Whether you work for an advertising platform, a brand, an agency, or publisher, you’re probably all aware that the digital data generated by consumers’ behavior and tendencies lack real-life insight. Very few companies can identify where consumers are, where they have been, or where they are going without some guesswork. Even when customer whereabouts are known, context about the environment around them tends to be missing or inaccurate.
Beyond simple maps, the advertising industry generally lacks accurate data about the static environment to cross reference with dynamic location data from consumers’ mobile devices. This makes it difficult to answer questions such as: Where do they go? What time are they home? How long does it take them to commute to work? What do they like doing on Saturday mornings? What media do they come across in the day–on their mobile, laptop, car radio, billboards, beacons in stores, on TV. How are ads they saw three days ago on their laptops influencing what they’re doing now?
Budget allocated to geo-targeted ads is expected to rise from $12.4 billion in 2016 to $32.4 billion in 2021, according to BIA Kelsey.
Location is the only source of data that spans every activity and media experience of a consumer throughout the day from what they see on the way to work, to what they watch on TV at night. And this is why advanced location-enabled intelligence is transforming advertising. It acts as a bridge between the digital and physical worlds–helping you make sense of, and leverage, all the data you hold about consumers.
A greater understanding of where consumers have been, where they are, and where they’re going leads to a clearer picture of who the consumer is–revealing patterns and enabling predictions. For example, if a consumer commutes into the center of a city from a wealthy suburb every day, they can be assumed to be an affluent individual. The shops and restaurants they visit might support this assumption. Similarly, if someone visits a university campus each day and returns to a student hall at night, they might be assumed to be a student.
Location intelligence combined with data sources, such as social media or mobile apps, can surface what people visiting certain places are feeling and thinking. Machine learning and other artificial intelligence techniques can help analyze and process all this unstructured data, to then build patterns and predictions.