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

Seo Master present to you:
By Satish Kambala, Staff Software Engineer

At Google we believe that speed matters and a faster web is better for everyone. That’s why we started the Make The Web Faster initiative. To improve the speed of a website, we need to measure how fast web pages load. The Site Speed report, which is now available by default to all users of Google Analytics, provides just that: it enables website owners to measure page load time for their web pages.

You can use the Site Speed report to correlate speed with other metrics in Google Analytics, such as page views and conversions. This enables website owners to identify and optimize those pages that drive these metrics. Page load times can be analyzed by browser type or user location to understand if specific optimizations are required. Recently, we enhanced the Site Speed report by adding a new section called Technical (see screenshot below) which displays network and server time components of page load time.


You can learn more about the Site Speed report here. This report, along with powerful page speed analysis tools such as Page Speed Online, will help website owners delight their users by building fast and responsive websites.

Have ideas on how to make your website faster or ways to speed up the entire Web? Send us your thoughts.


Satish Kambala works at Google on stuff that helps in making the web faster. In his free time, apart from watching cricket and movies, Satish likes exploring places with his wife.

Posted by Scott Knaster, Editor

2013, By: Seo Master

Seo Master present to you: We know it's easier for developers to program in the languages they know. So we updated the Google Analytics API Python Client library with all the new API version 2 features and added reference exampels for both the Account Feed and Data Feed. Now it's easier than ever to automate your analysis workflow using our API.

Taking The Library For a Spin

With the updated library, we thought it would be a great time to highlight the power of the new v2 features. So we created a sample application to do just that. The application uses the new Google Analytics Python client library to retrieve metrics for a series of segments. It then performs some calculations on the data and creates bar charts using the GChartWrapper package, an open source Python wrapper for the Google Charts API. Finally, it uses the Python Imaging Library to add a title and legend, and stitches all the charts together into a single image. We decided to release this application as open source so you can create visualizations with your own data.

Solving Business Problems

With social media all the rage, we wanted to use this new application to help Avinash Kaushik, our Analytics Evangelist, to measure "engagement" on his popular Occam's Razor blog. We also wanted to determine if the time he spends participating in social media sites is valuable and sends new readers to his blog.

First we created segments to pull all the referrals from Facebook and Twitter. Second, we chose five calculations and corresponding metrics to compare the performance of thee two segments. We then compared the segments to each other and, for context, to all the visits to the site as a control.

They say a picture is worth a thousand words, here are the results:



Let's Analyze

Some interesting observations become apparent.

• 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).

The bottom line? Even though social networking sites are all the rage, they actually contribute very little to Avinash's blog. If this blog were a company, it would be wise to ensure the time and effort put into driving traffic from social media is proportionate to the actual volume of traffic and goal conversions from those sites.

Hopefully this example shows how powerful our new features can be.

If you're interested in running this report against your own data, the application is free and open sourced. Additionally, we made it really easy to change the metrics, segments, calculations and all the other visual properties to power your own visualizations. So please download it here and give it a whirl, we would love to hear your feedback.

By Nick Mihailovski, Google Analytics API Team2013, By: Seo Master

Seo Master present to you: Over the past few months we've received a lot of great feedback from our developers about what they wanted to see in the Google Analytics API. Today we're excited to announce new powerful and flexible features to the Google Analytics Data Export API including:

Support for Advanced Segments

With advanced segmentation, you can look beyond the totals and into the nuances of the data for your site. For example, the average time on site for all visits could be 60 seconds, but when you segment by country, you might learn that average time on site of visits from Poland is over 2 minutes.

So we've added two new ways to use advanced segments through the API:

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.

This video describes exactly what advanced segments do and how you can use them with the API.



Goal 5-20 and Configuration Data

With the recent Google Analytics v4 launch enabling up to 20 goals, many of you asked for access to this valuable data, and we listened. Now there are 48 new metrics to access goal performance. We've also added all the goal configuration data, including name, type, step names for each profile.

Here's a great video describing the depth of the goal configuration data.



Custom Variables

Custom variables are powerful new ways to describe visitors, visits and pages within Google Analytics. In this new release, we've added 10 new dimensions to access custom variable data. In addition, every custom variable that you've used is now available through the Account Feed.

All the details of this release can be found on our public changelog and public notify group. We've updated all our documentatation at http://code.google.com/apis/analytics. Please continue to give us feedback to improve our product through our public google group.

Thanks!

By Nick Mihailovski on behalf of the Google Analytics API Team2013, By: Seo Master

Seo Master present to you:

Jeetendra

Nick

By Jeetendra Soneja and Nick Mihailovski, Google Analytics API Team

Today we are announcing the new Google Analytics Core Reporting API as a replacement for the Data Export API. This is the second phase in a larger project we started a couple months back to upgrade our APIs to new infrastructure.

The Core Reporting API has two versions.

Version 3.0 is a brand new API, with a 10x reduction in output size and support for many new client libraries, like PHP, Ruby, Python, JavaScript and Java. All new features will only be added to this version.

Version 2.4 is backward compatible with the legacy Data Export Version 2.3.

If you are building a new application or maintaining an existing one, we highly recommend migrating to version 3.0.

One of the biggest changes in switching to the Core Reporting API is that you now need to register your applications via the Google APIs Console and use a project ID to access the API.

With this change, we are also announcing the deprecation of the Data Export API version 2.3. This API will continue to work for 6 months, after which all v2.3 XML requests will return a v2.4 response. Also, we plan to terminate the Data Export API Account Feed. All configuration data should be retrieved through the Google Analytics Management API.

See our Data Export API changelog for all the details of the change and read our developer documentation for more details about each API.

If you have any questions feel free to reach out in our Data Export API Google group.


Jeetendra Soneja is the technical engineering lead on the Google Analytics API team. He's a big fan of cricket – the game, that is. :)

Nick Mihailovski is a Senior Developer Programs Engineer working on the Google Analytics API. In his spare time he likes to travel around the world.

Posted by Scott Knaster, Editor

2013, By: Seo Master