Datalicious Blog - Data Driven Marketing

Gigya info-graphic on the different social network identities people are using to sign-in online

The guys at Gigya created a great info-graphic showing what accounts and online identities people are using to sign-in online.

The most popular social identities are Facebook, Google, Yahoo, and Twitter, but popularity differs by user segments and website categories. Users are most likely to log on to entertainment sites via Facebook, but Twitter for news sites, etc. 

Given the data that is available to companies if their users subscribe using one of their social online identities you wonder why there's still normal subscription processes being used, especially as the social identities probably have better data quality anyway (i.e. you want Facebook to have your real/main email address).

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Filed under  //   christian bartens   facebook   gigya   ID   identities   infographics   linkedin   media   networks   social   twitter  

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Photo of our new exhibition stand at ADMA Forum

We're mighty proud of our new stand as you can tell ;)

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Filed under  //   adma   christian bartens   exhibition   forum   photos   stand  
Posted from NSW, Australia

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ADMA Forum presentation slides on Eliminating Waste and Increasing Relevance through Targeting

Below are the slides from our recent ADMA Forum presentation on Eliminating Waste and Increasing Relevance through Behavioral Targeting: An Introduction. Please let me know your comments and thoughts, keen to get your feedback.

(download)

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Filed under  //   adma   analytics   behavioural   christian bartens   data   forum   introduction   news   presentations   speaking   strategy   targeting  

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Watch our Managing Director, Christian Bartens, present today at the ADMA Forum 2010 in Sydney

At 12pm on the Exhibition floor in the Epsilon seminar center.

Eliminating Waste and Increasing Relevance through Targeting: An Introduction

  • Defining segments and input sources
  • Exploration of behavioral and transactional data sources
  • Developing integrated solution combining various different data sets and systems
  • Extracting greater program efficiency for an effective targeting program

At 4pm in the Data & Analytics stream upstairs.

Gaining a 360 View of Your Potential Customer: A Cutting Edge Approach at Telstra

Christian Bartens, Managing Director, Datalicious and Karen Ganschow, Executive Director Relationship Marketing & Online Telstra.

  • Developing a full profile of potential customers
  • Ensuring email addresses are captured at every interaction
  • Strategies for leveraging the information you have to gain social media identity
  • Connecting offline sales to online through email receipts

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Filed under  //   adma   forum   leila seith hassan   news   speaking  

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Photos of the Datalicious team ice skating at the Winter Festival in Sydney last Friday

                                   

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Filed under  //   christian bartens   ice skating   news   photos   sydney   team   winter festival  
Posted by datalicious 

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GetResponse study on impact of social sharing features in emails shows 30% increase in CTRs

GetResponse just released an interesting new study on the impact of social media sharing options in email.
 
Among other topics the study investigated if social emails improve click-through rates and found that if you let readers share your email messages on their social pages, they’ll generate on average 30% higher click-through rates. The click-through rates also varied by social network with Twitter and Facebook leading the field.
 
Download the full report here
http://www.getresponse.com/learning-center/reports/social-sharing.html

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Filed under  //   christian bartens   email   facebook   getresponse   media   networks   research   sharing   social   study   trends   twitter  

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New Syncapse empirical research on Facebook fan value, additional product spend and brand loyalty

"A fan base is a self-segmented group of highly valuable customers."

After all the buzz about the value of a Facebook fan, Syncapse just released the first proper empirical review on this topic which I encourage everyone to read (and it's free anyway). Especially the figures on average additional product spending by brand are pretty interesting (see below chart).

Summary of key findings
  • Fans spend an additional $71.84 on average compared to non-fans
  • Fans are 28% more likely than non-fans to continue using the brand
  • Fans are 41% more likely than non-fans to recommend a product
Many brands overcomplicate their measurement requirements by tracking dozens of independent variables. Many oversimplify by trying to apply a single number concept of value, and far too many fail to quantify ROI in such a way as to convince a CFO of the merit of increasing or shifting investment towards Facebook marketing. [...] This study will examine the five leading contributors to Facebook fan value. (1) Product Spending (2) Brand Loyalty, (3) Propensity to Recommend, (4) Brand Affinity and (5) Earned Media Value.
Download the full Syncapse research report here
http://www.syncapse.com/media/syncapse-value-of-a-facebook-fan.pdf

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Filed under  //   christian bartens   facebook   fans   media   networks   reports   research   social   syncapse   value  

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Flowtown identifies social profiles, demographics and influencers from customers email addresses

I just came across this new service called Flowtown which is pretty interesting. 
 
The platform lets you upload your contact's email addresses for which it then returns the respective social profiles on Facebook, Twitter, MySpace, LinkedIn, Flickr and StumleUpon. And if all you have is an email address, the service can also give you a name, age, gender, occupation and location. But the best part is the integration with Klout, a service that determines a person's influence level based on Twitter and basically identifies your most influential contacts for you. 
 
Have a look at the below chart, which shows the statistics for all my 1,800 contacts compared to the 50 identified influencers. Not surprisingly, but still interesting to see is that the influencers are definitely much more likely to have a social profile online across multiple networks and that all Twitter influencers also have a Facebook and LinkedIn account.
Visit the official Flowtown website or watch the below demo video to find out more.
 

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Filed under  //   analytics   christian bartens   crm   customers   email   Facebook   Flickr   flowtown   LinkedIn   marketing   media   MySpace   networks   profiling   social   StumleUpon   targeting   tools   Twitter  

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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?
 
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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Filed under  //   analytics   cookie   custom   google analytics   hamish ogilvy   super cookie   variable  
Posted by Hamish Ogilvy 

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Use Google Analytics custom variables and simple JavaScript to target site content in real-time

I was reading this on another blog earlier today and thought it was worth passing on. In essence Google Analytics have provided a function to read the custom variables for targeting purposes. Although this is essentially nothing more than being able to read a cookie and use it to segment and target page content, it's nice to be able to use the same variables used by Analytics, as the targeting immediately has context in reports.
 
If you already use the custom variables (index 1-5), you can now use the following function to read the value and switch out content using some simple JavaScript.
 
_getVisitorCustomVar()
 
_getVisitorCustomVar(index)
 
Returns the visitor level custom variable assigned for the specified index.
 
pageTracker._getVisitorCustomVar(1); 
 
Parameters
 
Int index The index of the visitor level custom variable.
 
Returns
 
String The value of the visitor level custom variable. Returns undefined if unable to retrieve the variable for the specified index.
 
Read the original blog post from Michael Whitaker here or check out the code reference in Google's help section if you want to find out more.

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Filed under  //   analytics   custom   google analytics   hamish ogilvy   targeting   variable  
Posted by Hamish Ogilvy 

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