Geo-fencing and Geo-framing are both forms of digital advertising targeting customers based on their locations. Although both have similar names, they are miles apart in their approach to attracting and engaging a targeted consumer.
The standard Geo-fence allows you to put a pin on a building and create an imaginary “fence” around that point of interest. That fence is generally a 90-meter radius, and some are up to a mile. You can then cookie any device seen within that fence and serve them with ads for the next 30-60-90-days. When you think about that in a retail setting – you’ve probably positioned your store in a high traffic area. So even at a 90-meter radius, you’re going to cookie the devices that are walking past the store, driving past the store, and even at the surrounding businesses. This means there will be a lot of irrelevant traffic and wasted impressions in your campaign. In densely populated regions, the number of irrelevant devices captured using this strategy can climb into the thousands. The impact of such broad strategies can be catastrophic to campaign success and greatly diminish the return on ad spend.
That’s the most striking difference between Geo-fencing and Geo-framing: granularity. Geo-fencing gathers audience data based on a very broad proximity to the desired point of interest. In contrast, Geo-framing, developed by Esquire Advertising, is accurate down to a single meter level, and frames are hand-drawn around each point of interest. Geo-framing pays careful attention when creating its targets, focusing solely on capturing the devices within the store’s four walls. Additionally, part of the geo-framing process uses a series of quality checks to identify and remove devices belonging to employees of the store – further enhancing the reliability of the audience data. This means there is zero outside noise.
Unlike the conventional digital tracking methods of the last twenty years, which heavily relies on cookies and online interactions, Geo-framing generates audiences based solely on offline behaviors. Consumers in your market can be monitored while they are out shopping and form audiences based on their near real-time decisions. Geo-framing is 100% cookie-free. In recent years, cookie-based advertising has become even more unreliable due to iOS and Google updates. A user’s cookie profile is not necessarily an accurate picture of who they are as a consumer. Everything people do online, every site we visit, every ad we click on, and every Geo-fence we pass through plants a cookie on your profile. This causes the standard cookie pool to become very muddy and leads to wasted impressions.
Instead of using cookies to track the devices within Geo-frames, each smart device’s UDID (unique device identifier) can be extracted and matched back to the household where it resides. This eliminates all fraudulent traffic and provides retailers with a 1:1 household match to their in-store sales.
Not only can you see what consumers are doing in the market in near real-time, but you can turn back the clock and look at device observations in a particular market over the past six months. This is especially valuable to big-box retailers that want to target in-market shoppers at various stages of the buying cycle. Additionally, Geo-framing uses this historical data to populate an interactive dashboard, which puts the power of the data in your hands.
One of the most frustrating aspects of digital marketing for brick and mortar stores is how to effectively measure the success of a digital campaign that is intended to drive traffic into a physical store. “We’ve seen an increase in sales, but how do we know which of the advertising channels in our marketing mix is responsible for the improved performance?”
Using the same principles of Geo-framing techniques, you can also use that method to observe the devices at a household address.
Once you capture a customer’s home address information at the point-of-sale and enter it into your CRM, that data can be used to observe all the device IDs of their current/past customers. Then, the list of confirmed customer devices can be compared against the list of devices ads were served to digitally and look for matches. This attribution method is what is refered to as a “Matchback Analysis.”
Stop marketing based on a guess. Start marketing based on the data.