10 interesting digital marketing statistics we’ve seen this week

See on Scoop.itWhat is Data Science

Here are some of the most interesting digital marketing statistics we’ve seen this week.

Carla Gentry CSPO‘s insight:

The data, which comes form Marin Software, also shows that shoppers continue to find PLAs more attractive than standard text ads, as evidenced by the fact that PLA clickthrough rates (CTR) have remained higher than standard text ads since November 2012.

See on econsultancy.com

Who Will Buy Foursquare?

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Location data is money. Foursquare’s got plenty of it, plus a new product that promises to make it profitable. Is the start-up finally having its moment?

Carla Gentry CSPO‘s insight:

As TechCrunch explains, the new Foursquare "will ping phones with personalized recommendations based on a user’s targeted physical location, without needing them to check in at all."

In a recent interview with Fast Company, co-founder and CEO Dennis Crowley explained this product is basically the holy grail of local discovery, and one that has the potential to turn Foursquare from a fun, though niche social check-in site, into a massive local data company. 

See on www.inc.com

The Data Scientist Team

The Data Scientist Team

20130826DataScientistTeam

I’ve been intrigued with all of the attention that the world of Data Science has received.  It seems that every popular business magazine has published several articles and it’s become a mainstream topic at most industry conferences. One of the things that struck me as odd is that there’s a group of folks that actually believe that all of the activities necessary to deliver new business discoveries with data science can be reasonably addressed by finding individuals that have a cornucopia of technical and business skills.  One popular belief is that a Data Scientist should be able to address all of the business and technical activities necessary to identify, qualify, prove, and explain a business idea with detailed data.

If you can find individuals that comprehend the peculiarities of source data extraction, have mastered data integration techniques, understand parallel algorithms to process tens of billions of records, have worked with specialized data preparation tools, and can debate your company’s business strategy and priorities – Cool!  Hire these folks and chain their leg to the desk as soon as possible.

If you can’t, you might consider building a team that can cover the various roles that are necessary to support a Data Science initiative. There’s a lot more to Data Science than simply processing a pile of data with the latest open source framework.  The roles that you should consider include:

Data Services

Manages the various data repositories that feed data to the analytics effort.  This includes understanding the schemas, tracking the data content, and making sure the platforms are maintained. Companies with existing data warehouses, data marts, or reporting systems typically have a group of folks focused on these activities (DBAs, administrators, etc.).

Data Engineer

Responsible for developing and implementing tools to gather, move, process, and manage data. In most analytics environments, these activities are handled by the data integration team.  In the world of Big Data or Data Science, this isn’t just ETL development for batch files; it also includes processing data streams and handling the cleansing and standardization of numerous structured and unstructured data sources.

Data Manager

Handles the traditional data management or source data stewardship role; the focus is supporting development access and manipulation of data content. This includes tracking the available data sources (internal and external), understanding the location and underlying details of specific attributes, and supporting developers’ code construction efforts.

Production Development

Responsible for packaging the Data Scientist discoveries into a production ready deliverable. This may include (one or) many components: new data attributes, new algorithms, a new data processing method, or an entirely new end-user tool. The goal is to ensure that the discoveries deliver business value.

Data Scientist

The team leader and the individual that excels at analyzing data to help a business gain a competitive edge. They are adept at technical activities and equally qualified to lead a business discussion as to the benefits of a new business strategy or approach. They can tackle all aspects of a problem and often lead the interdisciplinary team to construct an analytics solution.

There’s no shortage of success stories about the amazing data discoveries uncovered by Data Scientists.  In many of those companies, the Data Scientist didn’t have an incumbent data warehousing or analytics environment; they couldn’t pick up the phone to call a data architect, there wasn’t any metadata documentation, and their company didn’t have a standard set of data management tools.  They were on their own.  So, the Data Scientist became “chief cook and bottle washer” for everything that is big data and analytics.

Most companies today have institutionalized data analysis; there are multiple data warehouses, lots of dashboards, and even a query support desk.  And while there’s a big difference between desktop reporting and processing social media feedback, much of the “behind the scenes” data management and data integration work is the same.  If your company already has an incumbent data and analytics environment, it makes sense to leverage existing methods, practices, and staff skills.  Let the Data Scientists focus on identifying the next big idea and the heavy analytics; let the rest of the team deal with all of the other work.

via The Data Scientist Team.

Data Mining Is a Risky Business | The Descrier

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Algorithms can be manipulated by both the good guys and the bad guys

Carla Gentry CSPO‘s insight:

“Foreignness” is binary. You are either inside the territorial United States or you are not. Due to the complexities of how communications traffic is routed from server to server to server, some packets of information have a very high degree of location certainly and some have a very low degree. The point is that the NSA has set the bar quite low.  Technology can never give us 100 percent, or even 80 percent, certainty that only people outside the US are being monitored.

See on descrier.co.uk

With Playnomics, data science predicts which players will quit a game in the next month

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Playnomics can tell developers who is quitting a game and how to stop that from happening.

Carla Gentry CSPO‘s insight:

Chethan Ramachandran, the chief executive of Playnomics, said in an interview that 70 percent of players in free-to-play titles, where people can play for free and pay real money for virtual goods, will quit within the first 30 days. Playnomics can now predict how anonymized players will behave, who is likely to spend money, who should developers should target with promotions, and who the developer has to pay attention to in order to prevent the dreaded churn, or decision to stop playing a game
Read more at http://venturebeat.com/2013/08/28/with-playnomics-data-science-predicts-which-players-will-quit-a-game-in-the-next-month/#YLWGJ4DuCCVG6xaL.99 

See on venturebeat.com

40 facts on blogs every business needs to know to grow

See on Scoop.itWhat is Data Science

Blogs are the lowest cost marketing channel; yet, they produce a high return. More businesses are blogging. More people are reading them.

Carla Gentry CSPO‘s insight:

Anything worth doing is worth doing well. The benefits of blogs don’t accrue without regular publishing, listening to the needs of your audience and engaging.

See on barnraisersllc.com

Twitter and Facebook’s global impact as told through which governments want their data

Overall, data about Facebook users are much more coveted: The social network received at least 25,607 government requests in the first half of this year, compared with 1,113 for Twitter. That makes sense since Facebook is bigger, and Twitter stores less sensitive information about its users. There are simply more secrets hiding on facebook.com.

A Real Life Analytics Metaphor | Analytics & Optimization

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Suppose you own a restaurant in a highway rest area. You want to understand how your visitors behave and to optimize the restaurant to better serve your clients. How would you do that? And how is it related to Web Analytics?

Carla Gentry CSPO‘s insight:

You might be surprised, but that’s what Google Analytics does, in a somewhat larger scale! Why? Well, in this case you are the analytics tool as you are counting and reporting on what’s happening, who is coming, who is leaving and which transportation vehicle they are using. The restaurant is the website, where the visitors are supposed to make a transaction or complete a task.

See on online-behavior.com

Everyone In The Tech And TV Industries Is Passing Around This Speech By Kevin Spacey

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Everyone in the tech industry is passing around…

Carla Gentry CSPO‘s insight:

Spacey says there will be 146 pilots made this year at the cost of $300-$400 million. Only 56 of those will actually be made into a series. "That makes our ‘House of Cards’ deal for two seasons really cost effective," Spacey says in the speech.

Read more: http://www.businessinsider.com/everyone-in-the-tech-and-tv-industries-is-passing-around-this-speech-by-kevin-spacey-2013-8#ixzz2dAuUMFc2

See on www.businessinsider.com

Fast cars and furious data – Fortune Tech

See on Scoop.itWhat is Data Science

How NASCAR is using social listening technology to bolster its brand.

Carla Gentry CSPO‘s insight:

The ability to respond quickly in real time is crucial for an industry that’s not only based on the premise of speed, but which boasts a massive audience–around 75 million fans–with a huge social media footprint. During this year’s Daytona 500, HP Executive Vice-President of Enterprise Services Mike Nefkens estimates that they analyzed around 60,000 social media interactions per minute.

See on tech.fortune.cnn.com