Behavioral Advertising on Target
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Uila Supported Apps
Uila Supported Applications and Protocols updated Oct 2020 Application/Protocol Name Full Description 01net.com 01net website, a French high-tech news site. 050 plus is a Japanese embedded smartphone application dedicated to 050 plus audio-conferencing. 0zz0.com 0zz0 is an online solution to store, send and share files 10050.net China Railcom group web portal. This protocol plug-in classifies the http traffic to the host 10086.cn. It also 10086.cn classifies the ssl traffic to the Common Name 10086.cn. 104.com Web site dedicated to job research. 1111.com.tw Website dedicated to job research in Taiwan. 114la.com Chinese web portal operated by YLMF Computer Technology Co. Chinese cloud storing system of the 115 website. It is operated by YLMF 115.com Computer Technology Co. 118114.cn Chinese booking and reservation portal. 11st.co.kr Korean shopping website 11st. It is operated by SK Planet Co. 1337x.org Bittorrent tracker search engine 139mail 139mail is a chinese webmail powered by China Mobile. 15min.lt Lithuanian news portal Chinese web portal 163. It is operated by NetEase, a company which 163.com pioneered the development of Internet in China. 17173.com Website distributing Chinese games. 17u.com Chinese online travel booking website. 20 minutes is a free, daily newspaper available in France, Spain and 20minutes Switzerland. This plugin classifies websites. 24h.com.vn Vietnamese news portal 24ora.com Aruban news portal 24sata.hr Croatian news portal 24SevenOffice 24SevenOffice is a web-based Enterprise resource planning (ERP) systems. 24ur.com Slovenian news portal 2ch.net Japanese adult videos web site 2Shared 2shared is an online space for sharing and storage. -
Online Advertising
Online advertising From Wikipedia, the free encyclopedia Jump to: navigation, search This article may require cleanup to meet Wikipedia's quality standards. Please improve this article if you can. (July 2007) Electronic commerce Online goods and services Streaming media Electronic books Software Retail product sales Online shopping Online used car shopping Online pharmacy Retail services Online banking Online food ordering Online flower delivery Online DVD rental Marketplace services Online trading community Online auction business model Online wallet Online advertising Price comparison service E-procurement This box: view • talk • edit Online advertising is a form of advertising that uses the Internet and World Wide Web in order to deliver marketing messages and attract customers. Examples of online advertising include contextual ads on search engine results pages, banner ads, advertising networks and e-mail marketing, including e-mail spam. A major result of online advertising is information and content that is not limited by geography or time. The emerging area of interactive advertising presents fresh challenges for advertisers who have hitherto adopted an interruptive strategy. Online video directories for brands are a good example of interactive advertising. These directories complement television advertising and allow the viewer to view the commercials of a number of brands. If the advertiser has opted for a response feature, the viewer may then choose to visit the brand’s website, or interact with the advertiser through other touch points such as email, chat or phone. Response to brand communication is instantaneous, and conversion to business is very high. This is because in contrast to conventional forms of interruptive advertising, the viewer has actually chosen to see the commercial. -
CTR Prediction for Contextual Advertising: Learning-To-Rank Approach
CTR Prediction for Contextual Advertising: Learning-to-Rank Approach Yukihiro Tagami Shingo Ono Koji Yamamoto Yahoo Japan Corporation Yahoo Japan Corporation Yahoo Japan Corporation Tokyo, Japan Tokyo, Japan Tokyo, Japan [email protected] [email protected] koyamamo@yahoo- corp.jp Koji Tsukamoto Akira Tajima Yahoo Japan Corporation Yahoo Japan Corporation Tokyo, Japan Tokyo, Japan [email protected] [email protected] ABSTRACT if a user clicks their advertisements and visits the adver- Contextual advertising is a textual advertising displayed tiser’s site. To maximize revenue by choosing some ads from within the content of a generic web page. Predicting the candidates and ranking them to display, an advertising sys- probability that users will click on ads plays a crucial role in tem needs to predict the expected revenue for each ad. The contextual advertising because it influences ranking, filter- expected revenue from displaying each ad is a function of ing, placement, and pricing of ads. In this paper, we intro- both bid price and click-through rate (CTR). Bid price is duce a click-through rate prediction algorithm based on the the cost that the advertiser agrees to pay per click, so the learning-to-rank approach. Focusing on the fact that some advertising system already knows this. On the other hand, of the past click data are noisy and ads are ranked as lists, CTR for each ad can vary significantly in accordance with we build a ranking model by using partial click logs and then many factors such as the web page and user, so the system a regression model on it. -
Self-Regulatory Principles for Online Behavioral Advertising (FTC)
FTC Staff Report: Self-Regulatory Principles For Online Behavioral Advertising Behavioral Advertising Tracking, Targeting, & Technology February 2009 TABLE OF CONTENTS EXECUTIVE SUMMARY....................................................... i I. INTRODUCTION........................................................1 II. BACKGROUND.........................................................2 A. What Is Online Behavioral Advertising?.. 2 B. The FTC’s Examination of Online Behavioral Advertising. 4 1. Online Profiling Workshop.....................................6 2. Tech-ade Hearings and the Ehavioral Advertising Town Hall. 8 C. Staff’s Proposed Self-Regulatory Principles. 1 1 D. Recent Initiatives to Address Privacy Concerns. 1 2 III. SUMMARY OF THE COMMENTS RECEIVED AND STAFF’S ANALYSIS.. 1 8 A. The Principles’ Scope. .............................................2 0 1. Applicability to Non-PII. .....................................2 0 2. Applicability to “First Party” Online Behavioral Advertising. 2 6 3. Applicability to Contextual Advertising. 2 9 B. Transparency and Consumer Control...................................3 0 1. Choice for Non-PII...........................................3 1 2. Providing Effective Notice and Choice. 3 3 C. Reasonable Security and Limited Data Retention for Consumer Data. 3 7 D. Affirmative Express Consent for Material Retroactive Changes to Privacy Promises.........................................................3 9 E. Affirmative Express Consent to (or Prohibition Against) Use of Sensitive Data ............................................................4 -
Contextual In-Image Advertising
Contextual In-Image Advertising Tao Mei, Xian-Sheng Hua, Shipeng Li Microsoft Research Asia 5F Sigma Center, 49 Zhichun Road, Beijing 100190, P. R. China {tmei, xshua, spli}@microsoft.com ABSTRACT so called Web 2.0 wave) have led to the countless private The community-contributed media contents over the Inter- image collections on local computing devices such as per- net have become one of the primary sources for online adver- sonal computers, cell phones, and personal digital assistants tising. However, conventional ad-networks such as Google (PDAs), as well as the huge yet increasing public image col- AdSense treat image and video advertising as general text lections on the Internet. Compared with text and video, advertising without considering the inherent characteristics image has some unique advantages: it is more attractive of visual contents. In this work, we propose an innovative than plain text, and it has been found to be more salient contextual advertising system driven by images, which auto- than text [9], thus it can grab users’ attention instantly; it matically associates relevant ads with an image rather than carries more information that can be comprehended more the entire text in a Web page and seamlessly inserts the quickly, just like an old saying, a picture is worth thousands ads in the nonintrusive areas within each individual image. of words; and it can be shown to the users faster than video. The proposed system, called ImageSense, represents the first As a result, image has become one of the most pervasive me- attempt towards contextual in-image advertising. The rele- dia formats on the Internet [13]. -
A Case Study of Nairobi Consumers by Wambua, Glori
THE EFFECT OF REPETITIVE BEHAVIOURAL RE-TARGETING ON ONLINE CONVERSION RATES: A CASE STUDY OF NAIROBI CONSUMERS BY WAMBUA, GLORIA MWENDE UNITED STATES INTERNATIONAL UNIVERSITY - AFRICA SUMMER 2018 THE EFFECT OF REPETITIVE BEHAVIOURAL RE-TARGETING ON ONLINE CONVERSION RATES: A CASE STUDY OF NAIROBI CONSUMERS BY WAMBUA, GLORIA MWENDE A Project Report Submitted to the Chandaria School of Business in Partial Fulfilment of the Requirement for the Degree of Masters in Business Administration (MBA) UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA SUMMER 2018 i STUDENT’S DECLARATION I, the undersigned, declare this my original work and has not been submitted to any other college, institution or university other than United States University in Nairobi for academic credit. Signed __________________________ Date: ________________ Wambua, Gloria Mwende ID #:620162 This project report has been presented for examination with my approval as the appointed supervisor. Signed __________________________ Date: _________________ Dr. Peter Kiriri Signed: __________________________ Date: _________________ Dean Chandaria School of Business ii DEDICATION This is for my wonderful mother, who has been the wind beneath my wings all throughout my life. Your never-ending love, support, encouragement and prayers are what have made me realize this milestone. To my husband Paul, and my daughter M̯ uuo, your patience with me as I worked on this dissertation has been tremendous, thank you. To all my family and friends who took the time to fill out my online survey, and all those who’ve prayed, encouraged, and supported me in the journey towards the completion of this paper, this work is dedicated to you. iii ACKNOWLEDGMENT To the almighty God, who formed me, all glory an honour is yours. -
Systematic Scoping Review on Social Media Monitoring Methods and Interventions Relating to Vaccine Hesitancy
TECHNICAL REPORT Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy www.ecdc.europa.eu ECDC TECHNICAL REPORT Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy This report was commissioned by the European Centre for Disease Prevention and Control (ECDC) and coordinated by Kate Olsson with the support of Judit Takács. The scoping review was performed by researchers from the Vaccine Confidence Project, at the London School of Hygiene & Tropical Medicine (contract number ECD8894). Authors: Emilie Karafillakis, Clarissa Simas, Sam Martin, Sara Dada, Heidi Larson. Acknowledgements ECDC would like to acknowledge contributions to the project from the expert reviewers: Dan Arthus, University College London; Maged N Kamel Boulos, University of the Highlands and Islands, Sandra Alexiu, GP Association Bucharest and Franklin Apfel and Sabrina Cecconi, World Health Communication Associates. ECDC would also like to acknowledge ECDC colleagues who reviewed and contributed to the document: John Kinsman, Andrea Würz and Marybelle Stryk. Suggested citation: European Centre for Disease Prevention and Control. Systematic scoping review on social media monitoring methods and interventions relating to vaccine hesitancy. Stockholm: ECDC; 2020. Stockholm, February 2020 ISBN 978-92-9498-452-4 doi: 10.2900/260624 Catalogue number TQ-04-20-076-EN-N © European Centre for Disease Prevention and Control, 2020 Reproduction is authorised, provided the -
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The Definitive Guide to Contextual Advertising
THE DEFINITIVE GUIDE TO CONTEXTUAL ADVERTISING 1 TABLE OF CONTENTS Preface 4 CHAPTER 1 Contextual PPC advertising 8 CHAPTER 2 Creating context on social platforms 14 CHAPTER 3 Contextual messages and your brand identity 21 CHAPTER 4 How does creative automation help? 28 Rocketium 35 2 “Dear marketer, Before you target me, ask yourself why I’d want to care…” - Gen Z prospects Consider this “60% of Gen Z will not use an app or website that loads too slowly.” (Institute of Business Management), via Hubspot 3 PREFACE Ads were always contextual -- at least until digital behavioural patterns became common knowledge and behaviour targeting took center stage. However, we are now at an interesting turning point in the history of online ads. Browser policies and privacy mandates have made non-negotiable restrictions on targeting through third-party cookies. As a result, there is a renewed focus on user experience and consent. “” 4 Because Chrome, Safari, and Firefox will all no longer support this type of data tracking (third party cookies) by 2022, publications like Digiday are calling Google's phase-out the “death of the third-party cookie.” 5 Marketers are obligated to care not only about the mandates but also about the fact that the users they are targeting are evolving to distrust the media overall and subsequently, brands that try too hard. Besides, the steep costs of lawsuits (GDPR suits costing as much as €20 million or 4% of global revenue) are tight shackles that could potentially crush a company. Contextual targeting, is therefore, one of the key strategies you need to embrace to stay relevant. -
How to Make Money with Online Affiliate Programs
How to Make Money With Online Affiliate Programs There are many ways an industrious individual can make money online. Countless people have turned to the World-Wide-Web looking for a good money making opportunity they can accomplish from the comfort of their own home. It may seem daunting at first but you can make money online if you learn, duplicate and most importantly have the determination to just start. One of the most ingenious ways to supplement or even replace your income is through online affiliate programs. Online affiliate programs first sprang up in 1996 when Amazon.com started paying websites for referring customers to their site. Now in 2006 online affiliate programs are a mainstay in the e-commerce world. Even Fortune 500 companies such as Dell, Walmart and Apple have adopted the online affiliate program marketing module. Online affiliate programs provide home business professionals and online entrepreneurs a risk-free form of advertising to produce revenue from their websites. Affiliate marketing has become an increasingly popular home based business opportunity because: It requires no production costsVery low start-up costsNo employeesNo inventoryNo order processingNo shippingNo customer serviceVery limited risk So you don’t have a website? In many cases you do not even need to operate a website or know any HTML to make money with online affiliate programs. With the maturation of contextual advertising through Yahoo! Publisher and Google Adwords many pay-per-click (PPC) savvy online affiliate marketers have moved away from deploying websites and focus entirely on search engine marketing (SEM). This may be a good way for some to test the waters with marketing online affiliate programs but if you’re not familiar with setting up PPC campaigns tread lightly. -
Loree O'sullivan
SELLING THROUGH SOCIAL MEDIA: CHARMING PROSPECTS WITH CONTEXT & CONFIDENCE LOREE O’SULLIVAN 2 LOREE O’SULLIVAN DIGITAL MARKETING STRATEGIST AT FABRICUT INC. Digital Marketer dig·i·tal /ˈdijidl/ mar·ket·er /ˈmärkədər/ A marketer who does all the things on all the devices for all of the platforms to accomplish all the goals for all of the teams - and still somehow manages to achieve greatness (with the help of coffee). SELLING THROUGH SOCIAL MEDIA CHARMING PROSPECTS WITH CONTEXT & CONFIDENCE Social media is everywhere. It’s everything! Whether consumers are researching reviews of a product, looking for customer support for an issue, sharing their satisfaction or disappointment from a purchase, or asking WHY ARE WE questions around best practices - social media has become LOOKING AT the place where consumers can engage in a way that is fast, SOCIAL MEDIA? reactive, and fits into their established dialogue. JUST HOW OBSESSED WITH SOCIAL MEDIA ARE WE? DOWN TO THE NUMBERS 54 74 78 22 60 BUSINESSES GENERATED USE FACEBOOK USAGE ACROSS WORLD POPULATION USE INSTAGRAM DAILY LEADS FROM SOCIAL DAILY AGE GROUPS 30-49 ON FACEBOOK 54% of B2B businesses 74% of Facebook users Use any form of social media: 22% of the world’s total 60% users indicate that they visit and marketers said say they visit the site daily 88% of 18 to 29 population uses these platforms on a daily basis. they’ve generated leads 78% of 30 to 49 Facebook. from social media 64% of 50 to 64 SOURCE http://www.pewinternet.org/2018/03/01/social-media-use-in-2018/ A LITTLE BIT OF HISTORY -
Online Retargeting Based on Consumer Behavior: a Comparison Between Models
Online retargeting based on consumer behavior: a comparison between models Master thesis Menno Nelis Student no: 5929040 Supervisor: Prof. Dr. Dick Heinhuis University of Amsterdam Amsterdam, The Netherlands Faculty of Science Information Studies Track: Business Information Systems August 2017 Abstract Behavioral retargeting is a widely used technique within the field of online marketing to generate sales out of customers that already visited your website. These campaigns are based on the online behavior and interests of the customer. Although there is a huge growing market for behavioral retargeting, current online campaigns do not yet use models of consumer behavior or decision making as input for their campaign design. Behavioral retargeting is seen as a trick by marketers and their campaigns are optimized empirically. This research selected three models of consumer behavior or decision making; the Howard-Sheth model, the EBM model and the Bettman model. The phases of these models were translated from offline to online behavior and used to create three different retargeting campaigns. The fourth campaign is the control group, which is not based on a model. A Dutch travel company, Corendon, provided a platform to run the different experiments. Their visiting customers on the website were used in the experiment to sub divide customers among the different experimental campaigns, based on the selected models. The results obtained from these experiments were measured in six online metrics: the bounce rate, click through rate, conversion rate, cost per action, cost per click and the return on investment. After the campaigns ran for a week, results showed that the campaigns based on a consumer behavior or decision making model all scored better than the control group.