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Mar 31, 2010
March 30th, 2010 by Christian
Time and again in American politics, Republicans have voted as a unit to frustrate our disorganized Democratic majority. No matter what's on the table, a few Democrats will peel away from the party core; meanwhile, all Republicans will somehow manage to stay on-message.
Thus, they caucus block us.
There are many methods of looking at the political spectrum, but the best way I've come across is to hold social politics and economic politics separate, and measure a person's views on each in terms of permissiveness vs. restrictiveness on a 2-dimensional plane. Like so:
As you can see, I've superimposed some 'party' labels, to add some real-world context. One could quibble with the names I've chosen, but I feel that, in a broad sense, they fit: Democrats have a permissive social outlook and believe in restricting the financial sector (through regulation); Republicans essentially believe the reverse. In their corner, Libertarians would like to end restrictions across the board, and, down in the lower right, we have people who prefer that all aspects of life be guided by some authority: religion, the government, whatever.
These lines contain a neat little story:
Now, with this picture in hand, we can go a step further with our data. The American two-party system creates an interesting mathematical situation: we can bisect our political planea two-party system allows us to bisect the political plane and see which party more closely reflects a given age group's ideology simply by asking which side of the line the group lands on. People sitting in the upper right half should vote, in theory, for Democrats. People in the lower left, for Republicans. Like so:
But of course this line assumes that social and economic values are equally important to a person and that his or her priorities don't change as time goes by. Obviously, neither is the case in the real world. So let's see exactly how those values change and do even more with our graph.
Digging deeper into OkCupid's matching database, we find the following new information on people's political priorities:
The way this data bears on our political plane is mathematically cool, but arctan(x) really has no place in a political discussion (except in Flatland!), so I'll just summarize bya change in political priorities causes our
dividing line to rotate saying a shift towards either social or economic issues causes our Democrat/Republican dividing line to rotate about the center of our political plane. Here's exactly how it happens; this timeline is basically the sum of all the information we have shown so far. Use the slider to step through time.
age From this animation, we can consolidate all that we've learned about each group into a single plot. The blue dots are the ages likely to vote Democratic, the red are the Republican ones. In case you're keeping score, there are 21 blue dots and 22 red ones.
This detailed portrait of the electorate jives well with the actual exit poll numbers from the last few Presidential elections. The New York Times has collected this data and present it very well, if you have time to take a look. Here's the part that concerns us:
To wind up this section, I'd like to take one last look at our political plane, with a final set of overlays that I think are most illuminating:
The polygons I've drawn over the dots are called convex hulls; they are a geometric way to measure the spread of a set of points. In this case, the hulls tell us the size of the ideological/age base of our political party.
As you can see, the Democrat's base is much larger. And the range of political values it encompasses is vast. Here's party-to-party comparison in tablet form, for easy digestion:
Unlike in many things, size here is a liability. Yes, a political party that's this wide-open is probably a more intellectually stimulating organizationideological size is a liability to be a part of, and it has a lot more potential power. But bigger base is also just that many more competing viewpoints Democratic politicians must cater to and that many more different viewpoints in play among the actual elected officials themselves.
Also, well over half of the Democratic party's hull lies outside of its upper-right-hand ideological home, implying that you've got many groups of people who might tend Democratic, but who have disagreements with the party on particular issues and could defect, should the slant of the party or the country tilt the wrong way. On the other hand, the Republicans are concentrated in the lower-left-hand corner. This red cluster has multiple, apparently self-reinforcing, reasons to vote with their party, giving the Republicans both a more fervent power base and a little more ideological wiggle-room along either the social or economic axis.
So when you read about the thousands of Catholic nuns who recently came out in favor of health care reform, it's easy to get excited about being a Democrat. But do you think those same people will side with us on things like gay marriage? Or abortion rights? Hull no!
Let's look at ideological distributions on a few hot-button issues and see how the Democrats are spread out and exposed. We'll start with views on abortion. This chart shows the opinions of social conservatives and social liberals. Everything is as you'd expect: liberals are pro-choice; conservatives pro-life.
Now let's look at how economic liberals and conservatives view abortion:
Again, the conservatives are strongly pro-life. But the economic liberals have widely distributed views. A solid portion of the Democratic economic base actually sides with Republicans on this issue. It's those nuns again!
While the two conservative curves are nearly congruent, the liberals ones are totally different. The takeaway, the Republican advantage, is this: economic conservatives and social conservatives agree, while the liberal halves of these spectra don't. Furthermore, the purple overlap—in a sense "the swing vote"—is largely on the conservative side!
We see same pattern repeated again and again. Here, for example, is a look at the 'Gay Marriage' issue:
Below are two matrices, showing person-to-person match percentages. These numbers are a measure of how well two people get along. We've used them to facilitate over 100,000 marriages in the last few years; their accuracy is well-tested. Match percentages range from 100 (awesome) to 0 (terrible), and the site average is 61.
We excluded explicitly political questions, ran numbers for the different ideologies, and found these patterns, which I'll leave you with:
As you can see, conservatives of both stripes get along with each other better than liberals do with themselves, even on non-political issues. We calculate match percentage by posing a series of questions to our users. Just to give you a sense of what these questions are like, here are the top three most important (by user vote):
Time and again in American politics, Republicans have voted as a unit to frustrate our disorganized Democratic majority. No matter what's on the table, a few Democrats will peel away from the party core; meanwhile, all Republicans will somehow manage to stay on-message.
Thus, they caucus block us.
. . .
Articles noting this phenomenon anecdotally appear all the time, and despite the recent hopeful spate of Democratic victories, the fact that Republicans form an exceptionally effective opposition party is undeniable. Today, we're going to perform a data-driven investigation of this—and discover some fascinating things about the American electorate along the way. Our data set for this post is 172,853 people.A Picture Of Our Political Evolution
I should start off by pointing out that the Left/Right political framework we're usually handed is insufficient for a real discussion, because political identity isn't one-dimensional. For example, many Libertarians have Left-leaning ideas about social policy, and Right-leaning ideas about personal property. Where do they fit on a single ideological line? There are many methods of looking at the political spectrum, but the best way I've come across is to hold social politics and economic politics separate, and measure a person's views on each in terms of permissiveness vs. restrictiveness on a 2-dimensional plane. Like so:
As you can see, I've superimposed some 'party' labels, to add some real-world context. One could quibble with the names I've chosen, but I feel that, in a broad sense, they fit: Democrats have a permissive social outlook and believe in restricting the financial sector (through regulation); Republicans essentially believe the reverse. In their corner, Libertarians would like to end restrictions across the board, and, down in the lower right, we have people who prefer that all aspects of life be guided by some authority: religion, the government, whatever. . . .
Now, with the definitions out of the way, we can get to some information. We'll begin with the most basic measurement: people's economic and social values. Because our data set is so comprehensive, we can even measure the change in these values with age. These lines contain a neat little story: - Both socially and economically, teenagers prefer an anything-goes type situation.
- But as these teenagers grow up a bit and enter the job market, they quickly develop progressive economic ideas: perhaps a bit of "levelling" seems pretty good when you're staring up the professional ladder from the bottom rung. Meanwhile, their youthful live-and-let-live social philosophy begins to fade.
- In their late 20s, they start making real money. Economic progressivism goes out the window, preferably out the window of a building with a doorman. As the adult mind turns to more material matters, social views don't change that much.
- Finally, after the mid-40s, retirement looms. Our former teenagers check their collective 401(k)s and think, you know what, let's all get checks from the government. Social views take a hard turn for the more restrictive. At the end of the journey, economic and social views are again in agreement—only this time on the other side of the philosophical line!
Now, with this picture in hand, we can go a step further with our data. The American two-party system creates an interesting mathematical situation: we can bisect our political planea two-party system allows us to bisect the political plane and see which party more closely reflects a given age group's ideology simply by asking which side of the line the group lands on. People sitting in the upper right half should vote, in theory, for Democrats. People in the lower left, for Republicans. Like so:The Implication of Our Two-Party System
But of course this line assumes that social and economic values are equally important to a person and that his or her priorities don't change as time goes by. Obviously, neither is the case in the real world. So let's see exactly how those values change and do even more with our graph.Digging deeper into OkCupid's matching database, we find the following new information on people's political priorities:
The way this data bears on our political plane is mathematically cool, but arctan(x) really has no place in a political discussion (except in Flatland!), so I'll just summarize bya change in political priorities causes ourdividing line to rotate saying a shift towards either social or economic issues causes our Democrat/Republican dividing line to rotate about the center of our political plane. Here's exactly how it happens; this timeline is basically the sum of all the information we have shown so far. Use the slider to step through time.
The Effects Of Changing Political Priorities
age People's Ultimate Political Tendencies
This detailed portrait of the electorate jives well with the actual exit poll numbers from the last few Presidential elections. The New York Times has collected this data and present it very well, if you have time to take a look. Here's the part that concerns us: To wind up this section, I'd like to take one last look at our political plane, with a final set of overlays that I think are most illuminating: The polygons I've drawn over the dots are called convex hulls; they are a geometric way to measure the spread of a set of points. In this case, the hulls tell us the size of the ideological/age base of our political party.As you can see, the Democrat's base is much larger. And the range of political values it encompasses is vast. Here's party-to-party comparison in tablet form, for easy digestion:
Unlike in many things, size here is a liability. Yes, a political party that's this wide-open is probably a more intellectually stimulating organizationideological size is a liability to be a part of, and it has a lot more potential power. But bigger base is also just that many more competing viewpoints Democratic politicians must cater to and that many more different viewpoints in play among the actual elected officials themselves.Also, well over half of the Democratic party's hull lies outside of its upper-right-hand ideological home, implying that you've got many groups of people who might tend Democratic, but who have disagreements with the party on particular issues and could defect, should the slant of the party or the country tilt the wrong way. On the other hand, the Republicans are concentrated in the lower-left-hand corner. This red cluster has multiple, apparently self-reinforcing, reasons to vote with their party, giving the Republicans both a more fervent power base and a little more ideological wiggle-room along either the social or economic axis.
So when you read about the thousands of Catholic nuns who recently came out in favor of health care reform, it's easy to get excited about being a Democrat. But do you think those same people will side with us on things like gay marriage? Or abortion rights? Hull no!
. . .
That's the crux of the problem: Republicans cohere, Democrats don't. After the above mathematical dissection of the political plane, let's take our conclusion in hand and see how it plays with other dating data we have.Issues, Matching, and Politics
This whole Republican/Democrat situation reminds me (as it surely reminds you) I think of Mamluks sometimesof when Napoleon and his few French divisions dispersed the vast Mamluk horde by the banks of the Nile. Like an army, a political party must be coherent and disciplined to be effective, and these qualities alone can carry the day, even against greater numbers.Let's look at ideological distributions on a few hot-button issues and see how the Democrats are spread out and exposed. We'll start with views on abortion. This chart shows the opinions of social conservatives and social liberals. Everything is as you'd expect: liberals are pro-choice; conservatives pro-life.
Now let's look at how economic liberals and conservatives view abortion: Again, the conservatives are strongly pro-life. But the economic liberals have widely distributed views. A solid portion of the Democratic economic base actually sides with Republicans on this issue. It's those nuns again!While the two conservative curves are nearly congruent, the liberals ones are totally different. The takeaway, the Republican advantage, is this: economic conservatives and social conservatives agree, while the liberal halves of these spectra don't. Furthermore, the purple overlap—in a sense "the swing vote"—is largely on the conservative side!
We see same pattern repeated again and again. Here, for example, is a look at the 'Gay Marriage' issue:
. . .
Finally, I want to wrap up this burrito with a look at OkCupid's specialty: matching people up.Below are two matrices, showing person-to-person match percentages. These numbers are a measure of how well two people get along. We've used them to facilitate over 100,000 marriages in the last few years; their accuracy is well-tested. Match percentages range from 100 (awesome) to 0 (terrible), and the site average is 61.
We excluded explicitly political questions, ran numbers for the different ideologies, and found these patterns, which I'll leave you with:
As you can see, conservatives of both stripes get along with each other better than liberals do with themselves, even on non-political issues. We calculate match percentage by posing a series of questions to our users. Just to give you a sense of what these questions are like, here are the top three most important (by user vote):1. If you had to name your greatest motivation in life so far, what would it be?
I find groupthink frightening. But that fact that Democrats can't get together on some multiple-choice Q & A, speaks volumes about why they struggle with the infinite possibilities of government.- Love
- Wealth
- Expression
- Knowledge
- Passion
- Dedication
- Yes
- No
Last year, with help from my coworkers at Linkfluence, I created two sets of maps of the Perl and CPAN’s community. For this, I collected data from CPAN to create three maps :
Also, I won’t provide deep analysis for each of these graphs, as I lack insight about some of those communities. So feel free to re-use theses graphs and provide your own analyses.
For each profile, using the github API, I’ve tried to determine what the main language for this person is. And with the help of the geonames API, find the right country to attach the profile to.
Each profile is represented by a node. For each node, the following attributes are set:
As always, I’ve used Gephi (now in version 0.7) to create the graphs. Feel free to download the various graph files and use them with Gephi.
The first map is about all the languages available on github. This one was really heavy, with more than 17k nodes, and 130k edges. The final version of the graph use the 2270 more connected nodes.
You can’t miss Ruby on this map. As github uses Ruby on Rails, it’s not really surprising that the Ruby community has a particular interest on this website. The main languages on github are what we can expect, with PHP, Python, Perl, Javascript.
Some languages are not really well represented. We can assume that most Haskell projects might use darcs, and therefore are not on github. Some other languages may use other platforms, like launchpad, or sourceforge.
The Perl community is split into two parts. On the left side, there is the occidental community, driven by people like Florian, Yuval, rjbs, … The second part are the japanese Perl hackers, with Tokhuirom, Typester, Yappo, … And in between them, Miyagawa acts as a glue. This map looks a lot like the previous map of the CPAN. We can see that this community is international, with the exception of Japan that don’t mix with others.
There is no main project on github that gathers people, even though we can see a fair amount of MooseX:: projects. Most of the developers will work on different modules, that may not have the same purpose. Lately we have seen a fair amount of work on various Plack stuff, mainly middleware, but also HTTP servers (twiggy, starman, …) and web framework (dancer).
One important project that is not (deliberately) represented on this graph is the gitpan, Schwern’s project. The gitpan is an import of all the CPAN modules, with complete history using the Backpan.
To conclude about Perl, there are only 365 nodes on this graph, but no less than 4440 edges. That’s nearly two times the number of edges compared to the Python community. Perl is a really well structured community, probably thanks to the CPAN, which already acted as hub for contributors.
The Python community looks a lot like the Perl community, but only in the structure of the graph. If we look closely, Django is the main project that represent Python on Github, in contrast with Perl where there is no leader. Some small projects gather small community of developers.
PHP is the only community that is structured this way on Github. We can clearly see that people are structured based on a project where they mainly contribute.
CakePHP and Symphony are the two main projects. Nearly all the projects gather an international community, at the exception of a few japanese-only projects
As for the Github graph, we can clearly see that some countries are isolated. On the right side, we have: the Japan community is at the bottom; the Spanish at the top. Australian are represented on the upper right corner, while on the left side we got the Brazilians.
The main projects that gather most of the hackers are Rails and Sinatra, two famous web frameworks.
This one shows interesting features. Some countries are really isolated. If we look at Spain, we can see a community of Ruby programmers, with an important connectivity between them, but no really strong connection with any foreign developers. We can clearly see the Perl community exists as only one community, and is not split by country. The same is true for Python.
This community is unique on github. In 2007, Yappo created coderepos.org, a repository for open source developers in Japan. It was a subversion repository, with Trac as an HTTP front-end. It gathered around 900 developers, with all kind of projects (Perl, Python, Ruby, Javascript, …). Most of these users have switched to github now.
Three main communities are visible on this graph: Perl; Ruby; PHP. As always, the Javascript community as a glue between them. And yes, we can confirm that Perl is big in Japan.
We have seen in the previous graph that the Japanese hackers are always isolated. We can assume that their language is an obstacle.
This is a really well-connected graph too.
I would like to thanks everybody at Linkfluence (guilhem for his advices, camille for giving me time to work on this, and antonin for the amazing poster), who have helped me and let me use time and resources to finish this work. Special thanks to blob for reviewing my prose and cdlm for the discussion :)
- dependencies between distributions
- which authors were important in term of reliability
- and how the websites of theses authors are structured
This graph will be printed on a poster, size will be A2 and A1. Please, contact me (franck.cuny [at] linkfluence.net) if you will be interested by one.
all the graph are available on my flickr’s account.
- a graph of all languages
- a graph of the Perl community
- a graph of the Ruby community
- a graph of the Python community
- a graph of the PHP community
- a graph of the European community
- a graph of the Japan community
Also, I won’t provide deep analysis for each of these graphs, as I lack insight about some of those communities. So feel free to re-use theses graphs and provide your own analyses.
Methodology
I didn’t collect all the profiles. We (with Guilhem) decided to limit to peoples who are followed by at least two other people. We did the same thing for repositories, limiting to repositories which are at least forked once. Using this technique, more than 17k profiles have been collected, and nearly as many repositories.For each profile, using the github API, I’ve tried to determine what the main language for this person is. And with the help of the geonames API, find the right country to attach the profile to.
Each profile is represented by a node. For each node, the following attributes are set:
- name of the profile
- main language used by this profile, determined by github
- name of the country
- follower count
- following count
- repository count
As always, I’ve used Gephi (now in version 0.7) to create the graphs. Feel free to download the various graph files and use them with Gephi.
Github
properties of the graph: 16443 nodes / 130650 edges
The first map is about all the languages available on github. This one was really heavy, with more than 17k nodes, and 130k edges. The final version of the graph use the 2270 more connected nodes.
You can’t miss Ruby on this map. As github uses Ruby on Rails, it’s not really surprising that the Ruby community has a particular interest on this website. The main languages on github are what we can expect, with PHP, Python, Perl, Javascript.
Some languages are not really well represented. We can assume that most Haskell projects might use darcs, and therefore are not on github. Some other languages may use other platforms, like launchpad, or sourceforge.
Perl
properties of the graph: 365 nodes / 4440 edges
The Perl community is split into two parts. On the left side, there is the occidental community, driven by people like Florian, Yuval, rjbs, … The second part are the japanese Perl hackers, with Tokhuirom, Typester, Yappo, … And in between them, Miyagawa acts as a glue. This map looks a lot like the previous map of the CPAN. We can see that this community is international, with the exception of Japan that don’t mix with others.
There is no main project on github that gathers people, even though we can see a fair amount of MooseX:: projects. Most of the developers will work on different modules, that may not have the same purpose. Lately we have seen a fair amount of work on various Plack stuff, mainly middleware, but also HTTP servers (twiggy, starman, …) and web framework (dancer).
One important project that is not (deliberately) represented on this graph is the gitpan, Schwern’s project. The gitpan is an import of all the CPAN modules, with complete history using the Backpan.
To conclude about Perl, there are only 365 nodes on this graph, but no less than 4440 edges. That’s nearly two times the number of edges compared to the Python community. Perl is a really well structured community, probably thanks to the CPAN, which already acted as hub for contributors.
Python
properties of the graph: 532 nodes / 2566 edges
The Python community looks a lot like the Perl community, but only in the structure of the graph. If we look closely, Django is the main project that represent Python on Github, in contrast with Perl where there is no leader. Some small projects gather small community of developers.
PHP
properties of the graph: 301 nodes / 1071 edges
PHP is the only community that is structured this way on Github. We can clearly see that people are structured based on a project where they mainly contribute.
CakePHP and Symphony are the two main projects. Nearly all the projects gather an international community, at the exception of a few japanese-only projects
Ruby
properties of the graph: 3742 nodes / 24571 edges
As for the Github graph, we can clearly see that some countries are isolated. On the right side, we have: the Japan community is at the bottom; the Spanish at the top. Australian are represented on the upper right corner, while on the left side we got the Brazilians.
The main projects that gather most of the hackers are Rails and Sinatra, two famous web frameworks.
Europe
properties of the graph: 2711 nodes / 11259 edges
This one shows interesting features. Some countries are really isolated. If we look at Spain, we can see a community of Ruby programmers, with an important connectivity between them, but no really strong connection with any foreign developers. We can clearly see the Perl community exists as only one community, and is not split by country. The same is true for Python.
Japanese hackers community
properties of the graph: 559 nodes / 5276 edges
This community is unique on github. In 2007, Yappo created coderepos.org, a repository for open source developers in Japan. It was a subversion repository, with Trac as an HTTP front-end. It gathered around 900 developers, with all kind of projects (Perl, Python, Ruby, Javascript, …). Most of these users have switched to github now.
Three main communities are visible on this graph: Perl; Ruby; PHP. As always, the Javascript community as a glue between them. And yes, we can confirm that Perl is big in Japan.
We have seen in the previous graph that the Japanese hackers are always isolated. We can assume that their language is an obstacle.
This is a really well-connected graph too.
Conclusions and graphs
I may have not provided a deep analysis of all the graph. I don’t have knowledge of most of the community outside of Perl. Feel free to download the graph, to load them in Gephi, experiment, and provides your own thoughts.I would like to thanks everybody at Linkfluence (guilhem for his advices, camille for giving me time to work on this, and antonin for the amazing poster), who have helped me and let me use time and resources to finish this work. Special thanks to blob for reviewing my prose and cdlm for the discussion :)
Mar 28, 2010
Posted by Sandra Hanchard March 22, 2010
Earlier this month, Commonwealth Bank in conjunction with the University of Canberra launched Viewpoint - a report on the economic vitality of Australia. The report found that Gen Ys' spending habits were surprisingly resilient to the Global Financial Crisis - “Gen Y’s spending increased 6.2 per cent in 2009, despite earnings growing only 2.5 per cent and job losses in the age group rising 13.6 per cent.”
This insight corresponds broadly to a finding from our recent study on the “New vs. Returning” visitors to Shopping and Classifieds – Appliances and Electronics websites in the lead up to Christmas last year. Interestingly, 18-24 year olds were 44% more likely than the online average to be returning visitors to Appliances and Electronics websites in November 2009. Even though Gen Ys were amongst the hardest hit group by economic pressures, they were still the most engaged online researchers in one of the most popular online retail categories last year.
In comparison, users over 55 were the least likely to return to Appliances and Electronics websites. Given that 55+ users have high purchasing power, and are becoming a larger segment with Australia’s ageing population, retailers should be stepping up their efforts to market to this group and create compelling content that invites repeat visits.
We also looked at the broader Lifestyle Mosaic Groups and found that “Learners and Earners (students) and “Pushing the Boundaries” (young families) were the two household groups most likely to have repeat visits to Appliances and Electronics sites in November 2009. The strength of repeat visits by young families, in addition to students, suggests that households with tight disposable incomes may also be engaging in higher than usual comparison shopping activity.
March 24, 2010
Mobile carrier app stores are a long way from dead. Despite the fanfare around application stores tied to specific mobile devices such as iPhones and BlackBerries, a new Nielsen survey finds ongoing loyalty to carrier stores. As of the end of 2009, half of all applications users were accessing carrier app stores according to Nielsen’s new App Playbook.
That said, the Apple App Store was the clear leader in preferred application stores in the United States and, combined with the dedicated AT&T Application Store, devices running on the AT&T network have the most popular stores.
The relatively new BlackBerry App World Store was the second most popular app store due to BlackBerry’s industry-leading installed base.
Carrier application stores come in next on the rankings due to the size of their subscriber base, suggesting users of more standard feature phones drove much of this ongoing popularity.
Nielsen’s App Playbook surveys more than 4,000 application downloaders in the United States every six months about their mobile application usage. It allows companies in the mobile ecosphere to monitor the transition and growth of the application market as mobile users shift from feature phones to smartphones.
Not surprisingly smartphone users were generally using applications more than feature phone users – only 12% of feature phone owners have downloaded an app in the past 30 days while 46% of smartphone owners have — with application usage driven mostly by the iPhone and Android devices. This was due to the size of the application universe offered on both platforms, which was significantly larger than that of the other platforms. BlackBerry’s relatively low number was due to the significant corporate user share, which often locks the device and only allows corporate IT to install applications on the device.
Among recent acquirers, BlackBerry continued to be the market share leader among smartphones in the U.S., while Apple’s iPhone continued to increase its market share, especially after the launch of the iPhone 3GS in June 2009. The launch of a significant number of Android devices in the latter part of 2009, drove its market share to 5% at the end of the year and set it on a promising trajectory for 2010. Both Microsoft and Palm were unable to capitalize on their new launches in 2009 of Microsoft Windows Mobile 6.5 and the Palm Pre and Pixi, losing 7% market share and 4% market share respectively. Symbian, one of the leading smart phone operating systems globally continues to struggle to make significant inroads in the United States.
With only eight Android devices launched in 2009 – and the Droid launching late in the year – the Android Market Store saw modest popularity. Since then, a large number of Android devices launched, and the popularity of the Android Market Store is expected to increase as a result. This is further illustrated by the high satisfaction scores the Android Market received in the Nielsen study, second only to the Apple App Store. The Palm Application Store seems to be languishing due to the relatively modest success of the new Palm Pre and Palm Pixi, combined with a comparatively low number of applications available in its app store. The same is true for the Windows Marketplace, which is also suffering from a low number of applications and dwindling market share for Windows Mobile devices.
The Apple App Store and the Android Market Place have a sizable lead in terms of satisfaction compared to the other application stores. We see similar sized leads for both the Apple App Store and the Android Market Place when it comes to satisfaction with the pricing of the apps. While the often-overlooked carrier application stores are significantly behind the two leaders in terms of satisfaction, they are still marginally ahead of the BlackBerry App World and Windows Marketplace. It will be interesting to see how the just-announced revamp of the Windows Marketplace will impact satisfaction scores and what RIM will do in their next iteration of App World.
About Nielsen’s App Playbook
Insights from Nielsen’s App Playbook were gathered from 4,265 respondents who had downloaded an application in the past 30 days, 2,351 of which owned a smartphone and 1,914 of which owned a feature phone. The respondents were identified through Nielsen’s Mobile Insights syndicated tracking study, which surveys 80,000 mobile subscribers per quarter. The App Playbook sample is weighted back to the total qualified population from the Nielsen Mobile Insights survey. The survey covers a wide range of topics related to applications, including audience profiling, market sizing, download and purchase behavior, app store channel, app usage and satisfaction.
Insights from Nielsen’s App Playbook were gathered from 4,265 respondents who had downloaded an application in the past 30 days, 2,351 of which owned a smartphone and 1,914 of which owned a feature phone. The respondents were identified through Nielsen’s Mobile Insights syndicated tracking study, which surveys 80,000 mobile subscribers per quarter. The App Playbook sample is weighted back to the total qualified population from the Nielsen Mobile Insights survey. The survey covers a wide range of topics related to applications, including audience profiling, market sizing, download and purchase behavior, app store channel, app usage and satisfaction.
For more info: Contact The Nielsen Company
Roger Entner, Senior Vice President, Research and Insights, Telecom Practice
The iPhone, Blackberry, Droid and smartphones in general dominate the buzz in the mobile market, but only 21% of American wireless subscribers are using a smartphone as of the fourth quarter 2009 compared to 19% in Q3 2009 and 14% at the end of 2008. We are just at the beginning of a new wireless era where smartphones will become the standard device consumers will use to connect to friends, the internet and the world at large. The share of smartphones as a proportion of overall device sales has increased to 29% for phone purchasers in the last six months and 45% of respondents to a Nielsen survey indicated that their next device will be a smartphone. If we combine these intentional data points with falling prices and increasing capabilities of these devices along with a explosion of applications for devices, we are seeing the beginning of a groundswell. This increase will be so rapid, that by the end of 2011, Nielsen expects more smartphones in the U.S. market than feature phones.
The Smartphone User
Slightly more males than females are getting smartphones (53% versus 47%) which is what we would expect for technical early adopter products. In terms of demographics, Hispanic Americans and Asians are slightly more likely to have a smartphone than what their share of population would indicate, which is a trend we see in the adoption of other mobile data services. While smartphones started out in the business segment, two-third of today’s buyers of smartphones are personal users.
Slightly more males than females are getting smartphones (53% versus 47%) which is what we would expect for technical early adopter products. In terms of demographics, Hispanic Americans and Asians are slightly more likely to have a smartphone than what their share of population would indicate, which is a trend we see in the adoption of other mobile data services. While smartphones started out in the business segment, two-third of today’s buyers of smartphones are personal users.
Loyalty
In the last six months, roughly 77% of new smartphone buyers remained loyal to their wireless operator, while 18% switched to a new provider to get their new smartphone with the remaining percentage made up of first-time smartphone buyers. Interestingly enough, the percentage of people who switched carriers and got a new smartphone is not higher than that of the average wireless subscriber.
In the last six months, roughly 77% of new smartphone buyers remained loyal to their wireless operator, while 18% switched to a new provider to get their new smartphone with the remaining percentage made up of first-time smartphone buyers. Interestingly enough, the percentage of people who switched carriers and got a new smartphone is not higher than that of the average wireless subscriber.
This indicates that the portfolio of the wireless carriers in general is robust enough to prevent any wide-spread smartphone flight from one carrier to the other, with very few exceptions. The added bonus for wireless carriers is that smartphone owners are significantly more satisfied (81%) with their device than feature phone owners (66%).
Features, features, features
Smartphones show higher application usage than feature phones even at the basic built-in application level. During Nielsen’s Mobile Insights survey we asked the respondents about features they’ve used in the last 30 days. The good news for the smartphone market is that people are actually taking advantage of the device capabilities.
Smartphones show higher application usage than feature phones even at the basic built-in application level. During Nielsen’s Mobile Insights survey we asked the respondents about features they’ve used in the last 30 days. The good news for the smartphone market is that people are actually taking advantage of the device capabilities.
The percentage of people who use their phone for only voice communications drops from 14% among new feature phone owners to 3% of smartphone owners. The use of the built-in camera and video capability jumps by almost 20% for both categories, due to the generally better quality and user friendliness of the features. Smartphones also often have a better speaker which translates into more frequent usage from about half of feature phone owners to about two-thirds of smartphone owners. Not surprisingly the use of Wi-Fi increases 10-fold from 5% for feature phone owners to 50% for smartphone users to satisfy the need for fast downloads.
Posted by Dr. Pete on March 24th, 2010 at 7:28 pm Analytics
The history of web analytics has read a bit like the quest for the Holy Grail. We've gone through a list of candidates searching for the one true metric: Hits, Page Views, Visitors, Unique Visitors... stopping at each one to admire its purity and virtue while denouncing the heresy of whatever metric it replaced (usually, one whose purity and virtue we were just praising the week before).
While drinking from the wrong Grail in analytics won't melt your face like the bad guy in Indiana Jones 3, you may wish for some face-melting when you have to tell your boss how much money your bad conclusions just cost the company. This post will help you get control of your unhealthy obsession with Conversion Rate and avoid the most costly traps.
While drinking from the wrong Grail in analytics won't melt your face like the bad guy in Indiana Jones 3, you may wish for some face-melting when you have to tell your boss how much money your bad conclusions just cost the company. This post will help you get control of your unhealthy obsession with Conversion Rate and avoid the most costly traps.
Conversion Rate Crash Course
Let's start with some basics, both for the newcomers and because the industry doesn't always agree on how to define terms:
There are many variations on conversion rate, and "Action" can mean just about anything – a click, a form submission, an RSS subscription, an actual sale – but let's keep it simple for now. So, let's say that for February your site received 10,000 visitors, and 450 of them took action:
Pretty simple, right? Don't get me wrong – conversion rate is powerful, and it captures an important bottom-line measurement. Problem is, it's just one number (well, ultimately, two numbers). So, what's missing? To answer that question, I'd like you to consider three scenarios...
Scenario 1 – Sacrificing Traffic
This is a situation that comes up frequently in PPC management – cutting traffic to raise your conversion rate. Here are a few examples to illustrate the point:
All three of these cases have 5% CR, so they're all the same, right? Of course not - all else being equal, anyone in their right mind would pick (C). Where people get into trouble is when they over-optimize for CR at the expense of traffic.
For example, let's say you have a classic PPC scenario: (A) a campaign targeting branded keywords with low traffic and high CR, and (B) a campaign targeting product keywords with high traffic and low CR. Your client starts complaining about low CR, so what do you do? You cut spending in Campaign (B). CR goes up, but the unfortunate side effect is that traffic goes down and overall Actions (read that "sales") go down with it.
SOLUTION:
Pay attention to both conversion rate and overall leads or visitors. Once you collapse down to CR, you've lost the top and bottom numbers and are left with just a ratio. If you're a PPC manager, set an acceptable Cost-Per-Action (CPA). Traffic within your CPA limit may be worth going after, even if CR isn't ideal – traffic that costs more than your acceptable CPA may have to be sacrificed. Don't just start chopping visitors to see CR go up.
Pay attention to both conversion rate and overall leads or visitors. Once you collapse down to CR, you've lost the top and bottom numbers and are left with just a ratio. If you're a PPC manager, set an acceptable Cost-Per-Action (CPA). Traffic within your CPA limit may be worth going after, even if CR isn't ideal – traffic that costs more than your acceptable CPA may have to be sacrificed. Don't just start chopping visitors to see CR go up.
Scenario 2 – Dropping Prices
Want the secret to increasing conversion? Cut your prices in half. What's that? You say you'll make a lot less money that way? Yes, you probably will. Of course, you'd never do anything that radical, but many people create sales, price pressures, and information architectures that drive people to the cheapest product. This can boost CR but cost you money.
Let's look at an example – say you get 1,000 visitors per day, and experiment with pushing a cheaper product ($29) over a more expensive product ($99) to boost CR:
Looking at the CR, it's great news: you doubled conversion. Unfortunately, your revenue also dropped 40%. There may be times when you're willing to make this trade-off to draw in new customers, but make sure you have all of the information you need to make that business decision.
SOLUTION:
If you make a change that could drive visitors to lower-priced items, make sure you track not only CR but also changes in the average purchase amount. If you're running an A/B testing scenario, consider tracking the mean or median purchase for both groups (use the median if your products span a wide price-range).
If you make a change that could drive visitors to lower-priced items, make sure you track not only CR but also changes in the average purchase amount. If you're running an A/B testing scenario, consider tracking the mean or median purchase for both groups (use the median if your products span a wide price-range).
Scenario 3 – Losing Loyalty
An aggressive push to drive short-term conversion, including the pricing scenario above, could also lead to a drop in long-term revenue and customer loyalty. If you offer a sweetheart deal that pulls in new customers, it's possible that they'll take advantage of that deal and disappear forever. Today's Conversion Rate gain, if it's driven by bargain hunters or impulse buyers, could be next month's Conversion disaster.
That's not to say that sales and short-term incentives are never a good idea. Driving traffic in the front door is essential to building long-term relationships. The core point is that, whenever you take an action that may change the quality of your customers (and not just the quantity), you need to look at the big picture.
SOLUTION:
These metrics are a bit beyond the scope of this post, but there are a number of Key Performance Indicators built around repeat buying and the lifetime value of a customer. Whenever you pursue a short-term strategy, don't just measure CR, measure whether those new buyers are one-hit wonders or have real staying power.
These metrics are a bit beyond the scope of this post, but there are a number of Key Performance Indicators built around repeat buying and the lifetime value of a customer. Whenever you pursue a short-term strategy, don't just measure CR, measure whether those new buyers are one-hit wonders or have real staying power.
It's Still Pretty Good
I don't want to sound like I'm bashing Conversion Rate. I use it every day and have driven real, bottom-line improvements for clients based on CR metrics. We just have to remember to never get so enamored with one metric that we neglect the big picture. Every web metric that has ever existed or ever will exist is missing some critical piece of information for some set of situations and has the potential to lead us astray. Think about your objectives, think about the possible outcomes, and most of all, think about all of the analytics tools you need to see that big picture.
By thomascrampton ⋅ March 26, 2010 ⋅ Post a comment
Here’s some of those who attended the third of the Shanghai Tweetups, which took place this Wednesday evening. Thanks very much to everyone who joined!
Marcella Szablewicz: She’s a PhD Candidate in Communication & Rhetoric at Rensselaer Polytechnic Institute. She’s currently studying the Chinese gaming community, and the cleavages within it. Educated at Duke and Georgetown university, she is currently working for the China Institute. Her Twitter handle is @mszabs and find her on LinkedIn here, and on Facebook here.
Wang Lili: She’s a Chinese author who blogs at recordsoftoday.com. She’s written about her early life as a migrant worker and her work has been heralded in the Peoples Daily, and other international periodicals. Now working as an author and journalist in China, her first two books My Tears Won’t Fall and You Are Far Away are well known in China. If you want to add her on twitter she’s @wanglilinovels. Her adventures in images are available on Flickr.
Sera Hill: Sera is the current director at Trepantech, an Internet applications company based in Shanghai. Here she works on iPhone apps and VPN services. She studied at McGill University and previously worked for Ericsson in Canada. Follow her on Twitter @Serpah and read her bio on LinkedIn.
Mark Evans: The technical director in Shanghai for the Joint US-China Collaboration on Clean Energy (@JUCCCE), Mark focuses on environmental topics, as well as food and social media. He graduated from Reed College, and has worked in a variety of professions including work at Trepantech in Shanghai. He runs his own food and personal blog at tastingandcomplaining.com. In China Mark is now reduced to the ‘point, smile and pray’ method of acquiring food. To read more about Mark view his LinkedIn profile, and follow him on Twitter @Mark_E_Evans
Tony Luce: Tony works at Trepantech with Sera and has worked on iPhone apps and the vpNinja VPN. He’s an expat in Shanghai and a prolific tweeter. His Twitter is @tonyluce.
Andy Miller: Andy is a biking enthusiast currently based in Shanghai who specializes in youth marketing. He has a blog at pitythecool.com. He’s traveled the world in his pajamas and contributed his musing to FHM and other magazines. Say hi on Twitter @andyjmiller.
Patrick Searle: Patrick is a Business Development manager at CIC Data. He’s currently based in Shanghai and studied at the University of London for a degree in Chinese and Oriental studies. CIC Data is a Chinese based Internet research and consultancy firm, there Patrick is involved in business development in China’s marketing and social media sector. See all his attributes on LinkedIn, and drop him a line @PatrickSearle .
Kai Lukoff: Kai works at Bloggerinsight.com where he’s an analyst of Chinese social media and social gaming. Originally from California he studied at Stanford and has some basic Mandarin skills. Bloggerinsight provides companies interested in the Chinese market with insights from mainland bloggers. He also blogs atChinasocialgames.com and is a co-founder of Chinese group buying, group discounts website Cooltuan.com. His Twitter handle is @klukoff, and Chinasocialgames also twitters @CNSocialGames. Get details on Kai career on LinkedIn.
Emile MacGillavry: Based in Shanghai he’s worked with Maximum marketing in China and the Netherlands for 10 years. At Maximum he’s a managing director working on employee matters and managing clients in China. Maximum is a marketing and advertisement firm originally from the Netherlands. Two of Emile’s associates joined him at the tweetup but sadly they’re details were hard to hear in the video I recorded of attendees. Follow him on Twitter @MacGillavry and take a look at his LinkedIn profile here.
Jeffery Clark: Clark is a fashion specialist based in Shanghai. He runs his own fashion and personal blog atjeffreyliving.wordpress.com. According to his bio he’s been involved in the Fashion industry since the 80’s and founded SourceTheGlobe.com in 2008, a website allowing fashion designers to scour a database of global clothing providers to provide efficient supply chain management. Clark is from Canada but has been involved in Asian fashion and clothing sourcing for awhile now. He’s on Twitter @fashionsourcing
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