While the promise and reality of online local advertising is bright, the success of this market is dependent on the performance of local ads. As we will see in this analysis, the vast majority of geotargeting capabilities are still only targeted to a DMA (Designated Market Area, e.g. the San Francisco Bay Area). Is that local enough? What if our geotagging technology can help improve targeted advertising to a neighborhood level?

Let’s quickly recap some of the market data and industry trends. In 2011, Borrell Associates reports the US online local advertising market to be $23 billion, and the majority of this ad spend is divided up between search and display advertising on the Web.

US mobile advertising approximately accounts for only 11% of the total local online ad spend. As mobile data traffic is projected to grow 26x over the next 5 years, not only will the US mobile ad spend grow, but the size of the overall local ad market pie will grow as well.

Industry changes are reflecting this opportunity as well. Google, Amazon, AOL, and AT&T Interactive have entered the daily deals space in addition to bulking up their general local advertising sales efforts. There has also been the meteoric rise of daily deal companies, such as Groupon and Living Social.

As companies increase their local ad efforts, it will be important that local ads become fully optimized. In order to optimize local ads, geotargeting must be effective in displaying a relevant, contextual ad. Let’s review the current geotargeting methods and respective limitations:

		Description	Limitation
	IP Based	Ads are delivered using a user’s geographic location as determined by the IP address.	There are widely-known issues with the quality and precision of this method. IP-based can at best target to a DMA.
	Mobile/Location-Based	Ads are delivered using the geographic coordinates via the smartphone’s GPS capabilities.	IAC reports that the mobile inventory is by far mostly mobile Web (70%) rather than application (30%). Consequently, most available mobile ad inventory has only DMA-level targeting because mobile Web publishers generally have access to only derived-location data. In-app advertising, on the other hand, has been too expensive for advertisers to participate in (e.g. iAd).
	User Profile	Ads are delivered based upon the explicit user registration info submitted for a particular site or service.	The scale is limited to the service which required user registration, and there are other issues such as outdated information, or lacking location context. The targeting capability is city level, but typically targeted to a DMA.
	Behavioral	Ads are delivered based on the digital footprint (via cookies) of the user, and they are based upon the user’s browsing actions.	As these are derived locations, the precision of the geotargeting is inconsistent, especially if the browser cookies are often cleared. Behavioral ads typically has at best DMA-level targeting.
	Search	Ads are delivered based on the search query entered by users on search engines.	While these are very effective ads, it relies on the user entering the specified location within the search query, and if it the location is not included in the search query, it ultimately relies on IP-based targeting.
	Contextual	Ads are delivered based on the location context of the webpage, typically hyperlocal content that is relevant to the user.	The inconsistency with this form of geotargeting is limited to local news sites or weather sites. These sites cannot target beyond the city level, as they are inherently organized by zip code.

 

The untapped opportunity in advertising is contextual targeting.

There is a significant opportunity in location-targeted advertising and moving past DMA and city level to one step further: neighborhoods and places. Through geotagging, we can better understand the location context of a webpage and identify the street intersection, neighborhood, and city via the referenced places and locations. In turn, we can retarget local ads more effectively.

This type of contextual targeting also allows advertisers to also get more granular categorical information.  If a detected place in a piece of content is a hospital or a car dealership, categorical inference yields Health or Automotive categories, respectively.  This type of contextually targeted advertising allows advertisements from national advertisers to be more focused and delivered to local areas.  Combining categorical and location information allows advertisers like BMW to advertise for a specific dealership in Santa Rosa on an article about Mercedes Benz’s auto show at the Infineon Raceway.

The impact of such an improvement in targeting can be astounding for media and advertising companies.

In the case of national advertisers, targeting advertisements for national companies that have local presence continues to be a multi-billion dollar opportunity.  Bank of America, The Home Depot, Wal-mart, and the like are only a few examples of advertisers that need local intent and local targeting to bring online customers to stores.  The ability for The Home Depot to advertise for a lawn-mower deal at the West Los Angeles store on an article about home improvement in West L.A. is impossible without contextually-targeted geotagging.  

In the case of daily deals, Groupon spent $180 million in online marketing in the first quarter alone and acquired approximately 33 million new subscribers in that time. That is about $5.40 per new subscriber, and Groupon is on path to spend $720 million annually in local advertising. If Groupon and other services could understand the location context, targeted down to a neighborhood, they can buy advertisements on pages that are relevant to the deals they have inventory for, thereby increasing conversion and reducing marketing expenditures.

So, how does it work?  Well, it’s pretty simple.  Advertisers submit a URL to us for the page they want to advertise on and we’ll return a list of places and local categories associated with the page.  We can also return latitudes and longitudes associated with a page.  This meta data about the page can then be used to determine which ad to show on that particular page.

Advertising agencies want this data and advertisers want to find ways to reach real customers in local areas more effectively.  We’re excited about this massive opportunity, and we’re excited that geotagging can bring the right ads to the right content and to the right people.