attribution: the next phase in the age of mobile … · coupons and other areas whose nascent stage...

ATTRIBUTION: THE NEXT PHASE IN THE AGE OF MOBILE ADVERTISING

January 2014

Attribution: The Next Phase in the Age of Mobile Advertising

Copyright © BIA/Kelsey 2013 i

CONTENTS

Executive Summary .................................................................................................................. 1

Starting Point: ......................................................................................................................... 2

Where are We Now? ................................................................................................................ 2

Location Saves the Day ............................................................................................................ 3

Proving out ........................................................................................................................................... 4

Performance Grade ............................................................................................................................... 6

Not So Fast… Location Targeting’s Problems (AND Solutions) ...................................................... 6

Accuracy ............................................................................................................................................... 6

Attribution ............................................................................................................................................ 6

Location Powered “Big Data” .................................................................................................................. 8

Insightful Places .................................................................................................................................... 9

Drilling Down on Attribution .................................................................................................... 11

Location Data Analysis ......................................................................................................................... 11

Third Party/ Panel Data ....................................................................................................................... 13

Offline Purchase Data .......................................................................................................................... 13

Conclusion ............................................................................................................................. 15

About BIA/Kelsey ................................................................................................................................ 16

About BIA/Kelsey Sponsored Reports ................................................................................................... 16

About Verve Mobile ............................................................................................................................. 16


Copyright © BIA/Kelsey 2013 1

EXECUTIVE SUMMARY

Mobile tops the list of tech and media topics that have gained the marketplace’s attention and investment over the past few years. This focus is being sustained by hardware innovation, rising smartphone penetration and the resulting mobile Web and app usage.

A key part of this discussion is monetization. Though paid apps have succeeded in some areas such as games or niche vertical interest, ad support is a more broadly fitting choice for lots of app developers and publishers, due to mobile users’ high commercial intent.

But a challenge has arisen in the slower than expected advertiser adoption. Put another way, advertiser adoption hasn’t caught up with mobile usage. Mobile holds a 12 percent share of U.S. consumers’ media time, but only a 3 percent share of ad revenue.

The good news is that a few factors will counter this supply/demand imbalance that’s mitigating sell-side monetization. For one, mobile’s high engagement relative to other ad supported media will create premium inventory that boosts advertiser demand and rates.

One of the main sources of this premium lift will be location targeted ads. Their value will be a function of congruence with user intent, such as 50 percent of Google mobile searches that seek local information. Location targeted ad performance will also drive this.

Such performance deltas for mobile ads that carry various flavors of location relevance are already evident from data shared by mobile ad companies cited in this report. Advertiser demand

will follow in an inevitable process of market evolution towards higher ROI.

Mobile local campaign tactics won’t just involve where the ads show up and what they contain, but how they’re measured. A feedback loop of ad effectiveness drives towards one of the most important areas of mobile ad innovation in the coming years: Attribution.

Knowing which ads effectively drove brand lift and various forms of conversions will have a vital role in optimizing and assigning value to mobile ad tactics. And that will only come about through accurate measurement of actions taken after ad exposure and engagement.

This is developing in lots of ways that are getting attention such as “closed loop” point of sale technologies. This includes mobile payments, coupons and other areas whose nascent stage and fragmented standards compel more realistic near-term solutions.

Attribution technologies are developing along these lines to speak the language of large advertisers. Brand ad dollars are where the action is in mobile today, and attributing the success of those campaigns is the name of the game.

The question remains how? The science of mobile ad attribution is at an early stage, but we’re already seeing innovation and emerging best practices.

This report examines ways mobile advertising and attribution are materializing, how mobile location data is playing a central role in these approaches and where they’re moving next.



STARTING POINT: WHERE ARE WE NOW?

Since the iPhone launched in 2007, mobile has captured the tech world’s attention and investment.

In addition to ratcheting up the level of competition in mobile hardware, mobile’s ascent has inspired innovation in various flavors of content packaging and delivery.

US Smartphone Penetration on the Rise

Smartphone penetration is currently at 63 percent of U.S. mobile subscribers and BIA/Kelsey projects it will reach 92 percent by

2017. This has driven a flood of content consumption through apps, mobile web, email, and other developing formats.

Mobile holds a 12 percent share of consumers’ current media consumption. More importantly, mobile’s share is on the upswing, chipping away at maturing (television) and declining (newspaper) media.

(BIA/Kelsey, 2013)



LOCATION SAVES THE DAY

One downside to this rapid usage growth is that ad dollars haven’t kept pace. Compared to the 12 percent share of users’ media time cited above, mobile holds a disproportionately low 3 percent share of U.S. ad dollars.

Share of Consumer Time Versus Share of Ad Dollars

So what can mobile marketers do about the slow adoption of the buy side? For one, it will pick up as advertiser evolution and education drive more effective tactics. The feedback loop that results

from more effective practices will in turn boost adoption and demand.

Premium ad strategies will also play a key role in boosting mobile advertiser demand – and ad rates along with it. Mobile has the potential for various sources of premium inventory, data targeting and formats, whose value is a function of deeper engagement levels.

Examples of emerging forms of premium advertising include achievement based ads (i.e., “earned media”) in games or fitness apps. There are also organically generated “native” ads such

(Kleiner Perkins Caufiled & Byers)

as Facebook’s Sponsored Stories which show promising results at early stages.

Another impactful source of premium rates will come from local relevance. This aligns with mobile user behavior and expectations. The device’s portability and location awareness means the mobile use case carries a high degree of immediacy and local intent.



For example, BIA/Kelsey estimates (based on disclosures from Google and others) that half of mobile search queries have local intent. The comparative desktop share of local search queries is 18 percent.

PROVING IT OUT

One of the critical components of BIA/Kelsey’s ad revenue forecast is the breakdown of ad dollars associated with location-targeted campaigns. Weighing in at 37 percent of total ad revenues ($1.4 billion) last year, they’re project grow to 52 percent ($10.8 billion) by 2017.

US Mobile Spending Goes Local

Like all local media BIA/Kelsey cover, this is defined by geo-targeted ad placement. Examples

include in-app banner ads shown to users in certain geo-fences; search ads based on explicit or implicit (GPS) location queries; local calls to-action; or local ad creative.

Beyond classifications, the larger question remains why will location be so important? The share-shift may seem aggressive at first, but begins to make sense after considering the market factors impacting mobile advertising and driving value in location targeting.

As with many advertising media, the reasons begin with consumer behavior. If the typical new media pattern holds true, advertiser adoption will eventually follow the consumer’s lead. There’s evidence to suggest this is already happening.

(BIA/Kelsey, 2013)



For example, mobile ad platforms report significant growth in campaigns that are geo-targeted or targeted to reach specific location-determined demographics. xAd reports that in Q1 2013, these campaigns grew from 27 percent to 58 percent year over year.

Drilling down, Verve Mobile reports the breakdown of mobile advertising among different business categories on its network. Here retail is the clear leader due to a desire for traffic of the analog variety (foot traffic). Autos follow closely behind for the same reason.

Top Categories Leveraging Location Targeting Strategies

(Verve Mobile)

Within the influential and opportune retail category, Verve Mobile took the analysis a step further by examining 1,500 campaigns to find that geo-fencing is the most prevalent location targeting strategy. This includes geo-conquesting (targeting competitors’ locations).

TIME OUT: WHAT IS LOCATION TARGETING?

It’s important to define the flavors of location based ad targeting, alluded to in the previous data sets. There is traditional “geo-

aware” ad placements often targeted on a city or DMA level; geo-fencing which happens on more of a meter level; and geo-defined audience targeting.

There is a sliding scale among these options that trades off accuracy/reliability with ease/availability of data. Geo-based audience targeting is meanwhile a method quickly developing through the innovation of data providers (detailed further below).

Geo-aware: Traditional geo-targeting such as DMA-level. Ad creative can change accordingly (i.e., “You are 1.5 miles from the nearest McDonalds”)

Geo-fence: Ads delivered only to users within a set radius around a location or competitor’s location (includes geo-conquesting)

Audience: Targeting individuals and locations based on demographic and/or behavioral data



PERFORMANCE GRADE

Premium ad rates from these location targeting strategies are growing as a function of their performance. This includes higher click-through rates and other metrics when compared with non-location targeted benchmarks. And we’re already seeing lots of evidence of this.

Ad networks and ad tech companies focused on the location targeting opportunity continue to indicate proof points for the location targeting opportunity. Some leading names in this growing category include xAd, Verve Mobile, YP and PlaceIQ.

Verve reports that campaigns in the quick serve restaurant (QSR) category that apply location targeting achieved a 2X increase in click-through rates. Some saw a 3X lift in foot traffic. Campaigns applied a combination of geo-aware, geo-fencing and audience targeting.

Performance Comparison

(Verve Mobile)

NOT SO FAST… LOCATION TARGETING’S PROBLEMS (AND SOLUTIONS)

While location-targeted advertising will be the answer to many of mobile’s challenges and represents a large part of its future, it presents many current challenges. Before we get carried away with mobile location targeting’s

performance advantages, it’s important to acknowledge these barriers.

Mobile advertising’s problems and their solutions map to two main areas: accuracy and attribution. Below is a synopsis of these factors and a deeper dive follows.

ACCURACY

One weak link in location targeting is the data integrity of the location signals on which those tactics are based. In other words, it is often difficult to capture a user’s precise location to target ads effectively and in ethically sound ways.

ATTRIBUTION

Advertising attribution measures user behavior post-ad exposure. It’s a statistically relevant way to identify advertising effectiveness and ROI. This has many implications for optimizing and iterating campaign tactics, and shifting budget.

DRILLING DOWN ON ACCURACY

Sometimes known in the industry as the “10 percent problem,” location-based mobile ad targeting accuracy is a gating factor to its opportunities. Location targeting methods being developed – some outlined above – can only be as good as the location signals they utilize.

The most precise location targeting – defined by lat/long level accuracy – only happens in about 10 percent of mobile local ad impressions. The rest defer to desktop methods such as zip code



targeting or reverse IP lookups, which can be very imprecise or even inaccurate.

“There’s one very significant problem that is holding mobile location targeting back,” Verve CEO Tom MacIsaac recently wrote in an influential Business Insider article. “The market has been flooded with bad location data. And many are turning a blind eye to this fact because to acknowledge it would be contrary to their business interests.”

To quantify this, mobile ad network Telenav recently ran a study in which the company compared ad exchange supplied location data with its own location data. The test spanned half a billion ads, and exact locations were identified directly among a sample of its users.

• 26 percent of exchanges reported locations that were off by more than 10,000 meters.

• 42 percent were off by at least 3,500 meters. • 33 percent were accurate within 100 meters.

There are a few explanations for these location signal deficiencies. Here the science diverges between the different methods for collecting that location data. MacIsaac lists the following methods employed by mobile ad platforms to determine location, each of which has inherent challenges.

1. Deriving location from the phone's location services (GPS or WiFi triangulation).

2. Inferring location by reverse geo-coding a user's IP address.

3. Asking for and relying on the accuracy of a user's registration data.

The first method is the most accurate but sometimes the hardest to obtain. Native apps have more direct access to resident device hardware such as the GPS chip. But for privacy reasons, most mobile OS require user opt-in on an app-by-app basis to share location.

iOS Location Sharing Opt-in

(MyLocalWineStore.com)

For IP location data, some networks and exchanges convert IP address into an estimated lat/long, thus giving the false impression (no pun intended) of a more granular reading. This is done to boost the value of impressions.

MacIsaac provides a bit more color on this problem:

The methods being used to generate “inferred” lat/longs fall into two primary categories (although there are more): “centroids” or “randomized” lat/longs.

Centroids are lat/long coordinates that are generated by software programs that automatically pick the center of a geographic region as a substitute for either no location data — many corrupt lat/longs are dead center in the middle of the country — or for lesser-quality location data — they are in the middle of a state, DMA, city or zip.



Randomized lat/longs are generated by software programs that randomly choose lat/longs within a region. This is a really big problem for the evolution of the highly granular location targeting that has so much potential and has marketers so excited. If a marketer is targeting a particular store, mall, office building, neighborhood or city block, or an audience segment that is based on inferences drawn from the context of a user’s location, then bogus location coordinates are likely targeting the wrong place and the wrong people and driving down performance. Garbage in, garbage out.

And these flawed methods lead to real performance degradation. Verve reports that its impressions – based on first party location data – have a 4X better CTR than exchange based impressions. This is represented in the following “verified” versus “unverified” stat.

Verified vs. Unverified CTR Stats

But even with a reliable lat/long reading, there can still be missing context, such as deeper data on location-related variables. These include user profiles, intent and predicted behavior. Just in

time for the age of “big data,” this is where things start to get interesting.

LOCATION POWERED “BIG DATA”

A new batch of companies is bringing more data into the picture by layering in things like historical behavior, demographics, or users' spatial movements. The goal is to bring more context to a given device node, user profile, or spot on the map.

The implication is that this type of data can counteract the accuracy holes in the location data described above. Given that there’s weakness in those signals, other data points can compensate with equal or greater relevance signals that matter to advertisers.



“Demographic information, including age ranges [and] geographies, can be valuable predictors,” Localeze CEO Jeff Beard told BIA/Kelsey. “Yet, if you can merge this information with socio-economic and lifestyle data such as affluence and shopping trends; businesses can build a much more precise privacy-friendly profile for targeting.”

In addition to Localeze, startups like Factual and PlaceIQ are aggregating point of interest data to support these efforts. There are also mobile ad companies with in-house tech like xAd, JiWire and Verve. The latter players lead the way with the location-based audience targeting introduced earlier.

This movement is catching on among advertisers because it speaks their language: demographic and behavioral targeting. Buying audience segments is what brand advertisers have been doing for years. Mobile audience targeting is just a newer and more precise version.

INSIGHTFUL PLACES

Verve reports that location-based audience targeting is the second most utilized location strategy employed for retailers on its network (behind geo-fencing). Some advertisers are finding even better results with a combination of audience targeting and geo-fencing.



Verve has compiled about 20 consumer segments and mapped them to different hexagonal grids that overlay the physical world. These are each tagged with point of interest, demographic and other context-defining data for media buyers or programmatic placement.

“We ingest, store and make sense of massive amounts of demographic, transactional, commercial, and social data to understand the context of the user,” said Verve CEO Tom MacIsaac. “We then observe user location and context over time and assemble unique anonymous attributes to understand and segment mobile consumers.”

Advertisers can target grids whose characteristics align with targeted customers, service areas or other campaign objectives. This includes users who have demonstrated a psychographic, demographic or transactional affinity for the advertiser’s product.

“If a suburban shopping center occupies a certain hex-grid, we can understand the demographics of nearby neighborhoods, the mix of commercial establishments, and other data that will tell us a

lot about a user in that grid,” said Verve CTO Chris Nicotra. “And, over time, as we see a unique user in multiple grids we can develop an anonymous profile of a user based on their place-based attributes.”

The retail categories making the best use of these data include consumer electronics, department stores and home improvement. Retailers in these categories on average are seeing resulting CTRs of 1.6 percent, 1.25 percent and 0.95 percent respectively.



DRILLING DOWN ON ATTRIBUTION

Many of the above technologies lead up to a key question for advertisers: How do I know it’s working? Location data can also be an important part of the solution to measure mobile advertising effectiveness and ROI, also known as Attribution.

This principle also operates under the reality that most U.S. retail spending happens offline. And an increasing share of that total is influenced online and in mobile. So connecting those dots is the name of the game.

Here it’s important to differentiate advertising attribution strategies from “closed loop” lower funnel conversion tracking technologies generating buzz today (i.e., mobile payments1). The latter is an exciting area but its nascent stage compels nearer term solutions.

Many closed loop methodologies are nascent and fragmented, with no clear standards (NFC, barcodes, Bluetooth, etc.). Further, they fail to address the “upper funnel” branding issues that will draw in the big money from large national brands.

As such, the attribution technology standards examined below are focused on measuring brand awareness and other upper funnel advertising through various methods. Each involve measuring lift or conversions after ad exposure or engagement.

1 See BIA/Kelsey Report: Mobile Local Shopping and Payments: A Virtuous Cycle

Overall, we classify these developing attribution methods into three main categories:

Location Data Analysis

This involves tracking users’ spatial behavior after ad exposures using various extrapolation methodologies. Most of these solutions are built and applied in house by mobile ad companies. Examples are Verve’s Foot Traffic Index, Place IQ’s Place Visit Rate, and JiWire’s Location Conversion Index.

Panel Data

This involves third party players like Nielsen and Placed which administer panels of mobile users whose ad exposures and related physical world behavior are tracked. Large samples can derive pattern analysis and insights about ad effectiveness. This is similar to the online panels tracked and administered by comScore, except in the “real world.”

Offline Purchase Data

This involves using offline behavioral data post-ad exposure to measure effectiveness. This works in a manner similar to the other methods introduced above, except the extrapolated conversion metrics use transaction data through a network of partners (i.e., payment processors).

Each of these methods is at a different stage and has unique advantages and challenges. The following sections drill down on each one.

LOCATION DATA ANALYSIS

As introduced above, there are many technologies developing to draw correlations between online ad exposure and offline conversions. This usually involves some version



of tracking ad impressions in a given area, along with the associated lift in subsequent store visits.

One example is Place IQ’s Place Visit Rate (PVR). The company explains it as follows:

PVR is measured by aggregating all of the devices that were messaged during a campaign and analyzing the number of those same devices that were later seen within a specific location or place footprint. Additionally, PlaceIQ can also set up A/B testing to measure PVR lift by identifying control groups or messaging additional PlaceIQ audiences.

JiWire’s Location Conversion Index, meanwhile, works like this:

JiWire establishes a benchmark of how likely it is that an audience, such as young, professional urban women, will visit a store based on the historical store visit rate. Then, JiWire measures the increase in visits compared to the historical benchmark for those consumers who have received the ad. To address the natural fluctuation of in-store traffic, such as the holiday season, JiWire compares people who saw the ad to a control group of consumers over the same period of time.

Verve’s Foot Traffic Index likewise tracks post-impression behavior to derive effectiveness and is described as follows:

The Foot Traffic Index is a proprietary method that uses location data to measure effectiveness of mobile campaigns to drive foot traffic. FTI methodology compares test and control groups (users exposed to ads versus those not exposed).The comparison between advertiser location visits for exposed vs. non-exposed groups provides the final FTI metrics.

Verve recently applied this to a well-known (undisclosed) CPG brand campaign. The campaign’s objectives were to increase awareness of a household paper good among a target audience segment and to drive consumers to grocery chains where it is sold.

FTI showed a 2.7X lift in visits from ad exposure compared to a control group not exposed. Within the exposed group, users that engaged further with the ads showed a 3.7X lift in store visits. This demonstrated the efficacy of the location targeted tactics of the campaign.

All in all, many of the above technologies are powerful in that they are vertically integrated with an advertising network or offering. But this strength is also a limitation: These companies’ data only examines a piece of the puzzle, and is not necessarily federated across ad networks.

These companies generally acknowledge that in-house solutions can only measure post campaign impressions where they have visibility. There are also statistically small sample sizes, given that they only track people who open a location-aware app in the store.



Another potential disadvantage of this approach is a perceived conflict of interest. In other words, are there lingering doubts about attribution data when ad networks inherently have a vested interest in reporting higher performance? Some might say yes.

THIRD PARTY/ PANEL DATA

To alleviate a perceived conflict of interest in self-reported performance data, non-partisan third parties have emerged.

Nielsen – one of the largest and longest standing providers of third party metrics for media consumption – is now entering this opportune field. Its brand equity and trust factor should achieve the desired third-party affirmation and integrity that’s currently needed.

As with smaller startups like Placed, Nielsen employs a panel methodology. This involves a sample of opted-in mobile users who are vetted carefully to represent a cross section of the overall population. Their mobile behavior is then tracked for location relevant patterns.

According to VP of Innovation Jake Moskowitz, Nielsen’s philosophy divides advertising strategies into reach, resonance and reaction. These strategies map to the stages of the proverbial purchase funnel, and define tactics served for the corresponding stage.

These “three R’s” also map to Nielsen measurement products: Brand Effect, Location Effect and Sales Effect. Location is the step in the middle that ties it all together – taking to heart the reality threaded through the previous pages that most retail activity happens locally.

And that’s precisely where Nielsen’s opt-in panels come into play – measuring the steps between seeing an ad and making a purchase. These are the all-important steps taken (literally) in the “path to purchase,” which reveal valued behavioral data.

Overall, the benefits of third-party panels are clear. Besides those mentioned, Moskowitz mentions another benefit: Privacy. This elephant in the room impacts any location tracking discussion. But panels are privacy safe, due to their opt-in approach.

OFFLINE PURCHASE DATA

The third and final form of attribution taking form in today’s marketplace is utilizing offline purchase data to connect the dots between ad exposure, effectiveness and ultimately conversion. This follows logically, given that a purchase is the ultimate ROI indicator.

But purchase data from “closed loop” transactions is often limited and fragmented due to nascent mobile couponing and payments. These are also lower funnel technologies, whereas many brand advertisers are still focusing on “upper-funnel” branding and awareness.

“We need to show clients that branding campaigns do result in performance,” said Nuestar GM of Ad Solutions Dave Helmreich. “I have to be careful when I use the word “performance” because [it] doesn't connote direct response. But how do you actually show that a targeted campaign – wherever it is in the purchase funnel – produces the result that the marketers are expecting?”

Companies like Neustar, along with Datalogix, Nielsen Catalina and others address this by



measuring the impact of reach driven branding campaigns. This involves tracking purchase data to determine the rate at which users convert offline, after ad exposure or engagement.

For example, Neustar’s Campaign Conversion Insights (CCI) connects the dots through a network of partners including ad companies and payment processors. Extensive data on U.S. households and consumer identifiers are also central to its measurement methodology.

This plays out as Nuestar ingests campaign data such as impressions served from ad companies like Verve. It can then array location targeted impressions against household data to pinpoint ad exposures. This includes demographic and buyer profiles.

Resulting offline purchases are then measured through geographic sales lifts. This involves measuring transaction data from its payment processing partners. And it is done in an aggregated and anonymized way for privacy reasons.

The result is campaign effectiveness reporting, delivered back to advertising companies, publishers and advertisers. Going deeper, the specific buying patterns of certain demographics enable marketers to iterate campaigns tactics at more granular levels.

According to Neustar VP of Business Developmpent Ken Barbieri, this will only improve as the company reports data more frequently within campaigns. It typically does so mid-campaign and post-campaign. The feedback loop will tighten as advertiser adoption evolves.

“There is an education and data readiness challenge on the buy side,” said Barbieri, though he admits they’re catching on. In fact there’s

tangible progress that can be seen among advertisers, as well as mobile providers, publishers and ad companies.

“18 months ago, when offering data and attribution to mobile providers and advertisers, they mostly said they were fine with their own data,” he said. “A few months later, they wanted more, and today even more. There’s real deep interest in methods of attribution.”



CONCLUSION

The continued march of mobile technologies has spurred advertiser demand for more precise ROI metrics. Smartphone portability and location awareness enable better attribution, while mobile’s escalating share of media time compels it.

Attribution technologies are further compelled by their demand. This is especially true among the companies that spend the most on mobile advertising today: brand advertisers. They require tighter measurement of longstanding KPIs such as brand lift.

Further in support of attribution (and its timing) consumers are accessing increasing numbers of media across different screens, causing greater complexity in attributing sources of brand influence. This complexity calls for well-devised big data driven solutions.

Because a great deal of this evolving consumer behavior is ending up on – or spending increasing amounts of time with – mobile devices, it is a logical venue for innovating measurement standards. It’s a nascent area but one that’s developing quickly.

This can be seen in its practical application and real value to the advertisers that are the earliest adopters of mobile. Here, promising technologies like mobile payments and in-store engagement loom large. But ad campaign attribution programs are providing a vital bridge.

Like many things, the right tactics to adopt will be a combination of the methods introduced in this report. Regardless, advertisers, publishers and startups interested in discovering value in

mobile should keep a close eye on the quickly developing art that is attribution.



ABOUT BIA/KELSEY

BIA/Kelsey is a market research and analyst firm that focuses on all things local. Local media is an increasingly dynamic area of ad spending, and is quickly evolving with emerging digital platforms like mobile, social and search. Over the past three decades, BIA/Kelsey has been an authoritative voice on these developing technologies as well as their forbearers in traditional media, which continue to transform as they likewise compete for local ad dollars and consumer affinity.

Through a growing suite of products and mediums like research reports, articles, conferences, and client consulting, BIA/Kelsey analyzes the business, financial, social and technology trends affecting local media. Readers, event attendees, partners and clients are given the inside track on critical data, analysis, and recommendations needed to grow and transform in a rapidly evolving media and advertising landscape.

ABOUT BIA/KELSEY SPONSORED REPORTS

BIA/Kelsey Sponsored Research is commissioned by companies that wish to spotlight areas of coverage. BIA/Kelsey maintains full editorial

control and applies its longstanding standards of editorial practice and objective market analysis.

If you’d like to commission BIA/Kelsey to cover any topics or to profile sectors of local media innovation, please contact [email protected].

ABOUT VERVE MOBILE

Verve Mobile is a pioneering leader in location-based mobile advertising.

Its customers are national brand advertisers who want to engage consumers on their mobile devices with location-aware, data-driven and highly targeted marketing. It also serves premium publishers across the nation, delivering the platforms and services they need to power and monetize their mobile properties.

The Verve “location intelligent” technology platform and the proprietary data it generates are revolutionizing mobile media and advertising. It enables consumer and content brands to reach their desired audiences on the go, out and about, and closer to the buying decision than ever before.

Verve has been leading the way in location-based mobile advertising since 2005 and has offices in New York City, Washington D.C., Chicago, Los Angeles, San Francisco, Atlanta, Dallas, Detroit and San Diego.

http://www.vervemobile.com/

mailto:[email protected]

http://www.vervemobile.com/

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