We turn location data into real-world location intelligence to help companies solve today’s biggest challenges.
The Growing Location Intelligence Industry
According to Grand View Research, the global location intelligence market size was valued at $10.6 billion in 2019, and it is projected to be at $25.2 billion by 2025. This growth is driven by the rapid adoption of location intelligence solutions by leading companies.
By using location data from opted-in consumer mobile devices, companies gain valuable insight to solve their biggest business challenges.
What is Raw Location Data?
Location data is raw information on where consumers go. It is collected from opted-in mobile devices by the apps, products, and services that consumers use. Businesses can obtain location data from a variety of sources, including their own business systems and third-party location intelligence companies. Third-party location data providers are typically the preferred source of data, because they offer a wider variety of services tailored to business needs and maintain a heavy focus on data quality, including cleansing and contextualizing raw data to make it more useful.
This raw data could include:
- Pseudonymized mobile ID or mobile advertising ID (MAID)
- Latitude and longitude
- Mobile device information
However, raw location data often:
- Has inaccurate or duplicate information
- Contains erroneous location signals
- Doesn’t have the context needed for interpretation
How We Turn Raw Location Data into Intelligence
To deal with the issues mentioned above, location data should undergo a rigorous cleansing process that discards poor quality information while verifying other signals. It also needs to be contextualized so that analysts can easily interpret its meaning. The best cleansing processes use techniques like corroboration and dwell time to determine whether or not consumers were actually visiting a location. Contextualization can include information, or metadata, about the signal along with any relevant information about the place or time. It’s important to ask your location data provider about data cleansing processes and how the data is contextualized for use in analysis.
We have a multi-step process to turn location data into location intelligence.
Step 1: Validating and Deduplicating Location Data
First, we collect raw location data from many different sources. Then we find and remove instances of duplicates and spoofed signals in the data. We examine more than 15 billion location signals every day.
Purpose: This prevents you from wasting time and precious resources trying to analyze large amounts of raw location data that doesn’t add value.
Step 2: Enriching Location Data with Forensic Flags
Next, we use forensic flags to mark every location signal with information about its origin and its quality. There are many different forensic flags, each with its own meaning.
Purpose: This lets you work with only the data that is the best fit for your needs. It also ensures that only the best quality data is used to build Gravy’s data products.
Step 3: Contextualizing Location Data
Finally, we enhance our data with contextual information, such as event and venue information.
Purpose: Raw location data isn’t contextualized. Without context, the meaning of the data is unclear. Context is essential because it provides an explanation of what is happening at different locations, and why consumers would visit. Thus, it provides a more accurate view of consumer behavior.
Using Location Data for Real-World Results
Location data has 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.
The most common way of using location data for digital advertising is through advertising audiences. Location data allows businesses to create targeted advertising campaigns that deliver information to the consumers that are most likely to be receptive to it. We’ve seen businesses increase their click-through rate (CTR), foot traffic, sales, and ROI by combining advertising audiences with relevant messaging.
Foot Traffic Analysis
Businesses can analyze foot traffic to their stores or competitors’ stores. This allows them to be able to identify the types of consumers that visit them, and where they go before and after they visit their stores. With this information, companies can make better decisions when it comes to advertising, sponsorships, messaging, and more.
Conquesting campaigns are the most common use of location-based competitive intelligence. Businesses use competitive intelligence to monitor competitor locations or customers. By using this data, they can track the visits at their competitors’ stores, as well as where their competitors’ customers go before and after each visit. After the monitoring period, they can target their competitors’ customers directly to win them over and increase their market share.
Most market research relies on limited-scope sources, such as surveys and online behavioral data. That limits the value of the research and also leaves potentially valuable insights undiscovered. Location data, on the other hand, reflects the real-world behavior of large quantities of consumers, and is perfect for near real-time insights to drive research.