Q1 2015

MOBILE ADVERTISING’S GUIDE TO LOCATION ACCURACY

LOCATION SCORE™ INDEX

THIS REPORT FOCUSES ON HELPING MARKETERS UNDERSTAND:

The key drivers to location accuracy

The current state of location data quality in the mobile ecosystem

The opportunities and obstacles of mobile location data

MARKETER TOOLS ADAPT WHILE UNDERLYING DATA REMAINS A CHALLENGE

METHODOLOGY

This report marks the fourth edition of Thinknear’s Location Score Index (LSI). When we first published the Location Score Index in Q2 2014, our goal was to bring transparency to the mobile industry in an effort to encourage industry-wide improvements in data quality. While much has changed in the last year, location data accuracy still remains an issue for marketers.

Our most recent findings indicate that while the overall programmatic space continues to grow very quickly, the quality of location data remains relatively unchanged from a year ago, on average. As predicted, mobile publishers are flocking to programmatic and are also sharing generous amounts of location data. Unfortunately, publisher behavior is slow to change, and predictions that the quality of publisher location data would improve rapidly have yet to be realized.

Thinknear, along with other ad-tech players, is currently working with the IAB and the MMA to define industry standards and make adjustments to the OpenRTB specifications which will ultimately improve data reliability from publishers. Additionally, many in the industry have begun work to develop tools similar to Thinknear’s Location Score platform that allow marketers to filter out poor quality location data. The necessity for this technology has forced some providers out of the space and leaves marketers with a small handful of location-based marketing platforms capable of handling the complexities of the current mobile ecosystem.

Looking forward, we still anticipate that data quality will improve with time and adjustments to industry standards. We will begin publishing the Location Score Index on a semi-annual basis, while continuing to focus our efforts on bringing clarity to key mobile marketing issues. Our goal is to help marketers understand both the opportunities and obstacles that the mobile marketing world faces.

LOCATIONSCORE INDEX:A YEAR IN REVIEW

The Thinknear platform accesses the largest sources of location-enabled mobile inventory in the U.S. To compile this report, we sampled and analyzed data from more than one billion ad impressions and ran location accuracy tests on more than 500,000 consumer ad experiences.

2 Location Score Index Q1 2015 twitter @Thinknear

A YEAR IN CHARTS

eMarketer reported a nearly 400% increase in U.S. mobile RTB ad spend in 2014 and has forecasted a nearly 300% increase in 2015. The charts below further highlight growth in available RTB inventory.

LOCATION-ENABLED INVENTORY IS ALSO INCREASING, KEEPING PACE WITH OVERALL PROGRAMMATIC GROWTH. THE GROWTH IN LOCATION-ENABLED INVENTORY ALLOWS CAMPAIGNS WITH VERY SPECIFIC LOCATION TARGETING GOALS TO BE RUN AT SCALE.

QUARTERLY PROGRAMMATIC INVENTORY

(INDEXED TO Q2 2014)

QUARTERLY LOCATION-ENABLED PROGRAMMATIC INVENTORY

(INDEXED TO Q2 2014)

Q3 Q4 Q1Q2

3

2.5

2

1.5

1

0.5

Q3 Q4 Q1Q2

3

2.5

2

1.5

1

0.5

PROGRAMMATIC INVENTORY CONTINUES TO INCREASE. OVER THE LAST YEAR, THE INDUSTRY SAW A 2–3X GROWTH IN AVAILABLE INVENTORY. AS MANY ANALYSTS PREDICTED, PUBLISHERS ARE FLOCKING QUICKLY TO PROGRAMMATIC PLATFORMS.

PROGRAMMATIC AD BUYING HAS EXPERIENCED TREMENDOUS GROWTH OVER THE LAST YEAR.

62% OF AD REQUESTS INCLUDED LOCATION DATA IN Q1 2015, WHILE IN 2012, ONLY 10% OF ALL AD REQUESTS CONTAINED LOCATION DATA.

twitter @Thinknear3 Location Score Index Q1 2015

50LOCATION SCORE™

51 (Q4)

VOLUME BY ACCURACY LEVEL (Q1, Q4):

37% HYPER LOCAL Location data was accurate to within 100 meters

of the user’s true real-time location. (Size of a football field)

9% LOCAL Location data was accurate between 100 and

1,000 meters of the user’s true real-time location. (Approx. 0.6 miles)

26% REGIONAL Location data was accurate between 1,000 and 10,000 meters of the user’s true real-time location. (Approx. 6 miles)

18% MULTI-REGIONAL Location data was accurate between 10,000 and 100,000 meters of the user’s true real-time location. (Approx. 60 miles)

10% NATIONAL Location data was not accurate to within

100,000 meters of the user’s true real-time location. (Greater than 60 miles)

37%

10%

28%

17%

8%

UPDATED INDUSTRY SCORE

LOCATION SCORE BY EXCHANGE

We score location on a 100-point, non-linear scale. Thus, it’s easier for the industry score to grow from 25 to 35 than it would be to grow from 75 to 85. We constantly update the algorithm used to calculate Location Score, so industry scores fluctuate from time to time.

66

TOP QUARTILE

39

BOTTOM QUARTILE

70

60

50

40

30

0 Mobile exchanges are not equal in terms of data quality. The chart on the left shows the average Location Score for the top and bottom quartiles of exchanges. The score varies from the high 30s to the high 60s. Marketers need tools to identify the most accurate data within each exchange.

4 Location Score Index Q1 2015 twitter @Thinknear

THE INDUSTRY LOCATION SCORE HAS REMAINED LARGELY UNCHANGED OVER THE COURSE OF THE PAST 12 MONTHS.

INTERPRETING THE RESULTS

Over the last year, the average industry-wide Location Score has ranged from 49 to 55, indicating that publishers are still having difficulty sending high-quality location data through mobile programmatic exchanges.

Growth in the overall programmatic space means marketers have plenty of high-quality inventory available, but finding it requires location scoring technologies to filter out all but the most reliable data sets.

In the last two quarters, our analysis indicates that a large portion of lower-quality location data is attributable to apps that are new to the programmatic ecosystem. We also noted a marked increase in the volume of mobile web traffic entering the programmatic space (as opposed to mobile app traffic). Mobile web has traditionally had lower quality location data, which appears to be impacting the industry as well.

INDUSTRY LOCATION SCORE TREND

WHAT DOES THIS MEAN FOR MARKETERS?

As the mobile world becomes more complicated, mobile marketers should be aware of the existence and impacts of high-quality and low-quality location data. When planning and executing location-based campaigns, mobile marketers need to ask the right questions:

INDUSTRY LOCATION SCORE TREND

What is the source of the location data?

If the data is scored or filtered, how is it being done?

Is the data taken at face-value or is it scored for accuracy?

Q3 2014 Q4 2014 Q1 2015Q2 2014

60

50

40

55 51 5049

5 Location Score Index Q1 2015 twitter @Thinknear

As this report demonstrates, app and mobile web publishers often send inaccurate location data within their ad requests. There are multiple reasons why this happens and we’re frequently asked if the issue is driven by publisher fraud. While there will always be some element of intentional fraud when there are large sums of money involved, our analysis and interactions with inventory suppliers indicate that most of the inaccurate location data in mobile is driven by a lack of standards and a complicated ecosystem that most publishers aren’t able to effectively navigate.

WHY DO PUBLISHERS STRUGGLE WITH LOCATION DATA?

LOCATION INACCURACY: IS IT FRAUD?

1. LACK OF KNOWLEDGE WORKING WITH SUPPLY-SIDE PLATFORM SDKS:

Publishers are focused on building their applications; advertising technologies often become a secondary priority. Publishers have significant control over the data they pass as part of an ad request, but linking specific data to relevant fields in the RTB specification requires diligence on the part of the publisher.

2. THE ASSUMPTION THAT ANY LOCATION DATA IS BETTER THAN NONE AT ALL:

Publishers assume that marketers want any kind of location data, even if it is stale (pulled hours or weeks in the past) or not representative of a user’s current real-time location. However, publishers don’t realize that sending inaccurate location data impacts the user experience and results in fewer advertisers willing to purchase the inventory.

3. UX DRIVEN DATA DECISIONS:

Most publishers are genuinely interested in supplying accurate data, but do not want to negatively impact the user experience. Impacts of pulling high-quality location data can include introducing latency, utilizing too much network traffic, significantly draining the battery, or popping up a location request prompt. As a result, publishers sometimes opt for location data with subpar quality over a negative impact to user experience.

4. MALICIOUS INTENT:

Publishers know that attaching geo-data to an ad impression will have a positive impact on eCPMs. While rare, some publishers may intentionally provide false location data in an effort to generate higher revenues.

6 Location Score Index Q1 2015 twitter @Thinknear

EDUCATION THROUGHOUT THE VALUE CHAIN:

Demand-side buyers need to emphasize the value of quality location data to their supply-side partners. Supply-side platforms that represent the inventory of publishers in turn need to educate all of their publishers.

USER REGISTRATION AND CENTROID DATA: User registration data often captures a home zip or DMA. These locations are often converted to “centroids” and passed in the ad request as the center point of the provided zip code. While relevant in a broad context, these data do not provide any reliable context for behavioral or real-time marketing strategies.

AS DEMONSTRATED BY THE LOCATION SCORE INDEX, THE INDUSTRY HAS A LONG WAY TO GO IN IMPROVING DATA ACCURACY. BELOW ARE SOME OF THE STEPS THAT WOULD HELP IMPROVE THE STATE OF THE INDUSTRY:

STRICT SUPPLY-SIDE CONTROL OVER LOCATION DATA:

Supply-side platforms often leave it to publishers to pull location themselves and include the data in outbound ad calls—rather than the SSP’s SDK to fetch location data. More consistent use of SDKs with advanced location tools will help improve accuracy.

IP-BASED LOCATION DATA: IP data is typically based on the server location of an app and has nothing to do with the user’s true location. It is often used because it requires no user permissions and it can be done on the server side, thus requiring no extra client-side work. However, IP-based data is a poor tool for mobile targeting given its lack of relevance to the mobile user’s actual location.

CACHED DATA: Some publishers only fetch location data periodically in an effort to conserve battery life. The duration of time between location “pulls” can range significantly. Marketers seeking to leverage data about a user’s real-time location or the locations a user has been in the past need the freshest data possible. Many industry trade groups are exploring changes to the OpenRTB spec that would require publishers to communicate the “freshness” of data.

INACCURATE DATA CAN COME IN MANY FORMS. BELOW ARE A FEW EXAMPLES OF HOW INACCURATE LOCATION DATA CAN BE PRESENTED:

IMPROVED DETAIL AND TRANSPARENCY IN SPECS:

The OpenRTB specification should be updated to include references to when a location was fetched if the device’s Location Services were used.

WHAT DOES INACCURATE DATALOOK LIKE?

7 Location Score Index Q1 2015 twitter @Thinknear

Thinknear is a location technology company and full-service mobile advertising platform focused on delivering amazing advertising campaigns for agencies, brands and consumers. Thinknear’s platform delivers the accuracy, scale and technology required to effectively leverage mobile location data to power better consumer experiences. In mobile, accuracy matters, and as a division of Telenav, Thinknear leverages exclusive access to over 15 years of proprietary location data. To learn more, please visit www.thinknear.com and follow @thinknear on Twitter and Instagram.

Copyright © 2015 Thinknear by Telenav.

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