MatrixAdapt | Logiciel de gestion d'Entreprise, Création et référencement des sites web

• Far more visits originate from Twitter (3.6x) when compared to Facebook, perhaps not surprising given Avinash's Twitter followers (~16,120)
• Visitors from Twitter tend to be new visitors, a good thing, but they view fewer pages and spend significantly less time on the blog.
• On the other hand Facebook delivers an audience that is loyal. These visitors come back to the site more often and spend a significant time on the blog (compared to Twitter and all other visitors).

1. Create them on the fly by specifying their expression directly through an API query.
2. Use advanced segments created in the Google Analytics web interface through the API.

• Faster tracking code load times for your web pages due to improved browser execution
• Enhanced data collection & accuracy
• Elimination of tracking errors from dependencies when the JavaScript hasn't fully loaded

<script type="text/javascript">

  var _gaq = _gaq || [];
  _gaq.push(['_setAccount', 'UA-XXXXX-X']);
  _gaq.push(['_trackPageview']);

  (function() {
    var ga = document.createElement('script');
    ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 
        'http://www') + '.google-analytics.com/ga.js';
    ga.setAttribute('async', 'true');
    document.documentElement.firstChild.appendChild(ga);
  })();

</script>

import gdata.analytics.client

APP_NAME = 'goal_names_demo'
my_client = gdata.analytics.client.AnalyticsClient(source=APP_NAME)

# Authorize
my_client.client_login(
    INSERT_USER_NAME,
    INSERT_PASSWORD,
    APP_NAME,
    service='analytics')

# Make a query.
query = gdata.analytics.client.GoalQuery(
      acct_id='INSERT_ACCOUNT_ID',
      web_prop_id='INSERT_WEB_PROP_ID',
      profile_id='INSERT_PROFILE_ID')

# Get and print results.
results = my_client.GetManagementFeed(query)
for entry in results.entry:
  print 'Goal number = %s' % entry.goal.number
  print 'Goal name   = %s' % entry.goal.name
  print 'Goal value  = %s' % entry.goal.value

Nick

Pete

• Which content receives the most comments on social networks?
• What are the conversion rates for visits from social networks?
• For social buttons embedded in my property, which ones get used most often and for which content?

• What are the top 10 mobile devices used to visit my website?

• What are the Top 10 Campaigns for the San Francisco Bay Area DMA?

• What does the performance of my website look like across different sections?

• On a daily basis, how many views did my mobile app get?
• On a daily basis, how many exceptions occurred in my mobile app, and how many exceptions were fatal? 

• What were the time measurements for all user defined timings?

• Dimensions & Metrics Reference for a list of all available metrics & dimensions. 
• Google Analytics Query Explorer to help you build queries and try out the Core Reporting API.
• Core Reporting API changelog for a list of the 40+ new dimensions & metrics.

(Cross-posted from the Google Analytics Blog)

Back in Episode 10 of Web Analytics TV, (32:00), Lisa C from Melbourne asked how to pull a trending report from Google Analytics for the top organic search landing pages. This was such a great question, that we wrote 2 articles and released sample code describing how you can automate retrieving this data from Google Analytics Data Export API. But first let’s look at the results.

Here is a graph plotting traffic to the top 100 landing pages for organic search for all of June for www.googlestore.com.

Let’s Analyze:
This is the typical trend graph you can find across the Google Analytics web interface. By itself, all you can tell is that something happened during the spike. what you can’t figure out is which page actually increased in traffic; to do so would require lots more digging.

Now let’s try again:
Here is a stacked area graph of each of the top 100 landing pages for organic search.

Let’s Analyze:
Awesome right! So obvious why this is cooler. But let me explain.

Lisa’s graph, above, presents significantly simplified insights. Notice how much more we can get from this graph. We can see the green page is what caused the big spike. Also we see that the blue and orange pages had interesting changes in traffic patterns; changes we couldn’t identify from the graph on the left. Being able to break down the totals graph is indeed a gold mine for analysis.

Typical actions you, or Lisa (!), can take from this data are to get the organic search keyword to send traffic to the blue page. Then to identify the keywords sending traffic to the green and orange page and see if we can increase traffic to other pages.

Exporting the Data from the web interface:
Anybody can pull this data from the Google Analytics web interface. You simply create a custom report with landing pages and entrances. Then drill into each landing page, and export the data to a csv file. Finally you go through all csv files and compile them into a single file for analysis. Let's illustrate:

Going through each report individually is a LOT of manual work, but we can automate all of this using the Data Export API; reducing hours of work into a few minutes!

Using the Data Export API to Automate:
In part one of our series, we demonstrate how to use the Data Export API to automate the exact task above. A user specifies 1 query to determine the top landing pages. The for each landing page, a separate query is used to get the data over time.

This is great and we built it to work with any query with a single dimension. But notice that the number of queries grows with the number dimensions. In fact this program requires n + 1 queries so if you want data for 1,000 dimensions, it will take 1,001 queries.

This is bad because there is a daily quota of 10,000 queries for the Data Export API. So if you ran this program 10 times, with 1,000 dimensions, it would require 10,010 queries completely using your quota. ouch!

Optimizing Data Export API Requests:
To reduce the number of queries requires, the second part of this series describes an alternate approach to retrieving the same data, but minimizes the number of queries required. In the second approach, we use Data Export API filter expressions to return data for multiple dimensions in each request.

This approach dramatically reduces the amount of quota required. In the best case, only 2 queries are required.

Using this second approach, analysts can now run this report to their hearts content. They can do this for different time frames, and different dimensions, comparing organic vs paid traffic, trends of keywords by search engine, even compare traffic by geography.

As we mentioned, we wrote two articles describing both approaches and released the sample code for the application. Let us know the amazing insights you find through using this tool.

Have fun!

(Cross-posted from Google Analytics Blog)

On the Google Analytics API Team, we’re fascinated with what people create using the Data Export API. You guys come up with some really amazing stuff! Lately, we’ve also been paying a lot of attention to how people use it. We looked at whether the API has stumbling points (and where they are), what common features every developer wants in their GA applications, and what tricky areas need deeper explanations than we can give by replying to posts in our discussion group.

As a result of identifying these areas, we’ve written a few in-depth articles. Each article is meant as a “Deep Dive” into a specific topic, and is paired with open-source, sample reference code.

In no particular order, the articles are as follows:

Visualizing Google Analytics Data with Google Chart Tools
This article describes how you can use JavaScript to pull data from the Export API to dynamically create and embed chart images in a web page. To do this, it shows you how to use the Data Export API and Google Chart Tools to create visualizations of your Google Analytics Data.

Outputting Data from the Data Export API to CSV Format
If you use Google Analytics, chances are that your data eventually makes its way into a spreadsheet. This article shows you how to automate all the manual work by printing data from the Data Export API in CSV, the most ubiquitous file format for table data.

Filling in Missing Values In Date Requests
If you want to request data displayed over a time series, you will find that there might be missing dates in your series requests. When requesting multiple dimensions, the Data Export API only returns entries for dates that have collected data. This can lead to missing dates in a time series, but this article describes how to fill in these missing dates.

We think this article format makes for a perfect jumping off point. Download the code, follow along in the article, and when you’re done absorbing the material, treat the code as a starting point and hack away to see what you can come up with!

And if you’ve got some more ideas for areas you’d like us to expound upon, let us know!

Jeetendra

Nick

• Event goals are fully represented.
• An internal web property id that can be used to deep-link into the Google Analytics user interface.
• Profile configurations for the default page and site search query parameters.

Many developers have asked for a faster, more powerful way to access Google Analytics account configuration data through the Data Export API. We’ve listened and today we’re releasing a preview of the new Google Analytics Management API.

The Management API provides read-only access to Google Analytics configuration data. It consists of 5 new Google Data Feeds that map directly to the Google Analytics data model.

Previously, the API returned all the configuration data at once, which in many cases was inefficient if you only needed a subset of data. Now with separate feeds, developers can request only the data they need. For example, it’s now easy to get the Profile IDs for a single account or the Goal configuration data for only a single Profile.

To help you learn more we created a new Management API section in our developer documentation. We also created new reference examples in Java and have a live working demo in JavaScript. Check it out, no coding needed!

The Management API is being launched in Labs as an early preview. The API will change, grow, and get better over time. We recommend developers who aren’t committed to making updates to their applications only experiment with the new API and continue to use the Account Feed as their primary source for configuration data. We will strive to give you at least one month advanced notice of changes to this API.

The Management API represents a significant new piece of the Google Analytics developer platform. We encourage you to come try it out and give us feedback in our new Management API Google Group.

P.S. - Please make sure to sign-up for our notify list to stay up-to-date on all the latest Google Analytics Developer updates.

Thanks!
By Jeetendra M. Soneja, Google Analytics API Team

Jeetendra

Nick

1. Register with Google APIs Console.
2. Copy and paste the JavaScript code.
3. Configure this code to query your data and choose a chart type to visualize it.

<div id="chart1"></div>
<script>
var chart1 = new gadash.Chart({
      'type': 'LineChart',
      'divContainer': 'chart1',
      'last-n-days':30,
      'query': {
        'ids': TABLE_ID,
        'metrics': 'ga:visitors,ga:visits,ga:pageviews',
        'dimensions': 'ga:date',
  'sort': 'ga:date'
      },
      'chartOptions': {
        hAxis: {title:'Date'},
        vAxis: {title:'Visits'},
      }
    }).render();
</script>

• To track timings for AJAX actions before and after onLoad event. 
• A site can have their own definition of "User Perceived Load Time", which can be recorded and tracked with user timings.  As an example, news websites can record time for showing the above fold content as their main metric instead of onLoad time. 
• Detailed performance measurement and optimization of sub components on a page, such as time to load all images, CSS or Javascript, download PDF files and time it takes to upload a file.

• Avg. User Timing—the average amount of time (in seconds) it takes for the timed code to execute
• User Timing Sample—the number of samples taken

• Request an authentication token from your Google Account
• Create a URL with the data you'd like to get back from the API
• Make an HTTP request to the Export API using the authentication token and the URL you created

• What are the top referral sources by conversions to my site?
• What are the top browser language settings in the United States vs. the United Kingdom?
• What are the top keyword refinements and destination pages being used on my internal site search?