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Slide 1: The Science of Viral Campaigning in Saturated Mobile Markets N ealG orenfo l V i P resi ce dent F A S .research .gorenfo@ f research .com neal l as- w w w .f -research .com as 1 of 24

Slide 2: FAS applies the science of social network analysis to help people, products, and ideas reach their tipping points. 2 of 24

Slide 3: Trade M aps: Then and N ow 18 th C ent : M ap oft 21 st C ent : ury he ury N ort estP assage hw S oci alG raph 3 of 24

Slide 4: W hatf undam ent actaboutpeopl does segm ent i alf e aton f lt see ? ai o Segmentation, microtargeting, multichannel strategies That we are connected and influence each other. 4 of 24

Slide 5: W hatdoes t s t lyou about hi el t tonalm arketng ? radii i • • TR U S T D O W N : M ESSAG ES U P: Trusti advertsi has n i ng 3,500 t 5,000 perday, o unged 41% overt pl he nearl 2x 1970s l y evel hree years. l astt S ource : U S A T oday, 2005 S ource : 5 of 24 nsi express, 2006 i ght

Slide 6: W O M C hanneli O pen , Trust , s ed P osii & O f lne tve fi • O nl 12 brand m entons perday. S ource: K eller F ay, 2006 y i • 56% say f ends, f iy are pri ary i l nfuence . ri am l m S ource : F orrest R esearch , 2006 er • P osii ‘buzz’ outpaces negatve by 6 t 1. tve i o S ource : K eler F ay, 2006 l • 92% ofW O M happens of lne . S ource: K eller F ay, 2006 fi 6 of 24

Slide 7: ence … K now youraudi • S egm ent i anal aton yzes attributes of individuals • S oci et ork A nal s alN w ysi yzes attributes of links anal • “occer M om ” S S oci ndi ors have m ore ali cat predi i pow er S ource : T indal& ctve AN D W elm an , 2001. l “ C onnector” 7 of 24

Slide 8: … A nd how peopl are lnked i i e i nt I erpersonalLevelV i , nt ew ViralS t ure ruct ? C hange A gent , s C onversi on P eer-P ressure 8 of 24

Slide 9: A nd how com m unii are lnked tes i C om m uniy LevelV i t ew 9 of 24

Slide 10: M arketas system 10 of 24

Slide 11: M arketas syst si ul i em m aton 11 of 24

Slide 12: Tipping point effect  Network strategies are non-linear  Returns can be non-linear 12 of 24

Slide 13: W hi com m uniy w oul ch t d oi ? you m arkett frst E berndorf . S t K anzi an 13 of 24

Slide 14: From m i t m acro cro o + = N ear crii tcalm ass C om m uniy w ih t t W ihi densel tn y ofchange agent s hi vi gh ralpot iental connect net ork ed w 14 of 24

Slide 15: The tnderbox i D ensel lnked com m unii yi tes + N ear crii tcalm ass share + M any convert stl s ilavaiabl le + S al m om ent es um 15 of 24

Slide 16: The “ ralt ofa regi vi iy” on M i . 3000 resi n dents 16 of 24

Slide 17: V i argetng = l erchurn ralt i ow • Subscribers who are not strongly linked to other subscribers are 4x more likely to churn Call Volume Call Volume Alien Dominant Alian Change Agent SUBSCRIBER_ID Churn Index Total Net Provider Index Hub Index Spreader 10004381 0.290308 34360 16858 A1 0.3826923 0.0013201 0 10004414 0.52381 189 99 One 0.3550699 0 0 10004418 0.647169 3214 2080 One 0.3550699 0 0 10004423 0.693182 88 61 T-Mobile 0 0 0 10004464 0.462888 20411 18286 A1 0.4508741 0 0 10004498 0.82405 10736 10736 A1 0.3655594 0 0 10004535 0.212509 11256 3277 T-Mobile 0.3655594 0 0 10004536 0.13262 16536 2699 Telering 0.3655594 0 0 10004539 0.272317 30336 20644 A1 0.4662587 0.0013201 0 10004596 0.834437 302 302 A1 0 0 0 10004610 0.151627 47584 21548 One 0.4940559 0 0 10004613 0.040124 15203 1357 A1 0.3763986 0 0 10004622 0.328476 67868 47547 One 0.3826923 0.0013201 0 H i score , lkel H i score , att M easures probabiiy lt gh iy gh he 10004648 1 7 7 A1 0 0 0 10004650 0.428817 9890 9890 One 0.3655594 0 0 subscri w ill ber l eave til o nfuence cent ofuser er 10004678 0.717775 17603 17522 A1 0.4298951 0 0 acqui tons. com m uniy, lkel t usi soci ndi ors ng ali cat sii t i yo 10004985 0.497428 23135 17305 A1 0.4187063 0 0 10005327 0.106876 21988 2453 Telering 0.3655594 0 0 il nfuence ret i enton 10005403 0.047397 4494 371 One 0.3655594 0 0 10005449 0.54843 33760 33404 T-Mobile 0.5737762 0.0079208 0 10005458 0.227369 74909 20443 A1 0.3954545 0 0 17 of 24 10005461 0.812177 1741 1731 T-Mobile 0.3877622 0.0046205 0 10005465 0.13818 32255 8949 T-Mobile 0.3763986 0 0

Slide 18: W ho do you keep ? Vs. B artS i pson , $ 110 / m ont H elo K it , $ 90 / m ont m h l ty h 18 of 24

Slide 19: M arketas system • S ee your m arketas a syst w here peopl are em e lnked & i l i nfuence each ot her • S oci et ork A nal s m akes vi bl : alN w ysi si e – Pressure points i the system by m appi the n ng soci alst ure ofm arket ruct s – Change agents & hubs w ho have the bi ggest i pacton adds & ret i m enton – Viral regions to focus on to i ncrease acqui ton & sii ret i enton – Best entry points i o new m arkets nt 19 of 24

Slide 20: S t egy 1: S urpri cust ers w hen rat se om they are “ agi cont ous” C ust ers m ostaptt “ om o buzz” w ihi 2 w eeks ofpurchase : tn P rovi new cust ers w ih de om t unanticipated benefits G i new cust ers som et ng ve om hi t t k about o al F i w ays t supportt nd o he “buzzi ofnew cust ers ng” om 20 of 24

Slide 21: S t egy 2: C reat val uabl lnk m et -dat rat e ei a a M a p re a l n e tw o rk s so yo u ca n m e a su re in flu e n ce & le ve ra ge W O M  dentt , w / verii i R eali iy fcaton  Strengt off endshi h ri p  Lengt off endshi h ri p  A f ii i flaton C ouchsurfng .org i 21 of 24

Slide 22: S t egy 3: A ctvat change agent rat ie s ekeepers t com pett : C hange agent are gat s o iors  Gi t ve hem reasons t ent t com pett dom ai o er he iors n  Gi t ve hem ef ectve bri ng m essages fi dgi ? 22 of 24

Slide 23: S t egy 4: Take care ofhubs! rat S atsf t needs ofhubs t st lze exi i i y he o abii stng bercom m unii : subscri tes  M ake sure your hubs feelconfdentw ih your servi i t ce  Loose your hubs and you l oose your com m unii tes 23 of 24

Slide 24: Thank you .gorenfo@ f research .com neal l as- N ealG orenfo l V i P resi ce dent F A S .research w w w .f -research .com as 24 of 24