BusinessWeek has a truly exciting front page story today (February 21, 2010).

BusinessWeek has a truly exciting front page story today (February 21, 2010). Especially for somebody like me who repeatedly and openly declared an obsession with the state of News Media and in particular how Data and Analytics, in that regard, can help publishers increase their current performance.

BusinessWeek: AOL Moves to Build Tech `Newsroom of the Future’

I’ll leave the article to you, but have a look at the following comments (some of them, perhaps a tad naïve, but so aggressively data driven, that you have to love them):

• The [web analytics] numbers tell the growing number of journalists who work there how well their articles are performing
• Judicious use of Web-analytics software is a hallmark of what AOL Senior Vice-President Marty Moe calls the “newsroom of the future”
• We really want to enhance journalism with technology
• [Provide] journalists up-to-the-minute data on how much traffic those articles generate
• News editors’ computers come equipped with software—created internally by combining data from AOL’s own analytic tools with [other resources]
• Audience growth and audience engagement have to be the things that we judge the most off of our journalist investments
• AOL [is] considering sharing a portion of quarterly profits with staffers whose work fetches the most page views

That’s hardcore language, when you think about how data is shunned upon in other news organizations. I for one think that the newsroom of the future is data driven! Optimization is not about driving towards the lowest possible common denominator, and better performance could as easily push relevance, as compared to the general idea of pushing everybody toward easy digestible celebrity news.

Is Tim too aggressive for you ?

Cheers
/ Dennis (@dennismortensen)

More here:
Analytics is building the Newsroom of the Future

Popularity: 34% [?]

I wanted to write a much longer post about this than I am going to tonight, since it’s late - but I’ve noticed that many of my posts, recently, have been retweeted 30-40 times per post - sometimes by the same people, but oftern by different people. For example, my post on SEO, Web Analytics and Social Media have Merged was retweeted by my followers 35 times over the period of a day and each individual can reach others in their own networks, so a lot more than the 35 people who initially viewed my article from a retweet may have ultimately seen my post. Because tinyurls are used a lot of the referral traffic might not show up in Analytics, though I believe there are other ways to track information.

I wanted to write a much longer post about this than I am going to tonight, since it’s late - but I’ve noticed that many of my posts, recently, have been retweeted 30-40 times per post - sometimes by the same people, but oftern by different people.

For example, my post on SEO, Web Analytics and Social Media have Merged was retweeted by my followers 35 times over the period of a day and each individual can reach others in their own networks, so a lot more than the 35 people who initially viewed my article from a retweet may have ultimately seen my post.

Because tinyurls are used a lot of the referral traffic might not show up in Analytics, though I believe there are other ways to track information.

See original here: 
Power of ReTweeting

Popularity: 10% [?]

Now that I’m writing regularly for Entrepreneur.com I’ll probably need to make a page here at Webmetricsguru detailing all the articles I publish here - but to start, I was published twice this week and have two more articles I’m working on right now. On Wednesday my article on  Learn to Measure Your Web Presence - It’s one thing to generate online buzz, It’s another to know how to use it was published and on Friday a blog post on Making Connections in New York City detailed how vibrant and great New York can be for a Startup.

Now that I’m writing regularly for Entrepreneur.com I’ll probably need to make a page here at Webmetricsguru detailing all the articles I publish here - but to start, I was published twice this week and have two more articles I’m working on right now.

On Wednesday my article on  Learn to Measure Your Web Presence - It’s one thing to generate online buzz, It’s another to know how to use it was published and on Friday a blog post on Making Connections in New York City detailed how vibrant and great New York can be for a Startup.

Read both when you have a chance and I’ll be happy to answer any feedback here at Webmetricsguru.com.

See original here:
Writing for Entrepreneur.com

Popularity: 8% [?]

divstrong/strong/div
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divstrongExecutive Summary/strong/div
divnbsp;/div
pThe authors note the differences between conventional TV advertising and internet advertising when it comes to ad measurement. One of the main differences is that internet advertising is not bound to a schedule that defines an ad’s insertion into a broadcast. Thus traditional reach amp; frequency exposure models that aim to insert a certain amount of ads across a specific number of vehicles such that the ad is exposed to an audience once per insertion at most may not be the best to apply to online ads. It is argued that one of the main reasons why traditional models do not work online is that web users control their own navigation and can potentially be exposed to the same ad insertion more than once. /p
pHence, they proposed the use of a different exposure model based on Negative Binomial Distribution (NBD) which aims to represent user exposure rates to parts of specific sites where an ad is placed rather than the site as a whole. The model integrates well with web analytics as it relies on clickstream data for calibration. /p
pThe authors went on to conduct an actual study using data collected from panel-based software installed on the computers of 1012 users and applied the NBD model to it. They set out to determine whether the NBD model was effective in determining ad exposure for a given month as well as predictive abilities for a future month by creating 1000 ad schedules for a single ad placed in the top 100 subdomains visited by the sample. They concluded that the model’s predicted outcomes and the actual outcome from the user data were nearly identical. Thus, they were able to determine the optimal ad “schedule” for a particular set of ads for maximum exposure in the most visited subdomains on the internet. /p
br
pstrongReview/strong/p
pFor people responsible for online advertising budgets, this is a model that is worth looking into given its applicability to specific parts of a site rather than the site as a whole. If you utilize tactics such as subdomains and microsites with their own unique addresses, this is a worthy read. Insofar as its application to web analytics, it only makes sense that web analytics is an integral part of how and where online advertising dollars are spent given the sophistication of the tools amp; practitioners out there today. As an example, a model that optimizes reach and exposure can be coupled with web analytics by assigning dollar values to each creative element or determining the average order size/revenue per element to determine per-ad effectiveness and campaigns can be optimized on the fly as necessary. Non-revenue based metrics can also be linked to each element to allow for things like visitor segmentation, determine likelihood of repeat visits, etc. This can then inform future decisions on whether to tweak creative, change its placement on a particular site, or choose an entirely different ad space. /p
pIt is equally important to take a look at your own traffic. Web analytics tools can tell you a lot about referred traffic to your site. The study carried out by the authors in this article based their ad schedules on the 100 most visited subdomains from their sample panel. However, there are some sites out there that don’t necessarily get traffic from the most popular sources. Always keep this in mind – you know your site and who visits! nbsp;/p
pLike other math-based models out there, there are limitations. The authors state that this should only be applied to online marketing campaigns (versus multi-channel campaigns), ad executions that run for the exact same amount of time, and that it is an exposure model for banner ads only. There is clearly room for more research to be carried out to fill the gaps but this is a very good start. It will be interesting to see whether this model can be carried over into tactics like email or mobile. nbsp;/p
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pThis article is recommended for web analytics practitioners, agency account managers, or anyone who influences how and where external online ad dollars for marketing campaigns are spent./p
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divnbsp;/div
div**A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email a
pnbsp;/p
brbr26-Mar-09 9:00 AM
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Modeling The Audience’s Banner Ad Exposure For Internet Advertising Planning

Popularity: 18% [?]

div class=peerJournalArticleDetails
pspan class=peerJournalArticleAuthorsSingh, Nitish, Pereira, Arun, Baack, Daniel W., Baack, Donald., (2008)./span span class=peerJournalArticleTitleCulturally Customizing Websites for U.S. Hispanic Online Consumers/span. span class=peerJournalJournal of Advertising Research, Vol. 48, No. 2/span. span class=peerJournalArticlePages9 pages/span./p
p class=peerJournalReviewerReviewed by Christopher Berry, January 2009/p
/div
h2Executive Summary:/h2
pThe authors note the growing importance and influence of the Hispanic Online Consumer to US businesses, and the relative difficulty in reaching them. Their literature review identifies acculturation differences between US Hispanic Consumers as being a major challenge – and go onto define acculturation as “the process of learning a culture that is different from ones own, which in turn leads to change in values, attitudes, behavior patterns, and language use”. Also noted in the review are previous research on the US Hispanic Consumer acculturation levels and its impact on offline media usage./p
pThey use a survey methodology (n=400) to conclude “U.S. Hispanic customers have clear preferences regarding online marketing content and that acculturation level affects these preferences.” Specifically, that that weakly-acculturated US Hispanics in particular have a “significantly higher levels of preference for both English and Spanish language web pages in comparison to highly acculturated Hispanics.” They also found that only weakly acculturated US Hispanics had significantly higher preferences, attitudes, and even purchase intentions for websites that provided information about Hispanic community involvement./p
pThe authors also tested the use of creative involving grandparents, found that it was important to low acculturation Hispanics, and concluded that a “respect of elders” theme would be helpful for marketing to this group./p
h2Review:/h2
pThis article is directly applicable to web analytics, and raises a wicked web analytics problem./p
pThe decision to translate a significant portion of a website into Spanish involves the consideration of several direct and indirect costs. Aside from the direct cost of translation, there are less well known costs, such as the design consideration of navigation and template sizing due to content differences. Specifically, Spanish can require up to 20% more space in comparison to English content. As a result, if an existing website was not developed with Spanish translation in mind, errors such as crowded or broken navigation might very well result. In sum - the decision to translate into Spanish properly by offering a Spanish experience is not always a light or simple decision./p
pThe weak-acculturation finding has an important consideration for measuring the effectiveness of these pages and the overall value of the effort./p
pMany web analytics packages include “Visitors - Languages” reports, that generally sum up the type of language that the browser has been set up for. For example, ‘es’ is the code for ‘espanol’, or Spanish. Language selections are sometimes prefixed by country language localization. For instance, ‘es-ar’ would be ‘Argentina-Spanish’. The likelihood of weakly-acculturated US Hispanics using the default Spanish ‘es’, is low, making the creation of really meaningful custom segments in web analytics software, and then tracking their behavior, less robust./p
pMoreover, weakly-acculturated US Hispanics might not be likely to actually use the Spanish translated pages at all, but indication of their presence is enough to have a significant impact on conversion, brand perception, and likelihood to return. This is the direct implication of the study – the existence of Spanish translation is what is important to this group – not necessarily the actual use./p
pAs a result of these complicating factors, utilizing solely a pure web analytics approach might not be the most accurate way of gauging the value of translation and effort. Rather, a combined approach incorporating pure web analytics with survey methodology would yield more accurate assessment of translation efforts./p
pThis article is recommended to web analytics practitioners of companies that target US Hispanic consumers. It provides factual evidence that Spanish translated section of a website is important and well worth the effort./p
div class=peerJournalAccess
pA single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email
script src=/js/info_lindsay.js/script./p
/div

brbr22-Jan-09 2:00 PM
The rest is here: 
Culturally Customizing Websites for U.S. Hispanic Online Consumers

Popularity: 21% [?]