Remove 2011 Remove Experimentation Remove Optimization Remove Reporting
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When models are everywhere

O'Reilly on Data

The Entertainment” is not the result of algorithms, business incentives and product managers optimizing for engagement metrics. Television only lacked the immediate feedback that comes with clicks, tracking cookies, tracking pixels, online experimentation, machine learning, and “agile” product cycles.

Modeling 188
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Search: Not Provided: What Remains, Keyword Data Options, the Future

Occam's Razor

In late 2011, Google announced an effort to make search behavior more secure. As an analyst, I was upset that this change would hurt my ability to analyze the effectiveness of my beloved search engine optimization (SEO) efforts – which are really all about finding the right users using optimal content strategies.

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4 key roles that define transformational IT leaders today

CIO Business Intelligence

A majority (77%) of CIOs say their role has been elevated due to the state of the economy and they expect this visibility within the organization to continue, according to Foundry’s 22nd annual State of the CIO report. The vice president of IT reports to Johnson as well as the digital teams and transformation office.

IT 102
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Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Occam's Razor

If you are open to being challenged… then here are the short-stories inside this post… The World Needs Reporting Squirrels. The World Needs Reporting Squirrels. If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis. Bonus #2: The Askers-Pukers Business Model.

Modeling 127
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. But it is not routine.

Metrics 156
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Unintentional data

The Unofficial Google Data Science Blog

We data scientists now have access to tools that allow us to run a large numbers of experiments, and then to slice experimental populations by any combination of dimensions collected. Make experimentation cheap and understand the cost of bad decisions. This leads to the proliferation of post hoc hypotheses. Consider your loss function.

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How To Suck At Social Media: An Indispensable Guide For Businesses

Occam's Razor

In my Oct 2011 post, Best Social Media Metrics , I'd created four metrics to quantify this value. When you search for them, if you find them, you end up on sub-optimal landing pages. We all know that Page Likes is a profoundly sub-optimal metric. It is a good idea to separate value for a user from the value to the business.

B2B 167