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Intro To Predictive Analytics Reading List | Forrester Blogs

Intro To Predictive Analytics Reading List | Forrester Blogs | Daily Data | Scoop.it

Few understand the what, why, and how of predictive analytics. Here’s a short, ordered reading list designed to get you up to speed super fast:

The Signal And The Noise: Why So Many Predictions Fail — but Some Don’t by Nate Silver. Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, or Die by Eric Siegel.Uncontrolled: The Surprising Payoff Of of Trial-and-Error For Business, Politics, and Society by Jim Manzi.Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, and Mark Hall.The Forrester Wave: Big Data Predictive Analytics Solutions, Q1 2013 (Forrester client access only).
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marketingIO's curator insight, March 21, 2013 8:11 AM

Thank you Forrester! Click through for a quick summary of each read. NOTE: we believe Predictive Analytics to be the next step post-marketing automation, as the data collective through your MAP is a goldmine. As a B2B marketer, you need to get a good backgrounder on Predictive.


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Hui Li's curator insight, August 18, 2013 5:58 PM

Hello world

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Vinod Khosla: In The Next 10 Years, Data Science Will Do More For Medicine Than All Biological Sciences Combined | TechCrunch

Vinod Khosla: In The Next 10 Years, Data Science Will Do More For Medicine Than All Biological Sciences Combined | TechCrunch | Daily Data | Scoop.it
At TechCrunch Disrupt SF, TechCrunch and CrunchFund founder Michael Arrington and Khosla Ventures founder Vinod Khosla took the stage to discuss the areas Khosla is currently most interested in.

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How Online Retailers Use Predictive Analytics To Improve Your Shopping Experience

How Online Retailers Use Predictive Analytics To Improve Your Shopping Experience | Daily Data | Scoop.it
Retail leaders are under immense pressure to keep up with powerful consumers. Retailers are using predictive analytics to help them stay ahead. (How online retailers use #PredictiveAnalytics to improve your shopping experience.

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14 rules for data-driven, not data-deluded, marketing - Chief Marketing Technologist | #TheMarketingAutomationAlert

14 rules for data-driven, not data-deluded, marketing - Chief Marketing Technologist | #TheMarketingAutomationAlert | Daily Data | Scoop.it
We’re now in the crossfire of a peaking hype cycle for big data and its inevitable backlash.

 

Excerpted and condensed...

 

I confess, I emphasize with his frustration. Of course, I believe that data — the right data, used in the right ways — is immensely powerful in modern marketing. As I wrote in a post about pragmatic marketing, “Relying solely on gut-based, experience-driven decision-making in marketing is foolish in the digital age.”

 

But I also concluded: “The sensible answer for most companies is a balance of data analytics and human judgement.”

 

In the spirit of a balanced approach, I heartily encourage you to embrace data in marketing — but here are 14 rules of thumb to keep that data in perspective, to support a pragmatic approach to data-driven marketing (and avoid data-drowned or data-deluded marketing).

 

(iNeo favorites)

#8. Experimentation is the gold standard of causation.

Correlation is not causation. Every data scientist worth their salt will tell you this. But as marketers, it’s usually causation that we’re after — we want to know what we can do that will cause more customers to do more business with us. So what do we do when data shows a correlation that may reveal such a cause? We run a controlled experiment. Keep all other variables constant (as much as is practically feasible) and test the alternatives to prove or disprove our hypothesis.

 

#13. The model is not reality.

Data is not the reality that it claims to represent. At best, it is a reflection of reality, but one that is susceptible to being warped (see all the rules above). Certainly we want to use data — and maps, for that matter. As the great statistician George E. P. Box said, “All models are wrong, but some are useful.” But it’s prudent to maintain a little healthy skepticism about the correctness of the representation. In particular, we want to be alert to other signs — outside of the data — that suggest that reality differs.


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marketingIO's curator insight, September 16, 2013 12:13 PM

We see this story play out all the time: new concept, hype is built up, hype doesn't meet reality, rocks get thrown, concept publically goes away, but in the meantime, the smart people keep working away. That's what we have going on with Big Data. For us B2Bers, just skip the notion of Big Data, and settle on...Data. Yeah: Data. You're collecting the Data, and now you need to use the Data. See Scott's 14 Rules (or Commandments with apologizes to Mel Brooks).


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Marketers, Seeing A Changing Landscape, Recognize Need For Data Analysis Skills - Marketing Charts

Marketers, Seeing A Changing Landscape, Recognize Need For Data Analysis Skills - Marketing Charts | Daily Data | Scoop.it

Excerpt...

 

Whereas 5 years ago, marketers needed media planning and buying skills and the ability to implement overall campaign strategy (including timing and targeting) to be successful, the most important skills today are very different, according to a survey by the Economist Intelligence Unit, sponsored by Lyris. Attributing the largest drivers of change to a digital environment that requires much more agility and speed (58% selecting as one of top 2) and a much more knowledgeable customer (40%), respondents indicated that the most necessary skill today is the ability to use data analysis to extract predictive findings from big data (40% selecting as one of top 3).

 

Tied for second place: the ability to generate insights about the drivers of consumer behavior from multiple data sources (32%). Indeed, a significant proportion of marketers believe that harnessing complex data sets has become a critical success factor.

 


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marketingIO's curator insight, June 14, 2013 6:58 AM

Does not differentiate between B2B and B2C, but you get the point.


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