Free Eureqa tool from Cornell to detect equations and hidden mathematical relationships in your data

Hamish just came across this awesome piece of software called Eureqa that helps to detect equations and hidden mathematical relationships in your data

The goal of the free tool from the Cornell Creative Machines Lab is to identify the simplest mathematical formulas which could describe the underlying mechanisms that produced the data. Check out the introduction vide below!

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Facebook Insights introduces major update and adds new measurement for reach and referrals

Facebook just made some wholesale changes with big impact. Along with those changes came a new insights dashboard, which although simple, has greatly improved the measurement of reach and engagement. 

The screenshots below show statistics for a domain, with the open graph tags installed, along with like and share buttons (social widgets). As people click on these widgets, interactions are pumped into the users social graph and the reach is rather amazing. Below we see ~100,000 like/share clicks on the web site in a 30 day period, which generated 38 million impressions in peoples walls, streams and feeds. This is a staggering display of how the Facebook Open Graph can be used to extend reach. 
Screen_shot_2011-03-09_at_8
The second interesting development in here is the bottom graph, which illustrates the change Facebook made to the way Like buttons pushed content into peoples feeds around February 24th, essentially their functionality became more like a share button. At that point the green line indicating "Like story clicks" rises from almost nothing to be roughly equivalent with the "share story clicks" line. From this point the total clicks diverges from the "share story clicks" line, representing the increase in total clicks.

The take home here is that Like buttons are now generating more traffic and the Share button is probably going to disappear pretty quickly.

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Datalicious workshop on digital data, campaign measurement and optimisation at upcoming ad:tech

Screen_shot_2010-01-21_at_4

We're excited to announce that Hamish Ogilvy, our Head of Data, will be running a workshop on digital data, campaign measurement and optimisation at the upcoming ad:tech event in Sydney

The workshop will be held from 10-1pm on Wednesday, March 9th, and will cover a diverse range of topics from standardisation of metrics over digital data sources and their limitations to advanced media attribution and targeting to reduce media waste.

It doesn't matter whether you’re agency or client side, the workshop will enable you to make better data driven campaign optimisation decisions through a combination of marketing theory and hands-on group exercises.

For more information on the workshop and to register please visit the official event page.
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How to successfully implement Facebook's Open Graph to generate insights and start contacting fans

What is Facebook's Open Graph

The Open Graph is an open protocol for semantically labelling web content, but more importantly it provides the underlying logic for seamless integration into Facebook's social graph. Each configured URL becomes an object in the graph with various properties, like a title, author, image, URL, etc. People can be linked to objects through things like the Facebook like buttons (i.e. Johnny likes Datalicious). These relationships are then formatted nicely in Facebook news feeds and profiles to provide a means of virally sharing web site content. They also serve as semantic signatures, which can help search engines deliver more personable results.

Open-graph
Why would you bother implementing the tags?

There are many advantages to adding and configuring these tags properly. The key reasons are as follows:
  • Social relevance and influence is now officially part of Google/Bing search engine algorithms, so if you're into SEO and you're not looking at the Open Graph, then you're destined for rough times ahead. The "like" button is acting as a form of popularity score, much like Google's innovative PageRank, but each "like" is linked to a persons profile, allowing it to be authenticated and weighted accordingly.
  • Objects in the Open Graph drive traffic as they appear in news feeds and profile streams. For many sites this traffic is now greater than traffic from search engines. Without Open Graph tags and social widgets, you ARE losing potential traffic.
  • Properly formatted objects allow you to determine how the object will look in peoples profiles and searches. You can set the picture, title, description and many other important tags. This allows you to optimise your image.
  • Probably the most underutilised capability is the ability to contact users who have clicked like on your object. Effectively each like is functionally equivalent to subscribing to an email list, except it doesn't cost to send messages into users news feeds. Every "like" has a $ value, tapping into this new communication tool can be an extremely valuable exercise.
  • Objects in the graph are searchable in many other applications and likely will become a greater part of Facebook's built in search engine.
How to begin Implementing

There are several key pages on Facebook that explain the installation (see below), but don't believe everything you read, some of it is wrong (ironically eventually this post itself will likely be wrong). And to make matters more difficult, Open Graph tags do not create an object in the graph immediately (it takes time, sometimes a little, sometimes a lot!). The other thing that seems to influence the creation of the object is whether people have "liked" it, this appears to be important (although how many likes are required is also not clear).

Once you have worked through the creation of the meta tags on the site and they're testing ok (see the Linter below), you should be on track to start accumulating likes and generating traffic. To make use of the traffic you need to associate yourself as an administrator of the objects, to do this skip to the next section.

Resources
Facebook's Open Graph Developer Page
The Open Graph Protocol Page
The Facebook Linter - use this to check if things are working ok
Good blog article on how to get your web sites into your Facebook Insights

Sending messages into Users feeds

To do this you firstly need to make sure the URL has an object ID in the graph and secondly you must be an administrator of the object. To check the URL is indeed an object, go to the following address in your browser, but replace http://www.datalicious.com with the URL you want to check:

You want it to return something like the below (note the highlighted part means the object has an ID). Note: When you do this for thousands of URL's you won't do this manually, but initially it's a quick way to troubleshoot before attempting to post messages!

"http://www.datalicious.com/": { "id": "116002505130390", "name": "Datalicious | Data > Insights > Action", "picture": "http://profile.ak.fbcdn.net/hprofile-ak-snc4/hs463.snc4/50260_116002505130390_7413361_s.jpg", "link": "http://www.datalicious.com/", "category": "Website", "website": "http://www.datalicious.com/", "description": "Smart data driven marketing. Actively helping companies to optimise their marketing programs by providing accessible reports and actionable insights generated from solid data platforms.", "likes": 4 } 

Once you've confirmed your URL has an ID, you need to make sure you're an administrator of the object. This is usually easy to see, as next to a Facebook "like" or "recommend" widget you will see an "admin" button like below:

Screen_shot_2011-01-19_at_5
Note: The admin button does not appear next to like buttons without the "show faces" set to on, so if you have a button where you don't see peoples photos when they click like, then you won't see an admin button. If you're using a comments box, sometimes you need to click on "like", then "unlike" to make the admin button appear. Failing all of this, you need to check the open graph meta tags to ensure you're either giving admin access to either 1. An application, or 2. To specific user ID's, you should see something like one of the following on every page with OG tags:
<meta property="fb:admins" content="USER_ID1,USER_ID2"/>
<meta property="fb:app_id" content="1234567"/>
To send a message to the "likers" of your Open Graph Object, you can click on the "Admin Page" link and you will be taken to a page that looks similar to a fan page, from there you can post to the wall, which effectively pushes your post to all the people who have liked the object. Note: The post can also include URL's, etc.

If you want to send messages programatically or to thousands of objects at once, then you need to look at the programmable solution, the one thing to note here is that the "id" field must be numeric, it cannot be the URL as is incorrectly documented on the Facebook page and shown below:

Open_graph_issue
Case Study: GoPetition.com

Below shows the statistics from a site where the Open Graph tags were implemented properly. The site has recently peaked at over 3500 new likes in a single day, which is approximately an order of magnitude above new daily email subscribers. The staggering statistic is the ability to generate viral traffic and accumulate new subscribers to a key communication channel in a single step, with efficiency far beyond most other mechanisms.  

Fb_growth_dec_10

Contact us now if you need help implementing Facebook Open Graph on your website!

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Enhance Google Analytics with Super Cookies

Google Analytics mechanics are quite different to Omniture Stie Catalyst, for Google Analytics many of the calculations such as pages per visit, first visit, last visit, etc are stored in cookies, they are not calculated on the server side. Additionally the visitor ID is not necessarily the same for a given user, it isn't used to tie information together, instead Google Analytics relies on your cookies telling the truth. The problem is that cookies inherently only ever tell partial truths, the attrition rates are huge, so how can you trust this information? The answer is you can to a point, but be aware of what you're looking at, because its far from perfect. If you want to make it more accurate, then use super cookies. This post is aimed to touch on several of these areas where we think flash can add major value to existing Google Analytics solutions. We've already covered the super cookie technology in a previous post, so we won't dwell on the basics, if you need some more background please read the previous post:

Examples of Super Cookie additions to Google Analytics deployments
  1. Flash based persistent cookies work across multiple domains and multiple browsers - Use super cookies to re-set targeting and other custom variables across your network of domains, in any browser.
  2. Cookie deletion measurements - Find out how often do your existing users delete their standard cookies?
  3. Browser switching measurements - Do your users switch between multiple browsers? How does this affect your analytics?
Browser-logo-major
 
Which browser? Who cares!!!
1. Super persistant cookies
For those wanting to get better accuracy from google analytics, or if you're using the Google Analyitcs custom variables for targeting and reporting, this is for you. 
Persisting the profile - In order to keep the rich information on your users no matter whether they delete cookies or switch browsers, you have two main options:

a) Respawn their past GA cookies before loading GA - All their profile information is associated with their visitor ID. By reseting this to it's original value (stored either prior to cookie deletion, or from a previous browser), their profile remains intact. This method can also help you to keep more realistic figures on unique visitors as long as you can replace the visitor ID prior to sending any requests to Google. Although this gives the smoothest operation, the privacy issues are obvious and must be addressed. 
b) Keep a copy of targeting parameters in a super cookie - If you detect a cookie deletion, resend the the parameters to Google Analytics so they can be re-bind them to the new visitor ID. This is a little more privacy friendly, as you're allowing the user to remove association to a specific ID, but their profile remains. You no longer know who they are, but you still know a little about them to help serve them better.

2. Cookie Deletion Measurements
If you grapple with privacy concerns but are still desperate to know how many of your users delete their cookies, then you can use this method to find out without fear of privacy invasion. This technique is useful for adjusting data inaccuracies caused by cookie deletion. 

Super cookies remain after users delete their standard cookies. Because flash cookies are not currently dealt with by browser settings (Chrome has some functionality), or understood by consumers, they are rarely deleted (assume this will increase in the future). By comparing the super cookie value to the standard cookie value, you can quickly tell if a previous value existed and has since been deleted. The high level logic is found below (note: this over-simplistic and does not allow for browser switching, see section 3!). The following pseudo code would actually be done in JavaScript:

IF standardCookie(a) is not equal to superCookie(a) AND superCookie(a) is not null THEN
{
LOAD GA CODE
Set custom variable to indicate a cookie deletion
SEND GOOGLE REQUEST
} ELSE {
LOAD GA CODE
SEND GOOGLE REQUEST
}

The above logic would enable you to see several things including:

a) The total number of cookie deletions (using the prop or event)
b) Conversion rates of users who have deleted their cookies vs those that haven't (using the custom variable). Note: This is particularly useful for Targeting, where profiling enhances conversion. You can directly measure the uplift of normal users compared to users post cookie deletion.

3. Browser Switching Measurements
Many people now use multiple internet browsers for a variety of reasons, evaluation, different features, old bookmarks and probably most importantly, technical issues. The problem for analysts is that traditional cookies are browser specific, so each browser appears as a different user. Super cookies can quantify this issue. Super cookies provide the capability to keep a cross browser profile that remains even if a user uninstalls a specific browser and switches to a completely new one, but for the purposes of the exercise we are only looking to quantify the issue.

To create this capability the following logic can be used. Again this would be written in JavaScript. 
IF current browser is not equal to superCookie(browser) THEN
{
LOAD GA CODE
set custom variable "browser A > browser B"
SEND GOOGLE REQUEST
set superCookie(browser) = "browser B"
ELSE {
LOAD GA CODE
SEND GOOGLE REQUEST
}

The above logic would enable you to see:

a) Which browsers people are switching from/to. This can help you plan future testing resource allocations, etc.
b) Which pages browser switches are commonly associated with (above logic does not show a direct correlation to a specific page, but you can store the final session page in the super cookie and use that to see if the user has made a browser switch on the same page, which may indicate a browser issue).
c) How many browser switches have occurred (set the variable to be page specific)
d) How many users use multiple browsers (if you keep a common visitor ID across multiple browsers)

Hopefully this article has helped to show you how super cookies can be used to improve your Google Analytics deployment accuracy. For actual code examples, please see our original super cookie post or download the zip file below. For any questions or enquiries, please contact us at insights@datalicious.com

 

 

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