Location, location, location. This well-known phrase is a real estate agent’s mantra, but it should be the marketer’s too. Data-driven strategies have revolutionized digital marketing, and more recently, device location data has improved the ability to relevantly execute and effectively measure campaigns. Not only does location data help marketers better understand store visitation, but it also identifies who might be frequenting retailers without purchasing anything. This improves insight into previously data-weak industries such as sports and music events (where brands invest significant sponsorship dollars), or in the QSR segment (where consumers’ purchases are predominantly case-based).
For brands with brick-and-mortar presences, location also unlocks insight into how their competitors are faring in the market—historically, a notoriously difficult element to quantify. In an industry like automotive, location is uniquely suited to help brands understand long-term purchase journeys made up of many visits before any sale occurs. At Oracle Data Cloud, we’ve found location data to be an essential ingredient for helping our diverse client base achieve their marketing strategies.
For marketers who seek to leverage location data to achieve successful business outcomes, it’s critical to partner with data providers who get the details right. Duncan McCall, CEO of PlaceIQ, shared his take on this topic with us recently: “Location-based ads on mobile is on pace to reach $38.7B by 2022. Marketers are expanding beyond geo-fenced campaigns to leverage location-based insights to drive holistic marketing strategies, which makes accuracy critical. It’s imperative to use high-quality, validated data sets so marketers can see the complete consumer journey. Oracle Data Cloud is in a unique position to harness the leaders in their long list of partners, and work on behalf of—and, indeed, the industry—to be the arbiter of the types of location data that produce the best results.”
Small mistakes can make a huge impact when it comes to location data. If you’re trying to reach shoppers who have visited a sporting-goods store in a mall, you will mis-target the audience and waste resources if your location data is too broad and connects you with consumers who went to an adjacent hair salon and bridal boutique instead.
At Oracle Data Cloud, we set out to test the accuracy of location data by leveraging our unique data sets of verified offline purchases against store visits to ensure the accuracy and reach of the data. In one test, we picked three store types where individuals don’t go to window-shop— a big-box chain, a popular drugstore, and a quick-service restaurant—and we set out to verify the quality of each data set.
In our tests, we compared the accuracy of location data against a random baseline of historical shoppers by comparing store-visit data with purchase activity within those same data sets. We also dug deeper to compare the lift in using location data as opposed to simply finding shoppers who regularly purchased at an establishment. Finally, we analyzed how well we could model future buying habits of consumers based upon connected purchases at the store and location-inferred visits. Using each of those data points as a response variable fed into a model, we created predictive outlooks on purchasing behavior and then compared the results, looking for as much fidelity as possible with location data as that we see in purchase.
In the end, we learned that location data is definitely not a commodity. In our testing, we found that different approaches to gathering and curating location signals from our providers gave ranges of performance. Using that knowledge, we’re proud to partner with leaders who we’ve confirmed can give strong, location-based signals to help market efficiently and effectively, and to add additional value to our measurement and audience products. More important, we concluded that location data is an essential component of any marketing campaign—not just mobile—and through our rigorous testing, we understand the quality that these top-tier providers can deliver.
So, repeat after me: location, location, location . . .
About Alexander Sadovsky
Computer scientist turned neuroscientist turned data scientist, Alex has had a passion towards computers since he learned that hitting the right letters could let him play videogames on his Dad’s Commodore 64. Alex began working in internet technology by starting a web hosting company in high school during the first dot-com boom. He later pursued a degree in Computer Science at the University of Michigan and then detoured to explore the interface between computers and biology by obtaining a second bachelor’s degree in Molecular Biology followed up with a PhD in Computational Neuroscience at the University of Chicago. Today, Alex’s role as the Senior Director of Data Science at the Oracle Data Cloud is focused on applying machine learning techniques in the big data advertising technology landscape.