Getting Location Analytics Up to Speed for the Mobile Ad Revolution
Analyzing user data is standard practice for pretty much every website out there — large or small, consumer or B2B. In April 2012, Google announced that over 10m sites were using Google Analytics, and many more are using enterprise-focused solutions like Omniture, Coremetrics, and WebTrends.
The scale of adoption in web analytics highlights the direct benefits businesses see in measuring site activity — including key metrics like visitors, visits and page views, traffic source metrics like keywords and referring sites, as well as optimization metrics like conversion rates. And as companies have introduced services around mobile analytics, they’ve tried to create similar context for mobile measurement.
But while using similar metrics across web and mobile may feel more familiar for many businesses, it also discounts what makes mobile unique: location. Location data on the web is coarse, and limited to dimensions like country, state, and city — location analytics for mobile is different, in that it allows for a level of precision down to meters, and context down to place.
Thus, mobile location data, if used appropriately (and in connection with mobile analytics), can help drive revenue for both the publisher and the marketer by enabling mobile inventory to be sold against more granular, premium locations.
The most valuable inventory in mobile is going to be associated with geofences around neighborhoods in a city, a particular category of businesses, or a single chain of businesses. When using geofences, a more granular set of location analytics is required to identify this high-value inventory. By understanding the availability of mobile users nearby a certain category of business like banks or specific businesses such as Walmart, Home Depot, or Starbucks, a salesperson can go out into market and confidently sell against that inventory. Without this level of detail, a salesperson risks not being able to sell this high-value inventory, or could sell against a geofence with limited impressions, leaving money on the table and creating a less-than-optimal outcome for the client.
When planning a media buy, many marketers use services like Nielsen, Quantcast, and Comscore to get data on prospective inventory. Many of these services have limited data on mobile, so when making a mobile buy the media buyer becomes more reliant upon the internal reports of publishers and ad networks to understand details such as the audience composition, content, and targeting.
What is missing from the current data available to media buyers is what makes mobile unique: the ability to apply place context to a media buy. If an app sees 38% of interactions occur near a specific restaurant, that means 380 out of 1,000 impressions have the ability to change behavior in that moment. When you compare that to desktop inventory, a restaurant banner ad is much less likely to motivate a viewer to leave their computer, get into their car, and drive to that restaurant. The inability to apply value to the key differentiator of mobile (location) is part of the reason why mobile CPMs are still at 20% of desktop CPMs (Internet Trends, Mary Meeker, May 2012).
It is critical that media buyers push mobile inventory sources to provide location analytics that enable them to make buying decisions based on the unique features associated with mobile. By identifying the features unique to mobile, the media buyer can better identify opportunities for their clients to effectively move dollars to mobile inventory.
As an industry, we’re still in the early stages of mobile media buying and selling, where information is paramount and early adopters will be rewarded. Traditional metrics like visitors, audience, and content play a role in mobile, but location analytics is what quantifies the unique value of mobile for publishers and marketers.
David Shim is the Founder and CEO of Placed. By connecting the physical and digital worlds, Placed is creating a new class of analytics for mobile apps focused on location.