Google Analytics Glossary Below You Will Find the Definitions for Some of the Vocabulary That We Cover in the Better U Google Analytics Presentation

Total Page:16

File Type:pdf, Size:1020Kb

Google Analytics Glossary Below You Will Find the Definitions for Some of the Vocabulary That We Cover in the Better U Google Analytics Presentation TURN DATA INTO ACTIONABLE INSIGHTS Google Analytics Workshop Google Analytics Glossary Below you will find the definitions for some of the vocabulary that we cover in the Better U Google Analytics presentation. The goal is to give you a guide that you can reference quickly to gain clarity around some of the terminology that you may or may not be familiar with. Definitions Average Time on Page: Average Time on Page represents the average amount of time in seconds, a visitor spends on a particular page. Average Session Duration: The average length of a session. Bounce Rate: The percentage of single-page sessions in which there was no interaction with the page. A bounced session has a duration of 0 seconds. Keywords: A word that is used as a reference point for finding other words of its kind or any information regarding those words. Landing Pages: Landing Pages represent the number of sessions that started on a specific web page or group of web pages, helping you identify what content is bringing people into your site most. An entry page where a user accesses the website. New Session: An estimate of the first time visits. New Vs Returning Visitors: New visitors are users that have not visited your site before. Returning visitors will have made at least one visit to at least one page on your site previously. • Google Analytics uses cookies to detect previous visits. If Google cannot detect a cookie, one will be placed on the users browser, as long as the user has not disabled cookies in their personal browser preferences. Page/Session: Pages/Session (Average Page Depth) is the average number of pages viewed during a session. Repeated views of a single page are counted. willowmarketing.com 1 TURN DATA INTO ACTIONABLE INSIGHTS Google Analytics Workshop Pageviews: This is the number of times users view a page that has the Google Analytics tracking code inserted. It’s a count of viewed pages and not individual visitors. • Covers all page views. • If a user refreshes a page, or navigates away from the page and returns, these are counted as additional page views. • This helps you to identify your most popular pages. Sessions: Total number of Sessions within the date range. A session is the period of time a user is actively engaged with your website, app, etc. All usage data (Screen Views, Events, Ecommerce, etc.) is associated with a session. A session is ended after 30 minutes of inactivity or when a user exits your site. Total Pageviews: The total number of pages viewed. Multiple pageviews in a session are counted in the total. Unique Pageviews: The unique pageview is the count of all the times the page was viewed in an individual session. If a visitor only viewed the page once, or if they viewed it five times during their visit, the number of unique pageviews will only be counted as one. Users: People who engage with your site or app over a given date range. Traffic by Channels Definitions Direct Traffic: Users who are already familiar with you or your organization and typed your domain name directly into the browser. Email: Traffic that is acquired from email or email marketing. Events: Events are user interactions with content that can be tracked independently from a web page or a screen load. Downloads, mobile ad clicks, gadgets, Flash elements, AJAX embedded elements, and video plays are all examples of actions you might want to track as Events. Event Tracking: The process of Tracking specific user interactions with particular content, content types (such as video) and identified individual or organizational goals. Organic Search: Users who entered a keyword into a major search engine that drove organic traffic to your website. Other: Sources of traffic that Google has a hard time identifying and for some reason or another deems it as “Other” traffic. Paid Traffic: Traffic acquired from utilizing paid ad platforms like Google AdWords, Bing, and Facebook ads. Social: Traffic that is acquired from social media platforms. willowmarketing.com 2 TURN DATA INTO ACTIONABLE INSIGHTS Google Analytics Workshop Campaigns Definitions Campaigns: In its broadest definition, campaign tracking refers to a method of identifying how users discover your site. Specifically, you use campaign tracking in Google Analytics to accurately track online advertising campaigns to your website, both from AdWords- generated campaigns as well as from other advertising sources. Goals: Goals can be set up your Google Analytics account to monitor the conversion rate of the different activities that you want your users to take. Activities may include a visitor making a purchase, signing up for an email newsletter, or downloading a white paper. You can also assign a monetary value to each goal completion to help determine the return on investment from your website. UTM: Urchin Tracking Module (UTM) parameters are five variants of URL parameters used by marketers to track the effectiveness of online marketing campaigns across traffic sources and publishing media. NEED HELP? That’s what we’re here for. Let us know what questions you have. Keyon Whiteside, Director Of Digital Services, [email protected] Haley Kuehl, Digital Strategist, [email protected] willowmarketing.com 3.
Recommended publications
  • Causal Inference in the Presence of Interference in Sponsored Search Advertising
    Causal Inference in the Presence of Interference in Sponsored Search Advertising Razieh Nabi∗ Joel Pfeiffer Murat Ali Bayir Johns Hopkins University Microsoft Bing Ads Microsoft Bing Ads [email protected] [email protected] [email protected] Denis Charles Emre Kıcıman Microsoft Bing Ads Microsoft Research [email protected] [email protected] Abstract In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violated in settings where units are related through a network of dependencies. An example of such a setting is ad placement in sponsored search advertising, where the clickability of a particular ad is potentially influenced by where it is placed and where other ads are placed on the search result page. In such scenarios, confounding arises due to not only the individual ad-level covariates but also the placements and covariates of other ads in the system. In this paper, we leverage the language of causal inference in the presence of interference to model interactions among the ads. Quantification of such interactions allows us to better understand the click behavior of users, which in turn impacts the revenue of the host search engine and enhances user satisfaction. We illustrate the utility of our formalization through experiments carried out on the ad placement system of the Bing search engine. 1 Introduction In recent years, advertisers have increasingly shifted their ad expenditures online. One of the most effective platforms for online advertising is search engine result pages. Given a user query, the search engine allocates a few ad slots (e.g., above or below its organic search results) and runs an auction among advertisers who are bidding and competing for these slots.
    [Show full text]
  • Understanding Attention Metrics
    Understanding Attention Metrics sponsored by: 01 introduction For years and years we’ve been told that if the click isn’t on its deathbed, it should be shivved to death immediately. While early display ads in the mid-90s could boast CTRs above 40%, the likely number you’ll see attached to a campaign these days is below 0.1%. But for some reason, even in 2015, click-through rate remains as vital to digital ad measurement although its importance is widely ridiculed. Reliance on the click means delivering unfathomable amounts of ad impressions to users, which has hyper-inflated the value of the pageview. This has led to a broken advertising system that rewards quantity over quality. Instead of building audiences, digital publishers are chasing traffic, trying to lure users to sites via “clickbait” headlines where the user is assaulted with an array of intrusive ad units. The end effect is overwhelming, ineffective ads adjacent to increasingly shoddy content. However, the rise of digital video and viewability have injected linear TV measurement capabilities into the digital space. By including time in its calculation, viewability has opened a conversation into the value of user attention. It has introduced metrics with the potential to assist premium publishers in building attractive audiences; better quantify exposure for advertisers; and ultimately financially reward publishers with more engaged audiences. This is a new and fast developing area, but one where a little education can go a long way. In addition to describing current attention metrics, this playbook will dive into what you should look for in an advanced metrics partner and how to use these metrics in selling inventory and optimizing campaign performance.
    [Show full text]
  • WTF IS NATIVE ADVERTISING? Introduction
    SPONSORED BY IS NATIVE ADVERTISING? Table of Contents Introduction WTF is Native 3 14 Programmatic? Nomenclature The Native Ad 4 16 Triumvirate 5 Decision tree 17 The Ad Man 18 The Publisher Issues Still Plaguing 19 The Platform 6 Native Advertising Glossary 7 Scale 20 8 Metrics 10 Labelling 11 The Church/State Divide 12 Credibility 3 / WTF IS NATIVE ADVERTISING? Introduction In 2013, native advertising galloped onto the scene like a masked hero, poised to hoist publishers atop a white horse, rescuing them from the twin menaces of programmatic advertising and sagging CPMs. But who’s really there when you peel back the mask? Native advertising is a murky business. Ad executives may not consider it advertising. Editorial departments certainly don’t consider it editorial. Even among its practitioners there is debate — is it a format or is it a function? Publishers who have invested in the studio model position native advertising as the perfect storm of context, creative capital and digital strategy. For platforms, it may be the same old banner advertising refitted for the social stream. Digiday created the WTF series to parse murky digital marketing concepts just like these. WTF is Native Advertising? Keep reading to find out..... DIGIDAY 4 / WTF IS NATIVE ADVERTISING? Nomenclature Native advertising An advertising message designed Branded content Content created to promote a Content-recommendation widgets Another form to mimic the form and function of its environment brand’s products or values. Branded content can take of native advertising often used by publishers, these a variety of formats, not all of them technically “native.” appear to consumers most often at the bottom of a web Content marketing Any marketing messages that do Branded content placed on third-party publishing page with lines like “From around the web,” or “You not fit within traditional formats like TV and radio spots, sites or platforms can be considered native advertising, may also like.” print ads or banner messaging.
    [Show full text]
  • Google Analytics Education and Skills for the Professional Advertiser
    AN INDUSTRY GUIDE TO Google Analytics Education and Skills For The Professional Advertiser MODULE 5 American Advertising Federation The Unifying Voice of Advertising Google Analytics is a freemium service that records what is happening at a website. CONTENTS Tracking the actions of a website is the essence of Google MODULE 5 Google Analytics Analytics. Google Analytics delivers information on who is visiting a Learning Objectives .....................................................................................1 website, what visitors are doing on that website, and what Key Definitions .......................................................................................... 2-3 actions are carried out at the specific URL. Audience Tracking ................................................................................... 4-5 Information is the essence of informed decision-making. The The A,B,C Cycle .......................................................................................6-16 website traffic activity data of Google Analytics delivers a full Acquisition understanding of customer/visitor journeys. Behavior Focus Google Analytics instruction on four(4) Conversions basic tenets: Strategy: Attribution Model ............................................................17-18 Audience- The people who visit a website Case History ........................................................................................... 19-20 Review ..............................................................................................................
    [Show full text]
  • Auditing Wikipedia's Hyperlinks Network on Polarizing Topics
    Auditing Wikipedia’s Hyperlinks Network on Polarizing Topics Cristina Menghini Aris Anagnostopoulos Eli Upfal DIAG DIAG Dept. of Computer Science Sapienza University Sapienza University Brown University Rome, Italy Rome, Italy Providence, RI, USA [email protected] [email protected] [email protected] ABSTRACT readers to deepen their understanding of a topic by conveniently ac- People eager to learn about a topic can access Wikipedia to form cessing other articles." Consequently, while reading an article, users a preliminary opinion. Despite the solid revision process behind are directly exposed to its content and indirectly exposed to the the encyclopedia’s articles, the users’ exploration process is still content of the pages it points to. influenced by the hyperlinks’ network. In this paper, we shed light Wikipedia’s pages are the result of collaborative efforts of a com- on this overlooked phenomenon by investigating how articles de- mitted community that, following policies and guidelines [4, 20], scribing complementary subjects of a topic interconnect, and thus generates and maintains up-to-date and high-quality content [28, may shape readers’ exposure to diverging content. To quantify 40]. Even though tools support the community for curating pages this, we introduce the exposure to diverse information, a metric that and adding links, it lacks a systematic way to contextualize the captures how users’ exposure to multiple subjects of a topic varies pages within the more general articles’ network. Indeed, it is im- click-after-click by leveraging navigation models. portant to stress that having access to high-quality pages does not For the experiments, we collected six topic-induced networks imply a comprehensive exposure to an argument, especially for a about polarizing topics and analyzed the extent to which their broader or polarizing topic.
    [Show full text]
  • Chapter 3. Event Expressions: Filtering the Event Stream
    Chapter 3. Event Expressions: Filtering the Event Stream As we discussed in Chapter 1, the Live Web is uses a data model that is the dual of the data model that has prevailed on the static Web. The static Web is built around interactive Web sites that leverage pools of static data held in databases. Interactive Web sites are built with languages like PHP and Ruby on Rails that are designed for taking user interaction and formulating SQL queries against databases. The static Web works by making dynamic queries against static data. In contrast, the Live Web is based on static queries against dynamic data. Streams of real-time data are becoming more and more common online. Years ago, such data streams were just a trickle, but the advent of technologies like RSS and Atom as well as the appearance of services like Twitter and Facebook has turned this trickle into a raging torrent. And this is just the beginning. The only way that we can hope to make use of all this data is if we can filter it and automate our responses to it as much as possible. As we’ll discuss in detail in Chapter 4, the task is greater than mere filtering, the task is correlating these events contextually. Correlating events provides power well beyond merely using filters to tame the data torrent. This chapter introduces in detail the pattern language, called “event expressions1,” that we will use to match against streams of events. Event expressions are an important way that we match events with context.
    [Show full text]
  • The New World of Content Measurement Why Existing Metrics Are Flawed (And How to Fix Them)
    The New World of Content Measurement Why existing metrics are flawed (and how to fix them) Copyright © 2014 Contently. All rights reserved. contently.com Carta Marina, Olaus Magnus Death to the pageview. — Paul Fredrich VP of Product, Contently GAME OF METRICS: OVERCOMING THE MEASUREMENT CRISIS IN THE CONTENT MARKETING KINGDOM Editor’s Note At Contently, three things have been a constant topic of discussion over the past six months: basketball, cult comedies from the early 1990s, and the disastrous state of content measurement in the marketing and media industries. Simply put, everyone was using flawed, superficial metrics to measure success—like a doctor using x-ray glasses instead of an x-ray machine. Even as forward-thinking content analytics platforms like Chartbeat began to make headway towards measuring content in more sophisticated ways, we saw brand publishers we worked with getting left out. That’s because brands face a fundamentally different set of content measurement challenges than traditional publishing companies. In pursuit of a solution, we did a lot of research and hard thinking, and even built an analytics tool of our own. This report is the result of our findings. We hope you find it useful, and that it empowers you to firmly tie your content to business results. — Joe Lazauskas Contently Editor in Chief 3 CONTENTLY GAME OF METRICS: OVERCOMING THE MEASUREMENT CRISIS IN THE CONTENT MARKETING KINGDOM Table of Contents I. Introduction 5 II. History 6 III. The Present Day 7 IV. Flawed Proxy Metrics 9 V. The Key to the Content Marketing Kingdom 11 VI. The Future 13 4 CONTENTLY GAME OF METRICS: OVERCOMING THE MEASUREMENT CRISIS IN THE CONTENT MARKETING KINGDOM You’re measuring the success of your content all wrong.
    [Show full text]
  • Lessons Learned Globally: Tobacco Control Digital Media Campaigns
    Lessons Learned Globally: Tobacco Control Digital Media Campaigns Suggested Citation Gutierrez K, Newcombe, R. Lessons Learned Globally: Tobacco Control Digital Media Campaigns. Saint Paul, Minnesota, United States: Global Dialogue for Effective Stop-Smoking Campaigns; 2012. Listings of Case Studies Two lists of case studies are provided for more convenient review based on readers’ interests. The Table of Contents presents the case studies section in alphabetical order by country, then province or state (if applicable), and then in chronological order. It also lists the other parts of the document, such as the Executive Summary, Methodology, Lessons Learned, etc. The List of Campaigns by Goal found after the Table of Contents focuses only on the case studies, organizing them by the main tobacco control goal of each campaign. Campaigns are grouped according to whether they sought to: 1. Prevent initiation of tobacco use 2. Reduce tobacco use via assisting tobacco users in quitting 3. Reduce exposure to secondhand smoke The List of Campaigns by Goal may be helpful to those who are seeking insights from campaigns that have a common tobacco control goal. On the following page, there is a grid detailing the digital elements used in each campaign which may be helpful to those who are seeking information about effective use of particular digital vehicles/approaches. Table of Contents List of Campaigns by Goal 3 Grid of Digital Elements used in Each Campaign 4 I. Executive Summary 5 II. Introduction 8 Acknowledgements 8 Purpose of This Document 8 Methods 9 Limitations 11 Additional Campaign Information 12 III. Key Lessons Learned 13 IV.
    [Show full text]
  • Facebook Hotel Playbook CONTENTS
    Facebook Hotel Playbook CONTENTS 01. Landscape and Our Recommended Approach 3 02. Travel and Hotel Insights 4 03. Hotel Strategy on Facebook 5 04. Drive Hotel Demand 6 a. Dynamic Retargeting 7 b. Standard Retargeting 7 c. Dynamic prospecting 8 d. Boost Programs 9 05. Build Brand Confidence 10 a. Awareness 10 b. Inspiration 11 c. Loyalty Acquisition and Retention 12 06. Media Buying Efficiencies 14 07. Key Hotel Considerations 16 08. Global Hotel Insights and Considerations 17 09. Creative Tips for Recovery 18 10. Creative Best Practices and Dynamic Creative 19 11. General Measurement Guidance 21 12. Nimble Testing Ideas 23 13. Optimization and Signals 25 14. Signals for Enhanced Measurement 26 15. Other Considerations 28 a. Influencer Branded Content Marketing 28 b. Messenger 29 c. Facebook Marketing Partners Can Help 30 16. Additional Resources 32 17. Food and Beverage Playbook for Hotels 34 © Facebook company Facebook Hotel Playbook 2 01. Landscape and Our Recommended Approach The COVID-19 outbreak has affected And with 70% of people surveyed globally businesses around the world. Employee saying they are “now spending more time health, work patterns, production and on their smartphones,” hoteliers must consumer habits have all been affected. continue to adopt mobile-first strategies.3 The travel industry has been hit With this continued shift of consumer especially hard with an estimated behavior comes an opportunity to redefine $2.1T loss to the industry globally.1 media strategies and focus on driving new demand for hotels, regaining consumer And the hospitality industry has trust and fortifying customer loyalty. similarly been impacted as consumers change their habits.
    [Show full text]
  • Download Legal Document
    Case 1:15-cv-00662-TSE Document 168-6 Filed 12/18/18 Page 1 of 67 Wikimedia Foundation v. NSA No. 15-cv-0062-TSE (D. Md.) Plaintiff’s Exhibit 2 Case 1:15-cv-00662-TSE Document 168-6 Filed 12/18/18 Page 2 of 67 IN THE UNITED STATES DISTRICT COURT FOR THE DISTRICT OF MARYLAND WIKIMEDIA FOUNDATION, Plaintiff, No. 1:15-cv-00662-TSE v. NATIONAL SECURITY AGENCY / CENTRAL SECURITY SERVICE, et al., Defendants. DECLARATION OF JONATHON PENNEY Case 1:15-cv-00662-TSE Document 168-6 Filed 12/18/18 Page 3 of 67 TABLE OF CONTENTS I. Qualifications ...................................................................................................................... 1 II. Assignment ......................................................................................................................... 3 III. Summary of Opinions ......................................................................................................... 4 IV. Overview of Chilling Effects Theory & Supporting Empirical Evidence for Surveillance Related Chill Online ........................................................................................................... 4 V. Wikipedia Study—Design, Method, and Results ............................................................. 10 A. Design ......................................................................................................................... 11 i. Intervention Selection ................................................................................................. 12 ii. Data Selection ............................................................................................................
    [Show full text]
  • Modeling and Analysis the Web Structure Using Stochastic Timed Petri Nets
    JOURNAL OF SOFTWARE, VOL. 3, NO. 8, NOVEMBER 2008 19 Modeling and Analysis the Web Structure Using Stochastic Timed Petri Nets Po-Zung Chen, Chu-Hao Sun Department of Computer Science and Information Engineering, Tamkang University, Taipei County, Taiwan Email: [email protected], [email protected] Shih-Yang Yang Department of Information Management, Kang-Ning Junior College of Medical Care and Management , Taipei, Taiwan Email: [email protected] Abstract—Precise analysis of the Web structure can profiles left by all the browser users. As the profiles are facilitate data pre-processing and enhance the accuracy of only the sequentially recorded contents of the services the mining results in the procedure of Web usage mining. provided by the Web server, the profiles not only could STPN ( Stochastic Timed Petri Nets ) is a high-level contain multiple browsing profiles of different browser graphical model widely used in modeling system activities users but also could take in some extra or erroneous with concurrency. STPN can save the analyzed results in an profiles. The website management personnel must incidence matrix for future follow-up analyses, and some already-verified properties held by STPN, such as proceed with preprocessing to these usage profiles if they reachability, can also be used to solve some unsettled are to correctly analyze said users’ webpage-contents problems in the model. In the present study, we put forth usage sequence. Hence, a data preprocessing is needed to the use of STPN as the Web structure model. We adopt enhance information processing before we can analyze Place in the STPN model to represent webpage on the the usage profiles.
    [Show full text]
  • Wikibrain: Democratizing Computation on Wikipedia
    WikiBrain: Democratizing computation on Wikipedia Shilad Sen Toby Jia-Jun Li Macalester College University of Minnesota St. Paul, Minnesota Minneapolis, Minnesota [email protected] [email protected] ∗ WikiBrain Team Brent Hecht Matt Lesicko, Ari Weiland, Rebecca Gold, University of Minnesota Yulun Li, Benjamin Hillmann Minneapolis, Minnesota Macalester College [email protected] St. Paul, Minnesota ABSTRACT has raced towards this goal, growing in articles, revisions, 1 Wikipedia is known for serving humans' informational needs. and gigabytes by factor of 20. The planet has taken notice. Over the past decade, the encyclopedic knowledge encoded Humans turn to Wikipedia's 31 million articles more than in Wikipedia has also powerfully served computer systems. any other online reference. The online encylopedia's 287 languages attract a global audience that make it the sixth Leading algorithms in artificial intelligence, natural language 2 processing, data mining, geographic information science, and most visited site on the web. many other fields analyze the text and structure of articles However, outside the public eye, computers have also come to build computational models of the world. to rely on Wikipedia. Algorithms across many fields extract Many software packages extract knowledge from Wiki- meaning from the encyclopedia by analyzing articles' text, pedia. However, existing tools either (1) provide Wikipedia links, titles, and category structure. Search engines such as Google and Bing respond to user queries by extracting struc- data, but not well-known Wikipedia-based algorithms or (2) 3 narrowly focus on one such algorithm. tured knowledge from Wikipedia . Leading semantic relat- This paper presents the WikiBrain software framework, edness (SR) algorithms estimate the strength of association an extensible Java-based platform that democratizes access between words by mining Wikipedia's text, link structure to a range of Wikipedia-based algorithms and technologies.
    [Show full text]