With smartphones becoming the default mobile device, the process of showrooming is simplified with apps like Amazon Price Check, RedLaser, etc...  In response to this growing threat, Target and Best Buy have taken drastic steps to directly price match leading online retailers.  At a macro level the threat to brick and mortar retailers is clear, what has widely been ignored is understanding who is doing the showrooming.

In the largest showrooming study to date, Placed identified where Amazon customers who indicated they had showroomed in the past, shopped in January 2013.  This population was further segmented by Gender.  The results were significant, finding Male Showroomers were 39% more likely to visit Best Buy, 24% more likely to visit Home Depot, and 23% more likely to visit Lowe's than the US population.  For Female Showroomers the top three were completely different with Kohl's at 49%, PetSmart at 47%, and Bed Bath and Beyond at 46%.

These insights inform brick and mortar retailers on potential risks associated with showrooming and their customers.  Retailers not directly impacted by showrooming today, should take this as an opportunity to proactively address showrooming in their aisles, or risk reactive measures that drive down margins (ex. price matching).

For online retailers, this highlights an opportunity to leverage mobile to gain marketshare.  By leveraging geofencing, online retailers can proactively target audiences that have a propensity to view items offline, but purchase online (at a lower price point). Understanding these behaviors makes location a key feature to drive ROI for mobile media campaigns.

David Shim is the Founder and CEO of Placed, the leader in location analytics.  By connecting the physical and digital worlds, Placed has created a new class of analytics focused on location.  Prior to Placed, David has held leadership roles in product, marketing, and operations at Quantcast, WebTrends, Farecast, and Razorfish.

The ability to measure market share in real-time will disrupt the way retailers determine the success of the marketing efforts.  Today, retailers measure performance in various ways including return on ad spend (direct response), and in-store sales (branding, promotional).  By measuring market share, retailers are able to take a macro view on their marketing efforts that expands beyond their brick and mortar walls.

Quantifying performance by market share allows retailers to look at the entire retail ecosystem, versus a siloed approach to marketing.  By using market share as a key metric it allows retailers to optimize to gain share of wallet.  Understanding market share allows marketers to build campaigns to capture the $1.44 spent at competitors for every $1 spent with them.

Placed recently released their 2012 Holiday Retail Insights providing a first glimpse into retail market share, which includes surprising volatility.

Wireless Carrier Retail Stores - Market Share

• Verizon and AT&T wrestled for the top position in terms of wireless carrier store visits in the last six weeks of the holiday shopping season.  AT&T held the lead 4 out of the last 6 weeks.
• T-Mobile started and ended the holiday shopping season 4th in market share, but quickly closed the gap and at its peak came within one percentage point of Sprint (#3).

Big Box Retailers - Market Share

• Nearly 2/3 of visits to the largest big box retailers during the holiday season were at Walmart (65.4%), followed by Target (26.1%) and Kmart (8.6%).

Department Store - Market Share

• 1/3 of visits to the largest national department stores during the holiday season were to Kohl’s (32.3%), followed by Sears (26.7%), J.C. Penney (22.0%) and Macy’s (19.0%).

David Shim is the Founder and CEO of Placed, the leader in location analytics.  By connecting the physical and digital worlds, Placed has created a new class of analytics focused on location.  Prior to Placed, David has held leadership roles in product, marketing, and operations at Quantcast, WebTrends, Farecast, and Razorfish.

Placed recently released its Holiday Shopping Insights providing in-store activity reporting down to the retailer.  As part of this analysis, Placed looked at the busiest days for some of the largest retailers in the US including Walmart, Target, Toys 'R' Us, Macy's, Best Buy, and Kohl's.

This analysis highlights that a single days performance does not make or break a retailer's Christmas.  Rather these insights highlight consumers' retail preferences are variable based on time of year.  For example, Target's busiest day was Christmas Eve, while for Best Buy is was December 22nd.  The ability to look past macro retail trends and dive into retailer level insights enables marketers to react in season to offline behaviors of consumers.

As consumers retail preferences change based on time to Christmas, marketers are able to leverage consumer behaviors to drive incremental sales.  The ebbs and flows in terms of relative in-store visits were incredibly diverse across the last week in December (Super Saturday, Christmas Eve, and Day After Christmas).  Only American Eagle and Victoria’s Secret made the top five in these three days, while the rest of retailers were unique.  American Eagle was also the highest ranked retailer for Black Friday.

David Shim is the Founder and CEO of Placed, the leader in location analytics.  By connecting the physical and digital worlds, Placed has created a new class of analytics focused on location.  Prior to Placed, David has held leadership roles in product, marketing, and operations at Quantcast, WebTrends, Farecast, and Razorfish.

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.

Inventory Availability Based on Location
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.

Media Buying Based on Location
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.

This article initially appeared on Street Fight.

David Shim is the Founder and CEO of Placed, a location analytics company.  By connecting the physical and digital worlds, Placed has created a new class of analytics focused on location.  Prior to Placed, David has held leadership roles in product, marketing, and operations at Quantcast, WebTrends, Farecast, and Razorfish.

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.

This article initially appeared on AdExchanger.

David Shim is the Founder and CEO of Placed, a location analytics company.  By connecting the physical and digital worlds, Placed has created a new class of analytics focused on location.  Prior to Placed, David has held leadership roles in product, marketing, and operations at Quantcast, WebTrends, Farecast, and Razorfish.