Mobile Attribution POV February 2013

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Mobile Attribution POV February 2013

  1. 1. Mobile Attribution One of the Fastest Digital Channels Shows Significant Promise in Driving Online SalesInside:2 > The Authors2 > Executive Summary3 > Today’s Mobile Landscape3 > Mobile Measurement: The Issues to Date4 > Mobile and Channel Attribution5 > Approach and Methodology6 > Study Findings7 > Conclusion
  2. 2. The AuthorsMichael Kaushansky, EVP, Chief Analytics Officer Phuc Truong, Managing Director, MobextMichael has over 15 years of ex- Phuc Truong leads mobile mar-perience distilling huge amounts keting efforts in the US forof data into insightful, actionable Mobext; he founded the practicestrategies. He works closely with in 2008. Phuc has been focusedDannon, Volvo Cars of North in mobile marketing engagementAmerica, and Fidelity, applying since 2001 and is considered ahis expertise in database market- leading pioneer in the industry.ing, digital analytics, segmentation, modeling, and His team provides mobile engagement stewardshipdata 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 hisYork, 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 2011clients including UPS, Siemens, Time Warner Cable, & 2012 according to the Mobi Awards. Prior to joiningand TD Ameritrade. He has also held senior positions the Havas family, Phuc was a founding team memberwith Publicis Modem, GE Money, Target, Glaxo-Smith of MobileLime (Later Modiv Media; acquired byKline, 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 SummaryWith exponential investment occurring within the ment and conversion, but did a message seen on amobile media space, one thing must improve: meas- TV screen cause a consumer to visit a brand’s site onurement and analytics. For professionals within the their phone? Did that consumer search for the brandmobile advertising community, a common sound bite on their phone and later purchase the desired itemhas 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 aeffects. As most digital media professionals know, way to illustrate mobile media’s contribution withinmobile 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 tolack 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 audiencesThough we’ve observed strong YOY growth in mobile • While many media suppliers are using the sametraffic and transactions, it is difficult to tie that activity cookie pools in identifying online audiences, creatingto 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 duplicationtoggle back and forth between screens for engage- — extending reach for publishers and advertisersHavas Media > Mobile Attribution POV < 2
  3. 3. Today’s Mobile LandscapeIt is no secret that mobile consumption is growing vertiser/agency remains woefully immature and werapidly, 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 trueto 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 digitallimitless 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, 2011follows consumer eyeballs —therefore, media investmentsshould migrate as audiences moveto mobile devices. While Internetusage was only up 3%, eMarketerreports mobile-tablet usagejumped by 62% from 2011 to 2012,and mobile internet usage grewby 17% (Figure 1). However, eventhough mobile-tablet usage hasmore than doubled, the invest-ment in mobile advertising has notkept pace (Figure 2). Figure 2 > % of Time Spent in Media vs. % of Advertising Spending, USA 2011Why is mobile media investmentnot commensurate with consumeradoption? Why aren’t brands em-bracing mobile as they’ve em-braced digital video or socialmedia? The answer is simple — thelimitation of measurement andchannel accountability. Whilecompanies have been rapidlyinvesting in technology to bettertrack digital video, viewability, andFacebook 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 opportunitymeasurement for the average ad- calculated assuming Internet and Moshare equal their respective time spent share.Mobile Measurement: The Issues to DateWhat works for one channel, does not necessarily “In the name of progress, our officialwork for the other. Yet that is how we all have started culture is striving to force the new mediaoff 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 Messagethat the industry has depended upon — JavaScriptand http cookies — are unreliable on mobile devices. are going away with Apple’s directive that UDIDsThey do not work when it comes to in-app measure- will be deprecated and not available to 3rd partyment. Even the in-app method of UDID tracking companies.Havas Media > Mobile Attribution POV < 3
  4. 4. Many devices do not accept cookies at all; for the few ber of standards. Complicating matters is the fact thatthat do, the cookie is session-dependent and deleted some of the ecosystem players carry more weightimmediately after in order to save handset memory. than others to enforce their technologies upon theWhat’s more, third-party ad servers that have be- marketers, stifling innovation and truer insight. In thecome 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 Timesthird-party ad servers and server logs. It has gotten Company, “If the carriers and device manufacturersto 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 2011Apart from these technological Publisherschallenges, 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 highlyfragmented. There are numerouscompeting stakeholders, tech-nologies, and platforms that haveyet 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 AttributionCertainly in today’s media landscape where con- “Mobile is not a stand-alonesumers have multiple paths to content, consumers medium. It’s a connective piece ofoften toggle back and forth between channels — a broader media plan.”engaging with a brand in one medium, converting via — Michael Zimbalist, The New York Timesanother. In fact, they do not consume based on chan-nels — consumption is often based on whatever Artemis is a proprietary data management platformmedium is closest or easiest at the moment. With that can combine media and client conversion datamobile devices being the constant companion, the to draw out insights from campaigns across channelsimmediate reference device is the mobile phone. and screens — and it can definitively track across platforms and publishers. More specifically, HavasGiven the above, how do we capture that consumer Media agencies including Mobext use DoubleClick toactivity and attribute the mobile channel’s role within ad serve their digital media (online display, mobilea 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 deeppublishers — enter Havas Media’s Artemis platform. TM data link integrations on campaign performance andOne 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 digitalWithout connecting mobile and online, our conclu- campaigns such as:sions would be limited and less insightful. However, • User pathways to advertisers’ sites via digital mediathrough 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 conversionsvices — including mobile — to deliver a single view ofthe user. This was the game-changer for us, as we • Lifetime value of usersnow had the proper dataset needed to run cross-device mobile attribution.Havas Media > Mobile Attribution POV < 4
  5. 5. Artemis (Figure 4) can accept a Figure 4 > Artemis Data Management Platformwide range of data sets to derivefurther insights for clients pre- andpost-campaign. Some are deliv-ered via direct API, others via aFlexible Data Integration (FDI), APItogether with Data Overlays (OL)from an array of 3rd party dataproviders. OL FDIApproach and MethodologyUsing the cookie-level data we collected from booking revenue. Once we manipulated the data intoArtemis, we embarked on an effort to evaluate the an analysis dataset, we ran a multivariate stepwisead served data for one of our travel/entertainment regression model to test our hypothesis. The resultsadvertisers. Our hypothesis was that given mobile were stark and surprising.device adoption and consump-tion, mobile-tablet advertising Figure 5 > Behavioral Paths and Ad Exposuremust play a key role in conversion,and a role in contributing to the Path to Conversionrest 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 2ated data from April 1st 2012 Impressions Network Network 1 (Weather) (Travel) (Travel) $ Booking served = 63through the end of May 2012 cov- averageering display, mobile-smartphone, Impressions Publisher 1 (Travel) Mobile/Tablet (Travel) $ Booking served = 4and mobile-tablet advertisements. averageThe approach was to recreate the Impressions Branding Ad Network RTB Network 1 RTB Network 2 Targeted Ad Network $ Booking served = 79user-level journey online by stitch- averageing together every tracked digital Impressions RTB Mobile/Tablet $ Booking Network 1 (Travel) served = 6exposure and determining theappropriate statistical weight to average Branding Ad Mobile/Tablet Impressions Network (Travel) $ Bookingeach exposure/channel based on served = 7Havas Media > Mobile Attribution POV < 5
  6. 6. Study FindingsMillions of online and mobile im- Figure 6 > Overlap Correlationpressions were served — the cam-paign produced thousands ofcombinations, since behavioralpaths 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 userswhich did not overlap with otherforms of online media ads. Userswere not exposed to multi-screenads. The low correlation seen(<0.010 person’s coefficient) in Figure 7 > Contribution to Revenue Compared to % of Total Impressionsthe table below indicates there islittle overlap across Smartphoneand Tablet devices as comparedwith the larger online Real-TimeBidding (RTB) Ad Networks.Smartphone/Tablet advertisingreached new users; rather thanwith online RTB networks wherecompanies are sourcing audiencesfrom the same cookie pools.Not only did we see an influx ofnew users in the data, but we also Figure 8 > % Revenue Contributionsaw a strong statistical correlationbetween mobile-tablet and contri-bution (11%) to travel booking rev-enues compared to the volume ofserved impressions. The contribu-tion to revenue was higher com-pared to the Smartphone/Tabletimpression volume; which was low(< 0.04%). The significance withthis finding is that mobile mediawas 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 phonemobile audience is that these users are more apt to The last insight pointed a significant portion (85%) ofconvert than the normal online user (to be comfort- the booking revenue contribution is in fact not a resultable with using smaller screens to transact indicates of any mobile-tablet or online advertising but possiblya motivated user). Supporting this theory is that the a result of the inherent brand equity, offline media notfact that the top m-commerce sites for leading travel accounted for in the dataset and the natural bookingHavas Media > Mobile Attribution POV < 6
  7. 7. behavior regardless of in-market advertising. As a re- bution providers do not take this into considerationsult, our attribution models properly attributed the and may inflate their results.15% which we deemed as statistically related to onlineand mobile spend; therefore calculating the “true” re- We looked deeper in our initial findings for eachturn on investment from online and mobile spend. channel and ran a simulation by which we would re- align our impression volume to higher contributingTo 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 revenueput which determined that 85% of the online booked boding a triple-fold ROI.revenue could not be correlatedwith statistical confidence to our Figure 9 > Increase in Overall Revenueonline/mobile spend and there-fore was a result of external/unac-counted factors. This makes sense, Partners Investment Actual Revenue Investment Adjusted Revenuefor 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% $ 2,183,363our online and mobile spending, MobileTab 2.39% $ 213,747 6.69% $ 598,953we 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-ConclusionWe 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 consumersacross 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 Networkillustrated above, coupled with the continued explo-sive growth of mobile device penetration, and data that prove mobile’s effectiveness and credit towardsconsumption, it is critical that companies in the digital overall conversion. Consumers certainly float in andecosystem continue to produce data-driven insights out of media channels along the conversion path.Havas Media > Mobile Attribution POV < 7

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