Rvt.com Traffic and Demographic Statistics by Quantcast

 

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Rankings

RVT.com RV Classifieds Network

Monthly Uniques
362.9K US 424.5K Global

rvt.com

288.5K US 336.4K Global
  •  
  • Quantified

    Directly Measured Data

Find or Sell New or Used RV, Travel Trailers, Motorhomes, Diesel Pusher, Motorcoaches, Class A, C, B, Fifth Wheels, Toy Haulers, Pop-up Campers, Tent Trailers, and More. For sale by owner or dealer. Online RV Trader Classifieds, RVT.com (formerly RVTrader.com) is USA and Canada's Best Selling Online Classifieds for RV Buyers and Sellers, since 1999.


Related Links

US Demographics:   [ Web ]


Gender

Embed
segment this site vs. US average indexmultiple
Male
56% 
49% US average
 
1151.15x
Female
44% 
51% US average
  860.86x
US average
composition
 
Male 56%
 
Female 44%

}

Gender

Male



Age

Embed
segment this site vs. US average indexmultiple
< 18
13% 
18% US average
  700.7x
18-24
9% 
13% US average
  680.68x
25-34
11% 
17% US average
  660.66x
35-44
20% 
19% US average
 
1051.05x
45-54
22% 
17% US average
 
1271.27x
55-64
17% 
10% US average
 
1661.66x
65+
9% 
6% US average
 
1551.55x
US average
composition
 
< 18 13%
 
18-24 9%
 
25-34 11%
 
35-44 20%
 
45-54 22%
 
55-64 17%
 
65+ 9%

}

Age

Older



Age

Embed
segment this site vs. US average indexmultiple
Male < 18
7% 
9% US average
  710.71x
Male 18-24
5% 
6% US average
  730.73x
Male 25-34
6% 
9% US average
  710.71x
Male 35-44
11% 
10% US average
 
1161.16x
Male 45-54
12% 
9% US average
 
1431.43x
Male 55-64
10% 
5% US average
 
1951.95x
Male 65+
5% 
2% US average
2172.17x
US average
composition
 
Male < 18 7%
 
Male 18-24 5%
 
Male 25-34 6%
 
Male 35-44 11%
 
Male 45-54 12%
 
Male 55-64 10%
 
Male 65+ 5%

}

Age

Male Older



Age

Embed
segment this site vs. US average indexmultiple
Female < 18
6% 
9% US average
  690.69x
Female 18-24
4% 
6% US average
  640.64x
Female 25-34
5% 
8% US average
  610.61x
Female 35-44
9% 
9% US average
  940.94x
Female 45-54
10% 
9% US average
 
1101.1x
Female 55-64
7% 
5% US average
 
1381.38x
Female 65+
3% 
3% US average
 
1091.09x
US average
composition
 
Female < 18 6%
 
Female 18-24 4%
 
Female 25-34 5%
 
Female 35-44 9%
 
Female 45-54 10%
 
Female 55-64 7%
 
Female 65+ 3%

}

Age

Female Older



Children in Household

Embed
segment this site vs. US average indexmultiple
No Kids
63% 
50% US average
 
1251.25x
Has Kids
37% 
50% US average
  750.75x
US average
composition
 
No Kids 63%
 
Has Kids 37%

}

Children in Household

No Kids in Household



Household Income

Embed
segment this site vs. US average indexmultiple
$0-50k
44% 
51% US average
  860.86x
$50-100k
36% 
29% US average
 
1221.22x
$100-150k
13% 
12% US average
 
1061.06x
$150k+
8% 
8% US average
  970.97x
US average
composition
 
$0-50k 44%
 
$50-100k 36%
 
$100-150k 13%
 
$150k+ 8%

}

Household Income

Middle Income



Education Level

Embed
segment this site vs. US average indexmultiple
No College
43% 
45% US average
  970.97x
College
43% 
41% US average
 
1061.06x
Grad School
13% 
14% US average
  920.92x
US average
composition
 
No College 43%
 
College 43%
 
Grad School 13%

}

Education Level

College Graduates



Ethnicity

Embed
segment this site vs. US average indexmultiple
Caucasian
89% 
76% US average
 
1181.18x
African American
4% 
9% US average
  400.4x
Asian
2% 
4% US average
  400.4x
Hispanic
4% 
10% US average
  430.43x
Other
1% 
1% US average
  830.83x
US average
composition
 
Caucasian 89%
 
African American 4%
 
Asian 2%
 
Hispanic 4%
 
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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