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Boston Blogs Network

Monthly Uniques
176.9K US 182.1K Global

universalhub.com

176.0K US 181.0K Global
  •  
  • Quantified

    Directly Measured Data

All Boston, all the time.

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US Demographics:   [ Web ]


Gender

Largest Group and Index
Male


Segment This Site vs. US Average IndexMultiple
Male
59% 
49% US Average
 
1201.2x
Female
41% 
51% US Average
  800.8x
US Average
Composition
 
Male 59%
 
Female 41%

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Age

Largest Group and Index
25-34


Segment This Site vs. US Average IndexMultiple
< 18
7% 
18% US Average
  380.38x
18-24
11% 
13% US Average
  880.88x
25-34
32% 
17% US Average
 
1881.88x
35-44
24% 
19% US Average
 
1281.28x
45-54
17% 
17% US Average
  990.99x
55-64
5% 
10% US Average
  510.51x
65+
3% 
6% US Average
  520.52x
US Average
Composition
 
< 18 7%
 
18-24 11%
 
25-34 32%
 
35-44 24%
 
45-54 17%
 
55-64 5%
 
65+ 3%

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Children in Household

Largest Group and Index
No Kids


Segment This Site vs. US Average IndexMultiple
No Kids
68% 
51% US Average
 
1351.35x
Has Kids
32% 
49% US Average
  640.64x
US Average
Composition
 
No Kids 68%
 
Has Kids 32%

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Household Income

Largest Group
$50-100k
Highest Index
$150k+


Segment This Site vs. US Average IndexMultiple
$0-50k
35% 
51% US Average
  690.69x
$50-100k
35% 
29% US Average
 
1201.2x
$100-150k
15% 
12% US Average
 
1271.27x
$150k+
15% 
8% US Average
 
1771.77x
US Average
Composition
 
$0-50k 35%
 
$50-100k 35%
 
$100-150k 15%
 
$150k+ 15%

Embed

Education Level

Largest Group
College
Highest Index
Grad School


Segment This Site vs. US Average IndexMultiple
No College
17% 
44% US Average
  390.39x
College
50% 
41% US Average
 
1241.24x
Grad School
32% 
14% US Average
2232.23x
US Average
Composition
 
No College 17%
 
College 50%
 
Grad School 32%

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Ethnicity

Largest Group and Index
Caucasian


Segment This Site vs. US Average IndexMultiple
Caucasian
84% 
76% US Average
 
1111.11x
African American
6% 
9% US Average
  660.66x
Asian
4% 
4% US Average
  930.93x
Hispanic
5% 
10% US Average
  490.49x
Other
1% 
1% US Average
  920.92x
US Average
Composition
 
Caucasian 84%
 
African American 6%
 
Asian 4%
 
Hispanic 5%
 
Other 1%

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Political Affiliation

Political Affiliation

This new metric describes the political affiliation of a web property's US audience. Independent includes anyone who identifies as Independent, or is unaffiliated with any party.

Largest Group and Index
Democrat


Segment This Site vs. US Average IndexMultiple
Republican
10% 
24% US Average
  410.41x
Democrat
48% 
30% US Average
 
1601.6x
Independent
42% 
46% US Average
  920.92x
US Average
Composition
 
Republican 10%
 
Democrat 48%
 
Independent 42%

Embed

Political Engagement

Political Engagement

This new metric describes the degree of political engagement for a web property's US audience.

Highest Index
Active
Largest Group
Somewhat Active


Segment This Site vs. US Average IndexMultiple
Active
34% 
24% US Average
 
1411.41x
Somewhat Active
47% 
48% US Average
  990.99x
Inactive
19% 
28% US Average
  670.67x
US Average
Composition
 
Active 34%
 
Somewhat Active 47%
 
Inactive 19%

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Updated Mar 22, 2015 • Next: Apr 1, 2015 by 9AM PDT

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Getting Quantified

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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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