We may want to plot the number of new visitors by landing page over time:
Or perhaps we want to compare the number of transactions by customers based on the channel they were first acquired on (first touch referer source):
Creating the above slices of data is as simple as selecting the dimension / metric combination from the long list provided in the Looker UI.
Looker’s metadata model makes it very easy to define and analyze busines specific:
To give a very specific example: at Snowplow we are very interested in whether or not visitors to our website visit the ‘services’ pages, for example, as that indicates that they are potentially interested in our Pro Services offering.
We can add a dimension to our
events.lookerml model that categorises whether a specific event has occurred on a services page or not:
We can then create a metric that counts the number of events that occur on services pages, further down the
Both the above dimension and metric will now be available to include in any report produced in the Explorer. For example, we can now compare the number of events that occurred on services page by marketing campaign, landing page or over time.
We can define additional derived metrics (e.g. average events on a service page per visitor / session) or dimensions (e.g. classify visitors by whether or not they have visited the services pages at all) by simply extending the metadata model. The Looker metadata model is flexible enough to extend with your business, as you become more sophisticated in your use of data.
To illustrate this, let’s start by comparing visit and engagement levels by refer medium for the last month (i.e. a session-level analysis):
We can see visitors referered from other websites appear to engage more deeply, on average. We can explore that further, to see if it is true across e.g. all landing pages, by clicking on the Landing Page Count (which is “7” and circled above):
This opens another view, which lets us compare events per visit and bounce rates by the seven different landing pages that users refered to our websites from other websites were driven to. It looks like users refered from external websites to our recipe on market basket analysis engaged particularly deeply with our website:
We can explore this further by clicking on the visit count to see the actual visits. For example, if we click on the “17” visits to the market basket analysis (circled above)…
…we are shown an actual list of the 17 visits, including the cookie ID and the time each visitor spent on the website. (Note that all but the 3rd visitor were visiting our website for the first time).
It looks like the 9th visitor on the list was on our website for a particularly long period of time - let’s click on “Event Stream” (circled above) to find out what he / she actually did on the website:
We are now shown the complete event stream for that user on that session. Incredibly, the visitor only visited that recipe page (did not navigate to any other pages on our website).
<h2>4. Dashboards are a starting point for more involved analysis</h2>
It is straightforward in Looker to develop customized dashboards. The following is an example of one included in our Looker release:
Most BI tools offer great dashboarding facilities. What we like particularly about Looker’s is that clicking on any of the graphs sends you straight into the Explorer, so you can then start slicing / dicing and drilling in as described in the sections above. For example, if you clicked on the data point circled above (representing the number of visits from search engines to the website on January 6th) brings up a list of all those different sessions. We can then click on the Event Stream for any of those sessions to see what actually occurred.
<h2>5. Access your data from any application: Looker as a general purpose data server</h2>
As well as enabling users to plot graphs directly in Looker, it is also possible to use Looker as a data server to make your data easily available to other applications to visualize.
You can set Looker up to make specific slices of data available at designated URLs, in JSON, CSV or tab-delimited format, so that it can easily be ingested and refreshed from any application, including:
Say for example the following cut of data was important to us (the number of visits and events per visitor by web page, for the last week):
We can use Looker to publish the data to a URL. We’ve published the above view to the following URLs - check them out in your browser to see how easy it is to fetch the data:
You can see how the data looks in Google Spreadsheets below:
Because the data is being served live, it is always up-to-date. Pretty cool, huh?
Then get in touch with the team at Looker or the team at Snowplow to arrange a trial.