We see the future of location data providing a much more nuanced and detailed perspective for a variety of uses, from transportation planning to advertising. As our world gets increasingly connected through emerging technologies that become mainstream, location data will become much more prevalent as it stems from various new sources. The most common type of location data now is the mobile signal, but car sensor data and multiple types of Internet of Things-connected devices are also becoming more common.
Home assistant devices alone—Amazon’s Echo, Google’s Home and the Nest Thermostat, for example—have have recently exploded in popularity. According to McKinsey, the number of connected homes in the US has gone from 15 million in 2015, to 22 million in 2016, to 29 million in 2017. That’s 30% year-over-year growth.
Growth among IoT and connected cars is only expected to continue in the coming years. Cisco’s 2018 Visual Networking Index notes “machine-to-machine (M2M) connections that support IoT applications will account for more than half of the world’s 28.5 billion connected devices by 2022,” according to ZDNet. “By 2022, the connected home vertical will account for the bulk of total M2M connections, while connected cars will show the fastest growth, with a 28% compound annual growth rate.”
There will be a surge of location data in the next five years, and analysts and those looking to target consumers will welcome the addition of machine learning and AI to help make sense of this trove of information.
Use Cases for Location Data and AI in Segmentation
There are a number of future use cases for AI and how it can augment and improve a number of segmentation and personalization practices as it relates to location data, and how that can be applied or layered on with other ad tech partners. Several use cases, such as media buying and planning, when paired with location data and AI, enable advertisers, DSPs and other players to measure campaign performance, operational efficiency, the ability to make decisions in real-time during campaigns, and ultimately improve ROI.
Media buying and planning: Agencies and brands have been using AI, for example, for automated ad buying to find cheaper inventory and determine which bids they were unlikely to win.
Audience analysis and campaign performance: The market continues to apply AI to analyze audience insights, at scale, when they are served ads. After all, a person’s location when consuming any experience, including ads, is critical to understanding how it performs. For example, a video ad running on the Pandora app won’t do much good if the person is using Pandora while driving.
