Tmz.com Traffic and Demographic Statistics by Quantcast

 

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Rankings

tmz.com

Monthly Uniques
20.4M US 26.0M Global
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  • Quantified

    Directly Measured Data

A telepictures production and America Online joint venture that provides an inside look at the latest celebrity scandals. [Description from dmoz]

This just in! TMZ.com is a titillating tabloid for scandalous celebutainment. Ever since the Web site broke Mel Gibson's DWI arrest in 2006, TMZ.com has led the way in no-holds-barred celebrity gossip. The site delivers the goods faster than the paparazzi can take pictures, with constant updates featuring photos and videos of celebs caught off-guard. The site launched in late 2005 and a half-hour syndicated TV gossip-news show debuted in 2007. TMZ.com is a joint venture of AOL and Telepictures Productions, a subsidiary of Time Warner. [Description from Hoover's]

This site reaches over 20 million monthly people, of which 15 million (77%) are in the U.S.The typical visitor shops at Dollar Tree Stores, frequents MySpace, and sails on Princess Cruises.


Related Links

US DMAs

The volume of traffic originating from individual US DMAs in a 30 day period. Indexes are calculated by comparing the percentage of a site's traffic from a given USA DMA 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 USA DMA than the average Internet site.

DMA Uniques (Cookies) Uniques % Uniques Index Impressions % Impressions Index
Ft Smh-Fy-Sp-Rg 30,850 0.14 80 0.13 73
Fargo-Vally Cty 30,720 0.14 92 0.15 82
Youngstown 30,710 0.14 104 0.12 96
Bakersfield 30,547 0.14 105 0.15 113
Augusta 30,533 0.14 94 0.13 86
Greenvl-NB-Wash 29,557 0.13 84 0.13 78
Macon 29,021 0.13 71 0.12 80
Lafayette LA 28,966 0.13 83 0.12 88
La Crss-Eau Clr 28,676 0.13 83 0.12 71
Tri-Cties TN-VA 28,595 0.13 81 0.12 74
Wilmington 28,566 0.13 110 0.12 105
Tyler-Longview 28,435 0.13 85 0.11 80
Pdch-Cg-Har-Mtv 28,193 0.13 66 0.12 63
Rockford 27,790 0.13 111 0.12 99
Montgomery(Sel) 26,778 0.12 97 0.12 98
Gainesville 26,335 0.12 126 0.10 88
Updated Apr 2014 • Next: May 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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