A Software Engineer with a demonstrated history of working in the ad-tech industry along with the challenges and scale it brings. Skilled in Application Development and building highly Scalable Big Data Analytics platform. Lead Engineer managing the "Data Processing" Team at MiQ. I am a practitioner and love talking about Microservices, Big Data, Java, Scala, Kotlin and How to build systems for scale, efficiency, and performance.
Proximity Targeting is a marketing technique that uses mobile location services to reach consumers in real-time when they are around a store location or point of interest. This is done by defining a radius around a specific location. If a consumer has opted into location services on their mobile phone and enters within this radius, proximity targeting helps in triggering an advertisement or message to consumers in an effort to influence their behavior. This can be combined with the ability to purchase impressions through programmatic ad platforms that are powered by real-time bidding which can help businesses formulate the right strategy of influencing their users on a particular geographical area. They can build user groups based on certain characteristics (such as neighborhoods, demographics, interests, and other data), and subsequently launch another campaign that targets anyone which those characteristics.
The growth of mobile devices has led to enormous data generation which offers tremendous potential when used effectively for business. Thus we need an efficient platform where we can process such huge data efficiently and with minimum latency and cost. This talk describes MIQ's journey into building a fast and scalable processing platform using Big Data, delivering faster and actionable insights for Proximity targeting which has empowered the creation of a product generating ~30 million dollar revenue on a year to year basis.