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Everyone in digital advertising space talks about SEO, although they also do google for example "landing page optimization", people query about it approximately 17k times per month in malaysia but mostly failed to get the right answer.




I have also searched a lot on this topic. Honestly speaking, I have read more than 10,000 articles, only then, I understood and applied my findings to get my desired behaviour.

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The world of marketing is changing. On one hand, there is tension between the demand for financial transparency and accountability. On the other hand, there are more complex demands, from multi-screens to social media blogs, powering customers in an increasingly digital world. This means there's a growing need for Return On Advertising Spend (ROAS) in real time, so that synergy between ROAS and Real Time Engagement (RTE) will complement each other.

To understand more you have to analyse the performance of each network with a blending of two different marketing models: complete effect model and instant effect model.

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After reading the book, Web Analytics Action Hero, I decided to create a summary mashup of some of the approaches and information presented in the book. The book discusses applying the scientific method to web analytics in an efficient and systematic way the author calls the HEROIC approach. The author claims that this will help you answer important questions such as What, Why, and So What of an analysis. I put the below visual together to coalesce the information found in the book, and I use it as a reminder of important steps I'll likely have to go through when taking on a new website optimization project.

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Marketing tools are rapidly evolving and marketers are spending more money on technology than ever before. But 1 in 2 marketers report that fragmented technologies impede their ability to create a consistent experience for consumers across the web, mobile and other channels, according to a survey released today by Signal, a global leader in real-time, cross-channel technology.

The first global study of its kind, Signal’s Cross-Channel Marketing and Technology Survey found that marketers clearly recognize the importance of integrating the data-driven tools they utilize for email, ad-serving, search marketing, data collection, attribution, CRM and more. A powerful majority (9 in 10) believe that connecting the disparate tools in their company’s marketing technology stack would improve their ability to innovate, personalize consumer interactions, send timely messages, boost loyalty, evaluate campaigns, and increase return on marketing investments. 

However, 51% of marketers say they have yet to integrate marketing technologies beyond the most basic level. Fewer than 1 in 20 marketers reported having a fully-integrated technology stack. 

This lack of coordination is a key factor preventing marketers from reaching the holy grail of true, cross-channel engagement with consumers across their laptops, smartphones and tablets.  Signal conducted the survey in September to better understand the challenges faced by marketers in getting more value from their technology stacks. 
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Here’s the thing about data. You can shape it into anything. 

The great sin, of course, is to shape the data into nothing, to gather up the disparate numbers and pile them onto a page. “Here,” you say as you brush the serif edges from your hands and let them all tumble together. “Have some data.”

So you don’t do that. You wave your hands and swirl the data around into meaningful shapes and you create a story.

And therein lies the thing. The rub. The conundrum. What story will you create?

A true one, you say. Numbers don’t lie. Numbers are binary. Black and white. Concrete.

Only you know none of those things are true.

What you do know is this: there is meaning behind the numbers. You just have to find it.

The Stories in Search Data

I spend a lot of my time looking at search data. When companies tell me how well their sites are performing in search engines, they talk about rankings, and maybe how much traffic they get from search.

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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
(from www.weboptimeez.com)

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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