Build more predictive models

with Feature Mart

Highly-predictive features that help data scientists improve their feature engineering process and build more accurate machine learning models


Businesses are under more pressure than ever to deliver results. That means you’re under more pressure than ever to increase the precision and recall of your models to drive more informed business decisions. 
Mobilewalla Feature Mart is a collection of sophisticated, highly predictive consumer data and features that help to improve machine learning outcomes for key use cases across a variety of  industries. 

In data science, 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 a wide range of  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.


1. App Engagement

Features related to the apps being used on the device such as most seen app category, number of distinct apps used by the device, number of premium apps used by the device,etc.


2. Carrier

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


3. Demographic

Features like user age, gender, wealth, etc.


4. Device Engagement

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


5. Device Mobility

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


6. Householding

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


7. Location-derived Features

Features like common day, common evening and most-seen location of devices.


8. Segment-scoring Features

Features based on POI engagement scores of devices.


9. Time Engagement

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

Want to learn more about Feature Mart?

Get in touch with our experts to discuss your company's needs and targets. We can help you understand our consumer data features, provide samples, understand delivery options, and more. 

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