We turn location data into real-world location intelligence to help companies solve today’s biggest challenges.
The Growing Location Intelligence Industry
According to eMarketer, the location intelligence market size is well above $10 billion globally, and it is projected to grow to $20 billion by the middle of the decade. This double-digit growth is driven by the rapid adoption of location intelligence solutions by leading companies.
By using human mobility data, 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 this data from a variety of sources, including their own business systems and location intelligence companies. Location intelligence companies are typically the preferred source, because they collect a wider variety of location data, deliver location 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 the 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 precise location data with contextual information, such as event and venue information.
Purpose: Raw 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. This 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. Mobile 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.
Using location data to understand customers can increase revenue and up-sells, as well as improve the customer experience. It can also increase customer retention and market share. The companies we work with then use that information to identify and target lookalike audiences that are more likely to make a purchase, significantly improving their new customer acquisition strategies.
Data enrichment is when businesses add third-party data to an existing set of information, such as customer records housed in a CRM or database. When customer data is enriched with location data, companies can improve communication and offers throughout the customer lifecycle, increasing upsell and cross-sell revenue, loyalty, and customer engagement.
Location Data Uses by Industry
Many leading companies in different industries are already benefiting from the use of human mobility insights. Common use cases include digital advertising and competitive intelligence to increase their ROI and market share.
However, many of these industries also have unique, highly tailored use cases where they can use enterprise location intelligence to meet their respective industry challenges.
Retail & Restaurant
The retail and restaurant industry, especially brick-and-mortar stores, often uses location intelligence for loyalty marketing, competitor research, and audience targeting.
Location data helps real estate professionals better understand their market and helps them find tenants to fit vacant commercial spaces.
Financial Services & Insurance
Financial services companies use location information to manage risk and fraud, and reach clients with targeted ads.
Hospitality & Travel
Hospitality brands often use location information for competitive research, conquest campaigns, and audience targeting to bring more guests to their locations.
Sports & Entertainment
Like other industries, sports and entertainment companies use location intelligence for advertising, market research, and consumer insights.
Digital Out-of-Home (DOOH) & Connected TV (CTV)
Leaders in the DOOH and CTV industry have been using location data to tailor their messaging to the right audience and prevent ad waste.
Automotive companies use location-based insights to identify sponsorship opportunities, research and target competitors, and improve loyalty programs.
Real-World Location Data Case Studies
See how these leading companies used enterprise location intelligence to drive their business strategies.