Feature Mart & Data Dictionary | Mobilewalla
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Build more
predictive models
with Feature Mart

Mobilewalla Feature Mart is a collection of sophisticated, highly predictive consumer data and features to improve machine learning outcomes across various industries.

 

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Poor feature engineering is one of the leading culprits behind underperforming predictive models.

Predictive modeling success hinges on selecting the features that are most likely to affect the desired outcome. Mobilewalla offers solutions that effectively allow data scientists to outsource aspects of feature discovery.

Through years of refinement using a wide variety of modeling use cases, we have identified nine different feature categories, predictive of many business outcomes. 
These features are easy to use across a wide range of business, marketing and data science applications. All of the features are mapped to the IFA (identifier for advertisers) which is the device ID or MAlD (mobile advertiser ID).

Custom Features

Along with the nine features categories, Mobilewalla can also generate custom features based on your specific business requirements.
Talk to one of our data experts now to learn more.

 

App engagement

Features related to the apps being used on the device. Most seen app category, number of distinct apps used by the device, number of premium apps
used by the device, etc.
More About App Engagement

Carrier

Features based on device carrier like signal distribution by telco type, last seen cellular carrier, and most seen WiFi carrier.
More About Carrier Features

Demographic

Features like user age, gender, wealth, etc.
More About Demographic Features

Device engagement

Device profiling based on usage time like night riders, commuters, early risers, weekday, and weekend
engagements. 
More About Device Engagement

Device mobility

Features like area of mobility, average distance travelled in a day, total number of distinct locations visited by device, home/work location, commute distance, etc.
More About Device Mobility

Householding

Features focused on related devices and device attributes for users at that same location.
More About Householding

Location-derived features

Features like common day, common evening,
and most-seen location of devices.
More About Location Derived Features

Segment-scoring features

Features based on POI engagement scores of devices.
More About Segment-Scoring Features

Time engagement

Features focused on hours, days, and period
when the devices are active or engaged.
More About Time Engagement Features

Want to learn more about Feature Mart?

We can help you understand our consumer data features, provide samples, share delivery options, and more.

Talk to our experts