Matthew Butterick; Effluents influence affluence: The economics of a web-based book, year two; In His Blog; 2015-08-08.
- <quote>We used a modified version of Ghostery  to tag tracking and
- beacons, pixels, cookies
- content targeting
monitoring the consumer.
receiving & using consumer datums.
- Google AdSense
- Yahoo yieldmanager
Bot Benchmark Report: What Makes a Publisher Premium; White Ops, commissioned by Digital Content Next (DCN); 2015-09; 32 pages.
tl;dr → 32 publishers. <quote>DCN didn’t name the specific publishers in the study, but it considers its members “premium” publishers — think big brands like ABC, The New York Times, and National Geographic.</quote>
The Bot Baseline: Fraud In Digital Advertising; White Ops, commissioned by the Association of National Advertisers (ANA); 2014-12; 57 pages; press release; landing.
tl;dr → <quote>White Ops, which left out the names of the advertiser and website in its published study, declined to comment [on the report]</quote>.
- <quote>Bot rates appear to be tied to publishers’ own audience development and traffic-sourcing policies, the study found. Practices such as selling data to third parties or purchasing traffic from outside vendors were associated with those publishers showing higher bot rates. Notable, also, is the finding that publishers with less overall traffic were more likely to have higher sophisticated bot rates.</quote>
For color, background & verisimilitude
- Jason Kint, CEO, Digital Content Next (DCN)
Brian O’Kelley (AppNexus); The Ad Blocking Controversy, Explained; In Forbes; 2015-09-23.
tl;dr → ad blockers reduce viable inventory; this reduces supply; it
willmight increase prices.
- it will be interesting times.
- The 2015 Ad Blocking Report: The Cost of Ad Blocking; PageFair with Adobe; 2015-08-09; 17 pages; landing; previously noted.
- Widely Cited Ad Blocking Study Finding $21.8 Billion Loss Is Incorrect; Alex Kantrowitz; In BuzzFeed; 2015-09-06; separately filled.
How to Build a Content Farm in 20 Minutes; Carles (Carlos Perez); In Motherboard; 2015-09-25.
Teaser: How Much of Your Audience is Fake? As Much as Inhumanly Possible
How Much of Your Audience is Fake?; Ben Elgin, Michael Riley, David Kocieniewski, and Joshua Brustein; In Bloomberg Business; 2015-09-23; separately noted.
Teaser: Marketers thought the Web would allow perfectly targeted ads. Hasn’t worked out that way.
- <quote>However, everything trackable is all just data fields that can easily be populated to game traffic auditors, or your own computer can be taken over by malware.</quote>
- <quote>It’s hard to see programmatic advertising as anything other than the most complex, and highly profitable remnant inventory scheme.</quote>
- big box content farms
- Comcast data
- <quote>When a media company is acquired, does the acquirer see fake, cheap traffic as part of the value, or a misleading element of inflated value? Pumping and dumping media companies is easier than ever.</quote>
- <quote>We all must realize that clickbait is not the problem. It’s only a standard tool in the broken economy of traffic jacking. </quote>
- <quote>When I’m browsing the internet, I usually realize that I am on a low quality site if programmatic ads are following me around. Somehow, I’ve navigated the the farthest reaches of the internet that is serving the cheapest ads to the longtailiest users who may be influenced by them. Programmatic ads means that you are in no-man’s land, where publishers weren’t integrated into native, social, and live programmed events.</quote>
- <quote>There is no way for the reader to stand up for themselves because even if you chose not to read, a robot will take your place.</quote>
Frederic Filloux; Adblockers: The Only Way Out; In His Blog entitled Monday Note; 2015-09-27.
tl;dr → it’s going to get worse before it gets better
- Factoids are recited
- The suggested response
- Acceptable ads will be defined.
- Low-end formats will disappear
- Paid-for models for news will rise
Wait, what? Mobile browser traffic is 2X bigger than app traffic, and growing faster; John Koetsier; In VentureBeat; 2015-09-25.
tl;dr → two industry booster reports contradict each other; which is correct? Both! VentureBeat has a paywalled report too!
- Google: There’s an App for that: The Browser; Morgan Stanley; 2015-09-24; 18 pages.
tl;dr → puffing GOOG; you should buy GOOG at Morgan Stanley.
- The 2015 U.S. Mobile App Report; Adam Lella, Andrew Lipsman, Ben Martin comScore; 2015-09-22; 56 slides; paywall (pay with PII)
tl;dr → factoids of measurement & panels on app usage, Millennial-focused
A large-scale experiment involving 3.7 million treated subjects on Yahoo tests the ability of online display advertising to attract new customers. We track the number of new account sign-ups at an online business and demonstrate a statistically significant impact of one of the two types of advertising campaigns. We find that the ads shown on Yahoo Mail did not produce a statistically significant increase in sign-ups. Despite being derived using millions of subjects, this estimate is quite noisy, with the upper bound of the 95% confidence interval estimate being a 15% increase in new customers. By contrast, the ads served as Yahoo Run-of Network succeeded in generating a more precise and statistically significant increase in sign-ups of 8-14% relative to the control group. These figures call into question click-only attribution models, as the number of users that clicked on an ad and converted is less than 30% of the estimated treatment effect. Further, it is likely that many ad clickers would have converted in the absence of the ads, a likely possibility ignored by traditional click-attribution models.
John Battelle; It’s Time to Flip the Bit on Publishing and Data; In His Blog; 2015-09-27.
tl;dr → all talking heads, all of them; let’s compete on innovation!
- Factoids are recited
- Pundits are quoted
- Clayton Christiansen
- Cory Doctorow
- Frederic Filloux
- Tim O’Reilly
- Michael Schrage
- Doc Searls
- Xiaofeng Zheng, Tsinghua University and Tsinghua National Laboratory for Information Science and Technology
- Jian Jiang, University of California, Berkeley
- Jinjin Liang, Tsinghua University and Tsinghua National Laboratory for Information Science and Technology
- Haixin Duan, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology, and International Computer Science Institute
- Shuo Chen, Microsoft Research Redmond
- Tao Wan, Huawei Canada
- Nicholas Weaver, International Computer Science Institute and University of California, Berkeley
A cookie can contain a “secure” flag, indicating that it should be only sent over an HTTPS connection. Yet there is no corresponding flag to indicate how a cookie was set: attackers who act as a man-in-the-middle even temporarily on an HTTP session can inject cookies which will be attached to subsequent HTTPS connections. Similar attacks can also be launched by a web attacker from a related domain. Although an acknowledged threat, it has not yet been studied thoroughly. This paper aims to fill this gap with an in-depth empirical assessment of cookie injection attacks. We find that cookie-related vulnerabilities are present in important sites (such as Google and Bank of America), and can be made worse by the implementation weaknesses we discovered in major web browsers (such as Chrome, Firefox, and Safari). Our successful attacks have included privacy violation, online victimization, and even financial loss and account hijacking. We also discuss mitigation strategies such as HSTS, possible browser changes, and present a proof-of-concept browser extension to provide better cookie isolation between HTTP and HTTPS, and between related domains.
- 804060 – Cookies set via HTTP requests may be used to bypass HTTPS and reveal private information; CERT
- Cookies can render secure websites vulnerable in all modern browsers; Martin Anderson; In The Stack; 2015-09-24.