Tim Garcia is the Vice President of Business Development and Emerging Markets for Moasis Global, leading the growth of strategic partner alliances for the company. In his role, he is responsible for efforts in sourcing, managing, and implementing new domestic and international opportunities for … More »
Leveraging GIS Data for Mobile Marketing
August 5th, 2014 by Tim Garcia
Data companies are not a novelty in the marketing world. For instance, RL Polk, a leader in automotive data was founded in the late 1800’s, Acxiom emerged in the late 1960s, Experian flourished most notably in the 1990’s when it was purchased by GUS (Great Universal Stores) and later demerged. All provide valuable insights on audiences, specific consumer behaviors and tendencies. GIS companies, such as Esri, are also driving a stake in the ground as the mapping giant gathers a vast amount of info and redistributes to companies that can leverage the data. Each data provider brings their own insights and flavors to the table. Complementing how those insights are packaged, delivered and reinforced provide the real value.
Location-based advertising technology companies have been known to team with consumer data providers to draw insights from demographic and lifestyle data. This data is then presented to marketers with the ability to reach specific consumers on their desktop and mobile devices. The consumer currency can be pulled, sliced-and-diced from the provider’s proprietary database and suited to fit most ad technology, depending how granular the data can be packaged.
The latest frontier is how to effectively reach the mobile user by leveraging the data based on a consumer’s geographic position. Utilizing profiles integrated within geo-location, brands, agencies and even small businesses are able to locate consumers within a specific area and target them with the marketing message that is most relevant.
Just as consumer and demographic data is changing by the minute, Geo-Fencing is becoming more sophisticated to the point where machine learning is critical to keep up with the massive data. Machine learning enables a platform to become more effective as well as automated with location intelligence. For example, if consumers are showing an affinity to a specific brand in the financial district of San Francisco (where luxury buyers may be located) but not the Fisherman’s Wharf (where tourists may be located), the platform can immediately identify the affinity, and remove Fisherman’s wharf from the targeting. Understanding the consumer segmentation in an area is important enough that it needs a place to live and evolve, especially when first party data or immediate consumer behavior is collected at the point of engagement.
The teaming of data and ad companies is not necessarily new. Brands have been targeting mobile consumers with third party data for a few years now. In some cases, mobile data has been collected directly from the mobile device and sold as targeting categories to the advertiser (i.e. Facebook and Google). This is great for consumer and lifestyle categorizing, but what about location categorizing?
Advertisers will need the ability to create location rules (ex: I want to target all consumers in Palo Alto) and place rules (i.e. that are near a Tesla Dealership), and the audience rules (ex: have an affinity to Luxury Brands). This will enable the advertiser to generate the location target in an intelligent way, no matter how granular the brand wants to get.
GIS companies already have the attribution of locations and they will enhance those locations with consumer and demographic information. GIS data is quickly becoming an important aspect for how companies identify the types of people or companies likely to be interested in making a decision related to a nearby product or service.