Nationaloutdoors.net Traffic and Demographic Statistics by Quantcast

 

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Rankings

Single Track Trading Network

Monthly Uniques
4.7K US 7.6K Global

nationaloutdoors.net

4.6K US 7.5K Global
  •  
  • Quantified

    Directly Measured Data

National Outdoors provides its visitors with a unique outdoor gear reviews, a one-of-kind map based outdoor recreation area search tool, an outdoor website directory, a price comparison shopping engine, a continually expanding collection of articles about outdoor pursuits, and a regionally focused series of articles about outdoor destinations throughout the country.


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US Demographics:   [ Web ]


Gender

Embed
segment this site vs. US average indexmultiple
Male
62% 
49% US average
 
1261.26x
Female
38% 
51% US average
  750.75x
US average
composition
 
Male 62%
 
Female 38%

}

Gender

Male



Age

Embed
segment this site vs. US average indexmultiple
< 18
13% 
18% US average
  690.69x
18-24
11% 
12% US average
  890.89x
25-34
17% 
17% US average
  970.97x
35-44
21% 
19% US average
 
1101.1x
45-54
20% 
17% US average
 
1141.14x
55-64
12% 
10% US average
 
1231.23x
65+
6% 
6% US average
 
1141.14x
US average
composition
 
< 18 13%
 
18-24 11%
 
25-34 17%
 
35-44 21%
 
45-54 20%
 
55-64 12%
 
65+ 6%

}

Age

Older



Age

Embed
segment this site vs. US average indexmultiple
Male < 18
7% 
9% US average
  780.78x
Male 18-24
7% 
6% US average
 
1061.06x
Male 25-34
10% 
9% US average
 
1161.16x
Male 35-44
13% 
10% US average
 
1341.34x
Male 45-54
12% 
9% US average
 
1411.41x
Male 55-64
8% 
5% US average
 
1571.57x
Male 65+
4% 
2% US average
 
1701.7x
US average
composition
 
Male < 18 7%
 
Male 18-24 7%
 
Male 25-34 10%
 
Male 35-44 13%
 
Male 45-54 12%
 
Male 55-64 8%
 
Male 65+ 4%

}

Age

Male Older



Age

Embed
segment this site vs. US average indexmultiple
Female < 18
5% 
9% US average
  590.59x
Female 18-24
4% 
6% US average
  700.7x
Female 25-34
6% 
8% US average
  770.77x
Female 35-44
8% 
9% US average
  860.86x
Female 45-54
8% 
9% US average
  870.87x
Female 55-64
5% 
5% US average
  910.91x
Female 65+
2% 
3% US average
  720.72x
US average
composition
 
Female < 18 5%
 
Female 18-24 4%
 
Female 25-34 6%
 
Female 35-44 8%
 
Female 45-54 8%
 
Female 55-64 5%
 
Female 65+ 2%

}

Age

Female Older



Children in Household

Embed
segment this site vs. US average indexmultiple
No Kids
61% 
51% US average
 
1201.2x
Has Kids
39% 
49% US average
  790.79x
US average
composition
 
No Kids 61%
 
Has Kids 39%

}

Children in Household

No Kids in Household



Household Income

Embed
segment this site vs. US average indexmultiple
$0-50k
44% 
51% US average
  870.87x
$50-100k
33% 
29% US average
 
1131.13x
$100-150k
14% 
12% US average
 
1141.14x
$150k+
9% 
8% US average
 
1091.09x
US average
composition
 
$0-50k 44%
 
$50-100k 33%
 
$100-150k 14%
 
$150k+ 9%

}

Household Income

Affluent



Education Level

Embed
segment this site vs. US average indexmultiple
No College
38% 
45% US average
  860.86x
College
46% 
41% US average
 
1141.14x
Grad School
15% 
14% US average
 
1061.06x
US average
composition
 
No College 38%
 
College 46%
 
Grad School 15%

}

Education Level

College Graduates



Ethnicity

Embed
segment this site vs. US average indexmultiple
Caucasian
83% 
76% US average
 
1101.1x
African American
5% 
9% US average
  530.53x
Asian
4% 
4% US average
  850.85x
Hispanic
7% 
9% US average
  720.72x
Other
1% 
1% US average
  900.9x
US average
composition
 
Caucasian 83%
 
African American 5%
 
Asian 4%
 
Hispanic 7%
 
Other 1%

}

Ethnicity

Caucasian



Updated Apr 23, 2014 • Next: Apr 30, 2014 by 9AM PDT

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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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