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Mooga app personalizer


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Enhancing every App’s Salability

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Mooga app personalizer

  1. 1. Mooga App Personalizer   Enhancing  every  App’s   Salability   July 26, 2010
  2. 2. iKen’s  Purpose  and  Vision   Our  Core  Purpose   To   make   available   to   customers   what   they   want.   Trea@ng   each   individual   dis@nctly   and   Personalizing  his/her  experience  in  content/service  consump@on  lies  at  the  core  of  iKen’s  products.   Our  Vision   We  are  poised  to  bring  about  a  paradigm  shiH  in  the  way  market  treats  customers  today.  iKen  is   confident  of  taking  Mooga  from  present  day  “great  to  have”  percep@on  to    a  “must  have”  demand   in  the  following  years.  
  3. 3. iKen  Overview   •  An  IIT  Bombay  research  spin-­‐off   •  Opera@ons  began  in  June  2008   •  Headcount:  25,  with  offices  in  Mumbai,  India  and  Buenos  Aires,  Argen@na   •  Exper@se  in  Intelligent  Business  Systems  backed  by  Business  Intelligence  2.0  and   Hybrid    Ar;ficial  Intelligence  Techniques   •  iKen   has   a   comprehensive   soHware   framework   named   as   Mooga.   It   is   a   BI   2.0   pla[orm  for  N=1  analy@cs  services.     •  Mooga  can  be  applied  into  Telecom,  Mobile  VAS,  Internet  (Entertainment,  Retail,  e   Commerce),   Customer   Lifecycle   Management,   Customer   Care,   BFSI,   Billing,   ERP/ CRM,   Educa@on   and   with   Independent   SoHware   Vendors   having   respec@ve   Domain  Exper@se  
  4. 4. iKen  References  
  5. 5. iKen’s  Global  Presence  –  Clients  &  Partners   U   INDIA   BRAZIL   SRI  LANKA   KENYA   PARAGUAY   ARGENTINA   URUGUAYY  
  6. 6. iKen  Recogni@ons   •  NASSCOM  Innova@on  Awards  2008  Finalist   •  Selected  by  MicrosoH  to  par@cipate  in  Le   Web  ´08  as  one  of  the  Top  10  innova@ve   startups  in  the  world.   •  First  at  the  Tie-­‐Canaan  Entrepreneurial   Challenge  2008.   •  Mooga  won  Silver  Award  for  “Best   Technology  Innova@on”  at  the  Mobile   Content  Awards  2008.   •  Among  Top  25  start-­‐ups,  Silicon  India,  May   2010  hlp://   •  Among  DARE’s  “75  start-­‐ups  you  can  bet  on”   hlp://­‐startups-­‐you-­‐ can-­‐bet-­‐on/iken-­‐solu@ons.htm  
  7. 7. Today’s  Challenges   Apps  Apps   Everywhere..!!!   Operator’s  Dilemma   Customer’s  Dilemma   • Which  app  to  promote  to   • How  to  quickly  “get   which  user   navigated”  to  an  App  of  my   • How  to  mone@ze  the  en@re   choice/taste   App  inventory   • I  am  willing  to  pay  a  premium   • How  to  enable  App  Discovery   for  my  experience,  but  I  don’t   • How  to  Personalize  the  user’s   get  it.   experience  
  8. 8. What  is  Mooga   •  Next   genera;on   personaliza;on,   matching,   discovery   and   recommenda;on   framework   based   on   the   N=1   concept   •  Supports   various   types   of   structured   contents   and   generic  transac;ons  seamlessly  and  uniformly   •  Based   on   social   (collabora;ve)   filtering,   content   (logical   and   contextual)   filtering,   intelligent   matching   and   on   individual   tastes   along   with   adapta;on   to   ;me   and   loca;on  dimensions   •  Works   in   real-­‐;me,   self-­‐learning   and   is   completely   programmable,   configurable   and   customizable   based   on   products,  contents  and  required  func;onality  
  9. 9. Mooga  Hybrid  AI  Framework   Understanding wisdom of crowd (what people do?) Content filtering Adapting to and clustering changing personal tastes (including time and location ) Mooga Hybrid Artificial Intelligence Framework Business rules, Flexible modeling, Intelligent User configuration and Criteria Matching customization Lazy learning, adaptive and real-time framework
  10. 10. Mooga  App  Personalizer  (MAP)   Personal   Preferences   Business   Wisdom  of   Rules  &   Crowd   Policies   Dynamic   Personal   Behavior  &   Profile   Interac@on   App   Inputs   Market   Metadata   to  MAP   Informa@on   Mooga   Analy@cs   Engine   learns   each   user’s   taste   &   preference   thru  her  consump@on  palern  and   picks   up   the   most   relevant   app   that  suits  her  liking   Personalized  Apps  to  every  user    
  11. 11. Why  Mooga  App  Personalizer  
  12. 12. How  does  it  work?   INPUT   P&R  Processing   OUTPUT  (N=1)   User Transactions User Profile Users’ Transactions, Ratings, Tagging,   etc. Buy, browse, Personal download, referred Attributes(global Ratings and location and local) Clustering Individualized and Meta Contents, (based on feature Common contents matching)   Taxonomy, Keywords, Content Filtering   Tags,…   User Preferences   Dynamic and Domain   Incremental CFs Knowledge   Products or contents or promotional   Content Discovery material or advertisements (at what User Profile Data User and time What kind of products or contents user likes? True personalization Business Logic and when) the customer/user will What keywords, tags, etc. user searches? based on Hybrid AI     and Policy Rules likely What campaigns user responds? respond to or would like to buy/view/ Basic Ranked download or should be served. When user prefers transactions (day, time, DB Search   Content month)? Universe   Automatically Where user does transaction (location)? Hybrid AI skips the contents already What kind of likely personal characteristics user is Techniques   downloaded/bought etc.   having?
  13. 13. Example-­‐Clustering  based  on  N=1   N=G   N=LT   N=1   Customers   Broader   Long  Tail   Unique  and   Groups   (niches)   personalized   (Clustering/   experiences   Classifica@on)  
  14. 14. Create  Unlimited  Cluster  Types   Heavy Users Cluster can be created based upon different Parameters •  Usage (Heavy, Moderate, etc) •  Location •  Access Interface (Web/WAP etc) •  Content Category WEB(interface based cluster) •  Demographics IVR(interface based cluster) •  Other configurable cluster Enthusiastic users •  Combinations of defined clusters Common between two Clusters
  15. 15. All  this  Results  in   Operator’s  Delight   • User  specific  Personalized  App  promo@on   • Mone@za@on  of  Long  Tail  thru  Discovery   • Increased  Customer  S@ckiness     • More  revenue  from  each  user   Customer’s  Delight   • Superior  Experience   • Less  pain  in  naviga@on   • “I  get  what  I  want”    
  16. 16. Exploit  the  Unexploited    
  17. 17. P&R  Logical  level  diagram  
  18. 18. Mooga  Component  Level  Architecture   Application Application Application Application Front-end Front-end Front-end Front-end (Mobile) (Web) (Broadband (Digital TV) ) Client Application Server (Web/WAP/IVR, etc Server) Integration APIs to wrap web services User  info  &   P&R   Click  Streams Information Domain   Vocabulary     iKen  Studio   Mooga  P&R   Scheduler   Application  speciEic   extensions   Web  Services   Vocabulary   Domain  logic  and   Meta  data   models:  Business   creation  and  data   Rules,  logic  etc.   synchronization Mooga  P&R   Tag  Mapping   Database   CMS DB/Content DB/RSS Feeds
  19. 19. Case  Study:  Airtel   About  Airtel   •  Bhar@   Airtel   Limited,   formerly   known   as   Bhar@   Tele-­‐Ventures   LTD   (BTVL)   is   an   Indian  company  offering  tele-­‐communica@on  services  in  18  countries.     •  It   the   largest   cellular   service   provider   in   India,   with   more   than   135   million   subscrip@ons  as  of  May  2010.     •  Bhar@   Airtel   is   the   world's   third   largest,   single-­‐country   mobile   operator   and   fiHh   largest   telecom   operator   in   the   world   in   terms   of   subscriber   base.   It   also   offers   fixed  line  services  and  broadband  services.     •  It  offers  its  telecom  services  under  the  Airtel  brand   POC  for  Personalized  Ring  Back  Tones(RBT):  Scope   •  Aitel  proposed  a  market  with  high-­‐traffic,  diverse  demographics,  high  consump@on   of   music   and   which   could   be   representa@ve   for   other   markets.   Mumbai   was   the   chosen  circle.   •  RBTs   get   downloaded   through   various   channels   such   as   WAP,   USSD,   IVR,   *Copy,   OBD,   etc.   Implemen@ng   Mooga   services   on   a   Virtual   Number   (VN)   was   step   1.   Based  on  results,  integra@on  on  other  channels  was  to  be  encompassed.  A  virtual   number   is   a   short/long   code   which   subscribers   dial   in   to   listen   to   a   sequence   of   songs.  They  can  select  a  song  of  their  choice  any@me  by  pressing  a  *.  
  20. 20. Case  Study:  Airtel   POC  for  Personalized  RBTs:  Scope   •  Before  Mooga  deployment,  Airtel  would  play  a  set  of  5  songs  randomly  every  day   for  all  its  subscribers  (irrespec@ve  of  their  likings).  If  a  user  didn’t  find  a  song  of  her   interest  aHer  calling  the  VN,  she  would  hang  up  and  call  back  aHer  some  @me  to   get   to   listen   to   a   new   set   of   songs.   This   would   go   on   @ll   she   would   finally   come   across  a  song  of  her  choice.     •  We  started  off  with  providing  Personalized  Recommenda@ons  on  the  VN  from  the   1st   week   of   June   2010.   Mooga   gave   Personalized   Recommenda@ons   to   each   and   every   individual   based   on   her   taste   and   liking.   The   sequence   of   songs   would   dynamically  change  in  real-­‐@me  from  session  to  session.   •  Since   Mooga   is   a   self-­‐learning   system,   Recommenda@ons   get   more   and   more   precise  and  relevant  with  @me  (as  the  system  learns  more  about  the  user).  
  21. 21. Case  Study:  Airtel   Results     The  average  number  of  downloads  increased  by  a  staggering  150%  over  the  VN  in   just  a  span  of  1  month.     From  a  Sales  Distribu@on  perspec@ve,  Mooga  is  helping  Airtel  sell  in  one  day  what   they  used  to  sell  in  one  month.     The  total  numbers  of  calls  made  to  the  VN  have  increased  thrice  as  much  as  people   are   making   more   and   more   calls   as   they   are   hearing   up   to   100   songs   of   their   interest  from  earlier  5  earlier.  Because  it  is  a  toll  free  number,  people  have  made   this  like  radio.  Here  conversion  rate  is  higher  than  10%      
  22. 22. Contact  Details   India Latin America iKen Solutions India Pvt. Ltd. iKen Solutions – Americas 3rd Floor, SINE, CSRE Department Blanco Encalada 88, Piso 1, Oficina 6, Boulogne Indian Institute of Technology Bombay (CP 1609) Buenos Aires, Argentina Powai, Mumbai - 400 076, India Email: Phone1: +91-22-2572 2675 Phone2: +91-22-6518 2059 Email:
  23. 23.   Thank  You