SafeGraph’s Guide to Better Location-Based Marketing

Location-based data can give AdTech companies (and marketers) a competitive edge.

It’s Time for AdTech Companies to Marry Location-Based Data with Audience-Based Data

There are very few (if any) marketers today who would deny the value of programmatic advertising. In many ways, it has revolutionized how brands and businesses get closer to their target audiences and make more calculated bets on how to drive and improve conversion. Not to mention, it has helped marketers reduce a significant amount of marketing waste.

Defined broadly as the automated buying and selling of online advertising space, programmatic advertising, for those who might need the refresher, is essentially a digital marketplace where publishers can put up ad space for rent (aka, the “supply-side”) and advertisers can search for ad space that meets broader campaign goals and objectives (aka, the “demand-side”). 

The magic all happens on ad exchanges, where the smart technologies and algorithms of supply-side platforms (SSP) and demand-side platforms (DSP) come together to bring these advertising transactions to life (among other things). But more importantly, there’s a massive layer of audience-based data and insights that permeates the entire programmatic advertising marketplace. Simply put, advertisers can now easily “target users based on characteristics like demographics, geography, interests, behavior, etc. without any human intervention.” This alone has made it possible for marketers to build highly targeted and relevant digital marketing campaigns instantly that are well-optimized up front to achieve specific performance goals.

Location data is key to unlocking important advertising insights  related to the competitive landscape and consumer behavior.
Location data is key to unlocking important advertising insights related to the competitive landscape and consumer behavior.

More and more, location-based data is revolutionizing the AdTech industry. Even an ecosystem as advanced and data-driven as programmatic advertising can be better, more effective, and more competitive. The many opportunities to improve audience creation, targeting, and location-based marketing continue to grow. So we thought we’d break down exactly how POI, building footprint, and foot traffic data can be used to create winning AdTech strategies.

In this guide, we will explore how location-based data can effectively become a competitive differentiator for AdTech companies looking to break through the noise in a now very crowded programmatic advertising marketplace. Even more, it can provide marketers with a new level of sophistication around planning, buying, optimizing, and measuring programmatic campaigns—especially with respect to online-to-offline attribution. 

>> Want to learn how to use location-based data for store visit attribution? Download SafeGraph’s Technical Guide to Visit Attribution.  <<

Key takeaways at a glance

We’ll discuss why now’s the perfect time for AdTech companies to harness the power of location-based data, including: 

  • Why location-based data is the key for supercharging audience-based data insights.
  • How AdTech companies can help marketers close the online-to-offline attribution gap.
  • What must-have location-based datasets to begin using immediately.
  • How foot traffic data can be used to build accurate attribution models.
From offline attribution to geo-targeting, location data can help marketers understand the bigger picture of the customer journey.

Location Data 101: A Primer for Marketers (MarketingLand)

Location Really Does Make All the Difference

In the physical world, we place a lot of value on location. Where a brick-and-mortar business is located, for example, can determine its potential to succeed or fail. It’s always a question of how much organic foot traffic it can drive from either people living in its surrounding area or those visiting adjacent businesses. As a variation on a theme, think back to the last time you were at an airport. Were there more people congregating at the restaurants, cafes, and bars in the center of the terminal than those even a five- or ten-minute walk way?  

The moral of the story here is that it pays to be in a great location. We’d like to think the same philosophy applies to ads as well. After all, the more you know about where your target customers live, how they move about within and outside of their local area, what kinds of businesses they frequent, and how much time they spend within those places can add new meaning, value, and understanding to existing audience insights. Armed with those valuable insights, you can not only choose the right context for your ads but also develop messaging or offers that will ultimately resonate with (and convert) your target customers.

Detailed POI and building footprint information provides context for consumer behavior.
Detailed POI and building footprint information provides context for consumer behavior.

In this way, audience-based data is clearly indispensable for beginning to paint a picture of who your target customer is. It can also provide a clear roadmap for getting your brand or business squarely in front of the people who actually want or need what you’re selling. 

Location-based data, however, serves a slightly different purpose in this equation. It can amplify those audience insights to provide a real-time snapshot of how your target customers take action on those behaviors, needs, and impulses throughout the day. Together, audience- and location-based data can form the backbone of a powerful and precise marketing strategy that  ensures the right ads can reach the right people with the right message at the right time.

Bringing these types of data together can provide marketers with even more granular insights into their target audiences than ever before. This is just one of the reasons why we believe that location-based data is both the new frontier and the biggest opportunity for AdTech companies today. “Customer location data has emerged over the last decade as a wealth of information for marketers,” explains MarketingLand’s Taylor Peterson. It provides a “digital footprint of where customers are spending time and how they interact with brands—both online and offline.”

More than 84% of marketers use location data in their marketing plans, and 94% plan to in the future.

83% Increase in Customers Due to Location-Based Advertising (Martech Advisor)

Remember, not all data is created equal

AdTech is all about quality, accuracy, and precision. It’s important to remember that not every data provider follows the same quality standards as we do here at SafeGraph. Unfortunately, we can’t guarantee the quality of the data you get from other sources or that it will do exactly what you need it to do—with or without serious scrubbing. Even so, by asking yourself these four important questions, you’ll be in a better position to evaluate whether any dataset you get is really worth your while: 

  1. Does the data come from a credible source? 
  2. What can (and can’t) the data tell me? 
  3. Is the data immediately usable or will it need to be cleaned? 
  4. What do I plan to do with the data?  


>> To learn more about how to evaluate third-party datasets, be sure to
download SafeGraph’s Data Evaluation Checklist. <<

Closing the Online-to-Offline Attribution Gap

It’s one thing to pinpoint relevant audiences through programmatic advertising; it’s another thing to influence decision-making along the customer journey. Petersen goes on to say: 

“In addition to enabling a more highly targeted ad experience, location intelligence can be used to help close the gap between online and offline purchasing behavior. For businesses with storefronts, real-time location data can help fill in the holes of the customer journey by shedding light on the relationship between an online touchpoint and an in-person transaction.”

The world has transformed into a vast network of interconnected devices. In fact, it has been estimated that by 2025, there will be 75 billion IoT devices in circulation. This includes everything from mobile phones and wearables to home security systems and voice-activated home assistants to medical devices and connected vehicles—and the list goes on. 

The key takeaway here: As more of these IoT devices become part of the fabric of day-to-day life, there will be an even greater influx of location-based data to tap into for understanding the very nature of consumers and their daily habits—in a more intimate and hyper-relevant way.

Understanding what other stores consumers visit can help determine ad placement and messaging.
Understanding what other stores consumers visit can help determine ad placement and messaging.

This is the goal and, suffice it to say, untapped opportunity of today’s marketers. Programmatic advertising has taught us that putting the right message in front of the right consumer in the right digital context has a greater chance of driving an online conversion. With location-based data added to the mix—coupled with location-based in-app (mobile) targeting—it is possible for brands and businesses to capture people’s attention and influence their behaviors in real-time, when they’re simply going about their daily lives.

However, doing this well requires having access to highly accurate location-based data. Marketers and advertisers need to understand precisely where they should target consumers, how those consumers interact with those places, and what defining characteristics—including key demographic traits—could potentially impact the effectiveness of a future ad campaign. In many ways, by bringing together both the quantitative and the qualitative sides of data, marketers and advertisers now have a plethora of new possibilities at their fingertips for anticipating, shaping, and, ultimately, influencing the online-to-offline customer journey.

Marketers believe location-based marketing results in higher sales (90%), customer growth (86%), and higher engagement (84%).

83% Increase in Customers Due to Location-Based Advertising (Martech Advisor)

An important note about data privacy

We take a lot of pride knowing that our data maintains the highest level of security, quality, and compliance standards in the industry. But unlike other data providers, we don’t face the same legal issues or other GDPR and CCPA compliance-related limitations because all of our data is 100% anonymized. We do not track individuals whatsoever. For this reason alone, SafeGraph is the ideal data partner for AdTech companies looking to bridge audience-based insights with location-based data. (And because we provide clean, accurate, and high-quality data, too!) 

SafeGraph Places: 3 Types of Location-Based Data for Advertising

The SafeGraph Places dataset, updated monthly for utmost accuracy and precision, provides the in-depth POI, building footprint, and foot traffic data you need to add extra fuel to your audience-based data. The biggest perk of using SafeGraph Places data: It can help you make well-informed advertising decisions and better anticipate eventual campaign performance.

POI, building footprint, and foot traffic data work together  to indicate where a business is located and how consumers interact with it.
POI, building footprint, and foot traffic data work together to indicate where a business is located and how consumers interact with it.

Here are the three types of location-based data that make up this powerful dataset:

  1. Points of Interest (POIs)
    Places includes base information—such as location name, address, category, and brand association—for the places where people spend their time and money. It also sheds light on the relationship that exists between adjacent POIs. POI data is important because it offers marketers and advertisers a unique perspective for understanding the types of places their target audiences visit throughout the day or week.

  2.  Building Footprints
    Geometry offers building footprints for POIs derived from spatial hierarchy metadata to allow for geofencing as well as a more precise and accurate understanding of attribution. Check out this great overview of the ins and outs of building footprints for geospatial analysis, including highly targeted mobile marketing. With accurate building footprints, advertisers can be sure their ads reach the target audience at the right moment along the real world customer journey.

  3. Foot Traffic Patterns
    Patterns data measures hourly foot traffic to and from POIs precisely—powered by aggregated anonymized mobile device activity and other demographic data—to determine how often people visit certain POIs, how long they stay, where they came from, where else they go, and more. This helps marketers hedge smarter bets for where to spend their ad dollars and can also proactively identify real-time consumer trends that may influence ad creative and messaging.

>> Learn more about how these datasets work together to paint a clearer and more actionable picture around online-to-offline attribution. <<

Use Anonymized Mobility Data to Create Location-Based Audiences

The SafeGraph Places dataset provides the essential building blocks for developing a winning AdTech strategy. Powered by anonymized foot traffic data for non-residential places, Patterns data, more specifically, gives marketers and advertisers the information they need to identify, with a high degree of precision and accuracy, the most promising locations for either placing physical ads or deploying real-time mobile offers as consumers enter their properties. 

To create in-depth location-based audiences without advanced analytics, Patterns data also delivers an efficient way of understanding how consumers interact with brands and locations.

Anonymized foot traffic data, that is pre-attributed to POIs, takes the hassle out of AdTech analysis. Because Patterns data is aggregated and anonymized at the POI- and CBG-levels, the average file size for a week of Patterns data is only 1.2GB. In contrast, a file containing a week’s worth of processed mobile device pings is, on average, 45.5GB (and often much larger). 

Patterns data is pre-processed to accurately determine store visits from mobile location data.
Patterns data is pre-processed to accurately determine store visits from mobile location data.

Not only does the aggregated nature of the Patterns dataset help AdTech companies easily connect foot traffic insights to valuable demographic- and brand- related information, but it also helps cut down on the time and resources needed to understand store visit attribution. 

Use device-level data to understand store visit attribution

Even though many AdTech firms find value in aggregated foot traffic data, some prefer to create their own visit attribution models with raw mobility data. SafeGraph can help with that, too. 

If your goal is to use device-level data with our POIs and Geometry data for determining store visit attribution, there are a few important steps to take to ensure you drive the results you’re looking for—and with the highest level of accuracy possible:

  1. Clean GPS Data: Start by processing and cleaning datasets to account for GPS signal shift, spiking horizontal accuracies, and “jumpy” GPS pings.
  1. Cluster GPS Pings Together: The objective here is to take all of the GPS pings collected and turn them into potential visits on a map without relying on the Patterns dataset.
  1. Prepare the Clusters: This involves forming a geospatial join between the clusters identified in Step 2 and the polygons in our Geometry dataset in order to create a list of possible places that the clusters could be referencing.
  1. Predict the Best Places: Now comes the time to identify the best or most relevant place to associate with each cluster. This part involves a number of interconnected variables, so we find it most useful to leverage machine learning to aid the classification process. 
Attribution isn’t easy, but it is possible with the right location data.
Attribution isn’t easy, but it is possible with the right location data.

Less accurate attribution models to avoid

As we developed Patterns data, we tested a few, shall we say, simpler approaches in an effort to drive the same outcome. If you’re developing your own store visit attribution model, you may be considering some of these methods. To save you time, here’s a quick rundown of their most noticeable limitations:

  • Closest Centroid Wins: This strategy assumes that, for any given GPS ping, the closest POI centroid is considered a “visit” to that POI if the distance is below a certain threshold. This is the approach we relied on when we first started doing work around store visit attribution, but we quickly found that it was most effective at determining visits for large standalone stores (like the Walmarts of the world). The data can become flawed when trying to replicate this for smaller building footprints, either adjacent to or within larger polygons.

  • Any Ping Inside a Polygon: We also explored the approach of simply identifying a store visit as a sequence of GPS pings all within a single POI polygon. As in the case above, this approach works best for large POIs or outdoor places, like airports and theme parks. However, there are two primary issues we found with this approach:  Drifting GPS signals mean that certain pings might not be captured as entering the building polygon or, even worse, be attributed to a neighboring polygon; and device pings may automatically switch back and forth between two different locations, which requires a good amount of clean-up to make the data usable.
  • Any Ping Inside a Custom Geofence: The general idea behind this approach is to assume that any sequence of GPS pings that take place within a “padded” custom geofence constitutes a store visit. While the padding helps correct for some GPS drifting issues noted above, it simultaneously makes the outcome of data analysis less precise by default. As a work around for this, it’s possible to bring other attributes, like time of day or amount of time spent within a geofence, into the mix and let machine learning do the work of identifying the right place associated with a given ping. 

>> There’s a lot more where this came from.Be sure to download SafeGraph’s Technical Guide to Visit Attribution for step-by-step instructions around our unique attribution approach.  <<

Build Stronger Campaigns with Location-Based Data

Regardless of your approach to visit attribution and AdTech analysis, one thing is clear: The most successful ad campaigns are created with a combination of location- and audience-based data. 

Not only will combining these two data sources give you a competitive edge—versus your competitors who still rely solely on audience-based data—but it will also help you get closer to cracking the code around online-to-offline attribution (which is every marketer’s main goal!). 

The question today is not if you should weave location-based data into your analytics mix but rather when you decide to buck the trend and do so. Location-based data is the future of programmatic advertising. This is your chance to be an “early adopter” before the rest of the industry catches on. And trust us, it’s catching on!  

That being said, we understand that you might still be on the fence because you know that working with tons of new data may require a lot of work on your end. And although we’ve done everything in our power to make our datasets as easy as possible for you to use immediately, our team of data experts is always here to help get you over the finish line. So rest assured that we are here to support you throughout your location-data journey!


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