Location Targeting: Perception and Reality

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 theaters, 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 originally appeared in AdExchanger

Placed Wins the Unilever Mobile Commerce Startup Challenge at ad:tech New York

We are proud to announce that Placed was selected as the winner of the ad:tech Startup Spotlight Unilever Mobile Commerce Challenge last week in New York. Placed was chosen from hundreds of applicants to compete on-stage at the conference, which was judged by members of the Unilever team including Fabio Marciano, Senior Brand Manager for Knorr, Craig Rudner, Senior Brand Development Manager for Klondike Ice Cream, and Drew Corry, Communications Channel Manager for North America.

 

David Shim, Founder and CEO of Placed, had just 10 minutes to pitch the Unilever team on how location analytics could benefit their Knorr brand of products. David explained how, leveraging Placed Panels, Unilever could build their own location panel, providing never-before-seen insights into the physical world behaviors of their current and potential customers. Through these behavioral-level location insights, brands such as Unilever can improve their offline and online campaigns with highly actionable intelligence into their consumer segments.

 

The Unilever team was impressed, selecting Placed as the winner from a talented field of category finalists including Waze, Urban Airship and LevelUp. Not only did Placed walk away with the Innovation Award, but we now have the opportunity to take the pitch in-house at Unilever.

 

Needless to say, a very successful first trip to ad:tech for the team here at Placed.

 

Startup Spotlight Winners in the Unilever Mobile Commerce Category

Placed Founder and CEO, David Shim, accepting the Innovation Award with the Unilever judges.

How to Improve Your App with Location Analytics

Location Analytics for Apps You probably already use mobile analytics to understand what people are doing within your app – average session length, daily active users, most popular screens, etc.  But do you think about where people are, or what they are doing in the real world when using your app?  Mobile users can be almost anywhere when using an app or consuming content, which makes mobile different than other types of media.  To truly take advantage of the medium, it’s important to understand the impact that location has on app usage.

 

 

What is Location Analytics?

Understand the Real-World Context of App UsageUntil recently, there wasn’t really an easy way to get location insights for an app.  Google Analytics and Flurry are two excellent mobile analytics solutions, but they will only show location data down to the city level.  That’s why we launched Placed Analytics – a free service – to provide app developers with daily, aggregated reports around the places that users were nearby when interacting with their app.  The service supports Android, iOS and mobile web; the only requirement is that the app or site have permission to collect location data from its users.

 

Use Cases

There are numerous ways that you can use location analytics to better understand the behaviors and preferences of your users, and to shape plans for new features or improvements to your app.

 

Here are some example use cases:

 

App Engagement by MovementUnderstand how often people are moving (and how quickly) when using your app.
Are people usually in transit, walking or standing still?  This may seem obvious, but if they’re often walking or in transit, consider making buttons larger.  Or, you may want to add voice controls to make the app more user-friendly (not to mention safer).

 

 

 

Measure location by in-app activityTrack specific events within your app to identify where people were when they completed important actions.  Where were people when they first registered for your app?  When they upgraded to the paid version?  When they made a purchase or shared something on Facebook?  Using location analytics, you can start to identify relationships between certain types of places and key activities, and adjust your messaging and marketing tactics accordingly.

 

 

Granular Geographic Insights Ensure that your app addresses all the needs of your users.  If your app has a price scanning feature, make sure that you are set up to support all of the various types of products sold in the businesses where people are opening your app most often.  Or, if your app connects people with local services, you can identify which neighborhoods you should expand into next based on the specific areas where people are opening your app.

 

Learn more about your users and their habits.  See if they use your app in urban areas, suburban neighborhoods or rural places.  Determine if they prefer fast food or French restaurants.  Identify whether they shop at Walmart or Nordstrom.  Get a better sense of who your users are and what they like to do.

 

Takeaway

 

Of course, these are just a few general examples.  There are as many use cases as there are types of apps on the market.  If your app or site uses location, you can now access a wealth of valuable data – for free.  Simply implement an SDK, update your app on the market and start discovering new insights about your app and its users the next day.

 

This article originally appeared in MobileDevHQ.

Quantifying the Effects of Hurricane Sandy on Physical World Behaviors

As news reports filtered in of the possible severity of Hurricane Sandy, many people along the east coast spent days preparing for the storm that unfortunately lived up to many forecasters’ worst predictions.

 

In order to understand the effects of Hurricane Sandy on people’s activities in the physical world, we analyzed the behaviors of opted-in panelists via their mobile device who were located in the affected regions of New York City and along the New Jersey coast.  This data is an aggregation of pilot participants of Placed Panels and is not necessarily representative of the general population.

 

Intuitively, one would correctly assume that grocery and hardware store visits would increase, but for the first time these assumptions can now be validated with quantifiable metrics. By contextualizing these assumptions, Placed observed interesting shifts in behavior ahead of the storm.

 

People in these impacted areas were nearly 4X more likely to visit a grocery or hardware store during the weekend of October 26 – 28, when compared to the monthly average, as many people stocked up on food and supplies in anticipation of the storm.  People were also more than 2X as likely to visit gas stations and convenience stores during those three days as they made their way out of town or filled up in anticipation of post-storm shortages.

 

Category Visits Trend

As the storm made landfall on the 29th though, Placed saw a precipitous drop in visits to grocery and hardware stores as many people hunkered down at home. Additionally we saw a more gradual decline in relative visitation to gas & convenience stores. The day of the storm saw visits to hotels & motels increase by fourfold as many sought refuge away from their homes. The uptick in hotels & motels from October 5th to the 8th was associated with the Columbus Day holiday weekend.

 

Often overlooked, Placed analyzed the places people were less likely to go as the storm approached.  Placed found restaurants, gyms and places of worship all saw around three-fourths less relative visitation during the weekend of October 26 – 28 compared to the monthly average for these activities.

 

Taking a deeper dive into the places people visited during the weekend of the storm’s approach, we saw that people in these impacted areas were nearly:

 

  • 4X more likely to visit a RadioShack compared to the October average.
  • 3X more likely to visit the supermarket chain Stop and Shop.
  • 2X more likely to visit drugstore chain Duane Reade, Walmart and Home Depot.

 

As expected, Hurricane Sandy clearly affected people’s physical world behaviors, but quantifying these changes in behavior brings a new level of dimension to understanding how real-world events influence people’s paths throughout the physical world.

Placed to Take the Stage at ad:tech New York

Next week the Placed team will head to ad:tech New York to take part in this year’s Startup Spotlight Competition. Placed is among four startup finalists chosen to compete for the coveted Innovation Award in the Unilever Mobile Commerce category.  Placed will demonstrate how location analytics can help Unilever solve the following challenge:

 

For startups that can make mobile a real sales channel for a CPG brand. The product or service that wins will help Unilever wade through the barcodes, coupons and check-ins to find the right tactics for driving mobile purchases.

 

If you will be at ad:tech, come check out the competition in-person on Thursday, November 8 from 2:45 – 3:45pm in Room 15, Hall 1E, Level 1 at the Javits Center.

 

Additionally, swing by our booth in Innovation Alley to meet us in person or message @placedinc on Twitter to connect.

 

UPDATE: We are pleased to announce that Placed was selected as the winner in the Unilever Mobile Commerce Challenge!