1. Mobile
Attribution
One of the Fastest
Digital Channels Shows
Significant Promise
in Driving Online Sales
Inside:
2 > The Authors
2 > Executive Summary
3 > Today’s Mobile Landscape
3 > Mobile Measurement: The Issues to Date
4 > Mobile and Channel Attribution
5 > Approach and Methodology
6 > Study Findings
7 > Conclusion

2. The Authors
Michael Kaushansky, EVP, Chief Analytics Officer Phuc Truong, Managing Director, Mobext
Michael has over 15 years of ex- Phuc Truong leads mobile mar-
perience distilling huge amounts keting efforts in the US for
of data into insightful, actionable Mobext; he founded the practice
strategies. He works closely with in 2008. Phuc has been focused
Dannon, Volvo Cars of North in mobile marketing engagement
America, and Fidelity, applying since 2001 and is considered a
his expertise in database market- leading pioneer in the industry.
ing, digital analytics, segmentation, modeling, and His team provides mobile engagement stewardship
data strategy to critical marketing challenges. for Fortune 500 clients within retail, CPG, automotive,
Michael joined the company from OgilvyOne New finance and travel & hospitality industries. Under his
York, where he led the agency’s marketing analytics leadership, Mobext has won numerous mobile indus-
capability, serving domestic and global fortune 500 try awards including Mobile Agency of the Year in 2011
clients including UPS, Siemens, Time Warner Cable, & 2012 according to the Mobi Awards. Prior to joining
and TD Ameritrade. He has also held senior positions the Havas family, Phuc was a founding team member
with Publicis Modem, GE Money, Target, Glaxo-Smith of MobileLime (Later Modiv Media; acquired by
Kline, and Union Pacific Railroad. Catalina Marketing), one of the first U.S.-based com-
panies to turn the mobile phone into a marketing,
loyalty and payment device.
Executive Summary
With exponential investment occurring within the ment and conversion, but did a message seen on a
mobile media space, one thing must improve: meas- TV screen cause a consumer to visit a brand’s site on
urement and analytics. For professionals within the their phone? Did that consumer search for the brand
mobile advertising community, a common sound bite on their phone and later purchase the desired item
has been that “mobile works best in conjunction with on their PC?
other channels.” However, to date, the incremental ef-
fects of mobile marketing have been difficult to prove As our team captured and measured consumers’
within the channel itself, let alone with cross channel toggling behaviors, we believe that we have found a
effects. As most digital media professionals know, way to illustrate mobile media’s contribution within
mobile measurement presents many challenges — the purchase funnel. We conducted tests and analy-
ecosystem fragmentation, technology barriers, and ses with a client in the travel/entertainment sector to
lack of standards — leaving us frustrated and left to unlock key findings, including:
define mobile contribution with either insufficient • Consumers’ pathways to conversion include a com-
tools or using non-standardized methods. bination of publishers’ mobile and online audiences
Though we’ve observed strong YOY growth in mobile • While many media suppliers are using the same
traffic and transactions, it is difficult to tie that activity cookie pools in identifying online audiences, creating
to the variety of media choices where consumers are duplication and inefficiencies for advertisers, the mo-
receiving our message. We know that consumers bile audience does not suffer from this duplication
toggle back and forth between screens for engage- — extending reach for publishers and advertisers
Havas Media > Mobile Attribution POV < 2

3. Today’s Mobile Landscape
It is no secret that mobile consumption is growing vertiser/agency remains woefully immature and we
rapidly, specifically among smart devices and tablets. feel the only way to overcome this hurdle to invest-
Consumers and business professionals alike are able ment is to demonstrate whether mobile delivers true
to do more with mobile devices today than ever be- value — not simply as a single channel — but as a sig-
fore. The trend is gaining momentum with the estab- nificant contributor to the entire media mix. The chal-
lishment of apps, technology integration, and lenge is capturing that data across multiple digital
limitless connectivity to the internet in our daily lives. channels utilizing a uniform methodology and toolset.
As we know from previous spend-
ing trends, marketing investment Figure 1 > The Increase in Mobile Device Use Source: eMarketer, 2011
follows consumer eyeballs —
therefore, media investments
should migrate as audiences move
to mobile devices. While Internet
usage was only up 3%, eMarketer
reports mobile-tablet usage
jumped by 62% from 2011 to 2012,
and mobile internet usage grew
by 17% (Figure 1). However, even
though mobile-tablet usage has
more than doubled, the invest-
ment in mobile advertising has not
kept pace (Figure 2).
Figure 2 > % of Time Spent in Media vs. % of Advertising Spending, USA 2011
Why is mobile media investment
not commensurate with consumer
adoption? Why aren’t brands em-
bracing mobile as they’ve em-
braced digital video or social
media? The answer is simple — the
limitation of measurement and
channel accountability. While
companies have been rapidly
investing in technology to better
track digital video, viewability, and
Facebook activities, we have seen
Source: eMarketer, 2011. *Internet (excl. mobile) advertising reached $30B in USA in 2011 per IAB,
far less effort in mobile. Mobile Mobile advertising reached $1.6B per IAB. Print includes newspaper and magazione. $20B opportunity
measurement for the average ad- calculated assuming Internet and Moshare equal their respective time spent share.
Mobile Measurement: The Issues to Date
What works for one channel, does not necessarily “In the name of progress, our official
work for the other. Yet that is how we all have started culture is striving to force the new media
off in our approach to mobile measurement — only to do the work of the old.”
to find that the measurement techniques and tools — Marshall McLuhan, The Medium is the Message
that the industry has depended upon — JavaScript
and http cookies — are unreliable on mobile devices. are going away with Apple’s directive that UDIDs
They do not work when it comes to in-app measure- will be deprecated and not available to 3rd party
ment. Even the in-app method of UDID tracking companies.
Havas Media > Mobile Attribution POV < 3

4. Many devices do not accept cookies at all; for the few ber of standards. Complicating matters is the fact that
that do, the cookie is session-dependent and deleted some of the ecosystem players carry more weight
immediately after in order to save handset memory. than others to enforce their technologies upon the
What’s more, third-party ad servers that have be- marketers, stifling innovation and truer insight. In the
come the standard for online advertising still experi- end, as indicated by Michael Zimbalist, VP of Re-
ence unacceptable levels of discrepancy between the search and Development at The New York Times
third-party ad servers and server logs. It has gotten Company, “If the carriers and device manufacturers
to the point that many are considering supporting and networks don’t play, we’ll be shadow boxing”.
two ad serving technologies —
one for online and one for mobile. Figure 3 > Mobile’s Ecosystem is Fragmented Source: Radar Research, Oct 2011
Apart from these technological Publishers
challenges, the key issue with mo- Carriers (e.g., ESPN, Marvel, OEM
(e.g., AT&T, Burbn, etc.) (e.g. Motorola,
bile measurement is the fact that Verizon, etc.) Samsung, etc.)
the mobile ecosystem is highly
fragmented. There are numerous
competing stakeholders, tech-
nologies, and platforms that have
yet to converge and define meas-
urement standards. As shown Retailers
OS
(e.g. iTunes, Amazon,
here (Figure 3), there are a num- (e.g., Android, IOS.) Ad Networks Google Android Market)
ber of different parties that need (e.g., AdMob, Millennial,
JumpTap, etc.)
to come together to define a num-
Mobile and Channel Attribution
Certainly in today’s media landscape where con- “Mobile is not a stand-alone
sumers have multiple paths to content, consumers medium. It’s a connective piece of
often toggle back and forth between channels — a broader media plan.”
engaging with a brand in one medium, converting via — Michael Zimbalist, The New York Times
another. In fact, they do not consume based on chan-
nels — consumption is often based on whatever Artemis is a proprietary data management platform
medium is closest or easiest at the moment. With that can combine media and client conversion data
mobile devices being the constant companion, the to draw out insights from campaigns across channels
immediate reference device is the mobile phone. and screens — and it can definitively track across
platforms and publishers. More specifically, Havas
Given the above, how do we capture that consumer Media agencies including Mobext use DoubleClick to
activity and attribute the mobile channel’s role within ad serve their digital media (online display, mobile
a purchase cycle? In general terms, one needs a tool display, and search) campaigns for advertising clients.
or methodology that can track across platforms and Subsequently, DoubleClick feeds Artemis via deep
publishers — enter Havas Media’s Artemis platform.
TM
data link integrations on campaign performance and
One of the most challenging aspects of running this view through. Combined with client conversion data,
type of analysis is the fragmentation of mobile data. Artemis can derive insightful intelligence from digital
Without connecting mobile and online, our conclu- campaigns such as:
sions would be limited and less insightful. However, • User pathways to advertisers’ sites via digital media
through the use of our ad-server and Artemis we TM
(display, mobile, rich media and video)
were able to unify our cookies across multiple de-
• Partial media credit attribution for conversions
vices — including mobile — to deliver a single view of
the user. This was the game-changer for us, as we • Lifetime value of users
now had the proper dataset needed to run cross-
device mobile attribution.
Havas Media > Mobile Attribution POV < 4

5. Artemis (Figure 4) can accept a Figure 4 > Artemis Data Management Platform
wide range of data sets to derive
further insights for clients pre- and
post-campaign. Some are deliv-
ered via direct API, others via a
Flexible Data Integration (FDI),
API
together with Data Overlays (OL)
from an array of 3rd party data
providers. OL
FDI
Approach and Methodology
Using the cookie-level data we collected from booking revenue. Once we manipulated the data into
Artemis, we embarked on an effort to evaluate the an analysis dataset, we ran a multivariate stepwise
ad served data for one of our travel/entertainment regression model to test our hypothesis. The results
advertisers. Our hypothesis was that given mobile were stark and surprising.
device adoption and consump-
tion, mobile-tablet advertising Figure 5 > Behavioral Paths and Ad Exposure
must play a key role in conversion,
and a role in contributing to the
Path to Conversion
rest of the online media mix. average
Impressions
Publisher 2 $ Booking
served = 5
(Travel)
To test this hypothesis, we evalu-
average
Branding Ad RTB Publisher 1 Publisher 1 Publisher 2
ated data from April 1st 2012 Impressions
Network Network 1 (Weather) (Travel) (Travel)
$ Booking
served = 63
through the end of May 2012 cov-
average
ering display, mobile-smartphone, Impressions
Publisher 1
(Travel)
Mobile/Tablet
(Travel)
$ Booking
served = 4
and mobile-tablet advertisements.
average
The approach was to recreate the Impressions
Branding Ad
Network
RTB
Network 1
RTB
Network 2
Targeted Ad
Network
$ Booking
served = 79
user-level journey online by stitch-
average
ing together every tracked digital Impressions
RTB Mobile/Tablet
$ Booking
Network 1 (Travel)
served = 6
exposure and determining the
appropriate statistical weight to average
Branding Ad Mobile/Tablet
Impressions
Network (Travel)
$ Booking
each exposure/channel based on served = 7
Havas Media > Mobile Attribution POV < 5

6. Study Findings
Millions of online and mobile im- Figure 6 > Overlap Correlation
pressions were served — the cam-
paign produced thousands of
combinations, since behavioral
paths and ad exposure vary signif-
icantly from one user to the next.
Figure 5 illustrates the most com-
mon combinations.
Our first insight was that mobile-
tablet ads reached new users
which did not overlap with other
forms of online media ads. Users
were not exposed to multi-screen
ads. The low correlation seen
(<0.010 person’s coefficient) in Figure 7 > Contribution to Revenue Compared to % of Total Impressions
the table below indicates there is
little overlap across Smartphone
and Tablet devices as compared
with the larger online Real-Time
Bidding (RTB) Ad Networks.
Smartphone/Tablet advertising
reached new users; rather than
with online RTB networks where
companies are sourcing audiences
from the same cookie pools.
Not only did we see an influx of
new users in the data, but we also Figure 8 > % Revenue Contribution
saw a strong statistical correlation
between mobile-tablet and contri-
bution (11%) to travel booking rev-
enues compared to the volume of
served impressions. The contribu-
tion to revenue was higher com-
pared to the Smartphone/Tablet
impression volume; which was low
(< 0.04%). The significance with
this finding is that mobile media
was more efficient at driving con-
versions for our client.
There may be a few reasons for mobile media’s effi- brands indicates that over half of the mobile book-
ciency toward conversion. However, one critical fac- ings occur within the same day.
tor that cannot be overlooked with the smart phone
mobile audience is that these users are more apt to The last insight pointed a significant portion (85%) of
convert than the normal online user (to be comfort- the booking revenue contribution is in fact not a result
able with using smaller screens to transact indicates of any mobile-tablet or online advertising but possibly
a motivated user). Supporting this theory is that the a result of the inherent brand equity, offline media not
fact that the top m-commerce sites for leading travel accounted for in the dataset and the natural booking
Havas Media > Mobile Attribution POV < 6

7. behavior regardless of in-market advertising. As a re- bution providers do not take this into consideration
sult, our attribution models properly attributed the and may inflate their results.
15% which we deemed as statistically related to online
and mobile spend; therefore calculating the “true” re- We looked deeper in our initial findings for each
turn on investment from online and mobile spend. channel and ran a simulation by which we would re-
align our impression volume to higher contributing
To define the unattributed portion of the online rev- channels and partners; this simple realignment sig-
enue, we ran our models and relied on statistical out- naled a potential for a 5% increase in overall revenue
put which determined that 85% of the online booked boding a triple-fold ROI.
revenue could not be correlated
with statistical confidence to our Figure 9 > Increase in Overall Revenue
online/mobile spend and there-
fore was a result of external/unac-
counted factors. This makes sense, Partners Investment Actual Revenue Investment Adjusted Revenue
for if we were to “go-dark” with Specialty Travel Site 8.20% $ 1,381,153 9.70% $ 1,633,483
RTB Ad Network 1 11.67% $ 549,916 46.34% $ 2,183,363
our online and mobile spending,
MobileTab 2.39% $ 213,747 6.69% $ 598,953
we should still expect to see online
Weather Site 37.10% $ (814,461) 37.10% $ (814,461)
bookings, though at a lower
RTB Ad Network 2 3.82% $ (115,121) $ –
threshold and in this instance on- RTB Ad Network 3 0.48% $ (25,428) $ –
line and mobile deliver 15%. Hav- Travel Blog Site 15.14% $ 169,608 $ –
ing this understanding allowed us Travel Aggregator Site 19.53% $ (187,297) $ –
to calculate the true return-on- OwnerIQ 0.17% $ (7,722) 0.17% $ (7,722)
investment for both online and Priceline 1.50% $ (22,785) $ –
mobile since we knew exact con- $ 53,648,157 $ 56,100,164
Increase in revenue 4.57%
tribution. Most conventional attri-
Conclusion
We highly recommend continued investment in the “It is no longer the linear purchase funnel,
mobile channel with testing increased spend levels but purchase pretzel [as consumers
across varied mobile formats. Per our recommenda- weave between channels to convert].”
tion for more mobile investment due to the insights — Walt Doyle and David Chang, PayPal Media Network
illustrated above, coupled with the continued explo-
sive growth of mobile device penetration, and data that prove mobile’s effectiveness and credit towards
consumption, it is critical that companies in the digital overall conversion. Consumers certainly float in and
ecosystem continue to produce data-driven insights out of media channels along the conversion path.
Havas Media > Mobile Attribution POV < 7