AUDIENCES - MARKETPLACE

AUDIENCES - ACCESO

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MEASUREMENT

Participación - Informes de usuarios

Apps are naturally built to appeal to certain segments of the population, and knowing who an app caters to, and whether those users are really invested in that app, is key to getting the attention of your audience.

Find out more about a person by their stage in life, their interests, and their activities. App Usage Personas take a step beyond a person's age and gender, to reveal more about where and how they like to spend time on their device.

Utilizing User Data

Discover which demographic groups of users are truly engaged with an app.
Compare demographic based engagement for apps across various business verticals.
Track the change in appeal of apps to different demographic segments over time.
Find the top ranked Ownership Personas for an app and rank them comparatively to cohorts.

User Engagement by Age Report

Engagement by Age for 3 different apps during an arbitrary date range.

The Engagement by Age bar chart shows the bucketed percentage (%) of devices that engage with a give app during a date range of interest, based on the age of the device owner.

All device owner ages are bucketed into one of four groupings:

Device owner age is equal to or less than 24 years old (< 25).
Device owner age is between 25 years old and 34 years old, inclusive (25 - 34).
Device owner age is between 35 years old and 54 years old, inclusive (35 - 54).
Device owner age is equal to or greater than 55 years old (55 +)

The age of the device owner determined by self-reported data, which is submitted at the time that a new device is initially set up by the user.

Age Bucket Coverage {WIP}

All four age buckets will always total to 100% for any given app; this is because there are no gaps in the age ranges of the buckets.

For Example: App A has the following engagement by device owner age for December 12th to December 21st...

08% of devices that engaged with App A during this time belong to owners age 24 or younger.
26% of devices that engaged with App A during this time belong to owners age 25 to 34.
46% of devices that engaged with App A during this time belong to owners age 35 to 54.
20% of devices that engaged with App A during this time belong to owners age 55 or greater.
100 Unique Devices on Day 5 that were not a part of day 1, 2, 3, or 4.

Together, these age breaks represent 100% of all engagement with App A during this time:

(08% + 26% + 46% + 20%) = 100%

User Engagement by Gender Report

The Engagement by Gender pie charts show the percentage (%) of devices engaging with a given app during a date range of interest, as based on the device owner's gender designation of Male or Female.

Engagement by Gender for 3 different apps during an arbitrary date range.

Gender Bucket Coverage

All four age buckets will always total to 100% for any given app; this is because there are no gaps in the age ranges of the buckets.

For Example: App A has the following engagement by device owner age for December 12th to December 21st...

08% of devices that engaged with App A during this time belong to owners age 24 or younger.
26% of devices that engaged with App A during this time belong to owners age 25 to 34.
46% of devices that engaged with App A during this time belong to owners age 35 to 54.
20% of devices that engaged with App A during this time belong to owners age 55 or greater.
100 Unique Devices on Day 5 that were not a part of day 1, 2, 3, or 4.

Together, these age breaks represent 100% of all engagement with App A during this time:

(08% + 26% + 46% + 20%) = 100%

Persona Over-Indexing Report

The Persona Over-Indexing ranker displays the proportional difference (as a 'factor increase’) that a device which engages with a given app will also belong a given App Graph Ownership Persona.

Por ejemplo
In a Users report run that includes App A for the date range of December 2nd to December 21st, the Persona Over-Indexing report shows that the "Coffee Connoisseurs" Persona had a value of 11.06x for App A. This was the highest ranked Persona for App A during this time frame.

The report results can be read as: Devices that engaged with App A during this time frame are 11.06 times more likely than the general population to be included in the "Coffee Connoisseurs" App Graph Ownership Persona.

This means that there is a high overlap of devices that both engage with App A during this time frame, and which posses the attributes necessary to be included in the "Coffee Connoisseurs" App Graph Ownership Persona:

Coffee Connoisseurs are predominantly working professionals 25-44, with above average household income $40k+. They often grab a cup of joe with a pastry on their way to work, and often have apps such as Starbucks, McDonalds, or Panera Bread to track frequent visiting points.

If there is a need to speak to an audience that includes "Coffee Connoisseurs", then reaching those devices via App A is a good option. Within the same report run, App B had a proportional over-index for "Coffee Connoisseurs" of 3.1x, and App C returned 11.01x for this Persona. While App B would still be a reasonable option for reaching this audience, it would likely not perform as well as App A or App C in this regard.

Apps Which Report No Persona Data

When the Engagement report is run for a given app, and a given date range, it could be the case that no data returns on screen for the Persona Over-Indexing report as it utilizes a backend threshold that will prevent apps with very small reporting sample from populating report results.

The Over-Indexing Personas report counts the number of real reporting devices (unscaled) to determine if there is sufficient statistical significance to generate report results, and if the reporting device count is less than 200 devices per day during the days in the report's date range, the report will not provide results back for those days below threshold.

App Graph Ownership Personas

App Graph Ownership Personas allow marketers to target users based on which mobile apps they have installed on their devices. These associations are based on Propensity modeling, in which device owner traits are inferred based on the combination of actual apps installed on, and attributes of, each device.

App Ownership Personas include such segments as...

Intent

Bargain Shoppers: While we all love a bargain, this audience is much more likely to be shopping for sales. The audience tends to be older, in the range of 55-64 years old. Ownership of apps such as eBay, Walmart and OfferUp are included in this audience.

Life Stage

New Moms: New moms, predominately in their 20s and 30s, with household income most often in the $40-60k range, who are doing a lot of shopping and planning to adjust for the newest member of their family. With apps like Pinterest, Target, and Ebay to keep their nursery and baby wardrobe well stocked, they are busy sharing photos with family and friends on Instagram, Vine, and Snapchat. They also stream a lot of content in the background while caring for baby, with apps such as Youtube, Pandora, Spotify, and Netflix.

Interest & Activity

Green Friendly: The Green Friendly audience is equally distributed across genders. While most likely in the 25-34 age group, these users appear evenly across age ranges. Owning app such as Paper Karma, iRecycle, WattBuddy and Eco Footprint help keep their passion for all things green in check.

Exporting Personas to CSV

When exporting the Persona Over-Indexing report via the CSV button, its important to note that the export will always be ordered via the original Independent sort order, and will not specifically export ranked by any particular app on screen.

Additionally, in cases where an app has "Insufficient Data" for a given Persona, the CSV export will interpret these as NULL values, and no data point will be provided in the cell.