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Designing Breakthrough Products
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Innovating Through Design
Innovation
From the October 2011 Issue
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Executive Summary

Reprint: R1110H

Thanks to the collaboration the internet has made possible and the open innovation it has spurred, we live in a world where ideas and solutions are abundant. The main challenge facing innovation managers today is how to take advantage of this wealth of opportunities, says the author, a professor of innovation management. He contends that being first to launch a new technology is less important than being first to envision its greatest untapped market potential. Most companies focus on employing new technologies to better serve customers’ existing needs. Those that have strive to create products and services that will provide customers with a completely new reason to buy a product: Think of Nintendo’s Wii, Apple’s iPod, and Swatch.

Verganti explains how companies can systematically produce technology epiphanies. He illustrates the process with the story of Philips Electronics’ creation of Ambient Experience for Healthcare, a system that uses LED displays, video animation, radio-frequency identification sensors, and sound-control systems to relieve the anxiety of patients undergoing CT, MRI, and other scans.

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In conventional product development, companies look for new technologies that will better serve the existing needs of their customers.

But if they want to create breakthrough products, they should seek to understand how those technologies could be used to address needs that customers may not realize they have.

To come up with these companies should turn to interpreters—experts from far-flung fields with a novel perspective on users—rather than to the users themselves.

Well-known examples of companies that did the latter include Nintendo, Apple, and Swatch. All three have used technology to radically change the meaning of offerings in a category—why customers buy or how they use a product. Nintendo’s clever application of MEMS (micro-electro-mechanical systems) accelerometers transformed the experience of playing with game consoles from passive immersion in a virtual world into active physical entertainment. Apple’s creation of the iPod and the iTunes Store made it easier for people to discover and buy new music and organize it into personal playlists, and provided a solution to the piracy that was threatening to destroy the music industry. And Swatch used inexpensive quartz technology to change watches from timekeeping tools into affordable fashion accessories. These companies weren’t necessarily the first to introduce a new technology in the product category (the iPod was released in 2001, four years after the first MP3 player), but they unveiled its most meaningful and profitable form.

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The 10 Types Of NFL Quarterback

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By Noah Davis and Michael Lopez

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This weekend, two perennial MVP candidates and two up-and-coming stars will be under center in NFL conference championship games. Tom Brady and Aaron Rodgers are the establishment; Andrew Luck and Russell Wilson are the hopeful usurpers. They’ve all reached the conference title game, in part, because they’re among the highest-performing quarterbacks in football — all falling within a career average Total Quarterback Rating of 59.6 and 70.1. 1

ESPN’s QBR, measured on a scale from 0 to 100, debuted in 2006. It’s not retrofitted to quarterback performances before 2006.

But that’s just an average, and sometimes averages can deceive. Turning several observations into a single metric like a Total Quarterback Rating (QBR) obscures some information. For example, post-2006 Brett Favre 2

His final five seasons.

A more thorough analysis makes use of the complete set of observations. The distribution of each quarterback’s QBR can offer a more thorough understanding of performance. When we look at quarterbacks this way, we find some players who consistently minimize bad games and others who can be brilliant one week and horrible the next, regularly handicapping their teams. 3

Quarterbacks who post a single-game QBR above 90 in a game win 90 percent of the time, whereas a score of less than 10 corresponds with a loss more than nine times out of 10.

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Comparing each quarterback’s distribution of game-by-game QBR can be done using a density curve, which is used to estimate the fraction of a player’s performances that occurred in a given interval — in this case, the probability that a quarterback had a QBR of a specific number in any individual game. 4

For a description of density curves and its application to hockey players, see this post on the blog WAR on Ice .

We used k -means clustering on different percentiles of each quarterback’s distribution. Although there is no correct value for k in implementing k -means, we found the best performance with between k =8 and k =10. As the curves looked much easier to interpret with k =10, we went with that. Once we had fixed 10 clusters, we ran the algorithm about a dozen times and used the clustering with the highest within-cluster similarity of those iterations. This helps justify the choice of player groups but doesn’t exactly imply that the QB groups are perfectly stable from one iteration to the next. That is because the k -means algorithm is non-deterministic — it doesn’t give the same answer with each run.

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