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September 21, 2010

Dashboard: Analyze your website traffic with Google Analytics in Bime

Today we bring you a Bime dashboard to demonstrate to you the Google Analytics Connector. We have connected directly to data from Google Analytics using the SaaS version of Bime.

Taking a look at the Bime dashboard below, we have utilized one of the newest functions available - grouped dashboards.

The Main tab contains the heatmap and the treemap visualizations. You'll notice to the right of the Main tab, there is a second one entitled "visits by source". This tab contains visualizations containing source data.


Take a closer look at our heatmap, which uses Google Maps on top of Bime to display geocoded data. This is particularly useful because no doubt you have already used Google Maps at some point in your life and are already familiar with its interface!


This heatmap displays pages per visit by country. So the countries that visit the most pages per visit are represented by larger circles (if you zoom in you can see this more clearly!). Don't forget that you can change the rendering mode using the options in the bottom left hand corner of the heatmap, to switch between heat circles and graduated circles. When you switch to graduated circles, the number of pages per visit is displayed inside the circle, and is also represented by the size of the circle too.

heatmap graduated circles

To keep it simple, we just stuck to one measure, but if you wanted to look at another measure on the same heatmap, you could do so on top of "size" or "color" in the pivot table, and this new measure would be represented respectively in the size of the circle radius or in the color.

Another cool flexible feature that Bime offers is the ability to regeocode your data if need be. Sometimes the Google Maps API can misread your data (for example: Georgia can be confused with the state in the USA, instead of the country, and vice versa). Should this confusion occur, it's only a simple matter of regeocoding your data manually. Simply click on the offending point and this will bring up a box containing location information. You'll find a box at the bottom where you can further specify your location, using our example, you would write "Georgia Europe" or "Georgia USA" depending on your requirements. Clicking "(re)geocode" will send a request to Google to change the location on your heatmap. It's as simple as that!


Moving onto the second visualization in the Main tab, here we have an example of the treemap, displaying visits and time spent on page for each month for a number of segments. Each box represents a segment, the size is relative to time spent on page, and the color represents the number of visits for that month. By clicking the play button in the top left hand corner of the chart this will start an automatic loop that will take you through each individual month. Alternatively you can click through manually, using the key on the left hand side.


The time spent on page decreases going from top left to bottom right, so you can instantly see which segments (type of visitors) spend the most time on a page. The color legend to the right of the treemap tells you that the more visitors there are the more blue the box is, and the less visitors there, the more pink it is. You can therefore infer that the site is normally most visited by new visitors, but non bounce traffic spend more time on each page. Armed with knowledge like this, you could then go on to dissect your online activity to see which pages are performing well, and why.

Now let's take a look at the second tab, visits by source. The first visualization is the guages. Here we filtered down to the top 10 traffic-producing sources. The guage makes a nice comparison tool as you can see the angle of the needle of the dial corresponds to the number of visits from each source. For more detailed analysis, the absolute number is also displayed at the bottom of the dial.

Last but not least, our fourth visualization is the simple-but-effective pie chart. We saw earlier on that August was one of our best months, so we decided to analyze a bit further. So we took the time on page and visits per source dimensions for the month of August and stuck them into the pivot table. We put time on page on top of color (blue representing the most, and pink representing the least) and left visits as the section size. One glance at the pie chart tells you that the sources that generate more traffic, also generate visitors that are more engaged, in terms of spending more time on a page. How can you be absolutely sure that direct traffic is the best quality traffic? During your analysis in the pivot table, simply hover over the direct segment and choose "decompose" to drill down even further!


Don't forget: available on the right hand side of every Bime dashboard is the ability to filter the view of your attributes and measures to facilitate analysis.


This was a basic overview of some of the things you can do with Google Analytics in Bime. But don't underestimate Bime's power - there are unlimited analyses you can do with Bime's powerful calculation engine that are otherwise impossible in the regular Google Analytics application. So what are you waiting for? Test it out now here !

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