Location Targeting: Perception And Reality

“Data-Driven Thinking” is a column written by members of the media community and containing fresh ideas on the digital revolution in media.

Today’s column is written by David Shim, CEO at Placed.

While there are a handful of companies close to realizing the potential of location-based targeting, as an overall industry there is a gap between perception and reality. Let’s use Jane as an example:

Jane walks by a Starbucks and receives a push notification­­­ for 10% off a drink order.   Jane then goes into the Starbucks and purchases a grande latte.  This is the perceived future of mobile advertising: to target a user in the real world to guide behavior. However, the reality of mobile ads looks a bit different today.

Perception: Jane walks by a Starbucks.
Reality: Jane walks within 100 meters of a Starbucks (length of a football field) and receives a notification of 10% off a drink order. Today’s location-based targeting is limited in the ability to precisely identify that Jane is walking by a Starbucks, rather the norm is to identify that Jane is within a few blocks of a Starbucks.

Perception: Jane receives a push notification for 10% off a drink order.
Reality: Jane needs to either (a) have opted in to receive push notifications from Starbucks or a Starbucks partner before walking by the store, or (b) be consuming mobile content that has the ability to target ads based on location in real time.

With (a), only a few companies have the ability to reach the Janes of the world at scale. With (b), real time bidding for mobile is limited by the reliability of cell network speeds. Akamai recently released a report that stated average ad load times of 12 seconds on mobile devices, which defeats the purpose of real time bidding (requires a decision to be made and an ad to be served in milliseconds).

Perception:  Jane goes into the Starbucks to purchase a grande latte.
Reality:  Prior to walking by the Starbucks, Jane walked by three other Starbucks.  It is of questionable value to attempt to convert Jane, if three other conversion opportunities failed.

Perception:  Jane was driven to the Starbucks by the push notification.
Reality:  Jane was planning on going to Starbucks; thus, the push notification unnecessarily provided a discount to an already loyal customer.

Jane and Starbucks highlight the difference between perception and reality when it comes to location-based ad targeting. This isn’t to say that perception won’t eventually be converted into reality, but to leverage location today, it requires taking a step back to evaluate what is technically possible at scale and the accompanying value proposition. Note that there are exceptions to parts of this example in the market today but they are limited.

Mobile Apps and Content

In the early days, online ad networks found they needed to sell the deliverability of today, rather than try to sell the promise of tomorrow. This meant not offering publisher-level transparency but packaging up sites into categories, optimizing campaigns by CTR versus waiting until third party ad serving became more widely available, and selling in-banner rich media, as expandable ads weren’t available at scale through all publishers. These ad networks understood there were dollars to be spent today and that selling the promise of tomorrow only delayed the distribution of ad dollars, and set the client up for disappointment due to unrealistic expectations.

By learning from the early lessons of online ad networks, mobile can start to bridge the gap in CPMs, where mobile inventory is priced at 20% of desktop inventory. The first step to realistically taking advantage of what makes mobile distinct — location — is to quantify this unique feature. Geotargeting by country, state, and city are available at scale with both mobile and desktop. The differentiator in mobile is the ability to close the last mile of location. Mobile has the potential to contextualize location to neighborhoods, categories of businesses, and individual storefronts. While location-based targeting may still have its challenges, an important starting point to unlocking its potential value is to understand the landscape of where users are currently consuming mobile content.

Understanding users’ proximity to restaurants, movie theatres, and grocery stores when consuming mobile content provides a baseline of place affinity. This baseline of places allows publishers to identify inventory available in proximity to a business or category of businesses. With this availability metric, publishers can start to package inventory based on affinities (similar to ad networks selling content categories) or explore opportunities for more advanced targeting. Referencing the early tactics from online ad networks, mobile publishers should be wary of starting with 1:1 targeting (ex. push notification within 10 meters of a Starbucks), as there are a number of inherent risks previously mentioned. Instead, they should take a crawl, walk, run approach when it comes to location-based targeting.

By understanding the current limitations of location targeting and working within the available technology stack, publishers and mobile ad networks can monetize location by packaging apps and mobile content based on place affinities.  This approach allows large marketers to shift dollars into mobile at scale by selling location at the aggregate app level versus selling at the user level.

Mobile Marketers

With online, almost all marketing efforts can be quantified. Banner ads use third party ad serving to measure impressions, clicks, and conversions.  Paid search is optimized by platforms that measure max bid, CPC, match type, inventory source, conversions, and return on ad spend.  In addition, social media, email, etc, all have become billion dollar categories because of their ability to quantify advertising efforts.

With mobile, that level of quantification is not yet available; until recently location measurement was limited to a count of users based on country, state, and city. Does this mean businesses should not commit dollars to mobile advertising? No. While there isn’t a level of quantification at a micro level matching that of banners or paid search, macro level opportunities exist.

Macro level measurement means understanding the activities of current and future customers in the physical world. This measurement is critical to establish a baseline of real world preferences for places, prior to exposure to location-based advertising. As location-based ad campaigns go live, marketers can analyze the change from the baseline to determine if the campaign was successful in changing behavior. It is important to work within the constraints of technology to move forward, rather than sitting on the sidelines waiting for all the stars to align.

While location-based advertising is still in its infancy, there are actionable steps that can and should be taken today by both publishers and marketers. These steps allow for the quantification and monetization of location-based targeting on available technologies. In order for the category to grow, publishers and marketers need to work from the technology available today to ensure that early adopters are able to achieve success and continue to invest in location, thus growing the entire ecosystem.

Follow David Shim (@davidshim) and AdExchanger (@adexchanger.com) on Twitter.

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  1. “average ad load times of 12 seconds on mobile devices” may make RTB a even better choice, since the percentage of time spent on BidRequest and BidResponse between exchanges and DSP will be smaller compared with RTB of desktop?

  2. Perception and Reality 1 & 4 could be seen differently:
    – Arguably, what is interesting is to target her when she is 100 meters away, to attract her to Starbucks. Why would Jane get an offer to buy at Starbucks if she is at Starbucks?
    – Also, location-based offers need to go beyond the fact that the consumer is within a radius of a certain store; you’ll need to look at frequency, habits, time of decision, etc… In other words, mobile is just another channel, not a reason to make an offer.

  3. This is one of the most concise explanations I’ve read regarding the real-world applications and perceptions of location-based technology. I tend to agree with the other commenters that mobile is a channel, or conduit to solve the problem, not the solution itself. The algorithms needed to track and assess individual costumer needs is possible, but is it feasible and cost efficient to incorporate those metrics into an app? I don’t know the answer to that question, but I think the point is that customer connections cannot be made with just a mobile technology alone.