We go beyond conventional ad tech methodologies to understand human behavior in both the physical and digital realms, and employ a multidisciplinary approach to data science. Traditional data science focuses on mining as many data sources as possible, then distilling and analyzing them against specific digital behaviors such as views, clicks, or online purchases. The approach works well in conventional ad tech attempting to understand and optimize data around a singular consumer behavior. It’s one-dimensional data science in the service of specific digital behaviors typically based in a mathematical discipline.

PlaceIQ’s data science methodology is different. It is founded on better understanding human behavior in both the physical and digital realms. Our insights reveal a multi-dimensional view of consumers—therefore we must employ a multi-disciplinary approach to data science. PlaceIQ’s data scientists stem from computer science, math, statistics, applied and theoretical physics, sociology, operations research, signal processing, computational finance, cartography, history, economics, and geography backgrounds.

For data science to be effective it has to be engrained in everything we do. The data science team is part of our product development and delivery, and informs every layer of our solution stack. They prototype to comprehend product behaviors, build predictive algorithms, and create mathematical models that help our clients improve business metrics. Our models are implemented as scaleable computing architectures that process billions of data points every month.

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