Bime 2.09: Google Analytics API 100% mapped, Improved Gauges and Bullet Chart [RELEASE]

127 New Dimensions And Metrics Available.

I can now say with confidence that everything available in the Google Analytics API is available in Bime. So all the data visualization goodness, clever calculations and interactive dashboards of Bime can be applied to ALL your web analytics data!

This is the original post on the Google Analytics Blog. It took us 4 days to map the new elements in the API, and we also added support for custom variables.

In order to take advantage of the new model, you'll have to create a new Google Analytics connection. The organization of dimensions and metrics has changed (for the better) and can't be applied in an old connection.

Other improvements include:

  • A search to easily find attributes and measures in the pivot table.
  • Goals are filtered to only bring back the goals linked to the profiles you selected in the connection.
  • In the new model, custom segments can be imported selectively. This is good for performance as under the hood the "All Segments" attribute fires one query per available segment.

Bullet (and gauges) time

Based on your feedback we boosted the set of features and level of personalization in Bullet and Gauge charts.

Let's take an example with this sample data set:

sample data set

Only actual data

actual data

Actual data with target data

bime desktop

Now let's talk about the ranges in the background that define good / bad performance.

The default setting is split into thirds. Below 33% of the maximum of the Actual data will be considered in the "bad" range and above 66% will be in the good range. As you can see all those settings can be changed:

  • You can change the percentage
  • Move from percentage of Actual to percentage of Budget
  • Define the exact value (not percentage) you want


Now, what happens if you don't want all your gauges have the same rules. In our case, it is that regions are so different that they require special handling. Well, then you can use measures to define the low and high range definition value.


Now you are happy with your data and range definition, you can change all the colors used in the gauge:


Alternatively, you can use a bullet chart that is much more space efficient in a dashboard.


Closing comments

That's pretty much it for this release. Stay tuned for the upcoming dual axes support in cartesian charts and the final release of our high compression rate format: Cloud Pack.