It feels like it has been the "Year of Mobile" for the past five years, with bloggers just like myself claiming this is the year consumers will shift and advertisers will finally provide users with the experience they are looking for when searching on a mobile device.
The issue is, technology continues to develop at a rate that not all advertisers can keep up with. But in some cases, advertisers need a little help, and the companies we advertise through can help us get there.
How Location Data Can Change Search
We all have mobile devices and we all expect to be served results based on our query when using Google or Bing. But with their goal being to provide us with the best user experience possible, what more can they do for us? We’ve had updates like Hummingbird that help with semantic search; we have had changes to algorithms to finally show us relevant results when we search for queries like "restaurants near me," as shown below.
So how can Google and Bing improve this experience even more? By leveraging location data from our mobile devices.
Companies like Pulse IQ, xAd, ThinkNear, and others are all leveraging location data in ways we are mostly not able to combine with search yet. Not only are they targeting users on their mobile devices based on where they are now, but they are also looking at past geographic locations, determining the context of their visit, and making ad placements decisions based on user behavior.
While Google and Bing are not there yet with this functionality, for certain advertisers, being able to understand where a user has been and how long they were there could be extremely powerful data to take advantage of. For example, if you happen to be a large auto manufacturer such as BMW, if you could geo-map all Mercedes-Benz and Lexus dealerships, then serve an BMW ad directly to any user who had visited two locations during one day, you would be serving an ad to someone who is aggressively searching for a luxury automobile. Right now, through the companies mentioned above, traditional mobile ads can be served based on where a user has been.
Connecting Location Data to Search
While following a pattern of visits like the example above could be a effective advertising tactic, adding this type of behavioral targeting to the intent that comes along with search could help even more finely target that individual who is ready to buy/convert for you.
Right now, advertisers are geo-targeting their service area and serving ads as far as they are willing to travel. Some SEM managers see performance by geographic locations skew significantly different and structure their accounts to take advantage of the best locations. But what if you could create if>then statements within your AdWords and Bing Ads campaign to increase or decrease your bid based on historical location data?
For example, to make the scenario outlined above come full circle, if that user sitting in the Mercedes parking lot searched for "top luxury car makers," you could bet the agency managing the SEM campaign for BMW would pay top dollar for their ad to appear in the top ad spot. This user not only has shown intent through search, but also is obviously in shopping mode looking to find the next dealership to visit.
What’s Holding This Back?
For a company like Google, Android device data is already being utilized to populate traffic patterns on Google Maps. The data and information to do this is available and it is just a matter of time before all SEM managers have another set of tools to play with.
The biggest issue is the quality of the data available. While location data is improving, most of it is not precise enough to guarantee a user is within a specific store. Sure, if they happen to be in a big box store, car dealership, or conference hall, the data is great to work with. But inside a mall or strip mall, it is still difficult for these advertising companies to determine if the user is in Starbucks or a hair salon.
With mobile phone technology improving with each new iteration of HTC, iPhone, and Android that gets released, it will just be a matter of time until the data is fine enough to leverage at a large scale.