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21 Top Metrics for Online Display Advertising

In this post I am listing the 21 metrics to measure the success of your display advertising.  Most of these are also applicable, with some variation, to other forms of advertising such as Paid Search, Social Media Ads, Print and email. I will cover these other channels and mediums in the future posts.

  1. Impressions – It is the number of times your ad is displayed. The number by itself does not hold much value but it is a metric used to calculate other metrics and KPIs. Keep in mind that an impression does not mean that someone actually saw the ad, it just that the ad was shown on a web page/app.
  2. Reach –This is the number of unique people (generally identified by cookies) that were reached by your ad. This number is always lower than the impressions because your ad is generally shown to same person (cookie) multiple times.
  3. Cost – The total cost of running the ad campaigns.  This is calculated differently by different tools and organizations. Some use actual media cost while other use a fully load number that includes the agency cost, creative cost etc. Whichever number you use, be consistent in your approach. If you are going to do comparisons with CPC models such as Paid Search then I suggest using the actual media cost. Most of the publicly available benchmarks are based on actual media cost and are expressed in CPM (explained later in this list).
  4. Engagement Rate or Interaction Rate– This applies to the Rich Media Ads, where a user can interact with the ad without leaving the Ad unit/widget.  Engagement Rate is the percentage of interactions per impression of the ad unit and is calculated as (Number of Interactions/Total Impressions)*100%.
  5. CPM – This is the cost for 1000 Impressions of the ad unit. Display advertising is generally sold on CPM basis. (For more information on CPM, see  Cost of Advertising: CPM, CPC and eCPM Demystified).
  6. Clicks – Number of clicks on an ad unit that lead to a person leaving the ad unit.  Keep in mind that a click does not mean that a person landed on the intended destination of the banner ad click. There are multiple factors that could lead to a click but not a visit to the destination (I won’t cover those here but am happy to discuss over email or a call).
  7. CTR (Click though rate) – It is the number of Clicks generated per impression of a banner ad. This number is expressed as a percentage. CTR = (click/impressions)*100%
  8. CPC – Cost per Clicks is the cost that you pay for each click.  Generally, display advertising is sold by CMP (see above), you can easily convert the cost in to Cost Per Click to compare it against other channels such as paid search. Cost per click is the effective amount you paid to get a click.  It is calculated by dividing the cost with number of clicks.  CPC = Cost/Clicks. Sometime this number is also referred as eCPC (effective Cost per Click).
  9. Visits – As stated above in the definition of clicks, not every click turns into a person landing on your destination (generally your website). Visits measures the clicks that did end up on your site.  (For more definition of visits, please see Page Views, Visitors, Visits and Hits Demystified)
  10. Visitors – Visitors metric goes one step ahead of the visits and calculates the number of people (as identified by cookies) who ended up on your site as a results of the clicks on the banner ads.
  11. Bounce Rate – Is the percentage of visits that left without taking any actions on your site. It is calculated as Number of Visits with one page view /Total number of visits resulting from the display ads. (Bounce Rate Demystified for further explanation).
  12. Engaged Visit Rate – Generally this is opposite of bounce rate (though you can have your own definitions of engagement).  It measure the quality of the visits arriving from your display advertising. You can calculate Engaged Visits as  (100 – Bounce Rate expressed as percentage).
  13. Cost/Engaged Visit – This is effective cost of each engaged visits. It is calculated as total Cost divided by number of engaged visits.
  14. Page Views/Visit – Page views the number of pages on your site viewed by each visit. With a lot interactions happening on one single page, this metrics is losing its value. However, for now, it is still a valuable metric for ad supported sites.
  15. Cost/Page View – As above, this is valuable metrics for ad supported site to figure out the cost of generating on extra page view.
  16. Conversions – Conversion is defined as the count of action that you want the visitors to take when they arrive from you display ads. Some examples of conversions are – purchase, signup for newsletter, download a whitepaper, sign up for an event, Lead from completions etc.
  17. Conversion Rate  – This is the percentage of visits that resulted in the desired conversion actions.  Conversion Rate = Total conversions/visits*100. If you have more than one conversion actions then you should do this calculation for each one of the action as well for all the actions combined.  In case of Leads, you can take it one step further and calculate not only the “Leads Generation Rate” (Online Conversion Rate) but also Lead Conversion Rate, which is, Leads that convert to a customer divided by total leads generated.
  18. Cost per Conversion – This is the Total Cost divided by the number of conversions achieved from visits coming via display ads.
  19. Revenue – This is total revenue that is directly attributed to the visits coming from display advertising. It is pretty straightforward to calculate in eCommerce but gets a little tricky when you have offline conversions.
  20. Revenue per Visit   – Shows the direct revenue achieved per visit originating from the display advertising. It is calculated as Revenue Generated from Display Ads divided by the total Visits.
  21. Revenue per Page – This is useful for ad supported business models. This is sometimes expressed as RPM (Revenue per thousand impressions of ads) = (Total Ad Revenue/Number of page views) * 1000

Note: In addition to Clicks, you can also looks at View Through and calculate your other related metrics by view through.  View Through is sum of all the cookies that visited a page that showed your ad on it, and then landed on your site. The assumption, in this calculation, is that you landed on the brands site because of that ad exposure.

 Where can you get these metrics from?

  • Impressions, Reach, Cost, Engagement Rate, Clicks, CTR and CPC data is available from your agency or ad server tool.
  • Visits, Visitors, Page Views, Bounce Rate, Engaged Visit Rate, Conversion, and Conversion Rate are available in your Web Analytics tool.
  • Revenue is available in either your Web Analytics tool or other offline sales database.
  • Cost/Conversion, Cost/Engaged Visits, Cost/Page view and Revenue/page are calculated using data from multiple tools.

Questions/Comments?

Originally Posted on http://anilbatra.com/analytics/2014/05/21-metrics-to-measure-online-display-advertising

Understanding Data – Context (Excerpt from Data Points: Visualization That Means Something)

Look up at the night sky, and the stars look like dots on a flat surface. The lack of visual depth makes the translation from sky to paper fairly straightforward, which makes it easier to imagine constellations. Just connect the dots. However, although you perceive stars to be the same distance away from you, they are actually varying light years away.

If you could fly out beyond the stars, what would the constellations look like? This is what Santiago Ortiz wondered as he visualized stars from a different perspective, as shown in Figure 1-25.

The initial view places the stars in a global layout, the way you see them. You look at Earth beyond the stars, but as if they were an equal distance away from the planet.

Zoom in, and you can see constellations how you would from the ground, bundled in a sleeping bag in the mountains, staring up at a clear sky.

The perceived view is fun to see, but flip the switch to show actual distance, and it gets interesting. Stars transition, and the easy-to-distinguish constellations are practically unrecognizable. The data looks different from this new angle.

This is what context can do. It can completely change your perspective on a dataset, and it can help you decide what the numbers represent and how to interpret them. After you do know what the data is about, your understanding helps you find the fascinating bits, which leads to worthwhile visualization.

Figure 1-25

Without context, data is useless, and any visualization you create with it will also be useless. Using data without knowing anything about it, other than the values themselves, is like hearing an abridged quote secondhand and then citing it as a main discussion point in an essay. It might be okay, but you risk finding out later that the speaker meant the opposite of what you thought.

You have to know the who, what, when, where, why, and how — the metadata, or the data about the data — before you can know what the numbers are actually about.

Who: A quote in a major newspaper carries more weight than one from a celebrity gossip site that has a reputation for stretching the truth. Similarly, data from a reputable source typically implies better accuracy than a random online poll.

For example, Gallup, which has measured public opinion since the 1930s, is more reliable than say, someone (for example, me) experimenting with a small, one-off Twitter sample late at night during a short period of time. Whereas the former works to create samples representative of a region, there are unknowns with the latter.

Speaking of which, in addition to who collected the data, who the data is about is also important. Going back to the gumballs, it’s often not financially feasible to collect data about everyone or everything in a population. Most people don’t have time to count and categorize a thousand gumballs, much less a million, so they sample. The key is to sample evenly across the population so that it is representative of the whole. Did the data collectors do that?

How: People often skip methodology because it tends to be complex and for a technical audience, but it’s worth getting to know the gist of how the data of interest was collected.

If you’re the one who collected the data, then you’re good to go, but when you grab a dataset online, provided by someone you’ve never met, how will you know if it’s any good? Do you trust it right away, or do you investigate? You don’t have to know the exact statistical model behind every dataset, but look out for small samples, high margins of error, and unfit assumptions about the subjects, such as indices or rankings that incorporate spotty or unrelated information.

Sometimes people generate indices to measure the quality of life in countries, and a metric like literacy is used as a factor. However, a country might not have up-to-date information on literacy, so the data gatherer simply uses an estimate from a decade earlier. That’s going to cause problems because then the index works only under the assumption that the literacy rate one decade earlier is comparable to the present, which might not be (and probably isn’t) the case.

What: Ultimately, you want to know what your data is about, but before you can do that, you should know what surrounds the numbers. Talk to subject experts, read papers, and study accompanying documentation.

In introduction statistics courses, you typically learn about analysis methods, such as hypothesis testing, regression, and modeling, in a vacuum, because the goal is to learn the math and concepts. But when you get to real-world data, the goal shifts to information gathering. You shift from, “What is in the numbers?” to “What does the data represent in the world; does it make sense; and how does this relate to other data?”

A major mistake is to treat every dataset the same and use the same canned methods and tools. Don’t do that.

When: Most data is linked to time in some way in that it might be a time series, or it’s a snapshot from a specific period. In both cases, you have to know when the data was collected. An estimate made decades ago does not equate to one in the present. This seems obvious, but it’s a common mistake to take old data and pass it off as new because it’s what’s available. Things change, people change, and places change, and so naturally, data changes.

Where: Things can change across cities, states, and countries just as they do over time. For example, it’s best to avoid global generalizations when the data comes from only a few countries. The same logic applies to digital locations. Data from websites, such as Twitter or Facebook, encapsulates the behavior of its users and doesn’t necessarily translate to the physical world.

Although the gap between digital and physical continues to shrink, the space between is still evident. For example, an animated map that represented the “history of the world” based on geotagged Wikipedia, showed popping dots for each entry, in a geographic space. The end of the video is shown in Figure 1-26.

The result is impressive, and there is a correlation to the real-life timeline for sure, but it’s clear that because Wikipedia content is more prominent in English-speaking countries the map shows more in those areas than anywhere else.

Why: Finally, you must know the reason data was collected, mostly as a sanity check for bias. Sometimes data is collected, or even fabricated, to serve an agenda, and you should be wary of these cases. Government and elections might be the first thing that come to mind, but so-called information graphics around the web, filled with keywords and published by sites trying to grab Google juice, have also grown up to be a common culprit. (I fell for these a couple of times in my early days of blogging for FlowingData, but I learned my lesson.)

Learn all you can about your data before anything else, and your analysis and visualization will be better for it. You can then pass what you know on to readers.

Figure 1-26

However, just because you have data doesn’t mean you should make a graphic and share it with the world. Context can help you add a dimension — a layer of information — to your data graphics, but sometimes it means it’s better to hold back because it’s the right thing to do.

In 2010, Gawker Media, which runs large blogs like Lifehacker and Gizmodo, was hacked, and 1.3 million usernames and passwords were leaked. They were downloadable via BitTorrent. The passwords were encrypted, but the hackers cracked about 188,000 of them, which exposed more than 91,000 unique passwords. What would you do with that kind of data?

The mean thing to do would be to highlight usernames with common (read that poor) passwords, or you could go so far as to create an application that guessed passwords, given a username.

A different route might be to highlight just the common passwords, as shown in Figure 1-27. This offers some insight into the data without making it too easy to log in with someone else’s account. It might also serve as a warning to others to change their passwords to something less obvious. You know, something with at least two symbols, a digit, and a mix of lowercase and uppercase letters. Password rules are ridiculous these days. But I digress.

Figure 1-27

With data like the Gawker set, a deep analysis might be interesting, but it could also do more harm than good. In this case, data privacy is more important, so it’s better to limit what you show and look at.

Whether you should use data is not always clear-cut though. Sometimes, the split between what’s right and wrong can be gray, so it’s up to you to make the call. For example, on October 22, 2010, Wikileaks, an online organization that releases private documents and media from anonymous sources, released 391,832 United States Army field reports, now known as the Iraq War Logs. The reports recorded 66,081 civilian deaths out of 109,000 recorded deaths, between 2004 and 2009.

The leak exposed incidents of abuse and erroneous reporting, such as civilian deaths classified as “enemy killed in action.” On the other hand, it can seem unjustified to publish findings about classified data obtained through less than savory means.

Maybe there should be a golden rule for data: Treat others’ data the way you would want your data treated.

In the end, it comes back to what data represents. Data is an abstraction of real life, and real life can be complicated, but if you gather enough context, you can at least put forth a solid effort to make sense of it.

Excerpted with permission from the publisher, Wiley, from Data Points: Visualization That Means Something by Nathan Yau. Copyright © 2013

Author Bio
Nathan Yau, author of Data Points: Visualization That Means Something, has a PhD in statistics and is a statistical consultant who helps clients make use of their data through visualization. He created the popular site FlowingData.com, and is the author of Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, also published by Wiley.

For more information please visit http://flowingdata.com, and follow the author on Facebook and Twitter

 

Buy From Amazon: Data Points: Visualization That Means Something

Five Reasons Siegel's Book "Predictive Analytics" Matters to Experts

 
My new book — Predictive  Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die  — is a revealing, accessible primer positioned to appeal well outside our industry.
But, if you’re already an expert, here are five reasons to read it nonetheless:

  1. New detailed case studies
  2. Advanced topics (ensembles, uplift, etc.)
  3. An in-depth, startling treatise on privacy
  4. A compendium of 147 mini-case studies
  5. A means to share your field with your family, friends, or  supervisor

I took on a rewarding challenge: sharing with layreaders at  large a complete picture of predictive analytics, from the  way in which it serves actionable value to organizations, down to      the inner workings of predictive modeling. It’s high time the predictive power of data — and how to analytically tap it — be demystified to reveal its intuitive yet awe–inspiring nature. As you and I know, learning from data to predict human behavior is not arcane. Rather, it is a broadly applicable no–brainer. If we  spread the word with an appropriately friendly overview, we’ll readily earn broad buy in, much to the benefit of our blossoming  industry.

More than a string of anecdotes, this book delivers complete   conceptual coverage of the field and places predictive analytics into a worldview perspective, defining its societal and even      cultural context. Although packaged with catchy chapter titles and brand name stories, the conceptual outline is fundamental: 1) deployment, 2) civil liberties, 3) data, 4) core modeling, 5) ensembles, 6) IBM’s Watson, and 7) uplift modeling (aka net lift or persuasion modeling).

Although this pop science, mathless introduction is readable by everyone, you as an expert will also benefit from reading it. While some endorsers proclaim it is “The Freakonomics of big data”    that “reads like a thriller!”, others speak to the    practitioner:

“The definitive book of this industry has arrived. Dr.  Siegel has achieved what few have even attempted: an  accessible, captivating tome on predictive analytics that is a  ‘must read’ for all interested in its potential — and peril.” —Mark Berry, VP, People Insights, ConAgra Foods

“Written in a lively language, full of great quotes,  real-world examples, and case studies, it is a pleasure to  read. The more technical audience will enjoy chapters on The          Ensemble Effect and uplift modeling — both very hot trends. I highly recommend this book!” —Gregory Piatetsky-Shapiro, Editor, KDnuggets; Founder, KDD          Conferences

Here’s a bit more on the five reasons this book matters to you:

1. New case studies. Find detailed stories you have  never before heard from Hewlett-Packard, Chase, and the Obama Campaign. And did you know that John Elder once invested all his  own personal money into a blackbox stock market system of his own design? That’s the opening story of Chapter 1.

2. Advanced topics. Dive into ensemble models, crowdsourcing predictive analytics, uplift modeling (aka net lift or persuasion modeling), text analytics, and social media-based financial indicators. Plus, enjoy a fun yet fairly deep chapter on IBM’s Jeopardy!-playing Watson computer.

3. Privacy and other civil liberty concerns. This ethical realm is so intractable and inconstant, no one is a true expert, in a sense. My treatise on it, a chapter entitled “With Power Comes Responsibility,” addresses the questions: In what ways does predictive analytics fuel the contentious flames surrounding data privacy, raising its already-high stakes? What civil liberty concerns arise beyond privacy per se? What about predictive crime models that help decide who stays in prison?

4. A cross-industry compendium of 147 cases. This comprehensive collection of mini-case studies serves to illustrate just how wide the field’s reach extends. This color insert includes a table for each of the verticals: Personal Life, Marketing, Finance, Healthcare, Crime Fighting, Reliability Modeling, Government and Nonprofit, Human Language  and Thought, and Human Resources. One PhD-level technical book reviewer complimented me by saying, “The tables alone are worth the price of admission.”

5. Share your field of expertise. Would you like your colleagues and manager to better understand the value and potential of your work? Would you enjoy seeing your loved ones        not only learn what the heck it is you do and why it’s so  important, but enjoy it and get excited? Give this book to your  family, friends, and boss.

Author Bio        

Eric Siegel, Ph.D., founder of Predictive Analytics World  and Text Analytics World, and Executive Editor of the Predictive  Analytics Times, makes the how and why of predictive analytics      understandable and captivating. In addition to being the author of   Predictive Analytics: The Power to Predict Who Will Click, Buy,  Lie, or Die, Eric is a former Columbia University professor      who used to sing to his students, and a renowned speaker, educator  and leader in the field.

For more information please visit http://www.thepredictionbook.com

Data Analyst at Baystate Health, Springfield, Massachusetts

Data Analyst Departments of Strategic Planning and Digital Marketing Maximize your data analytical skills by supporting two departments utilizing various databases. We will provide challenging work and the ability to foster and sharpen your talents!As the Data Analyst, you will be part of a dynamic team responsible for strategic planning/business development and marketing analytics. Your work will support the business needs of the Strategic Planning & Business Development department (70%) and Department of Marketing & Digital Strategy (30%).

You will responsible for data collection, analysis and interpretation; best practice research; maintenance and quality control of multiple databases; and market/competitive intelligence for a wide range of business needs, constantly providing you challenging and rewarding work!

What do you need?
Bachelors Degree required.
Minimum of two years of experience analyzing, interpreting, reporting and communicating data.
Advanced skills in Excel and Access (0 preferred); strong understanding of HTML and web protocols; knowledge of Google Analytics or SEO is preferred.
Masters degree and previous experience analyzing healthcare market and service line data (DRG, ICD-9, CPT codes) is preferred.

In addition, you must have the following:
High energy, team focused and flexibility working between two departments
Strong sense of curiosity with demonstrated ability to proactively investigate data inconsistencies and anomalies,
Demonstrated capacity as a resourceful individual with proven ability to seamlessly balance multiple priorities,
Strong analytical skills (both qualitative and quantitative), ability to identify key points and trends, and develop succinct summaries of findings. Experience in statistical modeling/analysis highly is desirable.

What will you receive?

Opportunity to build a core set of skills and tools while contributing to the strategy of a world class healthcare system as well as growing your career;
Ability to work with a highly effective team that has a reputation for being innovative and influential;
Receive a competitive salary with great benefits including employer-paid retirement plan and leading work-life programs.

To apply, please visit www.baystatehealthjobs.com and search for position number 62554. For more information, please contact Jason Pacheco, Workforce Planning Consultant at Baystate Health at 413-794-2384.

Baystate Health is an equal opportunity employer committed to an inclusive and diverse workforce. EOE/AA

Apply at http://www.web-analytics-jobs.com/a/jbb/job-details/802691

Senior Web Analyst Sirius Xm Radio at District of Columbia

Lead Web Analytics for Siriusxm.com, leveraging Omniture and Google Analytics to provide insight into user behavior, business goals, and key performance indicators. Responsibilities include defining business requirements, implementation, data management, and analytics. Leverage data and analysis to optimize site performance for best user experience and conversion, partnering with finance and business units to develop and execute A/B and multivariate tests to maximize value of siriusxm.com.

Duties and Responsibilities:

Responsible for managing, organizing, and presenting Web analytics data; Establish key metrics and develop reporting and analysis using Omniture Site Catalyst, Test & Target and other tools.
Produce daily, weekly, monthly, and quarterly reports relating to outcomes based on the data analysis for various audiences, including management, marketing, product and editorial teams.
Create dashboards for senior management and various business units, synthesizing data from various sources.
Design, execute and evaluate A/B and multivariate tests that improve site experience and optimize contribution of siriusxm.com inventory. Partner with Finance to provide insights on value and opportunity cost of various site promotions.
Work with stakeholders (marketing, product, programming) to develop and execute landing page optimization testing.Work with marketing,product, and programming stakeholders to understand, define and prioritize tracking and reporting requirements. Work with IT and BI to identify gaps in data capture strategy.
Provide individual and group training sessions within the organization and ongoing support.
Create, manage and maintain data repositories and documentation and for exec-level analytics, training, internal processes and implementation details.
Provide insights, trends, and analysis of key performance indicators related to the category using competitive tools such as Hitwise.
Provide quick-turn-around responses to ad-hoc data queries.
Performs other duties as assigned.

Minimum Qualifications:

Bachelors degree or equivalent experience, MBA preferred
4 to 6 years of experience with Web Analytics Tools (Omniture, ClickTracks, WebTrends, HBX, CoreMetrics etc.)

Requirements and General Skills:

Expert in use of Omniture Site Catalyst and Test & Target; Functional proficiency with Google Analytics reporting required.
Proficiency in Excel is required, including charting and large data sets.
Experience driving the data strategy across multiple business areas and touchpoints including websites, surveys, testing, CRM systems, marketing databases, market research etc.
Ability to create useful dashboards based on the Web analysis.
Ability to lead the drive for data and to suggest, create and execute multivariate or a/b/c tests that drive fundamental improvements to the site experience.
A passion for numbers and the ability to use them to tell a story regarding site performance, consumer trends, and competitive analysis. Proactive in analyzing data and customer behavior. Willingness to take initiative and to follow through on projects.
Demonstrate ability to partner with technology teams to identify gaps in the data capture strategy and collaboratively implement enhancements. Should be able to partner with other business units and outside the company to ensure that best practices in metrics and decision making are being exposed to management and website decision makers.
Experience creating and managing Multivariate and A/B testing documents (from hypothesis creation to influencing creative to identifying success metrics) and post test analysis.
Manage Business requirements from multiple sources including product managers, development teams, and functional groups.
Create documentation relating to existing processes and suggestion / presentations for improving those processes.
Detail-oriented and organized with excellent verbal and written communication skills.
Ability to work independently as well as within a team structure.
Ability to work independently and remain flexible and motivated to work in a fast-paced environment.
Good public speaking and presentation skills; Ability to project a professional image over the phone and in person.Interpersonal skills and ability to interact and work with staff at all levels.
Commitment to internal client and customer service principles.
Strong organizational skills and attention to details.
Excellent time management skills, with the ability to prioritize and multi-task, and work under shifting deadlines in a fast paced environment.
Must have legal right to work in the U.S.

Technical Skills:

Basic knowledge of general internet technologies such as HTML, JavaScript and tracking/pixel structures is required.
Thorough knowledge MS Excel, and Access, ability to manage large data sets.
Strong analytic skills.
Experience with online ad serving, tracking, and reporting toolsExpertise in the SEM (Search Engine Marketing) / PPC (Pay Per Click) and SEO (Search Engine Optimization) strategies and a minimum one year experience measure success of SEM/PPC and SEO campaigns / efforts.

 

Apply at http://www.web-analytics-jobs.com/a/jbb/job-details/783614

Senior Web Analyst at Sirius XM Radio New York

Lead Web Analytics for Siriusxm.com, leveraging Omniture and Google Analytics to provide insight into user behavior, business goals, and key performance indicators. Responsibilities include defining business requirements, implementation, data management, and analytics. Leverage data and analysis to optimize site performance for best user experience and conversion, partnering with finance and business units to develop and execute A/B and multivariate tests to maximize value of siriusxm.com.

Duties and Responsibilities:

Responsible for managing, organizing, and presenting Web analytics data; Establish key metrics and develop reporting and analysis using Omniture Site Catalyst, Test & Target and other tools.
Produce daily, weekly, monthly, and quarterly reports relating to outcomes based on the data analysis for various audiences, including management, marketing, product and editorial teams.
Create dashboards for senior management and various business units, synthesizing data from various sources.
Design, execute and evaluate A/B and multivariate tests that improve site experience and optimize contribution of siriusxm.com inventory. Partner with Finance to provide insights on value and opportunity cost of various site promotions.
Work with stakeholders (marketing, product, programming) to develop and execute landing page optimization testing.Work with marketing,product, and programming stakeholders to understand, define and prioritize tracking and reporting requirements. Work with IT and BI to identify gaps in data capture strategy.
Provide individual and group training sessions within the organization and ongoing support.
Create, manage and maintain data repositories and documentation and for exec-level analytics, training, internal processes and implementation details.
Provide insights, trends, and analysis of key performance indicators related to the category using competitive tools such as Hitwise.
Provide quick-turn-around responses to ad-hoc data queries.
Performs other duties as assigned.

Minimum Qualifications:

Bachelors degree or equivalent experience, MBA preferred
4 to 6 years of experience with Web Analytics Tools (Omniture, ClickTracks, WebTrends, HBX, CoreMetrics etc.)

Requirements and General Skills:

Expert in use of Omniture Site Catalyst and Test & Target; Functional proficiency with Google Analytics reporting required.
Proficiency in Excel is required, including charting and large data sets.
Experience driving the data strategy across multiple business areas and touchpoints including websites, surveys, testing, CRM systems, marketing databases, market research etc.
Ability to create useful dashboards based on the Web analysis.
Ability to lead the drive for data and to suggest, create and execute multivariate or a/b/c tests that drive fundamental improvements to the site experience.
A passion for numbers and the ability to use them to tell a story regarding site performance, consumer trends, and competitive analysis. Proactive in analyzing data and customer behavior. Willingness to take initiative and to follow through on projects.
Demonstrate ability to partner with technology teams to identify gaps in the data capture strategy and collaboratively implement enhancements. Should be able to partner with other business units and outside the company to ensure that best practices in metrics and decision making are being exposed to management and website decision makers.
Experience creating and managing Multivariate and A/B testing documents (from hypothesis creation to influencing creative to identifying success metrics) and post test analysis.
Manage Business requirements from multiple sources including product managers, development teams, and functional groups.
Create documentation relating to existing processes and suggestion / presentations for improving those processes.
Detail-oriented and organized with excellent verbal and written communication skills.
Ability to work independently as well as within a team structure.
Ability to work independently and remain flexible and motivated to work in a fast-paced environment.
Good public speaking and presentation skills; Ability to project a professional image over the phone and in person.Interpersonal skills and ability to interact and work with staff at all levels.
Commitment to internal client and customer service principles.
Strong organizational skills and attention to details.
Excellent time management skills, with the ability to prioritize and multi-task, and work under shifting deadlines in a fast paced environment.
Must have legal right to work in the U.S.

Technical Skills:

Basic knowledge of general internet technologies such as HTML, JavaScript and tracking/pixel structures is required.
Thorough knowledge MS Excel, and Access, ability to manage large data sets.
Strong analytic skills.
Experience with online ad serving, tracking, and reporting tools Expertise in the SEM (Search Engine Marketing) / PPC (Pay Per Click) and SEO (Search Engine Optimization) strategies and a minimum one year experience measure success of SEM/PPC and SEO campaigns / efforts.

 

Apply at http://www.web-analytics-jobs.com/a/jbb/job-details/783616

Web Conversion Specialist Destination at Maternity Corporation, Philadelphia, Pennsylvania

Destination Maternity Corporation is the world’s largest designer and retailer of maternity apparel. In the United States and Canada, as of September 30, 2012, Destination Maternity operates 2,008 retail locations, including 625 stores, predominantly under the tradenames Motherhood Maternity, A Pea in the Pod, and Destination Maternity, and 1,383 leased department locations, and sells on the web through its DestinationMaternity.com and brand-specific websites. Destination Maternity also distributes its Oh Baby by Motherhood collection through a licensed arrangement at over 1,100 Kohl’s stores throughout the United States and on Kohls.com. In addition, Destination Maternity is expanding internationally and has exclusive store franchise and product supply relationships in India, the Middle East and South Korea. As of September 30, 2012, Destination Maternity has 119 international franchised locations, including 103 shop-in-shop locations and 16 Destination Maternity branded stores.

The Web Conversion Specialist, reporting to the Internet Metrics Manager, is primarily responsible for optimizing website conversion. The Web Conversion Specialist is responsible for the day to day management of website testing and conversion efforts to various traffic channels, while creating the best user experience possible.

Job Responsibilities:
– Manage Monetate Testing
– Site Search and Landing Optimization
– Maintain reporting of key metrics

Requirements:
– Minimum of a Bachelors degree in math, science or related field, along with 2-4 years of relevant experience
– Power user of Omniture, Google Analytics, or Core Metrics Applications (Familiar with the technology used to build, tag and track web sites)
– Proficient in a standard database queries and manipulation, using statistical tools like SASS
– Power Excel/ Access user capabilities
– Understanding of web marketing and use of cookies for tracking and behavioral targeting.
– Must be user and customer experience driven

 

Apply: http://www.web-analytics-jobs.com/a/jbb/job-details/776116

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Senior Analytics and Optimization Manager – ZAAZ Seattle, Washington

Are you a thought leader? Put your MBA and web strategy experience to use in a dynamic environment with opportunities to influence the evolution of web analytics as it relates to global campaigns for our clients. ZAAZ is a pioneer in the use of analytics to develop strategic site optimization plans that become an integral part of our clients overall business model. Our analysts work closely with our creative, development and client relationship teams to deliver exceptional, results-driven work that is based on insightful analytics data. Were looking for passionate, number-crunching aficionado to join our team. Are you the one?

Responsibilities:
Helping clients define web channel goals and key drivers.
Determine appropriate metrics to measure goals.
Create and deliver custom Scorecards based on site goals to measure website performance.
Analyze web analytics data as well as other on- and offline data to evaluate site performance.
Discover underperforming areas of sites, identify opportunities to improve site performance.
Collaborate with usability experts, IA specialists, designers and developers to recommend solutions to the problems identified.
Define A/B/MVT test scenarios and success metrics.
Perform monthly analysis of clients site performance, delivering recommendations to improve performance.

Required Skills:
MBA or equivalent business experience.
Web Strategy Background (5+ years).
Excellent communication & presentation skills.
Experience working with senior managers and executives.
Data skills and experience are a must. Strong familiarity with behavioral and attitudinal data sets.
Some travel, including Helsinki.

Ideal Candidate has:
Direct response marketing or CRM.
Experience managing a team.
Web Analytics: Omniture, HBX, WebTrends, Google Analytics and/or other web analytics tools.
Ad serving tools: DoubleClick, Atlas.
Social monitoring tools: Visible Technologies, Nielsen BuzzMetrics, Radian6, TNS Cymphony and other tools.
Agency background.
Strong, working familiarity with statistics and statistical concepts.
Benchmarking – industry, competitive and/or vertical views.
A/B/MVT Testing.
Multilingual/multi-cultural experience. French, Finnish and other languages a plus.
Experience in the telecommunication services and device markets are also a plus.

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Source: http://www.web-analytics-jobs.com/a/jbb/job-details/704745