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In this article: The pitfalls of a one-dimensional strategyStrategies provide the framework for collaborationDecision-makers are crucial Strategies create sustainable project flow…
Read MoreDrawing insights from geolocation data to optimize targeted ad campaigns.
Service: Delivery Services, Analytics
Industry: Marketing
Tech Stack: AWS S3, Apache Spark, Apache Hive, AWS Lambda, Pyspark, Jupyter
Our client - a major marketing company - desired to create new location and behavior-based attributes for target consumers that could be leveraged in model and audience creation. The attributes would be derived from data related to driving patterns and locations provided to the client by a major vehicle manufacturer. This data set contained vast amounts of information but lacked the necessary context relating one action to another, making the data hard to parse and interpret for our client’s purposes.
By optimizing existing approaches to extract better results, GAP developed a new proprietary methodology for the creation of these attributes. Algorithms were created to identify drivers’ home and work location and “stopped by” locations - places a consumer is likely to visit before or after work. Using these data points, GAP created audience profiles for frequent and recent visitors to these businesses. By identifying places and patterns of consumer visits, this unique model allowed our client to create and place better-targeted ads based on a consumer’s rider journey.
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In this article: The pitfalls of a one-dimensional strategyStrategies provide the framework for collaborationDecision-makers are crucial Strategies create sustainable project flow…
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