Mahapolice.gov.in Traffic and Demographic Statistics by Quantcast

 

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mahapolice.gov.in

Monthly Uniques
N/A US
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  • Not Quantified

    Data is estimated

We do not have enough information to provide a traffic estimate. When Quantified, this report will provide detailed, accurate audience information.


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Updated Apr 12, 2014 • Next: Apr 23, 2014 by 9AM PDT

Audience Also Likes

This list shows other sites an audience frequents, which can reveal brand preferences and other lifestyle traits. The Audience Also Likes section of a site's profile shows other sites that the audience is likely to visit, and the affinity indicates how much more likely than average. For example, if the profile for wsj.com listed barrons.com with an affinity of 10x, a randomly chosen visitor to wsj.com is ten times likelier to visit barrons.com than the average internet user.

Demographic Index

Index represents how a site's audience compares to the online internet population as a whole. An index of 100 indicates a site's audience is at parity with the total internet population.

Affinity

The affinity numbers represent how likely a given visitor is to visit one of the listed sites or categories compared to the internet average. For example, an affinity number of 10.2 would say that a user on "X" website is 10.2 times more likely than the average internet visitor to visit the other site or category that is provided.

Directly Measured

This website is Quantified, and the data displayed here is directly measured by Quantcast.

Addicts

Addicts are the hardcore segment of a site's audience, who have 30 or more visits to that site in a month.

Regulars

Regulars refers to a segment of a site's audience that frequent a site more than once per month but not as much as addicts who frequent a site 30 or more times per month.

Passers-By

Passers-by have a single visit over the course of a month.

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Getting Quantified

Getting Quantified means you get free, directly measured and reliable audience and traffic data for the web properties you manage. Non-Quantified sites are still listed, but the data shown is estimated.

Request Site Get Quantified

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You have sent a request to this site's owner to get quantified. You will be notified via email when this site becomes quantified.

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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