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Pardot Pro Features Overview Extra Bells & Whistles That Will Get You More Sales & Leads
Pardot Pro Features Overview Extra bells & whistles that will get you more sales & leads A Pardot Client Advocate Topical Office Hours Clare Kelly EMEA Client Advocate Pardot, Salesforce The Client Advocates help our customers achieve their marketing goals by providing Pardot strategy, marketing automation best practices, and industry trends. Next topical session: ‘Game Changer: 3 strategies to shorten your buyer journey’ Friday 15th April, 6pm GMT (register for recording) Check our calendar for all upcoming sessions! http://www2.pardot.com/advocates The Pardot Client Advocates Adam Waid Ginny Richardson Nicole Conley Molly Morris Futrell Kylie Nickles Richard Lewis Hannah Freeman Camille Barnet Dane McKinley Lindsay Stanford Caitlin Clark Howell Jazmyne Dodd Cristi Moscoso Paymaun “P$” Rezai Rob Valdez Jessica Williams Ami Kamei Jessica Marsh Cole McIntyre Virginia Baaklini Clare Kelly Leigh Falgoust David DiGiammarino Samantha Pang Whitney Rudeseal Pro Features Overview Businesses that personalize web With Pardot Pro Edition, you have access to: experiences see an average 19% ● Advanced Dynamic Content (avail as increase in sales. separate add on to Standard) ● Multivariate Landing Page Testing (addon) (MarketingProfs) ● Email A/B Testing (only Pro & Ultimate) ● Advanced Email Analytics + Email Rendering and Preview Analysis (addon) ● Google Adwords Connector (addon) ● Chat Support ● Social Profiling & Lookups (addon) ● Increased file storage, more SEO keyword and competitor monitoring ● Unlimited emails per month ● 10,000 mailable contacts ● File Hosting: 500 MB (standard 100MB) Advanced Testing Pro and Ultimate accounts have access to: ● AB Testing for emails ● Multivariate Testing for Landing Pages ● Advanced Email Analytics (Rendering and Spam Analysis) AB Email Testing AB Testing for emails allows you to change elements in your email, segment your recipient list into a test audience and winning audience, and then use the email’s engagement data to automatically determine and distribute the winning email. -
Pattern Discrimination PATTERN
Apprich, Chun, Cramer, Steyerl Pattern Discrimination Pattern PATTERN DISCRIMINATION APPRICH CHUN CRAMER STEYERL Pattern Discrimination IN SEARCH OF MEDIA Götz Bachman, Timon Beyes, Mercedes Bunz, and Wendy Hui Kyong Chun, Series Editors Communication Machine Markets Pattern Discrimination Remain Pattern Discrimination Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer, and Hito Steyerl IN SEARCH OF MEDIA University of Minnesota Press Minneapolis London meson press In Search of Media is a joint collaboration between meson press and the University of Minnesota Press. Bibliographical Information of the German National Library The German National Library lists this publication in the Deutsche Nationalbibliografie (German National Bibliography); detailed bibliographic information is available online at portal.d-nb.de. Published in 2018 by meson press (Lüneburg, Germany ) in collaboration with the University of Minnesota Press (Minneapolis, USA). Design concept: Torsten Köchlin, Silke Krieg Cover image: Sascha Pohflepp ISBN (PDF): 978-3-95796-145-7 DOI: 10.14619/1457 The digital edition of this publication can be downloaded freely at: meson.press. The print edition is available from University of Minnesota Press at: www.upress.umn.edu. This Publication is licensed under CC-BY-NC-4.0 International. To view a copy of this license, visit: creativecommons.org/ licenses/by-nc/4.0/ Contents Series Foreword vii Introduction ix Clemens Apprich [ 1 ] A Sea of Data: Pattern Recognition and Corporate Animism (Forked Version) 1 Hito Steyerl [ 2 ] Crapularity Hermeneutics: Interpretation as the Blind Spot of Analytics, Artificial Intelligence, and Other Algorithmic Producers of the Postapocalyptic Present 23 Florian Cramer [ 3 ] Queerying Homophily 59 Wendy Hui Kyong Chun [ 4 ] Data Paranoia: How to Make Sense of Pattern Discrimination 99 Clemens Apprich Authors 123 Series Foreword “Media determine our situation,” Friedrich Kittler infamously wrote in his Introduction to Gramophone, Film, Typewriter. -
PROTECTING MY PERSONAL SPACE User Perceptions
PROTECTING MY PERSONAL SPACE Devan S Since the arrival of early social networking sites in the early 2000s, online social networking platforms have expanded exponentially, with the biggest names in social media in the mid-2010s being Facebook, Instagram, Twitter and Snapchat. The massive influx of personal information that has become available online and stored in the cloud has put user privacy at the forefront of discussion regarding the database's ability to safely store such personal information. The extent to which users and social media platform administrators can access user profiles has become a new topic of ethical consideration, and the legality, awareness, and boundaries of subsequent privacy violations are critical concerns in advance of the technological age. A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. Privacy concerns with social networking services is a subset of data privacy, involving the right of mandating personal privacy concerning storing, re-purposing, provision to third parties, and displaying of information pertaining to oneself via the Internet. Social network security and privacy issues result from the large amounts of information these sites process each day. Features that invite users to participate in messages, invitations, photos, open platform applications and other applications are often the venues for others to gain access to a user's private information. In addition, the technologies needed to deal with user's information may intrude their privacy. The advent of the Web 2.0 has caused social profiling and is a growing concern for internet privacy. -
Time-Aware Egocentric Network-Based User Profiling
Open Archive TOULOUSE Archive Ouverte ( OATAO ) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15356 The contribution was presented at ASONAM 2015 : http://asonam.cpsc.ucalgary.ca/2015/ To cite this version : Canut, Marie-Françoise and On-At, Sirinya and Péninou, André and Sèdes, Florence Time-aware Egocentric network-based User Profiling. (2015) In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), 25 August 2015 - 28 August 2015 (Paris, France). Any correspondence concerning this service shoul d be sent to the repository administrator: staff -oatao@listes -diff.inp -toulouse.fr Time-aware Egocentric network-based User Profiling Marie-Françoise Canut, Sirinya On-At, André Péninou and Florence Sèdes IRIT, University of Toulouse, UMR CNRS 5505, 31062 TOULOUSE Cedex 9 {marie-francoise.canut, sirinya.on-at, andre.peninou, florence.sedes}@irit.fr Abstract — Improving the egocentric network-based user’s In this work, we focus on taking into account the profile building process by taking into account the dynamic evolution of user’s interests in social network-based user characteristics of social networks can be relevant in many profiling process in order to build a more relevant and up-to- applications. To achieve this aim, we propose to apply a time- date social profile. We try to answer the following problems: aware method into an existing egocentric-based user profiling (i) how to select the relevant individuals in the user social process, based on previous contributions of our team. -
Social Measurement Depends on Data Quantity and Quality
POINT OF VIEW SOCIAL MEASUREMENT DEPENDS ON DATA QUANTITY AND QUALITY Social Measurement Depends on Data Quantity and Quality As social platforms proliferate, enthusiastic users are generating more data than ever. Social media data are fast becoming the hottest commodity in market research. But can social data yield measurements that are comparable to those from other, more established The future of actionable forms of research? Is it really possible for brand managers to tap into these data streams to gain social media measurement insight into brand equity? is only as strong as its We believe it is too early to say for sure, even standards for data quality. though social data have been used effectively by PR and marketing departments for years. Anne Czernek For crisis management and on-the-fly campaign brands and more than 30 million online conversations Senior Research Analyst assessments, social monitoring involves to determine the most appropriate methodologies Emerging Media Lab Millward Brown/Dynamic Logic watching a wide stream of updates in real time for working with social measurement from a brand and using those to gauge immediate next steps. perspective. Our conclusion? The future of actionable For these purposes, a qualitative sense of the social media measurement is only as strong as its consumer mood is adequate; the precision of standards for data quality. quantitative research is not required. THE “SOCIAL” VOICE: HOW IS But increasingly, insight and brand strategy teams IT DIFFERENT? are interested in using social data, and they would like to place social measurement alongside other Social data are fundamentally different from types of brand metrics (attitudinal, behavioral, and traditional brand measurement data. -
PAN 2017: Author Profiling
PAN 2017: Author Profiling - Gender and Language Variety Prediction Notebook for PAN at CLEF 2017 Matej Martinc1;2, Iza Škrjanec2, Katja Zupan1;2, and Senja Pollak1 1 Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia 2 Jožef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia [email protected],[email protected], [email protected],[email protected] Abstract We present the results of gender and language variety identification performed on the tweet corpus prepared for the PAN 2017 Author profiling shared task. Our approach consists of tweet preprocessing, feature construction, feature weighting and classification model construction. We propose a Logistic regres- sion classifier, where the main features are different types of character and word n-grams. Additional features include POS n-grams, emoji and document senti- ment information, character flooding and language variety word lists. Our model achieved the best results on the Portuguese test set in both—gender and language variety—prediction tasks with the obtained accuracy of 0.8600 and 0.9838, re- spectively. The worst accuracy was achieved on the Arabic test set. Keywords: author profiling, gender, language variety, Twitter 1 Introduction Recent trends in natural language processing (NLP) have shown a great interest in learn- ing about the demographics, psychological characteristics and (mental) health of a per- son based on the text she or he produced. This field, generally known as author profiling (AP), has various applications in marketing, security (forensics), research in social psy- chology, and medical diagnosis. A thriving subfield of AP is computational stylometry, which is concerned with how the content and genre of a document contribute to its style [4]. -
Author Profiling: Predicting Age and Gender from Blogs
Author Profiling: Predicting Age and Gender from Blogs Notebook for PAN at CLEF 2013 K Santosh, Romil Bansal, Mihir Shekhar, and Vasudeva Varma International Institute of Information Technology, Hyderabad {santosh.kosgi, romil.bansal, mihir.shekhar}@research.iiit.ac.in, [email protected] Abstract Author profiling is the task of determining age, gender, native language or personality type of author by studying their sociolect aspect, that is, how lan- guage is shared by people. In this paper, we propose a Machine Learning ap- proach to determine unknown author’s age and gender. The approach uses three types of features: content based, style based and topic based. We were able to achieve an accuracy of 64.08%, 64.30% for age and 56.53%, 64.73% for gender in English and Spanish respectively. Keywords: Author Profiling, Topic Modelling, Text Categorization, Natural Lan- guage Processing 1 Introduction The problem of identifying the user’s profile from the text is always of importance as it helps in various fields like forensics and marketing. For example, in marketing, a manager might want to find the gender and age group of people who like or dislike their products from the public reviews. The increasing accessibility of public blogs offers an unprecedented opportunity to harvest information from texts authored by hundreds of thousands of different authors. In this paper, we tried to exploit these public blogs to find the relations between the author’s profile and the language style used by them. The main idea behind this task is to analyse how everyday languages reflects basic social and personality traits. -
Automatic Author Profiling Based on Linguistic and Stylistic Features Notebook for PAN at CLEF 2013
Automatic Author Profiling Based on Linguistic and Stylistic Features Notebook for PAN at CLEF 2013 Braja Gopal Patra1, Somnath Banerjee1, Dipankar Das2, Tanik Saikh1, Sivaji Bandyopadhyay1 1Department of Computer Science & Engineering, Jadavpur University, India 2Department of Computer Science & Engineering, NIT Meghalaya, India {brajagopal.cse, s.banerjee1980, dipankar.dipnil2005, tanik4u}@gmail.com, [email protected] Abstract. The rapid expansion of blog and electronic data in Web 2.0 is abounding and thus it is becoming important to identify the author‟s profile also. The problems of automatic identification of author‟s gender and age based on linguistic and stylistic pattern have been a subject of increasingly research interest in the recent years. The research methodologies are also helpful for several other applications like criminal detection, security and author detection etc. We have used lexical, syntactic and structural features for identifying the gender and age group of the authors. We have employed the Decision tree classifier for classifying the author profile. We have achieved the accuracies of 56.83% and 28.95% for gender and age group classification, respectively. Key words: Automatic author profiling, gender identification, age group identification, word class, decision tree. 1 Introduction In the 21st century, popularity of the Internet is increasing abundantly. Internet media like emails, blogs/internet forum and websites have been identified as the ideal communication platform for the people. In the recent years, the researchers have been paid increasingly interest to analyze of authorship of emails, electronic messages [5] and plagiarism detection [7] etc. Thus, analyzing the web content has become more important to the intelligence and security agencies that monitor the author information as much as possible [1]. -
Arabic Twitter User Profiling: Application to Cyber-Security
Arabic Twitter User Profiling: Application to Cyber-security Rahma Basti1, Salma Jamoussi2, Anis Charfi3 and Abdelmajid Ben Hamadou4 1Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), University of Sfax, Tunis, Tunisia 2Higher Institute of Computer Sceience and Multimedia of Sfax, 1173 Sfax 3038, Tunisia 3Carnegie Mellon University Qatar, Qatar 4Digital Research Center of Sfax (DRCS), Tunisia Keywords: Author Profiling, Arabic Text Processing, Age and Gender Prediction, Dangerous Profiles, Stylometric Features. Abstract: In recent years, we witnessed a rapid growth of social media networking and micro-blogging sites such as Twitter. In these sites, users provide a variety of data such as their personal data, interests, and opinions. However, this data shared is not always true. Often, social media users hide behind a fake profile and may use it to spread rumors or threaten others. To address that, different methods and techniques were proposed for user profiling. In this article, we use machine learning for user profiling in order to predict the age and gender of a user’s profile and we assess whether it is a dangerous profile using the users’ tweets and features. Our approach uses several stylistic features such as characters based, words based and syntax based. Moreover, the topics of interest of a user are included in the profiling task. We obtained the best accuracy levels with SVM and these were respectively 73.49% for age, 83.7% for gender, and 88.7% for the dangerous profile detection. 1 INTRODUCTION researchers to use it including those who focus on user profiling (Feldman and Sanger, 2006). Social media networks allow users to share Research in psychology (Frank and Witten, 1998) information, opinions and communicate with each has revealed that the words used by an individual can other. -
The Complete Guide to Marketing Attribution
The Complete Guide to Marketing Attribution A MARKETER’S GUIDE TO REBUILDING MARKETING ATTRIBUTION www.queryclick.com Contents 04 Marketing attribution: the basics 09 Marketing attribution models explained The problem with current attribution 15 solutions Why your data is the foundation for your 18 attribution success Marketing attribution: a guide to your 26 choices A new approach to attribution: visit-level 33 attribution The powerful data views you need to 38 accurately drive marketing ROI 40 Closing thoughts ATTRIBUTION PLAYBOOK 3 The purpose of attribution is deceptively In this guide, we are going to take a close Overview simple: to most fairly share the value of a goal look at some of the key aspects of attribution conversion across all touchpoints that may including: have influenced that conversion. However, in the real world the complexity of touchpoints • What marketing attribution actually is and media opportunities in marketing create • Why it matters more now than ever an attribution challenge, even in a perfect • An introduction to the main attribution world of data availability. models including some of their limitations The customer journey is now very often • Why data is key to all of this – and some of a highly complex one. And being able to the challenges and opportunities around attribute the impact of specific marketing collecting data across websites, offline touchpoints is crucial, as pressure from media, social platforms and CRM/CDP/ERP internal stakeholders - including finance and • How techniques like Machine Learning and the boardroom - to link marketing to revenue, Deterministic and Probabilistic matching and prove ROI intensifies. Unravelling the can help overcome limitations in current impact of specific touchpoints on conversion attribution approaches is priority number 1 for marketers. -
Author Profiling for English and Spanish Text
Author Profiling for English and Spanish Text Notebook for PAN at CLEF 2013 Upendra Sapkota1, Thamar Solorio1, Manuel Montes-y-Gómez2, and Gabriela Ramírez-de-la-Rosa1 1 University of Alabama at Birmingham, Birmingham, AL 35294, USA {upendra,solorio,gabyrr}@cis.uab.edu, 2 Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico [email protected] Abstract This paper describes an approach for the author profiling task of the PAN 2013 challenge. This work is based on the idea of linguistic modality3 that has been successfully used in other classification tasks such as authorship attri- bution. We consider three different modalities: syntactic, stylistic, and semantic, each representing a different aspect of text. For each modality, we extract infor- mative meta features by computing the similarity relations between the feature vectors in the test files and the centroids of modality specific clusters. Since we were provided texts in both Spanish and English, we build a language indepen- dent framework for author profiling. For both English and Spanish documents, our system performed well for the age identification task. For gender prediction, although our system could not perform as expected for English, it yielded good results on Spanish. Keywords: author profiling, linguistic modality, similarity relations 1 Introduction With the proliferation of social media (blogs, chats), it has been possible to access large online texts. Such large texts can be utilized to understand how the writing style among users of different age groups, as well as between male and female vary [1,2]. Unlike the authorship attribution problem where the task is to identify the true author of a given piece of text, the author profiling task tries to learn as much information as possible (demographics, personality) about the unknown writer of the given text. -
Approaching the Meaning of Measurement in the Digital Everyday
MCS0010.1177/0163443720907017Media, Culture & SocietyBolin and Velkova 907017research-article2020 Main Article Media, Culture & Society 2020, Vol. 42(7-8) 1193 –1209 Audience-metric continuity? © The Author(s) 2020 Approaching the meaning of Article reuse guidelines: sagepub.com/journals-permissions measurement in the digital https://doi.org/10.1177/0163443720907017DOI: 10.1177/0163443720907017 everyday journals.sagepub.com/home/mcs Göran Bolin Södertörn University, Sweden Julia Velkova University of Helsinki, Finland Abstract This article argues for an expansion of existing studies on the meaning of metrics in digital environments by evaluating a methodology tested in a pilot study to analyse audience responses to metrics of social media profiles. The pilot study used the software tool Facebook Demetricator by artist Ben Grosser in combination with follow-up interviews. In line with Grosser’s intentions, the software indeed provoked reflection among the users. In this article, we reflect on three kinds of disorientations that users expressed, linked to temporality, sociality and value. Relating these to the history of audience measurement in mass media, we argue that there is merit in using this methodology for further analysis of continuities in audience responses to metrics, in order to better understand the ways in which metrics work to create the ‘audience commodity’. Keywords audience measurement, audiences, digital media, disruptive methods, everyday life, metrics Introduction Digital ‘media life’ (Deuze, 2012) is increasingly permeated by metrics. Social media platforms, apps and personal gadgets quantify an ever-expanding array of social life that has ‘never been quantified before – friendships, interests, casual conversations, Corresponding author: Göran Bolin, Department of Media & Communication Studies, Södertörn University, SE-14189 Huddinge, Sweden.