Nydailynews.com Traffic and Demographic Statistics by Quantcast

 

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Rankings

nydailynews.com network

Monthly Uniques
24.1M US 30.7M Global

nydailynews.com

24.0M US 30.5M Global
  •  

  • Quantified

    Directly Measured Data

Online edition. [Description from dmoz]

This daily news might not always be fit to print, but it at least keeps New Yorkers entertained. Daily News, L.P. publishes the Daily News, the big city tabloid that goes toe-to-toe with the New York Post (owned by Rupert Murdoch's News Corp.) by penning over-the-top headlines and sensational stories. The paper, founded in 1919, is distributed primarily in the New York City area and boasts a circulation of about 700,000. It also publishes stories and features, as well as classified ads, online through its Web site. Real estate magnate Mortimer Zuckerman, who also owns news magazine U.S. News & World Report, and Fred Drasner bought the paper in 1993. [Description from Hoover's]

This site reaches over 26 million monthly people, of which 20 million (76%) are in the U.S.The typical visitor plays games on king.com, frequents addictinggames.com, and visits phoenix.edu.


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Countries

The volume of traffic originating from individual countries in a 30 day period. Indexes are calculated by comparing the percentage of a site's traffic from a given country to the pattern of all Internet traffic measured by Quantcast - e.g, an index of 500 indicates that the site gets five times as much of its traffic from the given country than the average Internet site.

Country Uniques (Cookies) Uniques % Uniques Index Impressions % Impressions Index
Belize 1,886 - 147 - 116
Grenada 1,866 - 274 - 158
Nicaragua 1,852 - 25 - 21
Bolivia 1,812 - 12 - 11
Saint Lucia 1,783 - 215 - 118
Syrian Arab Republic 1,655 - 22 - 15
Isle of Man 1,617 - 91 - 50
Suriname 1,600 - 123 - 52
Jersey 1,550 - 101 - 30
Angola 1,543 - 39 - 39
Malawi 1,525 - 84 - 146
Palestinian Territories 1,461 - 13 - 5
Maldives 1,440 - 89 - 31
Mozambique 1,394 - 42 - 31
Saint Vincent and the Grenadines 1,377 - 157 - 141
Montenegro 1,327 - 42 - 12
Updated Mar 2014 • Next: Apr 2014

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People from Sites & Syndicators

These percentages usually sum greater than 100% due to overlap in site and syndicated audiences.

Reading Demographic Graphs

1. Index

This compares audience composition of the site or mobile app to each platform population. The higher the index number, the more concentrated the property is in a particular demographic.

As an example, if a property indexes 100 for age 18-24, that means a given visitor to it is as likely to be 18-24 as any internet user chosen at random. An index of 200 means the visitor is twice as likely to be 18-24, 50 means half as likely, and so on.

2. Segments are represented with icons. Segments include gender, age, household income, and education.

3. Very High Indexes (over 200) are denoted with a plus symbol.

4. Internet Average is represented by the dotted vertical line.



Reading Demographic Graphs

This compares audience composition of the site or mobile app to each platform population. The higher the index number, the more concentrated the property is in a particular demographic.

As an example, if a property indexes 100 for age 18-24, that means a given visitor to it is as likely to be 18-24 as any internet user chosen at random. An index of 200 means the visitor is twice as likely to be 18-24, 50 means half as likely, and so on.

1. Segment refers to the demographic composition attribute.

2. Very High Indexes (over 200) are denoted with a plus symbol.

3. Internet Average is represented by the dotted vertical line.

4. Expand the data to see the numbers which make up the index calculation.


The expanded view shows the percentage composition, the Internet average and the multiple.

1. A Colored Bar indicates that a segment exceeds the Internet average, whereas a gray bar indicates the segment is below the Internet average. Internet average is represented by the dotted vertical line.

2. A Multiple is the percentage of the segment on this property divided by the average of the same segment on the entire Internet.

Example:
80% female segment on property ÷ 32% female internet average = 2.5x


This chart breaks down the property's audience for a demographic. All the segments collectively equal 100%.

As an example, if a property indexes 100 for age 18-24, that means a given visitor to it is as likely to be 18-24 as any internet user chosen at random. An index of 200 means the visitor is twice as likely to be 18-24, 50 means half as likely, and so on.

1. The Top-Indexing Segment is shown in color.



Understanding User Retention

This graph examines user retention patterns for a mobile app, which tells the story of how much of app's user base continues to use the app after installation over time.

1. The x-axis is comprised of cohorts based on when users installed the app. For example, if we look at the column "+3 Days", this means that regardless of whether users installed the app a week ago or a month ago, what ratio of these users have returned within three days after installation.

2. The gray bars indicate the average retention rate across all days the app was downloaded.

3. The yellow line represents the average retention rate by period of all apps measured by Quantcast.

4. Install grouping details can be found by clicking on the down arrow.

In the expanded view, each row shows the retention patterns based on a point in time. Click on each row to compare that cohort against the average of all users installing the app.

1. The average day row shows the general retention rate for the entire app.

2. The highlighted row shows the retention rate compared against the average. In this example, 29% of users who installed the app one month ago returned at some point within two days, compared to the average of 35%.

3. The Add Date button allows you to add custom dates to determine retention patterns.

4. The Close button collapses the details and returns you to the default view.



Understanding Visit Frequency

This chart shows the number of return visits for unique users over the last 30 days.

1. Toggle between visit patterns of Logged In and Non Logged In users. In order to enable the toggle, the publisher must designate that the app has a logged in user base. The Logged In number represents the visit frequency of users that have logged in order to use this app.

3. For example, over the last 30 days, 3,644 unique users visited 4-7 times.


Understanding Return Usage of Logged in Users

Digital brand offerings span across many device types and media channels. Quantcast allows brands to measure mobile web, online and app traffic. This feature allows a network property to demonstrate how logged in users migrate between these various platforms.

1. First Platform and First Cohort allow you to isolate a platform — for example mobile apps — and examine how users that start on mobile return over time to online, mobile web, apps or all of these platforms. The first cohort time range is for selecting a group of users you would like to track — for instance all users first seen within the course of a particular week an online ad campaign. Once defined, you can explore how this defined group of users returned, by platform, over time. A first-seen cohort may span up to 30 days.

2. Display Options allows you to choose available platforms to show return visits. More than one platform means that the logged in user returned on more than one platform (such as mobile apps and online) within the time period viewed. You can also select the amount of data points to review — 30, 60, or 90 days worth.

1. Bundled advertising inventory
Media properties often bundle together advertising inventory across platforms into a single package for their clients. By showing that customers are continuously engaged across multiple platforms, networks can demonstrate what packaging options make the most sense in all of the contexts and formats these platforms provide.

2. Measure efforts to migrate an audience from one platform to another
This feature is a great way to isolate marketing efforts made to drive usage from one platform to another by looking at historical changes in platform adoption.

3. Compare return usages nuances between platforms
Understand the nuances of usage pattern of customers on a particular platforms for product development decisions.



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