Kevin Hannan is vice president of product management at where he built and launched Nielsen’s next-generation mobile audience measurement and analytics platform.
The is looking to location-based advertising as one way to address its monetization challenges. This approach means mobile users receive targeted ads only when using their phones within an "invisible fence" around a specific retail location.
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The problem with doing it this way is that marketers miss some big opportunities and don't necessarily get to the best audience. Here are the three major obstacles with this trend and two ways to address them.
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A user has to use their phone or tablet when inside the fence, and they need to also be on an app or mobile website that your ad can reach. The net effect is a dramatic under-reaching of the audience that lives, works, or regularly shops in the area.
There are a few small-dollar categories for which spontaneous or impulse offers make sense. Examples include coffee discounts and on-the-go lunches. Research shows, however, that almost are planned and not spontaneous.
The rate of planned purchases is even higher in large-dollar categories such as travel, computers, and furniture. So, offering $100 off a $1,500 TV when the consumer is walking by the electronics store, frankly doesn't work. A consumer isn’t going to stop, head into the retailer and walk out with a major purchase without some forethought.
Most commercial areas are made up of diverse businesses and visited by people of all walks of life. If a mobile user is in a mall with twenty stores should they get offers from all twenty stores? No. And just because a mobile user is walking past Golfers Warehouse, should this affirmed non-golfer get a deal for 50% off a new golf driver? The appropriate ad should consider a person’s behavior, not just their location
Geo-fencing depends on the notion that targeting a mobile user with an ad based on location alone will somehow change their behavior and prompt them to take advantage of a deal or promotion to make the purchase on the spot.
A better approach is to target users with relevant ads and reach them when they are planning their purchases. Effectively using location data can help marketers identify their true audience and reach them at the right time. Here's how.
Most user-based targeting in mobile today is based on which mobile app or website the consumer is using. This has obvious limitations for apps and sites with broad appeal. That’s because it’s hard to identify unique behavioral characteristics for users of this content.
However, consumer opt-in location data -- where users allow certain data to be tracked -- offers an opportunity to target based on anonymous behavioral characteristics. For example, this type of data will reveal if someone dines out, shops at a certain type of store, and golfs. Thus, an advertiser can use this behavioral data to target a consumer with personally relevant offers.
Reaching a consumer with an offer from Target when he is walking into a Wal-Mart is just too late. The good news is there's an alternative approach. By using mobile-location data and predictive analytics, advertisers can reach users that are likely to be near Wal-Mart in the near future. This predictive targeting can be as straight forward as reaching people that live or work near a specific store-chain or have recently been to the chain. Some technologies allow brands to reach users when they are near a specific chain and also reach the same users elsewhere.
With this approach, brands can increase the frequency with which users view a specific campaign by at least three times. Advertisers can also implement more sophisticated predictive-targeting campaigns by, for example, reaching an audience of people that are likely to dine out on weekends, with offers and ads on Thursday night or Friday morning to influence where they make a dinner reservation. By using information about the user and reaching them in advance, marketers can increase interest by four times.
While the promise is huge, location data brings real challenges with consumer privacy. The leading solutions can manage data in an anonymous and safe way that benefits consumers, by extracting the intelligence from the location data and then discarding the raw data. For example, technology can capture that a mobile user shops at big box stores, and then throw away the raw data that shows the consumer was at a specific store at a specific time. In addition, solutions should be based on opted-in location collection, have the ability for users to opt-out, and allow the users to see their own data.
A handful of mobile players with user scale and location data have an opportunity to radically change mobile advertising by extracting behavioral information and applying location prediction in ad targeting. To succeed they must convince consumers of the benefits and give them significant control over their own data. If they can win over consumers then the big players have a chance to turn mobile into an advertising medium that’s on par with online.
This story originally published on Mashable .