The global location intelligence market size was valued at $8.12 billion in 2016, according to Grand View Research. It is predicted to expand at a compound annual growth rate of 15.3% from 2018 to 2025.
That growth is driven by the rapid adoption of location-based data and services by both businesses and consumers. For consumers, location-based services are now the norm, with most consumers expecting content and services tailored to where they are and, by association, what is relevant to their location. Navigation, restaurant selection, and augmented reality apps are just a few examples.
As consumers opt in to products, services, and apps that generate location data, enterprises gain valuable information on consumer habits and preferences that they can use to improve communication and business decisions.
What is Location Data?
Location data is raw information on where consumers go. It is typically collected from opted-in mobile devices by the applications, products, and services that consumers use.
Typically, when businesses purchase (or collect) location data, they get access to a variety of information, the main component of which are the locations mobile device users have visited in a certain time period.
Raw location data could include:
- Anonymized mobile ID or mobile advertising ID
- Latitude and longitude
- Mobile device information
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Location Data Use Cases and Business Results
Like other data types, location data have a wide variety of uses and applications for businesses. Below are some of the most common, but they are by no means an exhaustive list. In addition to the use cases below, many organizations use location data for industry-specific applications that provide an additional competitive advantage.
The most common way of using location data for digital advertising is through advertising audiences. Advertising audiences are targeted lists of anonymized consumers that businesses can deliver ads to.
Each advertising audience targets consumers based on a certain characteristic or combination of characteristics. For example, one advertising audience could target consumers that are interested in wine, which could be determined by using location data to determine how often consumers visit wine bars, vineyards, and wine stores. Consumers who visit wine-related location often are included in the audience. Another advertising audience may target consumers that are between the ages of 25 and 35, live in New York City, and regularly attend sporting events.
Advertising audiences can be pre-built or custom created to target the attributes that are most important to the business. Once the audience is created, businesses use their AdTech solution to upload the audience and deliver their ads to those specific consumers who are most likely to be interested in their services.
Location data can also improve advertising by providing information on what consumers are interested in, helping businesses fine-tune messaging and offers.
Business Benefits of Using Location Data for Advertising
Consumers are no longer interested in or fooled by flashy advertisements. They want relevant information and offers that cater to their interests. Location data allows businesses to create hyper-targeted advertising campaigns that deliver information to the consumers that are most likely to be receptive to it.
We’ve seen businesses dramatically improve their results by combining advertising audiences with relevant messaging. The most common results are increased click-through rate (CTR), foot traffic, sales, and ROI. For example, L’Oreal saw 320% ROI on a targeted advertising campaign in New York City. Seventy percent of stores that participated in the campaign increased sales.
Foot Traffic Analysis
Foot traffic data is a subset of location data that measures the number of visitors to a physical location. Businesses can analyze the foot traffic to their stores or competitors’ stores identifying the types of consumers that visit them as well as where they go before and after.
Foot traffic can also be captured for a specific group of consumers. For example, Gravy leverages mobile identity matching to help businesses learn more about their customers. Once customers are matched with a mobile device, Gravy can gather information about their foot traffic patterns, providing insight into where customers go in the physical world.