Iwin.com Traffic and Demographic Statistics by Quantcast

 

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Rankings

iWin Network

Monthly Uniques
1.1M US 1.7M Global

iwin.com

703.8K US 1.1M Global
  •  
  • Quantified

    Directly Measured Data

iWin.com is a casual game site that offers over 550 downloadable games. iWin.com is the largest provider of free ad supported download games with over 115 ad supported games.


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US Demographics:   [ Web ]


Gender

Embed
segment this site vs. US average indexmultiple
Male
42% 
49% US average
  860.86x
Female
58% 
51% US average
 
1131.13x
US average
composition
 
Male 42%
 
Female 58%

}

Gender

Female



Age

Embed
segment this site vs. US average indexmultiple
< 18
23% 
18% US average
 
1271.27x
18-24
11% 
13% US average
  840.84x
25-34
13% 
17% US average
  770.77x
35-44
17% 
19% US average
  910.91x
45-54
18% 
17% US average
 
1051.05x
55-64
11% 
10% US average
 
1111.11x
65+
6% 
6% US average
 
1171.17x
US average
composition
 
< 18 23%
 
18-24 11%
 
25-34 13%
 
35-44 17%
 
45-54 18%
 
55-64 11%
 
65+ 6%

}

Age

< 18



Age

Embed
segment this site vs. US average indexmultiple
Male < 18
10% 
9% US average
 
1041.04x
Male 18-24
4% 
7% US average
  690.69x
Male 25-34
6% 
9% US average
  630.63x
Male 35-44
7% 
10% US average
  750.75x
Male 45-54
8% 
9% US average
  880.88x
Male 55-64
5% 
5% US average
  930.93x
Male 65+
3% 
2% US average
 
1151.15x
US average
composition
 
Male < 18 10%
 
Male 18-24 4%
 
Male 25-34 6%
 
Male 35-44 7%
 
Male 45-54 8%
 
Male 55-64 5%
 
Male 65+ 3%

}

Age

Male Older



Age

Embed
segment this site vs. US average indexmultiple
Female < 18
13% 
9% US average
 
1511.51x
Female 18-24
6% 
6% US average
 
1011.01x
Female 25-34
8% 
8% US average
  910.91x
Female 35-44
10% 
9% US average
 
1071.07x
Female 45-54
11% 
9% US average
 
1221.22x
Female 55-64
7% 
5% US average
 
1271.27x
Female 65+
4% 
3% US average
 
1181.18x
US average
composition
 
Female < 18 13%
 
Female 18-24 6%
 
Female 25-34 8%
 
Female 35-44 10%
 
Female 45-54 11%
 
Female 55-64 7%
 
Female 65+ 4%

}

Age

Female < 18



Children in Household

Embed
segment this site vs. US average indexmultiple
No Kids
48% 
50% US average
  960.96x
Has Kids
52% 
50% US average
 
1051.05x
US average
composition
 
No Kids 48%
 
Has Kids 52%

}

Children in Household

Has Kids in Household



Household Income

Embed
segment this site vs. US average indexmultiple
$0-50k
51% 
50% US average
 
1011.01x
$50-100k
29% 
29% US average
 
1001.0x
$100-150k
12% 
12% US average
  950.95x
$150k+
9% 
8% US average
 
1021.02x
US average
composition
 
$0-50k 51%
 
$50-100k 29%
 
$100-150k 12%
 
$150k+ 9%

}

Household Income

More Affluent



Education Level

Embed
segment this site vs. US average indexmultiple
No College
48% 
45% US average
 
1071.07x
College
39% 
41% US average
  960.96x
Grad School
13% 
14% US average
  910.91x
US average
composition
 
No College 48%
 
College 39%
 
Grad School 13%

}

Education Level

People with No College



Ethnicity

Embed
segment this site vs. US average indexmultiple
Caucasian
79% 
76% US average
 
1041.04x
African American
8% 
9% US average
  920.92x
Asian
3% 
4% US average
  790.79x
Hispanic
8% 
9% US average
  840.84x
Other
1% 
1% US average
  980.98x
US average
composition
 
Caucasian 79%
 
African American 8%
 
Asian 3%
 
Hispanic 8%
 
Other 1%

}

Ethnicity

Caucasian



Updated Apr 12, 2014 • Next: Apr 23, 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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