Unacast gathers location data from smartphones and mobile apps, with opt-in consent from users (all of our data is privacy-compliant in the markets in which we operate). We then analyze and contextualize the data for purposes of providing accurate location intelligence, which can be used for advertising, audience segmentation, competitive landscaping, urban planning, and to generally understand people-based movement analytics.
Consumer privacy is foundational to our business. Our products are designed to meet all applicable laws and regulations, including GDPR and CCPA.
Raw location signals are aggregated from many different data providers & thousands of mobile apps.
Proprietary data verification methodology identifies flawed, problematic, & duplicate data.
Data from all sources is combined into one stream, removing duplicate signals & merging those close in space & time.
Valid data is filtered & classified by signal origin and other key characteristics.
We build our on-demand APIs, curated datasets, and location insights platform to meet our customers where they are in their data analytics journey.
Our Propriatary data processing technology ensures that no data from sensitive locations is used.
Consumers may request their information or opt-out of data processing at any time
To make raw location data usable, we apply our patented data processing technology. We process billions of raw location signals every day, eliminating fraudulent, problematic, and duplicate data by up to 65%, and creating a unified, high-quality data stream.
At Unacast, we understand the importance of utilizing location data effectively and ethically. We have seen firsthand how raw location data can be unreliable and how much of it is discarded due to quality issues. In fact, an average of 45% of location data is flawed or duplicative. With our expertise in identifying and rectifying problematic data, as well as augmenting valid data with new insights, we are committed to providing the highest standard of data processing.
For our Location Analytics datasets, GPS data is combined with contextual data including demographics, industry trends, venue attributes, historical data, and more. We then train our machine learning models to learn the relationship between different data sources and our target mobility insights. In total, the model comprises more than 120 features to train those relationships based on our long history of high-quality location data. Our machine learning-powered data is then curated into datasets to provide you easy to work with location analytics.
Our foot traffic dataset helps you understand visitation at a specific site or area – how many people visit, types of visitors, and traits of the visit.
Make better decisions by understanding the home and work locations of your visitors.
Discover other shops and locations your customers visit.
Determine what types of people are visiting a location.
To learn more about Unacast’s data privacy policies, read our privacy documentation.
Unacast’s data feeds are used by customers in a range of industries for many different purposes. Location intelligence is relied on by investors in real estate and equities, by retailers for site selection and measuring foot traffic at stores, by the public sector to plan public spaces and key infrastructure, by logistics companies to inform warehousing and transportation, and by the financial industry to inform alpha, detect market shifts, and measure portfolio risk. Find more information about our use cases.
Unacast offers fresh, high-quality data feeds with dense location data for multiple markets globally. Each dataset varies in its update frequency, from as short as 24 hours to up to quarterly.
Currently, our datasets extend back to Q1 2019. In some instances we have data into 2018, depending on the specific use case. We are continually abridging our models with complementary data sets, so rearview-looking timeframe may extend more accurately into the future.
Each of our datasets has a clearly defined schema that is well-documented for users ingesting our feeds. Read more in Unacast’s documentation.
