TruFactor Differentiation

Movement patterns are changing moment to moment in response to health, social, and economic changes. Is your data provider providing the accuracy you need to capture today's reality?

Accuracy depends on the data source and how the data is collected. We looked at New Years Eve in New York’s Time Square to show you how these differences matter.

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How does it all work?

Not collecting data frequently enough or long enough can significantly distort reporting. Location data providers often have large panels but, because of these differences, they have much lower signal density. For example, on New Year’s Eve in Time Square, TruFactor counted 3.5–5x more devices than location-based data.

Keep reading to learn more about these data inaccuracies and how TruFactor avoids them!

Accurate Routes

Location data providers receive device pings for foreground events, based on rules set by the OS (~40 signals per user per day). Sporadic pings mean that travel routes are inferred.

Background data collection delivers continuous pings (~600+ signals per user day). This enables accurate travel mapping, including origin, source and paths traversed.

Back to the New Year’s Eve Midnight Revelers - TruFactor detected devices for 2x longer than location-based data – 18 hours/day vs. 9 hours/day.

Complete Customer Journey

Infrequent device pings means that location data providers can miss critical points in the consumer journey - i.e., consumers visiting competitor stores.

“Always on” data collection captures the complete consumer journey, including travel modality.

How sporadic? Among the “Midnight Revelers,” Tru Factor received on average pings in 51 minutes of every hour, while location-based data received averaged 7 minutes of pings per hour.

Consistency in User Panel

Location data providers depend on continuous app usage to generate enough pings to build cohorts for measuring trends over time.

Based on an anonymized panel of 35 million users, TruFactor delivers the largest and most robust panel for trending digital behavior, physical locations, and demographics.

What does this mean for any given week? Looking at the “Midnight Revelers” before and after New Years Eve. TruFactor detected 84% of the midnight revelers all seven days of the week, while location-based data only detected 25% on all seven days.

Differentiating Visit Types

Location data providers classify all pings to a location as a visit.

More frequent device pings enable TruFactor to measure how long a person stayed in the location, known as "dwell time". Applying advanced machine learning algorithms, TruFactor then differentiates types of visitors based on dwell time (i.e., separating employees from consumer visits).

Representative Sample

Location data is dependent on the user base of those apps - their demographics, time of day those apps are used, and various other biases.

TruFactor intelligence is "always on" and based on mobile phone activity. Reported demographics and other data are verified by wireless carriers, ensuring higher accuracy (and appropriate weighting for population projections).

Privacy Controls

Privacy standards for Location data providers are dependent on the policies of each individual mobile app.

TruFactor closely partners with its data providers to ensure robust privacy policies are deployed. Subscribers retain complete control over the data they provide, a choice in whether they participate and transparency as to how their data is utilized.

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