions and the attributes of traffic, two Chinese analytics solutions are made to report vanity metrics only.

Take developing keyword insight as an example: Both Baidu Tongji and Tencent Analytics give you a flat table with the tabulated data. The default metrics are all vanity such as pageviews and visits. As opposed to Google Analytics, which brings at least two dimensions into your data exploration process. Baidu Tongji and Tencent Analytics assume that the flat table view is the fast answer and that you won’t need anything more.

clickz-jun2014-tongjiclickz-jun2014-tencent

So, how do we treat analytics as a discovery process? Let me show you two practices that I use all the time in my analytics discovery. First of all, don’t point and click randomly. Instead, create a standard model to begin with and make it a habit. In the diagram below (I use Google Analytics to illustrate), I call it a where-to-start model. Always start with checking the date range (I like to compare two date ranges for the depth of the trend), followed by the Acquisition metrics. Next, add a secondary dimension to the view.

clickz-jun2014-wheretostart

An analytics discovery process without exporting data is incomprehensive. For that reason, the next discovery practice that I want to share with you is to create a flat table custom report for keyword engagement.

For example, you can export the last six-month organic keyword engagements from Google Analytics to an Excel spreadsheet. Afterward, you can add the exported keyword data into a newly developed keyword list for your next pay-per-click (PPC) campaign. You should put them into two separate columns and then use Excel’s function (vlookup) to identify if there are keywords that had been previously brought in by organic search. Then, look at the keyword associated session depth or landing page to decide which keyword-landing page combination produces effective engagement. At the same time, you may also want to know whether there is jeopardization between paid search and organic search.

clickz-jun2014-flattable

There are in fact many ways to develop useful marketing insights using analytics. The principle that I am advocating here is not to settle with the default dashboard or to pick the numbers for reporting. If you treat analytics as a discovery process, it will reward you with a capability to shift hindsight to insight and to a treasurable foresight.

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