mobile wharton msi cs 12-301 mobile conference report

8/11/2019 Mobile Wharton MSI CS 12-301 Mobile COnference Report

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M A R K E T I N G S C I E N C E I N S T I T U T

ConferenceSummary

Marketing on the Move:

Understanding the Impact ofMobile on Consumer BehaviorPrepared by Lijia (Karen) Xie

February 2728, 2012

Philadelphia, Pennsylvania

Cosponsored with

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M A R K E T I N G S C I E N C E I N S T I T U T E

Contents

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ConferenceSummary

3 Mobility in Its Strategic ContextFelix Oberholzer-Gee, Harvard University

4 The Mobile Marketing Industry PanelModerator: John Deighton, Harvard Business School and MSI

Panelists: Maria Mandel Dunsche, AT&T AdWorks; Tim Reis, Google; and Fareena Sultan,Northeastern University

7 Mobile Marketing Analytic PanelModerator: John Deighton, Harvard Business School and MSIPanelists: Peter Farago, Flurry, and Greg Dowling, Semphonic

8 What Is the Value of Physical Distance on the Mobile Internet?Martin Spann, LMU Munich

9 The Hidden Dangers of Using Aggregate Data to Develop Mobile Marketing Strategies to

Reach ConsumersRafael Alcaraz, Ph.D., The Hershey Company

9 Assessing the Effectiveness of Mobile Phone Promotions

Peter Danaher, Monash University10 Geo-Social Targeting for Privacy-Friendly Mobile Advertising

David Martens, University of Antwerp

11 The Intersection of Mobile and Gaming: Now Everyones a GamerMatt Spiegel, Tap.me

11 Mobile Marketing: The Persuasive Impact of Real-Time ReviewsSam Ransbotham, Boston College

12 Embracing the Mobile ConsumerJosh Palau, Comcast Cable


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Marketing Science Institute1000 Massachusetts AvenueCambridge, MA 02138-5396Phone: 617.491.2060Fax: 617.491.2065www.msi.org

Conference summaries reported are providedto members of the Marketing Science Institute.All rights reserved. No portion of this reportmay be reproduced, in any form or by anymeans, electronic or mechanical, withoutpermission in writing from the MarketingScience Institute.

Marketing on the Move: Understanding theImpact of Mobile on Consumer Behavior 2012 Marketing Science Institute. All rightsreserved.


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C o n f e r e n c e S u m m a r y

C O N F E R E N C E S U M M A R Y 3

Marketing on the Move: Understanding theImpact of Mobile on Consumer Behavior

Prepared by Lijia (Karen) Xie

This conference, cosponsored with the Wharton Customer Analytics Initiative,

featured pioneering work in mobile customer analytics and addressed mobiles

impact on traditional media consumption, the relationship between customer

location and promotion effectiveness, mobile platforms as a tool for studying

social networks, and new methods to protect mobile user privacy.

IntroductionNo one can deny the explosive impact thatmobile devices have had on consumers,managers, public policy officials, and otherdecision makers. This dramatic growth inmobile activities has led to a correspondingtorrent of granular data that captures thesebehaviors. Whether mobile device users arecommunicating with others, consuming digitalcontent, acquiring information, alerting others

to their location, engaging in transactions, orposting content to be consumed by others, theyare creating an electronic trail that lends itselfto modeling and analysis that can ultimatelylead to a better understanding of customers andmore effective marketing.

MSIs conference on Marketing on the Move,with the Wharton Customer AnalyticsInitiative, took place on February 2728, 2012,at the Wharton School of the University of

Pennsylvania. It featured presentations frompractitioners who are pioneering the field ofmobile customer analytics, as well as the latestresearch findings from faculty researchers whoare measuring the impact of mobile on tradi-tional media consumption, exploring the rela-tionship between customer location and promo-tion effectiveness, using mobile platforms as a

tool for studying social networks, and inventingnew methods to protect mobile user privacy. Anumber of exciting new research questions andpotential monetization opportunities emergedfrom the conference discussions.

Mobility in Its Strategic ContextFelix Oberholzer-Gee, Harvard University

Firms face three critical challenges when theyseek to develop mobile marketing platformsand campaigns: What is the value proposition?

What complements are needed? Who willcapture the value?

Value propositionWhat relevant messages should you bring toconsumers? Can you create value for customers?Facebook, for example, has 483 million meanactive daily users. However, the revenue per visit($0.022) and profit per visit ($0.006) are trivial,

compared to those of McDonalds ($2.44 and$0.51, respectively). Companies that rely onFacebook in their marketing pushed to the Weband to phones a huge number of irrelevantmessages. As a result, neither the companies norFacebook captures significant value.

Lijia (Karen) Xie is a

Ph.D. candidate at the

Fox School of Business

and Management,Temple University.


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M A R K E T I N G S C I E N C E I N S T I T U T E 4

ComplementsThe impact of mobile marketing will depend onthe quality of complements. A complement is aproduct or a service that enhances the demandfor another product. A good example is ApplesiTunes. Apple makes almost no money on itsiTunes store, but iTunes creates strong demand

for iPods, iPhones, and other electronics. Evenbetter, in a world where content (i.e., music) isincreasingly free, Apple profits from a surge indemand for its technology. A fall in the price ofa complement further enhances demand.

Amazons e-reader Kindle provides anotherpowerful illustration of the importance ofcomplements. Sony offered a high-qualityreader before Amazon did, but the Kindleoutsold the Sony e-reader because of its

embedded wireless service, a small complementthat changed the reading behavior andconsumption patterns of consumers.

Where complements are missing, mobilemarketing campaigns will remain below theirtrue potential. For example, a recent CalvinKlein Jeans campaign showcased a mobile barcode on a huge billboard at a noisy street cornerin New York City. Passers-by snapped a pictureof the mobile code and followed a YouTube link

to an advertising video that built excitementwith erotic footage and throbbing music. But ona noisy New York street corner, consumers who

watched the video were unable to hear themusic, robbing the video of much of its effect. Inthis example, a quiet environment is a comple-ment that enhances the value of the ad.

Market powerHaving developed a clear value proposition andput the necessary complements in place,

marketers must also ask which parties will reapthe benefits of mobile marketing. Willconsumers be better off? Will mobile providerssuch as Verizon and AT&T capture most of the

value? In thinking about market power in theemerging mobile landscape, it is critical tounderstand the influence of proprietary stan-dards, network effects, and differences in priceelasticity across the value chain. All of these will

influence who captures the value that mobilemarketing creates.

The Mobile Marketing Industry PanelModerator: John Deighton, HarvardBusiness School and MSI

Panelists: Maria Mandel Dunsche, AT&TAdWorks; Tim Reis, Google; andFareena Sultan, Northeastern University

Five Big Bets: Where the Biggest,Most Powerful, and Far-reachingOpportunities Lie within the NextThree YearsMaria Mandel Dunsche, AT&T AdWorks

A survey of 300 media decision makers

addressed the question, What are the biggest,most powerful, and far-reaching marketingopportunities during the next three years formobile marketing?

Ability to target market.According to the survey,eight in 10 marketers believe that evolvingmobile marketing will result in better targeting.

Through mobile targeting, companies can getbetter information from consumers and reachthem with targeted messages; consumers can

get more types of ads they are interested in andget rid of things they are not interested in.

However, it is very difficult to effectively targetconsumers through mobile media. For example,

you dont have the same type of cookie-basedtracking as you do in online, so most mobiletargeting is done contextually.

Interactivity and engagement.Smartphones offermore interactivity, as Internet channels are now

approachable on mobile devices. A variety ofcreative ad formats available today do better

work in engaging customers. These includedisplay (i.e., whether it is a mobile Web or app),sponsored messaging, or customer solutions(developing your QR codes).

Location-based targeting. Consumers bring thedevice wherever they are. Thus, marketers can


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reach consumers at the right place at the righttime on the right device.

QR codes, RFID, mobile commerce, and couponing.All of these are new and emerging mobile tech-nologies that show tremendous promise andrevenue opportunities.

Mobile marketing trendsMobile marketing focuses not only on smart-phones but also on a wide range of smartdevices, including e-readers and tablets, which

will require a richer type of customer engage-ment in a larger format.

Current mobile spending share primarilyconcentrates on search and display. Less isbeing spent on SMS/MMS/P2P messaging,and there is a move toward rich media,including video. The app-like functionality ofrich media can reach a wide audience, withhigher use interaction, dynamic brand experi-ences, and detailed engagement metrics.

Targeting is key. This includes demographictargeting, geo-targeting, contextual relevance,behavior, search information, etc. One area ofparticular interest to AT&T as the carrier is theuse of subscription data, which is a fairly new

way of targeting in the mobile area. In an aggre-gate anonymous fashion, AT&T is able tocreate segmentation with actual subscriber dataand to match these segments to advertiserstarget audience. This offers a way to targetbeyond the contextual and behavioral targeting.

Three case studies from AT&TLevis store locator.When consumers click on aLevis mobile ad, they are directed to the storelocator. Once they select find a location nearme, they essentially opt-in to allow Levis toaccess the GPS function on their phone andmap the Levis location nearest to them. Itgenerated 50% engagement with over 10%click-through from consumers to find the Levi snearest to them.

Hewlett-Packards Shop Alert.Taking advantageof the location sensitivity of the mobile device,people were able to opt-in offers when they

were within the radius of the HP products.Consumers were then directed to offers instores. Among those engaged, 5% respondedand went in to purchase the HP products.

Pampers text coupon. Instead of using specificscanners to scan the mobile coupons in store,

AT&T used text messaging to have people textin for the offer that will apply to their AT&Tcell bill. With 100 million subscribers, they

were able to create a mass way to do mobilecouponing.

How to GoMo in 2012Tim Reis, Google

Optimize your site.While 40% of consumers willabandon a bad site experience to visit acompetitors site, ~50% of large online adver-tisers do not have a mobile-optimized landingpage. For example, TicketsNow saw a 30%improvement in efficiency and 50% in conver-sions by creating a mobile-optimized site.1-800-flowers.com saw a 53% decrease in aban-donment and a 25% increase in time spent afterimproving their mobile site experience. Mobile-optimized sites work!

Leverage local. Local is important. Customersmay want to know how many miles away from arestaurant they are. A display unit on the mobile

will create a branding experience. One in threemobile searches has a local intent; 90% of usershave searched for local information; 94% ofthem acted on the information (called or visiteda store) within 24 hours. For example, ThePriceline Negotiator app lets you quickly findand book a hotel room. How successful is it?Heres some data: 58% of users of this appbooked their room within 20 miles; a staggering35% booked within one mile. And 82% bookedrooms less than a day before their arrival,suggesting that app users had already reached


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their destination and were relying on mobile forreal-time decision-making.

Employ mobile at all points in the funnel. Mobileworks the entire funnel. It helps build aware-ness, audience engagement, and online and/oroffline conversions. Ads across all screens are

more effective in driving brand awareness. ANielsen study conducted with Volvo shows thatconsumers have a 48% lift in brand awareness

when they employ TV, PC video, phone video,and tablet video all together versus TV alone.

Scorecard for going mobile (all the possible things todo).This should include building a mobile-optimized site, breaking out mobile-onlycampaigns, mobile-specific ad copy, site links,click-to-call/call metrics, click-to-download,

adequate budget, and leverage mobile display.

Mobile Marketing: Brand in the HandFareena Sultan, Northeastern University

Mobile marketing enables organizations tocommunicate and engage with their audience inan interactive and relevant manner through anymobile device or network. The uniqueness ofmobile is its interactivity and location speci-ficity. The convergence of mobile handsets and

the Internet creates new opportunities for e-commerce and interactive marketing. Branding,mobile communications, and e-commerce canbe delivered in the pocket, in the car, and in thehand.

The mobile frontier is here and now. U.S. wirelesssubscriber connections reached 322.8 million in2011, and 31% of all U.S. households have onlya wireless phone. In January 2012, U.S. smart-phone usage was 48% of mobile phones. There

were 400 different models of smartphones onthe U.S. market at the end of 2011, with

Android and iOS accounting for 75%. Theannual U.S. wireless industry total revenue for2011 was $164.6 billion, with $55.4 billioncoming from data.

By 2015, mobile Internet usage in the U.S. willovertake PC Web usage. This means morepeople will access the Web through a mobilephone or a tablet than through a PC.

When mobile meets advertising.Total U.S. mobilead revenue is $1.45 billion. Google leads in

mobile advertising revenue with $750 million in2011. U.S. revenue from ad-supported mobilecontent was $284 million in 2011 (20% of allmobile content revenue), which has beenprojected to grow to $1.08 billion in 2015. Inparticular, mobile advertising is expected toincrease significantly for geo-targeting/loca-tion-based marketing.

M-commerce is here! U.S. mobile commerce wasworth $6.7 billion in 2011, including travel and

event ticket sales but excluding digital down-loads. U.S. mobile game revenue was $1.53billion in 2011. As forecasted by the industryobservers, U.S. mobile commerce will growfrom 2% of all e-commerce sales in 2011 to 7%in 2015.

U.S. mobile users behavior. In 2011, an averageU.S. adult spent 1 hour 5 minutes per day ontheir mobile phone, more than on readingmagazines and newspapers combined. Of all

Internet traffic in the U.S., 5.2% came from amobile phone. Of U.S. mobile subscribers 74%sent a text message, 48% downloaded an app,47% used mobile Internet, and 35% accessed asocial networking site via a mobile phone.

U.S. mobile shopping behavior. In 2011, 14.6% ofall online shopping traffic in the U.S. came froma mobile device, double from 2010. U.S. mobileshopper activity includes comparing pricesonline while shopping in a store (38%),browsing for products through mobile sites orapps (38%), reading online reviews for products(32%), searching for online coupons (24%),scanning a bar code to find additional productinformation (22%), using GPS capability tolocate a store (18%), and purchasing merchan-dise or mobile content (22%).


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The global mobile penetration.There were 6billion global mobile phone users at the end of2011, and global mobile app revenue in 2011

was $7.3 billion. Global mobile penetration isalmost 90%, with growth greatest in emergingmarkets and developing countries.

Two questions to consider: What are the driversof brand in the hand mobile marketing? Whatare the barriers to integration of mobile intocross-platform marketing strategies?

Mobile Marketing Analytic PanelModerator: John Deighton, HarvardBusiness School and MSIPanelists: Peter Farago, Flurry, and GregDowling, Semphonic

Peter Farago, Flurry

Companies that track how consumers interactwith mobile apps (anonymously and in aggre-gate) offer insights into consumer engagementand behavior.

On the consumer side, there are 480 millionsmart device active installed based worldwide in

January 2012. Of these, 109 million customersare based in the U.S., with fast-growing trends

in developing counties such as China andArgentina. In terms of the U.S. mobile appconsumption, time spent per categories ofmobile apps are 49% games, 30% socialnetworking, 7% entertainment, 6% news, and8% other. In terms of app retention, the datashow a big drop-off in user retention in the firstmonth post-acquisition for general iOS and

Android apps.

On the supply side, available apps in the iTunes

stores grow to 500,000 in October 2011 versus350,000 in Android market in the U.S. market.

Are apps cannibalizing the Web? People spendmuch more time in the mobile apps (94 minutes/day) than in Web browsing (72 minutes). Are

apps cannibalizing television? Applicationsession starts per second in the U.S. reached 4.5thousand during the 2012 Super Bowl. In addi-tion, while ad spending on mobile is still low(1%) compared to other media channels, italready accounts for 23% time spent byconsumers, more than print (6%), Web (22%),

and radio (9%), and second only to TV (40%).

Greg Dowling, Semphonic

Mobile app optimization should includestrategic considerations. How does mobile appusage compare to fixed Web or mobile Web?

Which campaigns are most effective at drivingapp downloads and repeat usage? Which typesof engagement patterns maximize customerlifetime value?

Mobile app metrics include reach (number ofcustomers), frequency (how often app is used),intensity (active engagement), duration (lengthof active use), and monetization (return ondevelopment investment).

A full picture should include key metrics oftotal downloads, app revenue (including trial topaid upgrades), number of unique app users,number of first-time users, ratio of app users tototal users/active users, acquisition/retention,time spent in app or on task, and number ofscreens viewed.

Mobile applications should include trigger-specific events that push out time- and session-based data to the measurement infrastructure.

The key events include install (on first run afterinstallation), upgrade (on first run afterupgrade), engaged user (daily/monthly), launch(any run that is not install or upgrade), back-ground (on set and on return to focus), offline

view (screen view while offline, i.e., cached),orientation (rotation of device portrait/land-scape), and crash (any exit not triggered byquit).


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Key variables include the following: date of firstlaunch after install; application name and

version number; days used in last week, month,lifetime; days since install, last use, upgrade;number of times launched, brought out of back-ground, or since upgrade; complete timestamp;current operating system version number; and

device, country, carrier, WiFi.

What Is the Value of Physical Distanceon the Mobile Internet?Martin Spann, LMU Munich

Location-based services may fundamentallytransform consumers search and purchaseprocesses. Location-based services bringcustomers to the store, since they see relevantoffers on their app and may take a detour for theoffer. However, consumers may go into thestore, scan the code, and scan reviews and pricesat other nearby stores. Would a customer walkfour hundred meters to get a discounted price atanother store? What types of price/locationtrade-off are consumers willing to accept?

The aim of the study was to analyze the impactof location-based services on consumerbehavior in a mobile advertising context. Thequestion is, What is the value of physicaldistance on the mobile Internet? Further, whichfactors impact choice of location-basedcoupons? This study used rich observedbehavior data to analyze these questions.

The study collected data of a location-basedcoupon app. The app provides information oncoupon offerings sorted by distances betweenthe current position of the user and the store. Inthe app, consumers can first click on an offeringto receive more information on the coupon(information stage) and second click to get acoupon code (redemption stage).

Overall, the data include more than 900different coupon campaigns, more than147,900 logins, more than 17,300 clicks on

coupon profile (i.e., information stage), morethan 6,100 clicks on coupon redemption code(i.e., redemption stage), and more than 31,000different users.

A fixed-effects sequential logit model was usedto examine effects on consumers choice to

redeem location-based coupons. The fixedeffect was to account for the heterogeneity atcustomer-level. Dependent variables werebinary choice to learn about (information stage)and to redeem (redemption stage) location-based coupons. Independent variables includeddistance between user and point-of-sale meas-ured in kilometers (based on GPS data), couponattributes such as face value and display rank,time of the day, and coupon category.

The results showed that distance has a signifi-cant negative impact on the probability to learnabout and to choose to redeem coupons. Face

value has a significant positive impact on theprobability to learn about and to choose toredeem coupons. Time of the day has a signifi-cant impact on the probability to learn aboutand to choose to redeem coupons in bothstages. For example, coupons for coffee aremore likely to be redeemed in the afternoon.Regarding the trade-off between distance and

face value, a one percent increase of the facevalue has the same impact as a reduced distanceof 104 meters on the probability to choose toredeem mobile coupons.

Location-based promotions are a promisingcustomer acquisition tool, since offers in close

vicinity have potentially high relevance forconsumers. Location-based services alsogenerate new (geo-) data for marketers, whichcan help better identify consumer preferences

and facilitate more precise targeting.



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The Hidden Dangers of UsingAggregate Data to Develop MobileMarketing Strategies to ReachConsumersRafael Alcaraz, Ph.D., The HersheyCompany

The use of mobile technology in the consumerpackaged goods industry has increased signifi-cantly in the past five years, adding a layer ofcomplexity to the marketing researchers task.

Although point-of-sale data have become moreaccessible, these data do not represent the indi-

vidual characteristics necessary to successfullysegment and target consumers at a granularlevel. Frequent shopper data have become moreprominent, and retailers across industries arenow collecting their own shopper information.

The question is, Are we flocking into themobile space without knowing what the returnsare going to be?

A masked analysis demonstrates that insightsbased on aggregate point-of-sale data may leadto a significantly different mobile marketingstrategy than insights obtained from miningfrequent shopper data.

In this study, three years of data include 240million records of transaction data in 200+ loca-tions, including 150 thousand loyalty cardmembers, 44 item categories, and 10 thousandUPCs. Data are segmented into frequent loyaltyshoppers, moderate shoppers, and infrequentshoppers. Only 35% of the customers gaveaggregate point-of-sale data.

In packaged goods, beverage, tobacco, and foodwere found to dominate the item categories of

store visits across three segments. The impor-tance of item across three segments was signifi-cantly different. Combined items into fivebundles based on purchase frequency allowedretailers to target more selectively.

In addition, each segment should be treateddifferently: incentivize more moderate shoppersand motivate higher redemption of frequent

shoppers and reward their loyalty. Most partici-pants in this study said the mobile couponsdistributed by the stores should be customized.

Assessing the Effectiveness of MobilePhone Promotions

Peter Danaher, Monash UniversityThe purpose of the study was to determine thelikelihood of uptake for mobile phone coupons.

A start-up interactive media company inMelbourne, Australia, conducted a two-yeartrial in a shopping mall in which about 5,500people were recruited to have an RFID tagattached to their mobile phone. They opted-into receive three SMS coupons whenever theyswiped their mobile phone at the mallentrances. Over 150,000 promotional messages

were delivered during the trial, comprising 40stores spread over three floors that generated136 different SMS coupons.

Usage and demographicsThe number of panelist swipes peaked within afew months but dwindled over time. Timebetween swipes steadily declined within a1100 day observation window. Only 61% ofpanelists supplied demographic information(67% female, 33% male). Redemption rates didnot vary much across gender. Older panelistshad more total swipes than younger, but theyredeemed less. The overall total swipes were8.3, and the redemption rate was 1.13%. As thedays until coupon expirations grew closer, theprobability that panelists would redeem thecoupons grew.

Study modelThe model used in this study was a multivariateprobit model for coupon redemption. The esti-mation method was Bayesian MCMC with10,000 burn-ins and 20,000 collection itera-tions. Results showed that multiple redemp-tions happen very rarely. The total number ofcoupons in a set of three is 44,901. However,43,399 coupons were redeemed zero times,1,474 once, 27 twice, and only 1 three times.



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FindingsDistance between the store and the redemptionpoint has a negative coefficient, indicating thefarther the store is, the less likely the panelist isto redeem. If the level difference between thestore and the redemption point is zero (the storeand the redemption point is at the same floor),

the more likely the panelist is to redeem. Bothresults indicate consumers are lazy.

On coupon format, the bigger the discount, themore likely the panelist is to redeem. Othercoupon attributes such as expiration length,dollar discount, and percent discount do nothave a significant relationship with the couponredemption. In terms of the redemption history,swipe frequency, prior redemption times, andtime on panel in days all have negative coeffi-

cients. Among all product categories, snackfood tends to drive the coupon redemption,

whereas shoes do not. Panelists tend to redeemmore during the morning than afternoon andon Mondays rather than Thursdays.

For policy simulations, the model is able toshow the redemption outcome for changes incovariates. In this study, the baseline redemp-tion rate is 0.92%. Increasing the discount by10% leads to a 1.24% increase in the redemp-

tion rate. If the distance to the store is halved,the redemption rate increases by 1.02%. Usingonly BOGOF coupon pricing leads to a 1.23%increase in redemption. Finally, delivering onlysnack food coupons seems to be the most effec-tive strategy, driving the redemption rate up by2.83%.

There is only about a 1% redemption rate forthe coupons. However, many of the covariatesare significant, showing, for example, that snack

food promotions are much more successful thanmens clothing. Items of low value, such as milk-shakes and pancakes, have much higherredemption rates than shoes and womens hair-dressing. Bigger discounts get higher redemp-tion rates, and 2 for 1 offers do much betterthan percentage or dollar discounts. Lastly,

there are large heterogeneity effects, withprevious redeemers being much more likely toredeem in the future (people we term promo

junkies).

A more robust model is needed to deal withpossible endogeneity issues. It would also be

interesting to develop a supply-side model fordispensing coupons and a strategy for optimalcoupon delivery that works in real time.

Geo-Social Targeting for Privacy-Friendly Mobile AdvertisingDavid Martens, University of Antwerp

This study combined the predictive power ofsocial network data and the targeting potentialof mobile customer data in a geo-socialnetwork, or GSN. The design is intended toidentify devices that belong to the same user orto different, but similar, users. The basic idea isthat two devices are similar, and therebyconnected, in the geo-social network, whenthey share at least one visited location (e.g., anIP address). They are more similar as they visitmore shared locations. Various analytical appli-cations were put forward, such as targetingadvertisements to mobile devices in a mannerthat is both effective and privacy-friendly. Thestudy looked at some real-life location datausing mobile check-ins from Real TimeBidding (RTB) environments. One week ofRTB data across 24 websites yielded 100million records/day.

Two questions were asked: Are we able to iden-tify the same user on multiple devices withinthe GSN? Is there any similarity in the geo-social cohorts behavior, specifically, website-browsing behavior?

To address the first question, several methodswere used to identify the user for the samedevice. The results show to what extent the useris connected to itself in the defined GSN andhow strongly the same user is connected toitself. A probability model can be used to



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retarget the same user on different devices andto target different users on the same device.

On the second question regarding similaritiesin website browsing behavior, prior research hasshown that website visit is a proxy for brandaffinity. A study of 100 neighborhoods shows

that the GSN neighbors of visitors to a widevariety of websites are substantially more likelyalso to visit those same websites. Highly similarGSN neighbors show very substantial lift. Theresults indicate that mobile devices that arenetwork neighbors are indeed similar in termsof browsing behavior, and closer neighbors areeven more similar. Reasons for this goodperformance include the ability to connect soul-mates (similar users visiting the same loca-tions), many screens (same user on different

devices), and multiple identities (same user onsame device with different IDs).

The findings offer useful potential for GSNtargeting, including computing GSN similari-ties for targeting, entity resolution, cross-channel campaign evaluation, and hyper-localtargeting.

The Intersection of Mobile and Gaming:Now Everyones a GamerMatt Spiegel, Tap.me

Mobile devices are much more than a utilitydevice; they are also becoming the dominantsolution for gaming. Women are the majority ofthe total mobile social gamers in the U.S. Mostgamers are between the ages of 1844. Gameapps are most used on mobile, with 64% appdownloaders in the U.S. Games matter ontablets, too: 58% of the activities of U.S. tabletusers are games. All these provide evidence ofthe potential of games as a massive media.

While gaming is increasingly important tomobile marketing, this is not reflected in adver-tising spend. Innovations in advertising place-ments can rapidly increase marketer adoptionof game-based advertising. For example, ads

can match game play. For example, Zyngatested reward advertising in CityVille.Players were exposed to the ads when they ranlow on energy. By developing game contentthat matches the brand, the game developersbring in the ad revenue that is also positive tothe game experience.

There are several new areas for data explorationThese include impact on game-playtime/frequency from advertising, impact ofgame genre on reactions to/interactions withadvertising, gamer segmentation and influ-ence on virtual goods and advertising, andincentive preferences (in game vs. real world,etc.) by gamer segment.

Mobile Marketing: The PersuasiveImpact of Real-Time ReviewsSam Ransbotham, Boston College

We talk about the consumption of mobilecontent, but mobile is also about creation.Consumers use mobile devices not only tosearch for information and make purchases butto communicate their product and service expe-riences to others in real time. Despite theirgrowing prevalence, little is known aboutmobile reviews.

This study compared reviews written usingmobile devices with those written on traditionadesktop computers. The questions were, Howdo they differ in content? How do they differ ininfluence? What mechanisms drive differences?

A total of 299,798 restaurant reviews wereanalyzed. Users were able to read and writerestaurant reviews with little governance. Foreach review, the study tracked user, restaurant,

date, title, text, mobile/desktop, recommend,and likes by other users. These evaluations

were compared in order to understand differ-ences in the content created on these platformsand in their relative influence. Specifically, textmining used Linguistic Inquiry and WordCount with 2007 dictionaries. This approach,



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developed by Pennebaker, Booth, and Francis(http://www.liwc.net), is used in text analysisresearch, often with transcription. Based on

word usage, it typically categorizes about 86%of words used.

Major categories include linguistic processes

(26 measures), psychological processes (32measures), personal concerns (7 measures), andspoken categories (3 measures), for a total of 68measures, many of which were highly corre-lated. This study focused on 11 measures.

Resultsn Word counts for desktops were higher than

those of mobile.

n Complexity of wording on mobile wasslightly higher than on desktop.

n Users on mobile tended to use less pasttense in their reviews than on desktop.

n Reviews on desktop tended to be less self-oriented, and reviews on mobile includedslightly less social wording such as myfamily, they, friends.

n There was more positive emotion in mobilereviews than in desktop reviews.

n Both mobile and desktop were associatedwith little anger and few swear words.

n Reviews on mobile were less cognitive(such as I believe, I think) and moreperceptive (such as I feel).

The study used ordered logistic regression(Bayesian) on the rating (dislike, neutral, like,really like) using 48,610 observations of 4,499

users with at least one mobile and one desktopreview of 5,000 iterations. The results suggestedthat mobile reviews were more likely negativethan non-mobile reviews.

The study further conducted negative binomialregression on the number of users who like thereview, using 48,610 observations of 4,499users, with at least one mobile and one desktop

review (additional controls for age, time, inter-cept). The results provide evidence that usersare less influenced by mobile reviews than bydesktop reviews.

Overall, the results indicate that, because of itsphysical and temporal nature, reviews on

mobile tend to be shorter, no less complex; havemore positive emotion; were closer to real-time;were surprisingly not negative in terms ofanger; were less cognitive (slightly) and moreperceptive; and more likely to be negative. Inaddition, reviews on mobile were less likely toinfluence users even after controlling for allthings mobilei.e., shorter length, etc.

What mechanisms drive differences? Theunderlying reasons may include review charac-

teristics (page placement), endogenous choiceof real-time (only if extreme?), use of likes asmeasure of influence (same as behavior?). Onlyusing the secondary data may not suffice toaddress this question.

In a scenario-based experiment, participantswere given the scenario Imagine that you arepicking a restaurant for tonight. A 2 2 design(mobile versus desktop and positive versusnegative) is used. After reading the same

reviews, ostensibly from different sources,participants were required to rate credibility,valence, similarity, influence, and timing of eachreview. Mobile was much less useful in negativereviews while almost the same as desktop forpositive reviews. Mobile was less credible innegative reviews and slightly more credible inpositive reviews.

Embracing the Mobile ConsumerJosh Palau, Comcast Cable

With the influx of tablets and smartphones,consumers demand ubiquitous access to infor-mation. Mobile search has outpaced desktop. Inorder to meet that demand, companies cannotsimply copy their desktop strategy, but mustthink multiscreen. However, only 33% ofcompanies have a mobile-enabled site.



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Comcast has leveraged both internal andmarket data to identify this opportunity anddevelop an experience to meet this demand.Comcasts strategy of multiscreen experience isanchored on three aspects: entrance, help, andsales.

Entrance. Comcast and Xfinity understand thatcustomers want to do a lot of things on mobile.Thus they provide multi-functional apps forsmartphones. For example, the Xfinity TV appallows people to watch movies and shows ontheir smart devices.

Help. Formerly, when people moved to a placewithout Comcast service, they had to findinformation on the Help and Service tab onComcast websites and call for technicians to

come. Now, Comcasts service of text messagesgives answers to consumers questions. By doingso, Comcast enables the mobile channel to helpand support consumers when facing technicalproblems.

Sales. Comcast developed a page with informa-tion about products; consumers talk to Comcast

via clicking a call button on that page. Aspecific call center at Comcast only answersmobile calls so they have a specific under-

standing of what the customers want and thedeals Comcast offers through the mobiledevices. This program is simple but quite effi-cient in driving sales of the products on mobile.Now mobile drives 10% of the online connectsfor Comcast with that page of information.



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mobile wharton msi cs 12-301 mobile conference report

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