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Google Analytics uses data from your Website to prepare a neat looking application where you can explore your data and answer what I consider your first-level questions. First-level questions are the most basic things you might want to know, such as How many? What Time Period? It is the second- and third-level questions where the analysis and action plans come into play. After all, it’s not enough to know how many people visited the site – you need to know what segment attracted them and how do you keep them coming back for more. [More articles about Google Analytics here]

Reviewing the Google Analytics Dashboard

In the Google Analytics site from the Home tab, you can design a dashboard to review what you consider key measurements for your site. This dashboard is easy to use and works great for checking daily traffic. However, these measurements answer your basic questions, such as the counts. It’s more difficult to use the information for in-depth discovery. For instance, in the following figure I am comparing month over month traffic for the site using the Dashboard.

This number indicates the total visits for each month. So, what can I learn from these metrics? Traffic to the site increased so that is positive, but wonder what caused it.
This spark line chart depicting the visits each day. Since the site primarily deals with a business related topic, most of the traffic occurs during the week. So, there are peaks and valleys for the weekends. No shocker – I knew that before I looked at this chart.

From viewing these measurements, I have learned a little about what is going on but this dashboard does not judge performance, which is what I really need.

Bring Value to the Measurements

Consider the following spark line chart from the SAS BI Dashboard that uses essentially the same data, but with more valuable detail. From this month-over-month data, I learned more with a single glance. For instance, the largest segment (Top Social Sites) had the most traffic but the traffic from those sites is down 11% from the previous month. However, it is okay because the Organic Search has increased by 41%. (Did all my SEO implementation tips work?)

This analysis actually helps me understand what I need to do next, which is determine the reason for the drop in the Top Social Sites (I suspect Christmas holidays) and to investigate the Google Keyword search to find out what the users were seeking. ?) There’s obviously more information here to explore – but the point is that the SAS BI Dashboard can bring richer analysis to the data than what Google Analytics does. In addition, I was able to understand instantly.

Building a SAS BI Dashboard Spark Table Indicator

In SAS 9.3, SAS introduced the Spark Table indicators for the SAS BI Dashboard. There are essentially three steps to creating a this indicator: prepare the data, build the range (colored arrows), and build the spark table. Here’s a quick overview of the process. The SAS Support site has complete documentation on building the Spark Table.

Preparing the Data

A Spark Table indicators groups your data. To provide the data as detail (for the aggregations) and partially summarized, I used PROC SAS SQL. To create the Prev Month column (see above figure), I summarized the month and previous month counts to create a PCT_DIFF column. Then I joined the main table with the temp table so I could add the PCT_DIFF column. The following figure shows the data table in SAS Enterprise Guide and then as the data source in the SAS BI Dashboard. (If this were for a customer, I would have used a stored process to pull this data.)

Note: This table (ga_data.ga_data_sparkline) is not registered in the metadata because it is only used for this purpose and to help keep the metadata slim.

Building a Range

Not all of the SAS BI Dashboard indicators use a range. A range allows you to control the red and green arrows. For these arrows, the PCT_DIFF value is used to judge the colors. Since my blog site traffic is small there are usually huge fluctuations in the percentage difference, I consider any change over 5% to be positive. Otherwise it’s yellow (basically unchanged). This method may not work for your organization, so obviously use measurements that make sense for your data.

Here is how I setup the range:

Building the Spark Table Indicator

You can have as many columns in your Spark Table as you have data. Here’s a quick reference showing how to build the Spark Table columns.

These three columns (Min Visit, Avg Visit, and Total Mth) all use the same data. The only difference is the Aggregate function chosen.
This Prev Month column uses the minimum, which prevents the aggregation and causes the range to work properly. Also note this is where I called in the Range create in the previous step. This is the only time it is used.
The Trend by Week requires two settings. Ensure the Display Settings show Week as the Spark X axis so the line is plotted correctly. Then the Aggregate function is set to Spark line.

Completing the Dashboard

Add the Spark Table indicator to the dashboard. In this dashboard, there were two other indicators: Visits and Difference, which were created with Dynamic Text indicators.

It’s helpful to know the counts, but it’s even better to analyze the measurements in one little minute. This article is part of my SAS Global Forum 2012 series that covers the paper Angela Hall and I will be presenting about our Building Business Intelligence with SAS book.

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