New ClickTale segmented heat maps show mouse data for prospects vs. existing customers

ClickTale launched two new heat maps feature today that are worth mentioning.

The Segmented Heap Maps (see screen shot below) allow analysts to show mouse movement, mouse click and page scrolling data for different segments to analyse differences in behaviour. The segmentation options include customer status, conversion status, media channels and any other custom segmentation variables such as age, gender or location but I especially like the fact that we'll now be able to analyse website usage for new prospects vs. existing customers separately.

Ultra Scale Heat Maps on the other hand allow analysts to show aggregate mouse data from up to 100,000 visitors in one single image enabling usability testing on a super large scale compared to standard eye tracking methods.

"With an 84-88% correlation between our Mouse Move Heatmaps and expensive eye-tracking studies, website owners can now conduct incredibly accurate usability studies on a massive scale, and at a fraction of the cost."

ADMA survey reveals lack of web analytics data usage in media attribution and single customer view

Over the past few months the ADMA Data & Analytics Council conducted an online survey to establish how evolved the direct marketing industry in Australia really is in terms of data and analytics

Key findings of the survey included

  • Although 62% of respondents said that they tie sales data back to campaigns and media channels driving them, 59% admitted that they were not actually using web analytics which is interesting given the increased importance of online channels in driving sales.
  • A similar trend emerged when asking marketers about whether they had a single customer view. A surprisingly large amount of respondents (44%) said they had a single customer view but interestingly only 41% of those companies were incorporating web analytics data into their single view. Given the growing amount of online customer touch points this raises the questions how complete these single customer views really are.

Although the survey was only a quick and dirty exercise I think the results are quite interesting and the council is now considering to extend and refine the survey to shed some more light on the highlighted issues above.

Please subscribe to the ADMA Councils blog if you would like to hear about research like this in the future or email councils@adma.com.au if you would like to help shape similar future initiatives.

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New TED video from David McCandless on the power and beauty of effective data visualisation

This is a must watch video for all data fans, it includes a few cool data visualisation examples. David also talks about data being the new oil and tries to coin the new phrase of "data being the new soil" (i.e. insights and innovation spring from it), great idea I think but it comes across as a bit wanky. Anyway, awesome examples and great video!

Aprimo/Omniture presentation slides on data driven marketing and effective cross channel targeting

Below are the slides from our recent presentation at the Aprimo/Adobe/Omniture breakfast seminar in Melbourne/Sydney on smart data driven marketing and how effective cross channel targeting can help increase campaign response rates. Thanks again to everyone who came to the event and please let me know your comments and thoughts, always keen to get your feedback.

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Datalicious presenting at Aprimo & Omniture breakfast seminar next week on effective targeting

If you want to know how to increase campaign response rates through effective targeting and you missed my recent ADMA Forum presentation you might be interested in the Aprimo/Adobe/Omniture breakfast seminars next Wednesday and Thursday in Sydney and Melbourne.

Your customers are interacting with you across a variety of channels and are pretty much telling you what they're interested in but are you actually collecting and acting on that information? Reality is that most companies are still operating their different tracking and communication platforms in relative isolation which leads to an inconsistent and irrelevant experience for prospects and customers.

I mean, who here hasn't clicked on a banner or search ad at some stage that directed you to a landing page that wasn't really related to the initial message that got you interested? Same with some of the email updates you're probably subscribed to, right? 

If you want to understand how your data can help you deliver a more relevant online experience and to get some thought starters then register for one of the breakfast seminars next week and I would be surprised if you would walk away without at least one idea to follow up on.

Melbourne breakfast seminar
When: Wednesday, 18th August, 8 am breakfast, 8.30-10 am seminar
Where: Hilton on the Park Melbourne, 192 Wellington Parade
Register here http://bit.ly/d5XIFo

Sydney breakfast seminar
When: Thursday, 19th August, 8 am breakfast, 8.30-10 am seminar
Where: Hilton Sydney, 488 George Street
Register here http://bit.ly/d6vJB5

Forrester Wave on listening platforms vs. Owyang's Altimeter report on social marketing analytics

Forrester have just released their latest Wave report on listening platforms which you can download from Converseon for free. However, looking at the matrix below I have to say I'm not entirely sure how well it was researched as some key players are plain missing

If social media monitoring is an area of importance for your company I would strongly recommend to also review Jeremy Owyang's Altimeter report on Social Marketing Analytics.
Comparing the below Altimeter chart to Forrester's above, it does seem that Jeremy was a little more thorough. In addition to a vendor list, the Altimeter report also contains some very useful thoughts on how to approach a social media monitoring strategy in general.

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.

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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.
 

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

 

 

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.