At the beginning of the movie "The Big Short" which depicts the radical fraud found in the housing market, a quote appears on screen:

It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so. ~ Mark Twain

Ironically this quote isn't a real quote from Mark Twain. Nor is what you've likely been told about mobile location data.

The goal of this article is to share with you the methodology PearlProx uses to understand and deploy successful geofencing campaigns, explain the differentiators, and expose false realities you've most likely been pitched in the past.

    What we want from geofencing:
  • Location data validated pre-bid.
  • Store visits seen within hand drawn conversion zones.
  • Blacklisting of repeat employee devices, delivery workers, and drive-by traffic.
  • Confidence in reporting attribution models.

Understanding the core challenges of location data:

For our purposes we will not be discussing beacon mobile pings as this method is severely limited by whether a device owner has your beacon app installed on their phone. Instead we will focus purely on device locations coming from the Adtech eco-system (Publishers). The flow of control is shown below:

We live in a world filled with countless types of devices all of which have technical limitations of their own as it relates to sending accurate data into the ad eco-system.

The speed of obtaining maximum precision is as long as 30 seconds from the time an app is launched or a webpage is rendered. Data entering the ecosystem sub-16 seconds generates a risk of polluting your campaign with often inaccurate coordinate samplings. GPS signals depend heavily on a clear line of site and cellular signal. Devices struggle with interference generated by dense buildings, concrete, or "Hive" blocking generated by too many devices in close proximity to one another. (I.E. Stadiums, Downtown skyscrapers, Subway stations).

It must also be noted that GPS strength can also be hindered by each publishers specific app settings. Since each of our campaigns has access to win ads on hundreds of thousands of apps/ millions of websites, it is extremely important to blacklist or optimize bid weight for imperfect GPS consensus. This does not mean that said app developers are "shady" by any means, as they might lower accuracy settings to preserve the battery life of a device that is accessing their service or for other various reasons.

In an OpenRTB protocol based exchange, a Demand Side Platform only has optional fields to further their understanding of each individual location a device is seen from. Each of the following fields translates critical information to Geofencing success "Estimated Accuracy", "Acceleration/ Speed", "Timestamp for when the coordinate was sampled". The movement speed of a device is critical to differentiating between drive by freeway traffic or someone standing at your cash register. Estimated accuracy is often submitted in feet and not meters by ill-informed publishers!

Is your geofencing platform accounting for these nuances?

A second look cleaning pre-bid location data:

Latitude and longitude data is depicted by decimal values such as:
33.812092, -117.918974 (Disneyland). These Disneyland coordinates are precise down to 6 decimal places OR 4 inches. This is considered heavily favorable precision. For fun, lets explore how far coordinate precision can actually go:

Decimal Places Precision In english
1 10 kilometers 6.2 miles
2 1 kilometer 0.62 miles
3 100 meters Roughly 328 feet
4 10 meters Roughly 33 feet
5 1 meters Roughly 3 feet
6 10 centimeters Roughly 4 inches
--- --- ---
10 10 microns A speck of pollen
12 0.1 micron Flew virus size
15 0.1 nanometers You can send ads to an atom

It's critical to distinguish the accuracy of each devices coordinates vs the precision of said coordinates. Showing false precision can lead others into believing accuracy is great than it really is. You can visualize accuracy vs precision here:

Info: Accuracy vs Precision visualization

Scoring: Keeping track of apps, publishers, and historical accuracy.

Improbable locations often surface in the bid-stream and thus need to be removed for optimum campaign ROI. Over time a historical score can be applied to each app/ publisher, while each score can and should manipulate your bid multipliers or suppression of said publisher. Invalid coordinates such as 0,0 tell us that an app has been designed poorly or an error in GPS processing by the device has occurred. This location points to the middle of the ocean, an unlikely target for your mobile ads. Out-of-bounds coordinates (Uninhabitable land/ water mass) should be removed pre-bid.

Info: 0, 0 coordinate location

Another example includes publishers sending latitude data variables where an exchange request is meant to have longitude variables. By flipping the variables you can visualize the intended location that was meant to be sent to the exchange. Algorithms can easily pick up flip-flop variables when looked at on the publisher level. It is possible to look at these publishers in a way that relates them to "misspelled searches" on Google and buying those keywords in your campaigns to lower overall costs while maintaining target accuracy. If your geofencing platform cannot identify flip flop publishers and use them to buy valuable device impressions at cheaper rates, you're missing out.

Info: Flip flopped location from publishers

Lastly, coordinates should be frequently analyzed using machine learning to discover and de-value bad publishers who bend or break their methodology over time. Often times bad actors will slightly manipulate their coordinates. The hope is for them to yield a higher frequency of impression sales when devices creep closer near highly populated areas often targeted by campaigns. Analyzing coordinates with extremely high volumes of devices in a very short time frequency either tells us that a significant event is underway (Super Bowl), or a publisher is hoping to trick your campaigns targeting functions. Often times we see publishers showing hundreds of devices standing on exactly the same 10 meter coordinate.

Info: Un-natural city center traffic

Inherently flawed reporting methods and how to represent the true ROI of geofencing:

Control vs exposed reporting methods are inherently misleading as it relates to store visit incrementation or uplift percentages. My reasoning behind this is that it is extremely easy to never perform badly and skew results by default towards a positive result. Many "Market leading geofencing companies" show uplift reports using control vs exposed groups of devices. I.E. "We noticed 100k devices at your target location (The control), while we exposed 30k of those devices to your geofencing ads. (Exposed)". Usually reports continue on by discussing how the exposed group showed positive traits in comparison to the control group. I.E. higher rates of store visits, quicker to visit, better click through rates.

This logic is extremely flawed at the core level as it relates to industry incentives for bidder engineers, but also for how it relates to the way exchanges throttle impressions towards your campaign.

  1. Programmers on bidding systems are literally paid to optimize their algorithms, remove bots, bad coordinates, take out fraudulent impressions, optimize the frequency of branding a message, etc etc. Without even changing the core of a basic AdTech bidder they are saying "If a device is seen in a geofence, show an ad to them... unless it is a bad impression (Bot, uninterested party unlikely to convert, etc)." If you go and pull a report for this campaign, the "exposed" group is always devices that received an ad from the campaign. The "control" group is everything they didn't want to bid on because it was either a bot, un-interested device unlikely to convert, or they ran out of budget from pacing at that moment in time and just skipped them.
  2. Another factor is exchange throttling. Ad traffic for the internet and all mobile devices is a massive stream of data to process and send to multiple parties in under 80ms (The amount of time an entire real time bidding transaction occurs within). Exchanges including Google ADX and many other leaders slow down the volume of mobile/ desktop traffic being sent to the buy side of the ecosystem to save costs. It is only when you show several days of buying ads related to your campaign targets that exchanges start to push optimum traffic volumes towards your campaigns bidding engines. This also is dependent on the volume of USD fiat targeting the throttled inventory and smaller campaigns frequently take weeks to ramp to full capacity. This throttling shows uplift of devices sent towards your conversion zones and campaign targets. Misleading customers into thinking they are moving the needle.

As you can see it's heavily without changing anything, skewed towards positive results.

At PearlProx we are as transparent as possible while leading buyers towards lucrative and measurable ROI. This is how geofencing reporting should be:

  1. We show store visits by geofence target. This includes the device ID that visited, date of visit, and which geofence they came from.
  2. We show you the uplift your campaigns generated for online search traffic. I.E. The week prior to running your campaign you experienced 500 searches for your brands name on Google, Bing, etc., however, during your geofencing campaign online searches for your brand or product increased by x %. This shows an external increase in traffic vs internal uplift which can be manipulated as shown in the previous section.
  3. We provide creative heat-maps that showcase every click reported with the user ID that clicked, the time of the click, and the time it took the device to click on your ad after it loaded on screen! This is extremely detailed information when trying to prove non-bot clicks as they often click instantaneously with page load or make straight mathematical line movements onto the ad region.
  4. Our pixel can track opt-in PII that is given on your landing pages, live chat modals, form submissions. If the user has also seen one of your ads we can legally list their PII and related ad engagements. I.E. "Name", "Email", "Phone", "Location", "Social networks", "The ad they saw from your campaigns", "The time the impression was served", "The domain or app it was seen on". This information can be easily correlated to physical or digital sales to generate ROI reports.

Using this reporting methodology you are able to make both marketing and business intelligence decisions without the burden of radical scale or spend. True ROI can be calculated with even low numbers of impressions and conversions! Statistical relevance depends on your use case. I.E: You might be tasked to use the store visits and device counts to figure out what new business location you might open in a certain town. Another media buyer might need a higher scale campaign in order to create labels for machine learning calculations. The greater the data a campaign generates, the greater the accuracy of a model a data scientist might use. I.E: Creating AI that decides how likely a customer is to convert at a physical location if they clicked on your banner or not.

Final notes on geofencing and mobile ad units:

Both post-view and post-click methodologies apply with geofencing as it is a heavily branding based channel. Mobile users are often not location static. Creatives need to be mindful of font-size and calls to action to ensure quick and assertive hand holding towards engaging with your ad. Mobile ads are often mis-clicked due to screen space constraints so it is extremely common for sessions to show up blank in non-log-level analytics software packages such as Google Analytics. Use HTTP level tracking to measure drop off! This result is also common due to page load times being slow for landing pages that are not fully optimized by the advertiser. This causes the analytics tag to not fully load before a session spanning as long as even 10 seconds to be counted. 3rd party analytics tags are extremely dependent on load times/ internet connection and where they are placed in your landing page code. Please ask us for advice if you wish for us to offer tips to help your landing page perform better in a mobile advertising environment.


At PearlProx, we know more about your customers than ever before. We see a MASSIVE amount of users across web, mobile, and social network traffic feeds everyday.

PearlProx’s data science team and programmatic learning software carefully determines the best individuals and locations to show your ads resulting in a giant leap in ROI for your business.

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