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The Google Analytics platform has been changing from a web analytics tool to a user-centric digital measurement tool (we’ve been calling it Universal Analytics). This evolution includes a number of changes to the system and completely new features. But what can you do when you put all of these pieces together?

I wanted to write a quick post about how a business could use the entire platform to better market to users on the web based on non-website activities. We’ll explore how to use offline and online data to create remarketing lists in Google Analytics.

Before I start a hat-tip to my buddy 
Dan Stone

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Note: This article was originally posted on the Marketing to Results blog.

Recently I was reminded of an article

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What's will you find in this post: 2 Books recommendations - Summer read for Digital Analytics addict: Fundamentals in Digital Analytics & broader Customer centric view

It's been a few weeks since summer is over and we are now in Back to School mood, showing off our nice pic and how tanned we are... And here comes the fateful question "What did you do this summer?". Well, now that I am a full grown-up and am living in Asia - I don't really get this feeling of end of Summer in September, as it's still 30 degrees in Hong-Kong and it's not going to stop before November approximately and I don't really get the fateful question anymore as in Asia July/August are month like any other ; the activity is not slower. Calvin-book-reportAnyhow, I don't blog in July/August and treat September as my Back to Blogging month. And instead of showing off how tanned I am, I'll show off how studious I was this summer and share 2 books I read during my trip back to Europe this summer.
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By: Shree Dandekar, General Manager | Sr. Director (Data Analytics), Dell

Predictive analytics is undeniably key for today’s marketing professional to gain insights that help grow businesses. A recent survey revealed that companies that rate themselves substantially ahead of their peers in their use of data are three times more likely to rate themselves as equally ahead in financial performance.

Predictive marketing provides value to everyone from analyst to technology experts to web content managers in all industries. Here are just a few examples:

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What you’ll get from this post: A deep exploration of significance testing, with useful recommendations for avoiding statistical error.

Estimated reading time:
 3 minutes; approximately 630 words.

When we run an A/B test, we are evaluating the performance of a page and its variations against one another. In statistical terms, we classify such an experiment as a hypothesis test—at the end of which, we can hopefully determine whether there’s a relationship between a change to the page and an increase in performance. To estimate the strength of such a relationship, we use

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What you’ll get from this post: In this Clickaways deep-dive, find out which comes first: the data or the test.

Estimated reading time: 2 minutes; approximately 400 words.

One of the takeaways from this year’s Click Summit was that “an organization must embrace data before it can leverage testing.” This seems obvious on the surface, but it is actually a powerful insight that hints at the mechanism by which a testing culture can grow within an organization. It also suggests some practical steps for building a data-driven business. 

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The Digital Analytics Association defines Digital Analytics as the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimising digital usage.

It’s not a minor thing that analysis and reporting are separated. Analysis and reporting are two really different things.

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What you’ll get from this post:
A clear explanation of significance testing methods and guidelines for ensuring test results are reliable.

Estimated reading time:

4 minutes; approximately 800 words.

Imagine two colleagues at a coffee shop during a mid-morning break. Both order large coffees and move to the condiment counter to add milk and sugar. “I like it when they do this before pouring the coffee,” one comments to the other. “It just tastes better.” Incredulous, the other challenges the first to a taste test to determine whether this preference bears any significance.

Back at the office, eight cups of coffee are prepared—four with sugar added before the coffee is poured and four with sugar added after. Presented randomly, the coffee connoisseur tastes each individually, and then announces whether sugar was added before or after the coffee. A certain number of correct guesses will be expected due to the nature of random chance. But if the taste tester performs well—say, by guessing all eight cups correctly—his astonished officemates will declare his palette exceptional and his performance significantly better than random chance.
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Digital Analytics Competency Framework

Progress Report #2


The CNN world headquarters in Atlanta takes up an entire city block and is a bit overwhelming. For three days in July, 2014, it graciously hosted the Digital Analytics Association Joint Task Force on Competency Framework Development.

It took about a half an hour of commute time for the 15 of us to reach our meeting room ("Ability") after arriving at the front door. Once ensconced - it was off to the races - with only minor distractions from breaking news.

This gathering in Atlanta was so interesting and so engaging that I only made it to the lobby bar on the first evening after dinner. We were all simply too tired after full days of wrestling with the creation of industry competencies, skills and required knowledge. What an effort!

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Nate Silver's the Signal and the Noise is a forecasting book with broad appeal. Read this book if you want to understand more about decision-making, statistics and predictive analytics without having to mine a text book. The book is richly researched, well organized and packed with engaging examples. It is especially valuable for digital analytics professionals and marketing executives who may be facing pressure to provide more predictions.

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We generate data everywhere, every day, in many different forms; from our phones, to our payment cards, to our smart televisions, and computing devices. Even the infrastructure of where we live creates data – the traffic signals, speed cameras, billboards, buses; anything that contains a micro-processor or sensor-equipped, provides data about us.

With an estimated

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This week in Atlanta, I will be meeting with 15 dedicated, digital analytics types in an attempt to answer some of life's imponderables:

Who are we?
What do we do for a living?
How many knowledge and skills levels are there?
What knowledge and skills are needed at each level?
What might a job description look like at each level?
How do you know which level you are?
What do you need to know to get to the next level?

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As the number of marketing channels continues to grow, measuring the influence of one channel on another has become a huge challenge. In fact, according to a recent report issued by Visual IQ and The CMO Club, more than 70 percent of marketers rated their ability to assess the impact of one channel on another as “poor” or “fair,” suggesting a lack of practical expertise harnessing and deriving insights from Big Data. The real bottleneck is not technology, but the availability of analytics experts with the skills and expertise to analyze and interpret it correctly, so it can be acted on effectively.

What Makes A Good Analyst?

While skilled analysts are essential for unlocking the power of Big Data, building an effective marketing analytics team poses some interesting challenges, including knowing what qualities to look for in an ideal candidate. When building out your analytics team, it’s important to focus on these skills and characteristics:

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Intro to Mobile Analytics – how does it differ from the fixed web?

Now that we are into 2014 I am sure all of you understand how important mobile is to your business.  But just in case you have been in a cave or under a rock for the last 4 years, here are some pretty staggering statistics about the Mobile web.

  • 58% of all US consumers already own a smartphone.  Source - 
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(Note: This is a repost from a guest blog, posted today at IQ Workforce; thank you to fellow DAA Research Committee members Christopher Berry and June Li, who aided me in gathering some details for this blog post!)

      Digital Analytics (or #measure on Twitter) has, over the past five years, emerged as a growing area of academic interest. Universities and colleges, worldwide, are taking an increasing interest in digital analytics, from offering courses, to full-fledged research centers producing journal articles, books and a growing body of academic literature focused on advancing and evolving our industry. The interaction between academia, digital analytics practitioners and vendors continues to evolve as well, as evidenced by the presence of academics at digital analytics conferences and symposiums, recent participation on the board of the Digital Analytics Association, and the launch of a new academically-focused, practitioner-oriented journal this fall.

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Reporting and analytics are an important part of website and software development. Tools are available to help with this process and can help to improve the efficiency of websites. When you integrate analytics reporting into your websites, you’re likely to improve your sales conversions. Our company can make optimization recommendations and provide performance insights. Here are some ROI reporting and optimization tips that you may need to know:

1. Mobile Device Reports

Business people can receive mobile accounts reports in any location that they are in at the moment. Not only can reports be deployed but also KPIs and other analytics. This helps people keep track of how their devices are performing.

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Email marketing remains one of the vital aspects of any healthy online marketing campaign. Over the past few decades, the best practices for this advertising medium have been refined to provide immense return-on-investment, due to low overall costs. Despite the age of the internet and email advertising, however, creative marketers have continued to create new methods for improving email marketing performance.

One such method is called email retargeting, and this option is a particularly new way to add value to a current marketing campaign. Put simply, email retargeting offers display advertising to individuals who have previously opened an email. When a person is part of an email marketing list and opens a recently delivered email, that potential customer is then shown relevant advertisements later.

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It was just 7 years ago when I first heard of this thing called Web Analytics. I had no idea what it was, but a recruiter found me on a career website and believed I had the qualifications necessary. Okay great, I was in graduate school and in the market for a new job….and yikes, I definitely had some homework to do in order to prep for this interview!

What I could find definitely made Web Analytics sound interesting. It was an industry that supported online businesses and involved both creative marketing and geeky data reporting. My personal Ying and Yang, as I like to call it these days; I was drawn to the fact that it seemed to be both a left-brained and right-brained career track.

On my interview day I met John Payne, who at the time was the Director of the DFW Coremetrics office. My first impression of John was amazing – he was passionate, energetic, and I loved his philosophy on hiring. He explained to me that Web Analytics had been around for a while, but was still up and coming and was sure to grow immensely as companies with an online presence realized the value it could provide. He was quite aware that few people had heard of it, very few people had job experience with it, and at the time, no one had studied it in school (at least not in our neck of the woods).

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By Guest Blogger, Damian Fernandez-Lamela.

In my experience there are several top marketing analytics mistakes that are common across different industries. Unfortunately, I have seen these problems repeated over and over, in many companies, during my career. If you want to be successful in analytics, you have to be particularly careful to avoid these mistakes. Here is a short list:

·         Not adequately influencing the decision maker

It is a completely wasted opportunity for the company if you are unable to influence the decision maker using your data-driven analytics insights. In some cases the issue is the lack of storytelling skills. Analytics professionals need to improve their communications skills and be able to explain the stories that the data is telling them in a way that business decision makers will easily understand. Sometimes, the problem is the lack of sufficient effort in convincing all necessary stakeholders. This is particularly challenging in large organizations where you need to convince a sizable number of stakeholders before any decisions are made. I know how frustrating it is to spend time on an analysis, only to see it rapidly discarded; to ensure that doesn’t happen to you, improve your internal “selling” skills. You and your organization will benefit tremendously.

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