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The Longer Tails of iTunes, Pandora, and YouTube


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While the limited bandwidth of FM radio facilitated widespread adoption of mainstream music preferences and spending habits, new digital music technologies recommend and feature music based on …

While the limited bandwidth of FM radio facilitated widespread adoption of mainstream music preferences and spending habits, new digital music technologies recommend and feature music based on personalized user profile data. Whether this includes tracking purchase history, song “likes”, users’ emotions, or otherwise, the shift from majority-based music recommendation to individual-based is a recent and relatively unexplored development in the music industry. The purpose of this study is two-fold: to determine the most influential factors shaping users’ choice of music technology, and the extent to which these new technologies affect music preferences, spending and engagement. Focusing on iTunes, Pandora, and YouTube, purpose-built surveys examine the reasons users choose each service and how they perceive the technologies have affected their music consumption. Additional survey questions seek patterns and correlations between demographics, musical experience, music preferences, and music listening environment. 125 college students voluntarily completed the survey, revealing strong correlations between variables currently ignored by music recommendation technology. By enhancing our understanding of how new music technologies impact individual users, this study may guide how music applications can improve user profiling, personalization, and the user’s music-listening experience as a whole.

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  • 1.     The Longer Tails of iTunes, Pandora, and YouTube: New Technology Shaping Music Preference and Spending     Andrew  D.  Penrose   Program  in  Science,  Technology,  and  Society   Stanford  University        The  author  wishes  to  thank  his  advisors  Professor  Robert  McGinn  and  Professor  David  Voelker  at  Stanford  for  their  valued  feedback  and  guidance  on  this  project.  Additional  thanks  to  all  survey  respondents,  interview  volunteers,  Professor  Fred  Turner  for  the  lecture  that  inspired  this  study,  and  all  others  who  contributed  support.      Correspondence  concerning  this  paper  can  be  sent  to  Andrew  Penrose,  675  Lomita  Drive,  Stanford,  CA  94305.  Address  email  to    
  • 2. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Abstract  While   the   limited   bandwidth   of   FM   radio   facilitated   widespread   adoption   of   mainstream  music   preferences   and   spending   habits,   new   digital   music   technologies   recommend   and  feature   music   based   on   personalized   user   profile   data.     Whether   this   includes   tracking  purchase   history,   song   “likes”,   users’   emotions,   or   otherwise,   the   shift   from   majority-­‐based  music   recommendation   to   individual-­‐based   is   a   recent   and   relatively   unexplored  development  in  the  music  industry.    The  purpose  of  this  study  is  two-­‐fold:  to  determine  the  most  influential  factors  shaping  users’  choice  of  music  technology,  and  the  extent  to  which  these   new   technologies   affect   music   preferences,   spending   and   engagement.   Focusing   on  iTunes,   Pandora,   and   YouTube,   purpose-­‐built   surveys   examine   the   reasons   users   choose  each   service   and   how   they   perceive   the   technologies   have   affected   their   music  consumption.   Additional   survey   questions   seek   patterns   and   correlations   between  demographics,   musical   experience,   music   preferences,   and   music   listening   environment.  125   college   students   voluntarily   completed   the   survey,   revealing   strong   correlations  between  variables  currently  ignored  by  music  recommendation  technology.  By  enhancing  our  understanding  of  how  new  music  technologies  impact  individual  users,  this  study  may  guide   how   music   applications   can   improve   user   profiling,   personalization,   and   the   user’s  music-­‐listening  experience  as  a  whole.    Keywords:  digital  music  technology,  the  Long  Tail,  music  preferences,  profiling,  multivariate  music  recommendation,  iTunes,  Pandora,  YouTube,  internet  radio           1  
  • 3. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Table  of  Contents  INTRODUCTION  (3-­4)  LITERATURE  REVIEW  (4-­10)   THE  LONG  TAIL:  NEW  TECHNOLOGY  REVEALING  NICHE  PREFERENCES   MUSIC  PREFERENCE  STUDIES  METHODS  (10-­16)   SAMPLING   CONCEPTS   Demographics   Musical  Experience   Music  Preferences   Listening  Environment   Music  Service  Features  and  Effects   CODING  AND  DATA  ANALYSIS   RESPONDENTS  RESULTS  (16-­60)   MUSIC  PREFERENCE  AND  DETERMINING  FACTORS   Song  Preference   Genre  Preference   Determining  Factors  in  Music  Preference   Correlations  Between  Genres   Demographics  and  Genre  Preferences   Musical  Experience  and  Genre  Preferences   Listening  Environment  and  Genre  Preferences     MUSIC  TECHNOLOGY  PREFERENCE  AND  DETERMINING  FACTORS   Factors  in  Music  Technology  Preference   Favorite  Feature   Demographics  and  Music  Technology  Preference   Musical  Experience  and  Music  Technology  Preference   Music  Preference  and  Music  Technology  Preference     MUSIC  TECHNOLOGY  INFLUENCING  PREFERENCE  AND  SPENDING   Effects  on  Music  Preference   Listen  More   Wider  Range  of  Genres   Deeper  Within  Familiar  Genres   More  Sharing  Music   Effects  on  Spending   More  Buying   Buying  Different  Music   Buying  Concert  Tickets   Music  is  Bigger  DISCUSSION  (61-­65)  REFERENCES  (66)  APPENDIX  (67-­75)           2  
  • 4. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose     Introduction     It’s   your   last   high   school   gym   class   before   moving   to   college.   You   return   to   your  locker   to   find   the   lock   broken   and   someone   has   stolen   your   iPod   and   entire   music  collection  out  of  your  backpack.  Fearing  you  may  turn  to  a  life  of  digital  piracy  and  cyber  crime,   your   parents   purchase   the   new   32GB   iPod   Touch   that   holds   7,000   songs   and  connects   to   the   Internet.   Having   lost   all   of   your   music,   you   research   some   popular   music  applications.  It’s  May  20,  2012  and  as  of   April  30th,  the  iTunes  store  offered  over  28  million  songs.  How  do  you  choose  which  0.025%  to  buy?  Do  you  instead  rely  on  the  endless  stream  of   YouTube   videos   your   friends   share   on   Facebook,   or   do   you   create   a   Pandora   station   like  the  150  million  other  Americans  that  enjoy  personalized  music  recommendations?   The  limited  bandwidth  of  AM/FM  radio  necessitated  a  popularity  contest  for  songs,  but   the   technical   constraints   of   terrestrial   radio   don’t   apply   to   digital   music.   The  combination   of   nearly   unlimited   music   choice   and   a   wide   variety   of   music   sites   make  modern   music   experiences   vastly   more   personal   than   terrestrial   radio.   The   proliferation   of  song  recommendations,  shared  playlists,  and  music  blogs  attest  to  the  power  of  the  digital  music  experience.   After  a  particularly  inspiring  lecture  on  digital  media  by  Professor  Fred  Turner  last  year,  I  designed  and  conducted  a  survey  on  Pandora  use  for  a  Communication  course  at  Stanford.  Asking  112  respondents  if  they  had  ever  bought  an  unfamiliar  song  after  hearing  it  only  once  on  Pandora,  59  students  equaling  53%  of  the  sample  indicated  that  they  had.  Even  more  surprising,  15  students  (13%)  indicated  they  had  bought  an  entire  album  after       3  
  • 5. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  hearing  a  song  from  it  for  the  first  time  on  Pandora.  Although  I  recognized  that  the  Stanford  students  that  made  up  the  survey  sample  were  not  representative  of  Pandora’s  entire  user  base,  it  seemed  likely  that  a  significant  percentage  of  Pandora’s  users  were  purchasing  unfamiliar  music  as  well.  I  wanted  to  know  how  these  new  transactions  would  affect  the  music  industry,  amidst  declining  sales  and  a  torrent  of  illegal  filesharing  applications.       Literature  Review  The  Long  Tail  of  Digital  Music:  New  Technology  Revealing  Niche  Preferences   After   Chris   Anderson   published   his   article   “The   Long   Tail”   in   Wired   magazine   in  October  of  2004,  it  quickly  became  the  most  cited  article  in  Wired’s  history,  and  his  book  became   one   of   the   most   influential   business   books   of   the   decade   (Anderson).   Using   e-­‐commerce   data   that   had   been   historically   restricted   to   executives,   the   book   outlines  Anderson’s  theory  that  the  Internet  has  expanded  the  range  of  effective  inventory  from  a  limited   number   of   “hits”,   as   seen   on   WalMart   and   Blockbuster   shelves,   to   nearly   infinity.  Since   the   post-­‐WWII   era   of   TV   and  radio,   businesses   have   traditionally  capitalized   on   the   power   of   the   top  100   or   even   100,000   mainstream  products,   ignoring   all   the   books,  songs,   and   goods   that   didn’t   make  the   charts   (Figure   1).   But   as   both                        Figure  1       4  
  • 6. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  Anderson   and   Lessig   point   out,   the   recent   success   stories   of   Amazon,   Netflix,   and   iTunes  prove   that   the   companies   providing   customers   with   the   most   choices   and   the   most  effective  ways  to  navigate  them  can  earn  as  much  as  40%  of  their  revenue  from  products  along  “the  long  tail.”   During   the   age   of   FM   radio   and   record   stores,   limited   inventory   and   constrained  choice   contributed   to   widespread   adoption   of   popularized   music   preferences.     Retailers  optimizing   limited   shelf   space   and   FM   radio   DJs   seeking   to   maximize   listenership  reinforced  a  culture-­‐wide  fascination  with  top  charts  and  superstars.    However,  as  digital  music   technologies   continue   to   proliferate,   the   seemingly   unlimited   number   of   musical  choices   and   their   innovative   recommendation   systems   are   shaping   listeners’   preferences  and  consumption  patterns  in  new  ways.    Although  the  possible  ramifications  of  unlimited  choice   and   user   profiling   are   numerous,   I   expect   these   technologies   to   both   widen   and  deepen   the   music   preferences   of   their   users.     In   other   words,   the   unique   features   of   new  music  services  will  not  only  enable  the  tracking  of  the  Long  Tail,  but  also  shift  demand  to  make  it  even  longer.    The  purpose  of  this  study  is  two-­‐fold:  to  determine  the  most  salient  factors   that   shape   listeners’   music   preferences   and   choice   of   music   service,   and   to   enhance  our  understanding  of  new  music  technologies’  impact  on  users.   Throughout   history,   from   Mary   Shelley’s   Frankenstein   to   George   Orwell’s   Nineteen  Eighty-­Four,  the  idea  of  technological  determinism  has  caused  society  to  irrationally  view  and   fear   technology   as   an   autonomous   juggernaut,   sometimes   causing   the   restriction   of  tools   that   extend   humanity’s   potential   (McGinn).     A   technological   determinist   might   use  phrases   like   identity   theft,   violation   of   privacy,   and   entertainment   piracy   to   describe   the       5  
  • 7. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  Internet’s  relationship  with  its  users.  In  his  book  Remix,  Lessig  argues  that  the  digitization  of   culture   and   the   economy   is   a   positive   change   to   be   embraced   and   understood,   rather  than   restricted   and   criminalized.   He   protests   against   outdated   copyright   laws   now  criminalizing  creative  actions,  calling  for  copyright  law  reform  to  realize  the  full  potential  of   the   new   hybrid   of   commercial   and   sharing   economies.   After   detailing   both   economies  individually,  he  argues  that  the  Internet’s  new  hybrid  economy  is  a  fusion  of  both  voluntary  collaboration   and   traditional   commerce.     He   provides   several   examples   of   companies   —  including  Netflix,  Amazon,  Google,  YouTube,  and  Second  Life  —  and  mechanisms,  such  as  user   reviews   and   recommendations,   crowdsourcing,   and   Anderson’s   Long   Tail   principle,  that   support   his   argument   that   the   new   hybrid   economy   is   “a   model   of   success,   not   a  compromise   of   profit.”   McGinn   also   testifies   to   the   vital   importance   of   resisting  technological   determinism,   acknowledging   technology   and   society   as   interdependent   and  co-­‐evolutionary,   and   monitoring   the   unique   powers   associated   with   each.     These   ideas  guided   this   study   throughout   the   various   stages   of   literature   review,   data   collection,   and  analysis.   Contrary   to   technological   deterministic   perspectives,   more   and   more   IT-­‐based  media  channels  and  corporations  are  capitalizing  on  their  control  over  technology  to  shape  user   interactions   online.     Amazon’s   book   recommendation   feature   is   one   example   of   a  navigational   tool   intended   to   both   maximize   profit   and   cater   to   users’   preferences.     As  Anderson  points  out  in  the  first  chapter  of  The  Long  Tail,  Amazon’s  pairing  of  the  best  seller  Into   Thin   Air   with   the   lesser-­‐known   Touching   the   Void   via   its   recommendation   feature  created   a   powerful   positive   feedback   loop   of   both   interest   and   revenue.   By   categorizing  media   based   on   similarity,   rather   than   —   or   in   addition   to   —   listing   them   by   popularity,       6  
  • 8. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  these   institutions   and   corporations   better   serve   both   the   user   and   the   long   tail   of   the  market.   In   his   book   The   Wisdom   of   the   Crowds,   author   James   Surowiecki   explores   the   notion  that  a  large  group  of  people  is  more  innovative  and  better  at  problem  solving  than  a  small  elite   creative   team,   concluding   that   this   technique   of   “crowdsourcing”   has   enormous  potential   and   has   already   begun   to   shape   online   interaction   (Surowiecki).     Taking  Surowiecki’s   advice,   the   popular   online   DVD   rental   service   Netflix   conducted   a   nearly  three-­‐year-­‐long   public   competition   for   an   improved   Netflix   recommendation   algorithm,  making   Netflix   usage   data   freely   available   in   an   effort   “to   substantially   improve   the  accuracy   of   predictions   about   how   much   someone   is   going   to   enjoy   a   movie   based   on   their  movie   preferences”   (   The   winning   team’s   algorithm   is   yet  another   user-­‐centered   tool   used   to   connect   niche   market   products   and   media   to   their  customers,  directly  facilitating  the  expansion  of  the  long  tail.  Studying  Music  Preference     Many   researchers   have   conducted   studies   revealing   correlations   between  demographical   information,   such   as   age,   gender   and   education,   and   music   preferences.    LeBlanc   et   al.   created   an   overall   music   preference   index   to   measure   subjects’   total  preferences   across   genres   and   compared   responses   between   different   age   groups.     After  surveying   2,262   respondents,   the   researchers   found   that   the   music   preference   index  declined  in  elementary  students,  rose  from  high  school  to  college,  and  declined  after  college  (LeBlanc   et   al.,   1996).     While   these   findings   may   not   provide   a   means   to   improve   music  recommendation   algorithms,   statistically   significant   correlations   between   age   and       7  
  • 9. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  preferences   for   specific   songs,   artists,   and   genres   would   certainly   help   predict   listener  reactions.     Although   surveys   may   be   able   to   determine   linkages   between   age   and   genre  preferences   (as   this   study   will   show),   this   method   is   obviously   not   feasible   for   collecting  larger  data  sets  regarding  artist  or  song  preference.    However,  this  is  one  of  many  examples  of   how   digital   media’s   growing   trend   of   “thumb”   or   “like”   feedback   could   be   utilized   by  companies  like  iTunes,  Pandora,  and  YouTube.     While   most   music   recommendation   sites   focus   on   users’   preferences   and   musical  similarities   between   songs,   several   Taiwanese   researchers   (Suh-­‐Yin   Lee   et   al.,   2009)  investigated  the  use  of  emotion-­‐based  music  discovery  within  the  context  of  motion  picture  scores.     Constructing   an   original   algorithm   called   the   Music   Affinity   Graph-­‐Plus,   Suh-­‐Yin  Lee  et  al.  achieved  an  impressive  85%  accuracy  in  matching  queried  emotions  with  music  of  the  same  emotions.  While  these  results  and  the  growth  of  music  recommendation  sites  like  Stereomood  and  Music  for  Emotion  prove  the  potential  of  emotion-­‐based  song  sorting  and   recommendation,   such   an   approach   has   yet   to   draw   a   fraction   of   the   audience   of  iTunes,   Pandora   or   YouTube.     In   acknowledgement   of   its   potential,   this   study   will   also  survey  respondents  on  their  level  of  demand  for  emotion-­‐based  music  recommendation.   In  2009,  Gaffney  and  Rafferty  conducted  a  study  investigating  users’  knowledge  and  use  of  social  networking  sites  and  folksonomies  (user-­‐generated  taxonomies),  focusing  on  the   potential   of   social   tagging   to   aid   in   the   discovery   of   independent   music.     Examining   the  four   music   discovery   sites   MySpace,   Lastfm,   Pandora   and   Allmusic   through   user   surveys  and  interviews,  they  found  that  although  respondents  use  social  networking  sites  for  music  discovery,   they   are   generally   unaware   of   folksonomic   approaches   to   music   discovery.       8  
  • 10. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  Furthermore,  those  who  do  use  and  contribute  to  folksonomies  are  mostly  self-­‐serving  in  their   motives   (Gaffney   and   Rafferty,   2009).     While   Gaffney   and   Rafferty   state   that   their  study  rests  upon  the  assumption  that  music  recommendation  and  social  networking  sites  push  users  and  revenue  toward  the  Long  Tail,  they  make  no  attempt  to  quantify  the  impact  of  any  particular  site  on  the  time  or  money  users  spend  on  Long  Tail  songs.    Additionally,  the  landscape  of  music  discovery  sites  has  changed  dramatically  since  they  conducted  the  study,  especially  in  the  case  of  Pandora’s  rapid  growth.   Unfortunately,   the   vast   majority   of   studies   involving   music   preferences   use   a  nomothetic  approach  to  choose  one  or  two  particular  factors  to  test,  whether  for  simplicity  or  convenience.    Christenson  and  Peterson  built  upon  earlier  studies  of  gender  and  music  genre   preferences   by   including   many   “metagenres”   previously   disregarded   by   social  scientists.     Consistent   with   similar   studies,   they   found   convincing   evidence   that   gender  predisposes   people   to   certain   music   preferences;   for   example,   that   females   gravitate  toward   popular   music   and   males   gravitate   away   from   it.     While   this   study   contributes   a  piece   of   the   music   preference-­‐mapping   puzzle,   Christenson   and   Peterson   admit,   “the  underlying   structure   of   music   preference   cannot   be   accounted   for   by   reference   to   two   or  three  factors,  but  is  multivariate”  (Christenson  et  al,  1988).    At  this  point,  the  need  for  an  idiographic  approach  to  music  preferences  is  clear.   This   study   is   partially   driven   by   the   lack   of   a   multivariate   or   idiographic   study  comparing   the   relative   impacts   of   age,   emotion,   social   network,   choice   of   digital   music  service,   and   more   factors,   on   music   preference.   iTunes,   Pandora   and   YouTube   certainly  have   a   wealth   of   data   on   their   services’   use   and   users,   but   data   points   like   relative       9  
  • 11. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  preference   between   services,   musical   education   and   experience   of   users,   and   listening  environment  are  often  ignored.  Not  only  do  I  find  this  information  intriguing,  I  suspect  it  could   prove   incredibly   relevant   to   both   music   marketing   strategies   and   music  recommendation   technology.   In   addition   to   enabling   the   examination   of   underlying  patterns   between   these   variables,   the   collected   surveys   provide   a   basis   for   predicting  economic   shifts   in   the   music   industry.     It   is   expected   that   by   aligning   recommendations  with   each   unique   users’   profile   rather   than   the   most   popular   songs,   new   music  technologies  like  iTunes,  Pandora,  and  YouTube  both  please  users  and  support  more  artists  further   down   the   Long   Tail.   Furthermore,   the   findings   presented   in   this   study   reveal  significant   relationships   between   variables   that   have   thus   far   been   excluded   from   music  recommendation  algorithms.     Methods  Sampling   Given  my  interest  in  the  college  student  demographic  and  my  immediate  network  of  friends   and   family,   I   focused   my   recruiting   efforts   on   three   different   colleges:   Stanford  University,   Glendale   Community   College   (GCC),   and   Arizona   State   University   (ASU).  Stanford  was  the  first  and  most  convenient  sampling  frame  for  me  as  a  Stanford  undergrad,  providing   38   respondents.   My   parents,   both   professors   at   Glendale   Community   College,  invited  their  students  to  take  the  survey  and  added  71  students  to  the  sample.    Last,  I  sent  a  brief   Facebook   message   to   recruit   ASU   students   from   my   high   school   network.   Response  and   completion   rates   were   lowest   at   ASU,   with   9   students   completing   the   survey.   The       10  
  • 12. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  shortage   of   both   time   and   funded   incentives   ruled   out   a   random   sampling   of   college  students,   but   I   minimized   potential   biases   by   recruiting   from   several   different   schools.  While  a  realistic  distribution  between  schools  would  have  been  preferable,  the  number  of  college  students  who  volunteered  for  my  unpaid,  20-­‐minute  survey  was  significantly  higher  than  I  anticipated.    Concepts   The  first  page  of  the  survey  addressed  respondents’  demographics,  including  age,  gender,  hometown,  current  school,  and  competence  with  computers.    Free  response,  or  open-­‐ended,  answer  formats  will  be  used  for  age,  hometown,  and  current  school,  while  gender  and  computer  competence  will  use  closed-­‐ended  questions.    The  question  “Please  categorize  your  competence  using  computers”  will  include  the  options  “Advanced”,    “Average”,  “Basic”,  and  “NoneVery  Little.”  These  items  were  carefully  chosen  for  clarity  and  appropriateness,  to  ensure  optimal  accuracy.  The  demographic  variables  were  chosen  for  potential  to  influence  both  music  preference  and  music  technology  preference.     The  second  page  of  the  questionnaire  features  units  of  analysis  addressing  respondents’  musical  experience,  in  order  to  gauge  how  each  influences  music  preference.    Each  concept  will  contribute  to  an  index  summarizing  overall  musical  experience,  assigning  quantitative  values  to  qualitative  responses  where  appropriate.    First,  subjects  were  asked  the  open-­‐ended  question  “Approximately  how  many  hours  per  week  do  you  spend  listening  to  music?”  Next,  respondents  selected  the  option,  “Which  best  describes  the  frequency  of  your  online  music  listening?”  from  the  list:  “Rarely”,  “Sometimes”,  “Often”,  and  “All  the  Time.”  Then,  using  a  check-­‐all  question  format,  respondents  indicated  the  school  years       11  
  • 13. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  during  which  they  took  at  least  one  music  class,  with  the  options  “Elementary  (K-­‐8th)”,  “High  School”,  “College”,  and  “None”.    Next,  subjects  indicated  how  many  years  they  have  taken  musical  instrument  lessons  (outside  of  school)  with  free  response.    Finally,  the  closed-­‐ended  question  “Do  you  currently  play  an  instrument?”  was  followed  by  the  contingency  question  of  “How  many  years  have  you  played  an  instrument?”    In  order  to  maintain  both  accuracy  and  the  respondent’s  attention,  these  questions  and  question  formats  were  chosen  based  on  their  clarity,  relevance,  and  brevity  for  each  unit  of  analysis.    Both  the  index  and  individual  units  of  analysis  will  be  used  in  determining  the  most  salient  factors  in  music  preference.   The  next  page  of  the  survey  investigated  subjects’  music  preferences.    For  the  purposes  of  this  study,  music  preferences  were  defined  as  genres  that  an  individual  simply  enjoys  listening  to.    As  mentioned  earlier,  genres  are  the  most  feasible  unit  of  analysis  for  music  preferences  using  a  survey,  given  the  large  numbers  of  artists  and  songs  in  existence.    Using  a  matrix  question,  participants  were  asked,  “What  are  your  attitudes  toward  the  following  music  genres?”    In  addition  to  operationalizing  this  concept  with  multiple  levels  of  enjoyment  (dislike,  neutral,  like,  and  love),  the  list  of  genres  included  those  common  throughout  all  three  music  services  in  question  (see  Appendix  for  full  survey).    The  primary  issue  carefully  controlled  in  this  question  was  the  respondent’s  understanding  of  music  genres.    For  this  reason,  the  selected  genres  were  pragmatically  selected  for  distinctness  from  one  another.    While  this  potential  confound  has  been  minimized,  it  cannot  be  fully  eliminated  without  including  potentially  distracting  full  definitions  of  each  genre.       12  
  • 14. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Although  the  varying  levels  of  preference  within  genres,  or  the  “depth”  of  music  preference,  have  been  accounted  for  with  the  four  options  listed  on  this  genre,  the  following  questions  utilized  a  different  approach  to  measure  the  same  concept.    After  subjects  indicated  their  favorite  genre  from  the  same  list,  they  were  asked,  “Within  your  favorite  musical  genre,  approximately  what  percentage  of  artists  and  songs  that  you  know  do  you  like?”  with  the  options  “0-­‐20%”,  “21-­‐40%”,  “41-­‐60%”,  “61-­‐80%”,  and  “81-­‐100%”.    Next,  the  questionnaire  asked  the  closed-­‐ended  question,  “Of  all  the  “top  40”  popular  music  you’ve  heard,  you  like:”  where  subjects  chose  between  “All  or  almost  all”,  “Most”,  “About  half”,  “Some”,  “None”,  and  “I  don’t  pay  attention  to  top  40  charts”.    Finally,  the  matrix  question  format  asked  respondents  about  the  importance  of  the  following  attributes  in  determining  whether  or  not  they  like  a  song.    These  attributes  included  “familiarity”,  “popularity”,  “fits  my  mood”,  “artistic  talent”,  “lyrics”,  and  “friends’  preferences”,  and  were  classified  as  either  “Not  important”,  “Somewhat  important”,  “Very  important”,  and  “Extremely  important”.    Again,  these  closed-­‐ended  questions  ensured  that  respondents  measure  their  perspectives  by  the  same  standards,  which  was  one  of  the  primary  reasons  for  using  the  online  survey  approach.   The  next  page  of  the  survey  examined  the  respondent’s  music  listening  environment.    Using  the  matrix  question  format,  the  respondents  indicated  how  often  they  listen  to  music  in  each  of  the  following  environments  and  activities,  including  “At  home”,  “In  the  car”,  “At  work”,  “By  yourself”,  “With  a  few  friends”,  “At  a  party”,  “While  studying”,  and  “While  sleeping”.    Potential  responses  utilized  the  Thurstone  scale,  and  included  “Never”,  “Rarely”,  “Sometimes”,  “Often”,  and  “Always”.    While  these  activities  and  locations  may  have  overlapped  somewhat,  each  item  was  chosen  for  relevance  and  potential  to       13  
  • 15. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  influence  music  preferences.    Perhaps  the  most  pivotal  of  the  entire  survey,  the  next  matrix  question  asked  respondents  to  rank  their  “Favorite”,  “2nd  Favorite”,  and  “3rd  Favorite”  music  services  between  iTunes,  Pandora,  and  YouTube;  respondents  could  also  select  a  fourth  option,  “Never  use  it”.    These  music  services  were  selected  because  they  are  widely  used,  legal  alternatives  to  music  piracy  and  because  I  wanted  to  understand  how  they  are  reshaping  the  music  industry  from  individual  users’  perspectives.     The  following  three  pages  contained  contingency  questions  depending  on  whether  respondents  use  the  services  iTunes,  Pandora,  and  YouTube.    Using  similarly  structured  matrix  questions,  these  pages  sought  to  ascertain  the  perceived  impact  of  each  service  on  users’  music  preferences  and  spending  habits  based  on  the  Likert  scale.    For  example,  respondents  were  asked  to  indicate  various  levels  of  agreement/disagreement  with  the  statements  “As  a  result  of  using  Pandora,”    “I  listen  to  music  more  often”,  “I  listen  to  a  wider  range  of  genres”,  “I  listen  to  more  music  within  the  genres  I  like”,  “I  share  music  with  my  friends  more”,  “I  buy  more  music”,  “I  buy  different  music  than  I  would  have  otherwise”,  “I  have  bought  concert  tickets  that  I  wouldn’t  have  otherwise”,  and  finally  “music  plays  a  bigger  role  in  my  life.”    Because  these  questions  directly  apply  to  the  hypothesis  of  this  study,  they  did  not  contain  negative  answers  or  answers  that  might  bias  results,  and  there  were  several  different  units  or  elements  intending  to  measure  the  same  concept.    The  final  question  on  each  page  asked  respondents  to  choose  their  favorite  feature  of  each  service,  choosing  between  “customizability/personalization”,  “its  interface”,  “its  wide  selection  of  music”,  “playlisting  and  song  recommendation”,  and  “Other:  Please  Specify”.    The  Likert  scale  was  chosen  both  for  its  speed  and  appropriateness  in  this  case,  and  the  use  of  similar       14  
  • 16. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  questions  on  the  pages  for  all  three  music  technologies  will  ensure  a  common  standard  of  measurement  and  enable  a  closer  comparison  of  their  relative  impacts  on  users.     Additional   measures   taken   to   ensure   accuracy   of   questionnaire   responses   include  carefully   ordering   the   questions   in   ascending   order   of   difficulty,   eliminating   double-­‐barreled  questions,  providing  questionnaire  instructions,  and  pretesting  the  questionnaire  on   a   number   of   classmates.     Wherever   possible,   questions   with   similar   potential   responses  were   grouped   as   matrix   questions   to   quicken   response   times   and   maintain   a   higher  response  rate.    Furthermore,  to  improve  the  relevance  of  the  questionnaire,  the  questions  that  may  not  apply  to  all  respondents  have  been  formatted  as  contingency  questions.  Coding  and  Data  Analysis   In  order  to  analyze  the  results  of  the  online  questionnaire,  I  downloaded  the  CSV  file  of  raw  data  for  138  respondents  from  and  imported  it  into  SPSS  Statistics,   which   I   purchased   through   Stanford   Software   Licensing.   Preparing   the   survey  data  for  analysis  involved  several  steps,  the  first  of  which  was  removing  the  incomplete  and  age-­‐inappropriate  cases.  After  deleting  the  few  cases  of  respondents  who  were  no  longer  in  college   or   hadn’t   completed   the   survey,   I   ended   up   with   125   total   respondents.   Next,   I  defined   each   of   the   variable   properties   by   classifying   them   as   either   ordinal,   nominal,   or  scale.    I  then  used  a  number  of  coding  techniques  to  enable  tests  of  correlation,  assigning  numeric   values   to   all   textual   responses.   For   example,   “Never”   =   1,   “Rarely”   =   2,  “Sometimes”  =  3,  and  so  on.  Next,  I  assigned   corresponding   labels   to   the   numeric   values   to  facilitate  my  interpretation  of  statistical  procedures.  Due  to  the  relatively  large  number  of       15  
  • 17. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  questions,  73  in  total,  the  various  strategies  used  to  assign  numeric  values  will  be  discussed  in  tandem  with  the  results  and  analysis  of  each  variable.  Respondents     Due   to   the   financial   and   temporal   constraints   of   this   study,   the   online   survey   was  distributed   to   a   convenient   sample.   Of   the   125   college   students   who   completed   the   survey,  66   (53%)   were   male   and   59   were   female.   Since   I   was   targeting   the   college   student  demographic,   respondents   had   an   average   age   of   21.43   with   a   standard   deviation   of   4.1.   In  response   to   the   third   question   of   hometown,   63   respondents   (50%)   indicated   they   were  from   Arizona,   40   of   which   were   from   Phoenix.   Another   26   respondents   (21%)   hail   from  various  cities  in  California,  and  the  remaining  subjects’  hometowns  included  18  states  and  4   locations   outside   the   United   States.   Although   the   survey’s   findings   may   have   a   slightly  southwest/west   coast   bias,   I   found   this   geographical   spread   acceptable   given   the   study’s  constraints.       Results     While  the  online  questionnaire  consisted  of  five  sections,  analysis  of  results  was  divided  into  three  sections:  music  preference  and  contributing  factors,  choice  of  music  technology  and  contributing  factors,  and  impacts  of  each  music  technology  on  preference  and  spending.  Each  of  the  three  sections  contains  several  different  variables  that  measure  similar  ideas  to  reinforce  findings.  Since  nearly  all  variables  were  coded  into  numeric  values  and  most  of  these  were  ordinal,  a  simple  function  in  SPSS  created  a  spreadsheet  of  all  correlations  between  variables  and  designated  those  of  significance  at  the  .05  and  the       16  
  • 18. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  .01  levels.  Because  the  survey  was  distributed  to  a  convenient  sample,  statistically  significant  correlations  cannot  be  generalized  to  larger  populations.  However,  these  findings  may  be  used  to  speculate  about  how  college  students  consume  music  online  and  how  technology  influences  their  preferences.  Due  to  the  length  and  comprehensiveness  of  the  survey,  the  three  results  sections  include  only  the  most  significant  and/or  surprising  results.      Music  Preference  and  Determining  Factors  Song  Preference     Perhaps  the  most  direct  question  addressing  the  factors  affecting  music  preference,  question  16  asked  respondents  to  indicate  the  importance  of  six  attributes  in  determining  whether   or   not   they   like   a   particular   song.   In   the   interest   of   saving   respondents’   time,   I  selected   attributes   that   were   highly   likely   candidates   of   influence.   Based   on   my   experience  with  music  and  friends’  preferences,  I  expected  popularity  and  friends’  preferences  to  rank  the  highest.  After  all,  it  seems  like  the  two  most  persuasive  reasons  to  check  out  a  new  song  are  that  friends  love  it  or  everybody  else  does.  I  also  speculated  that  lyrics  would  receive  polarized   ratings   of   importance,   and   that   “fitting   the   mood”   would   rank   as   more   important  than   most   of   the   other   attributes.   In   hindsight,   the   attribute   “artistic   talent”   should   have  either   been   reworded   as   “musicianship”   or   juxtaposed   with   “producer’s   talent”;   as   it  stands,  it  seems  hard  to  believe  many  respondents  would  indicate  that  they  don’t  care  if  the  artist  is  talented.       17  
  • 19. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose     The   results   for   these   six   attributes   were   fairly   surprising,   and   have   tremendous  implications   for   the   improvement   of   music   recommendation.   First,   my   predictions   about  popularity   and   friends’   preferences   were   almost   completely   wrong;   respondents   rated  both   lowest,   between   “somewhat   important”   and   “not   important”,   on   average.  Furthermore,   average   responses   for   familiarity   were   positioned   just   above   “somewhat  important”,   illustrating   users’   comfort   with   music   exploration.   Next,   lyrics   ranked   third  with  an  average  response  just  above  “very  important”,  in  contrast  with  my  expectation  that  some   respondents   preferring   instrumental   music   or   songs   by   Justin   Bieber   would   consider  lyrics   of   minimal   importance.   Interestingly   enough,   importance   of   lyrics   was   negatively  correlated  with  preferences  for  electronic  music  and  positively  correlated  with  R&B/Soul,  both  of  which  make  sense.  Although  I  guessed  “fitting  the  mood”  and  “artistic  talent”  would  rank   fairly   high,   I   didn’t   expect   them   to   rank   highest   overall   with   an   average   response  between   “very   important”   and   “extremely   important.”   While   these   findings   don’t   prescribe  an   ideal   way   to   incorporate   each   attribute   into   song   recommendations,   they   do   suggest  that   the   traditional   mechanisms   of   music   discovery   are   far   less   effective   than   new  recommendation  technologies  that  utilize  this  information.       18  
  • 20. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Determining  Factors  of  Song  Preference  -­  Mean  Response   4   Not  Important                                            Very  Important   3.5   3   2.5   2   1.5   1   0.5   0   Familiarity   Popularity   Fits  Mood   Artistic   Lyrics   Friends   Talent   Preferences    Figure  2     Admittedly  these  findings  are  self-­‐reported  and  it’s  entirely  possible  that  people  simply  don’t  want  to  recognize  how  much  a  song’s  popularity  or  their  friends’  tastes  influences  their  own  preference.  To  approach  the  question  of  how  popularity  impacts  song  preference  from  a  different  angle,  I  examined  the  frequency  of  responses  for  question  15  that  addressed  feelings  toward  top  40  music  (Figure  3).    The  average  response  was  halfway  between  “Some”  and  “About  Half”,  suggesting  that  the  previous  findings  were  correct.  Furthermore,  a  significant  portion  of  respondents,  reaching  almost  20%  of  the  sample,  state  that  they  either  don’t  pay  attention  to  top  40  charts  or  they  like  none  of  the  songs  on  them.  This  implies  that  although  many  users’  music  tastes  are  still  influenced  by  top  40  music  charts,  these  indicators  of  popularity  may  be  losing  the  power  they  once  held  over  AM/FM  radio  audiences.       19  
  • 21. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Preference  for  Top  40  Music   All  or  almost  all   Most   About  half   Some   None   I  dont  pay  attention  to  top  40s   0   5   10   15   20   25   30   35   40    Figure  3    Genre  Preference   The  survey’s  first  and  simplest  measure  of  respondents’  music  preferences  entailed  rating   fourteen   distinct   music   genres.   The   rating   scale   included   “dislike”   =   -­‐1,   “neutral”   =   0,  “like”   =   1,   and   “love”   =   2.     Rock,   Alternative,   and   Hip   Hop/Rap   scored   the   highest   on  average   among   the   125   respondents,   with   Latin   and   World   ranking   lowest   (Figure   4).  Additionally,   the   ratings   for   Hip   Hop/Rap   and   Country   were   the   most   polarized,   yielding  standard  deviations  over  1.         20  
  • 22. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Genre  Preferences  -­  Mean   2   1.5   1   0.5   0    Figure  4   After  seeing  how  respondents  ranked  each  genre  independently,  I  wanted  to  know  how   genres   clustered   together   based   on   these   ratings.   Using   multidimensional   scaling   in  SPSS,  I  determined  the  coordinates  for  each  genre  to  create  a  Euclidean  distance  model  that  provides   a   visualization   of   the   similarities   between   genres   based   on   the   respondents’  rankings  (Figure  5).  Though  the  interpretation  of  the  axes  is  essentially  meaningless,  this  graph   is   simply   a   way   to   visualize   perceived   similarities   between   genres   according   to  respondents.   For   the   most   part,   these   groupings   of   genres   make   sense   when   considering  musical  similarities,  probable  listening  environment,  and  several  other  characteristics.       21  
  • 23. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Derived  Stimulus  ConSiguration  -­  Euclidean  Distance  Model   Rock   Alternative   Jazz   Classical   Electronic   Vocal   R&B/Soul   Hip  Hop/Rap   Reggae   World   Pop   Dance   Latin   Country    Figure  5  Next,   respondents   indicated   their   favorite   genre,   choosing   from   these   fourteen   and   “Other”  (Table  1).  Consistent  with  Christenson  and  Peterson’s  findings,  the  “Other”  category  ranked  fourth   largest   with   14   respondents,   verifying   the   importance   of   accounting   for  “metagenres”  and  subgenres  in  music  classification  and  recommendation.  However,  for  the  purposes   of   this   analysis,   metagenres   and   subgenres   were   ignored   to   facilitate   quick   and  accurate  responses.     Favorite Genre Respondents Favorite Genre Respondents Rock 29 Pop 6 Hip Hop/Rap 18 Reggae 5 Alternative 16 Dance 4 Other 14 Classical 2 Country 13 Jazz 2     22  
  • 24. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   R&B/Soul 7 Vocal 2 Electronic 6 Latin 1Table  1  While   the   average   genre   ratings   and   favorite   genre   for   all   respondents   are   informative   and  fairly  interesting,  these  metrics’  true  function  is  to  provide  a  basis  for  correlations  between  subgroups  of  the  college  student  sample.  These  subgroups  are  drawn  from  four  categories  of   variables:   music   preferences,   user   demographics,   musical   experience,   and   listening  environment.  Factors  in  Music  Preference  Correlations  Between  Genre  Preferences     By   having   almost   daily   conversations   about   music   preferences   with   friends   and  strangers  for  at  least  ten  years,  I  developed  a  few  theories  regarding  relationships  between  genres.  I  got  the  sense  that  people  who  listened  to  at  least  one  niche  genre  tended  to  like  almost   all   others   as   well,   and   people   who   preferred   popular   music   had   much   narrower  tastes   for   genres.   While   portions   of   the   Euclidean   distance   model   conveyed   similar  information,   the   best   way   to   test   this   claim   was   through   bivariate   correlations.   Using   the  spreadsheet   of   Spearman   correlations,   I   calculated   the   number   of   significant   correlations  between  genres  and  found  two  groups  of  genres  separating  from  one  another.  I  created  one  table   using   the   genres   with   many   positive,   significant   correlations   (Table   2)   and   another  for   those   with   fewer   positive   correlations   and   more   negative   correlations   with   other  genres  (Table  3).         23  
  • 25. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose                    Table  2   Significant  Correlations  With  Other  Genres   Mostly  Niche  Genres   Positive   Negative   Reggae   7   0   Vocal   7   0   Latin   7   0   World   7   1   Classical   7   1   Jazz   7   1   Alternative   5   0   R&B/Soul   5   0                      Table  3   Mostly  Popular   Significant  Correlations  With  Other  Genres   Genres   Positive   Negative   Hip  Hop/Rap   6   2   Dance   4   1   Electronic   3   0   Pop   3   1   Rock   2   1   Country   2   1       These  tables  provide  strong  evidence  supporting  my  claim  that  users  who  like  one  niche   genre   are   likely   to   enjoy   many   more.   Not   only   does   it   show   that   niche   genres   are  positively   correlated   with   many   others   (Table   2),   the   more   popular   genres   have   twice   as  many   negative   correlations   (Table   3).     Hip   hop/rap   was   the   one   genre   positioned   in  between  the  distinct  groups  but  was  included  in  the  second  table  because  it  had  the  most  negative   correlations.   These   findings   seem   to   confirm   my   hypothesis   that   fans   of   niche  genres  have  wider  preferences  and  fans  of  popular  genres  have  narrower  preferences.    Demographics  and  Genre  Preferences       24  
  • 26. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose     I   predicted   a   few   demographical   variables   from   the   first   page   of   the   survey   would  correlate  with  genre  preferences  so  I  examined  their  Spearman  correlations.    As  I  expected  based   on   Christenson   and   Peterson’s   study   and   my   own   experience,   gender   was   negatively  correlated   with   preferences   for   Dance,   Pop,   and   Country.   Since   I   assigned   the   values  “Female”  =  1  and  “Male”  =  2,  this  means  that  females  are  more  likely  to  enjoy  these  three  genres  and  males  are  less  likely.  While  this  isn’t  an  especially  groundbreaking  conclusion,  it  both   makes   sense   and   matches   up   with   Christenson   and   Peterson’s   findings,   adding   a  degree  of  confidence  to  other  correlations  with  genre  preferences.     I   found   another   fairly   predictable   correlation   between   age   and   preference   for   jazz  and   classical   music.   Since   the   correlations   were   both   significant   and   positive,   we   can  conclude   these   two   genres   are   more   appealing   to   older   respondents.   While   this   isn’t  incredibly   surprising,   it’s   interesting   to   consider   that   the   standard   deviation   of  respondents’  age  was  only  4.1.  This  means  that  just  a  few  years  of  age  separates  the  fans  of  classical  and  jazz  from  those  who  enjoy  these  genres  much  less.  It’s  difficult  to  determine  whether   this   is   caused   by   a   generational   difference   or   perhaps   a   difference   in   maturity  levels,   but   simply   knowing   the   correlation   could   improve   song   recommendations  significantly.     On   the   other   hand,   I   found   an   unexpected   correlation   between   competence   using  computers   and   preferences   for   electronic   music   at   the   .01   level.   Put   simply,   the   more  experience   respondents   had   with   computers,   the   more   likely   they   were   to   like   electronic  music.     While   this   correlation   makes   sense   because   the   creation   of   electronic   music  requires  digital  signal  processing,  I  was  surprised  that  electronic  music  was  both  the  only       25  
  • 27. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  genre   correlated   with   computer   skills   and   it   was   significant   at   the   .01   level.   Again,   these  correlations   at   the   .01   level   can’t   be   generalized   to   the   population,   but   instead   indicate  particularly   strong   correlations   between   variables   for   the   college   students   in   the  convenient  sample.  This  particular  correlation  between  a  genre  preference  and  computer  skills,  a  characteristic  seemingly  unrelated  to  music,  begs  the  question  of  how  many  other  personality-­‐based  characteristics  correlate  with  music  preference.   Although   I   was   expecting   a   greater   number   of   correlations   between   demographic  information  and  genre  preferences,  those  that  I  found  present  convincing  evidence  for  the  implementation   of   demographics   in   music   recommendation   technology.   iTunes,   Pandora  and   YouTube   already   attain   demographic   information   and   incorporate   it   to   varying  degrees   when   serving   up   recommendations.   But   the   more   personality-­‐based   information  these  services  can  capture  without  annoying  users,  the  more  they  can  measure  correlations  and   target   recommendations.   Whether   this   implementation   involves   data   mining   from  public   social   media   profiles   or   building   extended   social   profiles   within   a   music   application,  it   has   potential   to   dramatically   improve   music   recommendation.   The   key   is   to   convince  users   they   are   benefitting   each   time   they   build   out   their   profile   and   use   A/B   testing   to  ensure  that  recommendations  improve.  Musical  Experience  and  Genre  Preferences     I  expected  the  survey  questions  addressing  musical  experience  to  correlate  strongly  with   genre   preferences.   I   based   this   hypothesis   on   two   observations   of   my   own   experience  with  music.  First,  the  more  time  I  spent  listening  to  music,  the  more  I  got  bored  listening  to  the   same   few   genres   and   tended   to   explore   unfamiliar   genres.   Second,   playing   guitar   has       26  
  • 28. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  had  a  tremendous  impact  on  my  music  preferences  and  listening  habits,  and  I  expected  this  trend  to  hold  true  for  other  people  regardless  which  instrument  they  play.  The  plethora  of  studies   on   the   effect   of   music   education   on   preferences   also   motivated   me   to   include   these  measures   in   the   study   (LeBlanc).   To   my   knowledge,   the   music   recommendation  technologies   of   iTunes,   Pandora,   and   YouTube   don’t   take   users’   musical   experience   into  account   and   I   felt   this   represented   an   opportunity   for   improvement.   Although   genres   were  the  only  feasible  metric  of  music  preference  for  the  purposes  of  this  analysis,  future  studies  may  address  correlations  between  musical  experience  and  song  preferences.     Upon  examining  the  spreadsheet  of  bivariate  correlations  generated  in  SPSS,  I  found  four   variables   of   musical   experience   that   correlated   strongly   with   several   genre  preferences.   First,   listening   hours   per   week   correlated   positively   with   electronic  and   jazz  music   at   the   .05   and   .01   levels,   respectively.   Although   correlations   with   other   genres  weren’t   statistically   significant,   all   were   positive   except   country   music.   This   proves   that  listening   to   music   more   often   facilitates   a   wider   range   of   preferences   and   correlates  strongest  with  electronic  and  jazz.       Next,   I   examined   how   musical   education   in   both   schools   and   private   lessons  correlated  with  genre  preferences.  I  expected  the  two  metrics  to  have  similar  correlations  with   genre   preferences,   and   hypothesized   that   higher   levels   of   music   education   would  correlate  positively  with  preferences  for  niche  genres.  As  it  turned  out,  “musical  education  in  school”  correlated  positively  with  classical  at  the  .01  level  and  with  jazz  and  world  at  the  .05   level.   On   the   other   hand,   “years   of   private   music   lessons”   correlated   positively   with  preferences  for  classical,  world,  and  rock,  but  negatively  with  country.    While  none  of  the       27  
  • 29. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  other  genres  had  statistically  significant  correlations,  I  noticed  a  general  trend  of  negative  correlations   between   musical   education   and   preferences   for   popular   genres   like   dance,  pop,   hip   hop/rap,   and   country.   Additionally,   I   found   statistically   significant   positive  correlations  between  years  of  experience  playing  an  instrument  and  preferences  for  rock  and   classical,   with   six   more   genres   producing   positive   correlations   that   were   above   the   .05  level.   These   findings   generally   confirmed   my   hypotheses   and   show   that   music  recommendations   may   be   improved   by   accounting   for   users’   music   experience,   though   a  more  thorough  study  using  song  preference  is  necessary  to  substantiate  these  conclusions.  Listening  Environment  and  Genre  Preferences     The  final  category  of  variables  I  analyzed  in  conjunction  with  genre  preferences  was  respondents’   listening   environment.     I   examined   respondents’   views   across   eight   distinct  listening   environments   according   to   the   following   coded   indicators   of   how   often   they  listened   to   music   in   each:   “Never”   =   1,   “Rarely”   =   2,   “Sometimes”   =   3,   “Often”   =   4,   and  “Always”   =5.   The   average   responses   and   their   standard   deviations   are   represented   in  Figure  6.       28  
  • 30. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose   Frequency  of  Listening  in  Environments  and  Activities   6   Never    Rarely    Sometimes    Often    Always   5   4.73   4.36   4   4.07   +1  σ   3.83   3.63   3.27   Mean   3   3.07   -­‐1  σ   2   1.89   1   0   At  Home   In  the  Car   At  Work   By   With   At  a  Party   Studying   Sleeping   Yourself   Friends    Figure  6     Again  I  explored  the  spreadsheet  from  SPSS  that  highlighted  significant  correlations  between  these  eight  metrics  and  genre  preferences.  In  much  the  same  way  that  correlations  between  genres  divided  the  genres  into  two  distinct  groups  (Tables  2  and  3),  the  variables  for  listening  environment  separated  into  three  separate  groups  (Table  4).  The  first  group  of  listening  environments  included  “In  the  Car”,  “Studying”,  and  “Sleeping”,  and  more  frequent  listening  in  these  environments  was  correlated  with  higher  ratings  in  several  genres,  with  no  negative  correlations.  The  next  group  consisted  of  “At  Home”,  “By  Yourself”,  and  “At  Work”,  and  had  one  or  fewer  correlations  with  genre  preferences.  The  final  group  of  environments  was  more  social  than  the  other  two,  and  had  an  equal  or  greater  number  of  negative  correlations  than  positive  correlations.         29  
  • 31. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  Table  4   Significant  Correlations  With  Genres   Listening  Environment   Positive   Negative   In  the  Car   Rock,  Hip  Hop/Rap,  R&B/Soul,  Country   0   1   Studying   Jazz,  Latin,  Classical,  Reggae,  Rock   0   Sleeping   R&B/Soul,  Latin,  Classical     0   At  Home   Hip  Hop/Rap   0   2   By  Yourself   Pop   0   At  Work   0   0   With  Friends   Hip  Hop/Rap   Classical,  World   3   At  a  Party   Dance,  Pop,  Hip  Hop/Rap   Classical,  World,  Vocal       At   first   glance,   the   first   and   third   groups   of   Table   4   might   appear   to   be   a   DJ   guide  indicating   which   genres   should   and   shouldn’t   be   played   in   each   environment.   However,  these   are   only   correlations   between   frequency   of   listening   in   eight   environments   and  ratings   for   genres;   respondents   were   not   asked   directly   which   genres   they   listen   to   in   each  environment.   But   since   they   follow   such   a   logical   pattern,   it’s   clear   that   listening  environment  plays  a  pivotal  role  in  determining  which  genres  users  listen  to.  At  the  very  least,   these   correlations   provide   evidence   that   music   services   using   recommendation  technology   should   experiment   with   allowing   users   to   adjust   for   different   environments,  especially  in  lean-­‐back  music  experiences  like  Pandora.      Choice  of  Music  Technology  and  Determining  Factors   Although   forcing   respondents   to   choose   one   favorite   service   may   have   made   for  simpler  analysis,  I  assumed  most  people  use  more  than  one  of  the  three  music  services  in  question.   So,   I   asked   respondents   to   rank   the   three   of   them   in   order   of   preference   and       30  
  • 32. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube   Penrose  included  the  option  “Never  use  it”.  I  then  organized  the  results  into  a  simple  bar  graph  for  comparison  (Figure  7).   Music  Technology  Preference   60   50   Respondents   40   iTunes   30   Pandora   20   YouTube   10   0   1st  Favorite   2nd  Favorite   3rd  Favorite   Never  use  it    Figure  7   The  first  conclusion  these  results  present  is  that  the  majority  of  college  students  use  several   music   services,   whether   for   different   situations,   genres,   moods,   or   for   other  undiscovered  reasons.  The  three  technologies  I  explored  are  some  of  the  most  popular,  but  if   Anderson’s   long   tail   theory   applies   to   music   services   as   well   as   songs,   the   majority   of  users   also   occasionally   take   advantage   of   other   niche   music   sites.     Responses   were  relatively   balanced   between   the   three,   with   50   survey   respondents   (40%)   indicating  Pandora   as   their   favorite.   Another   39   college   students   chose   YouTube   as   their   favorite   and  the  remaining  36  respondents  chose  iTunes.  YouTube  performed  the  best  overall,  ranking  highest   in   both   the   2nd   Favorite   and   3rd   Favorite   categories   thanks   to   the   fact   only   6%   of  respondents   never   use   it.   After   determining   the   music   apps’   relative   rankings   across   the  entire  sample,  I  explored  trends  among  subgroups  using  the  crosstabs  function  in  SPSS  as  well   as   bivariate   correlations.   Using   this   information,   I   speculated   about   causal       31  
  • 33. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  relationships   between   respondents’   preferred   music   service   and   all   other   collected  variables  and  set  out  to  determine  their  relative  influence.    Factors  in  Music  Technology  Preference  Favorite  Feature   Arguably  the  most  logical  factor  influencing  the  choice  between  iTunes,  Pandora  and  YouTube  was  respondents’  favorite  feature,  which  I  examined  first.  For  each  service,  survey  questions   asked   respondents   to   choose   their   favorite   feature   from   the   following   list:  “customizability/personalization”,   “interface”,   “wide   selection   of   music”,   “playlisting   and  song  recommendation”,  and  “Other:  Please  Specify”.  Using  these  canned  responses  and  the  option   of   free   response,   the   three   music   applications   could   be   easily   compared   while  capturing  any  features  missing  from  the  list.  Results  for  favorite  feature  of  the  three  music  services  are  visualized  in  Figure  8.       32  
  • 34. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose   Favorite  Feature   80   70   iTunes   60   Pandora   Respondents   50   YouTube   40   30   20   10   0   Customizability  /   Interface   Wide  Selection  of   Playlisting  and   Other   Personalization   Music   Song   Recommendation    Figure  8   When   inspecting   Figure   8,   it’s   important   to   keep   in   mind   that   respondents   chose  their   single   favorite   feature,   and   those   with   fewer   votes   aren’t   necessarily   poor   features.  YouTube’s   clear   favorite   feature   was   its   wide   selection   of   music,   earning   69   votes.   This  result  held  true  with  my  expectations;  not  only  does  YouTube  offer  the  widest  selection,  it’s  also  the  only  free  on-­‐demand  service  of  the  three.  Had  I  anticipated  Spotify’s  entrance  into  the   US   when   I   designed   the   survey,   “wide   selection   of   music”   would   have   been   an  interesting   metric   with   which   to   compare   Spotify   and   YouTube.   Respondents’   favorite  feature   of   Pandora   was   understandably   playlisting   and   song   recommendation,   though   its  customizability/   personalization   and   wide   selection   of   music   also   ranked   high.   The  simplicity   of   its   interface   may   account   for   Pandora’s   lower   score   on   this   metric,   but   this  also  makes  Pandora  especially  user-­‐friendly,  likely  contributing  to  it  earning  the  most  votes  for   favorite   music   service.   Respondents’   votes   for   favorite   feature   of   iTunes   were   fairly       33  
  • 35. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  evenly  distributed,  with  wide  selection  of  music  and  interface  ranking  highest.  Almost  20%  of   respondents   also   used   the   free   response   option   for   iTunes,   attesting   to   its   variety   of  useful  features.     Based  on  these  results,  I  came  to  three  conclusions  regarding  the  features  of  iTunes,  Pandora   and   YouTube.   First,   college   students   go   to   YouTube   first   to   find   any   song   that  exists.   These   findings   are   consistent   with   my   own   personal   experience,   and   more  respondents  chose  wide  selection  as  their  favorite  than  the  other  four  features  combined.  Second,  Pandora  has  the  strongest  song  recommendation  and  personalization  of  the  three  music   apps   under   review.   In   an   increasingly   fast-­‐paced   world,   users   appreciate   the   easy,  personalized   lean-­‐back   experience   that   Pandora   offers   for   free.   Third,   iTunes   is   the   most  robust  and  comprehensive  music  service  of  the  three  and  its  intuitive  interface  has  set  the  industry   standard.   Though   it   seems   unlikely   that   any   one   service   could   outcompete   the  others  on  all  four  features,  iTunes  appears  to  be  the  only  one  trying  out  of  the  three.   Besides  favorite  feature,  the  factors  that  I  anticipated  to  have  the  greatest  influence  on  choice  of  music  service  fell  into  three  categories:  demographics,  musical  experience,  and  music   preferences.   I   also   hypothesized   that   listening   environment   would   influence   the  choice   between   iTunes,   Pandora   and   YouTube,   but   there   were   almost   no   significant  correlations   between   them.   Additionally,   the   primary   reason   I   inquired   about   listening  environment  was  to  explore  how  it  correlated  with  music  preference,  not  music  technology  preference.           34  
  • 36. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  Demographics  and  Music  Technology  Preference     First,  I  hypothesized  that  respondents’  age  would  influence  their  choice  between  iTunes,  Pandora  and  YouTube.  Although  respondents  were  college  students  with  an  average  age  just  over  21  and  a  standard  deviation  of  only  4.1,  there  was  enough  variation  to  distinguish  between  the  preferences  of  younger  and  older  respondents.  My  instincts  told  me  that  YouTube  would  appeal  to  younger  respondents  because  they  were  more  social  media  savvy,  already  familiar  with  YouTube  from  viral  videos,  and  more  intentional  in  music  selection.  On  the  other  hand,  I  guessed  that  as  people  get  older  they  were  more  likely  to  use  Pandora  to  DJ  in  the  background  whether  for  familiar  songs  or  exploring  personalized  recommendations.    Additionally,  I  expected  older  respondents  to  favor  iTunes  for  its  functional  interface,  expansive  media  store,  and  library  for  organizing  CD’s.     As  it  turned  out,  my  intuitions  were  fairly  accurate.  The  correlation  between  respondents’  age  and  their  preference  for  YouTube  was  positive  and  significant  at  the  .01  level.  Since  lower  values  for  music  service  indicated  stronger  preference,  this  meant  that  younger  ages  correlated  with  stronger  preference  for  YouTube.  Next,  preference  for  iTunes  was  negatively  correlated  with  respondents’  age,  significant  at  the  .05  level.  In  other  words,  older  respondents  were  more  likely  to  rate  iTunes  as  their  favorite  service.  And  while  the  correlation  between  rating  of  Pandora  and  respondents’  age  wasn’t  statistically  significant,  it  was  also  negative.  Even  though  all  three  music  apps  collect  users’  age  upon  creation  of  new  accounts  and  have  quite  a  few  more  data  points  than  my  survey,  it’s  unlikely  they  track  users’  relative  preference  for  the  other  two  options.  And  since  most  college  students       35  
  • 37. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  use  at  least  three  music  sites,  it’s  important  to  know  which  they  prefer  in  order  to  speculate  and  test  why  they  do.   Next,  I  explored  gender  as  a  determining  factor  of  favorite  music  service.  Based  on  conversations  with  friends  and  DJs  from  Stanford  and  Phoenix,  I  predicted  that  females  would  gravitate  toward  YouTube  slightly  more  than  males,  but  that  males  would  prefer  iTunes  more  than  females  would  due  to  its  emphasis  on  customization  and  playlisting  features.  Last,  I  expected  Pandora’s  audience  to  be  more  balanced  between  genders.  Using  the  crosstab  function  in  SPSS,  I  organized  the  results  and  made  several  pie  charts  for  visual  comparison  (Figure  5).    Figure  9   Simply  looking  at  the  two  groups  of  respondents’  first  favorite  service,  it’s  clear  that  iTunes   was   most   popular   among   males   and   Pandora   was   the   favorite   for   females   (Figure  9).   Thus   my   hypothesis   regarding   males   was   correct   but   I   failed   to   accurately   predict       36  
  • 38. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  female   behavior,   which   wasn’t   incredibly   surprising.   YouTube   won   both   the   second   and  third  favorite  categories  among  both  males  and  females,  as  a  result  of  so  few  respondents  indicating   they   never   use   it.   Even   though   the   only   statistically   significant   correlation   was  between   females   and   stronger   preference   for   Pandora,   surveying   a   larger   sample   might  confirm   that   iTunes   tends   to   be   the   primary   service   for   males   and   YouTube   is   more   of   a  secondary  service  for  both  genders.   The   third   factor   in   favorite   music   service   that   I   explored   was   respondents’   school.    Though   I   focused   my   efforts   on   three   schools,   a   number   of   students   from   other   schools  found  my  survey  either  on  Facebook  or  Twitter.  For  simplicity’s  sake,  I  analyzed  the  three  schools   I   targeted   (ASU,   GCC,   Stanford)   and   put   all   others   into   an   “other”   category   for  analysis.   I   anticipated   YouTube   and   Pandora   would   be   most   popular   at   GCC,   which   was   the  largest  group  of  respondents  (71).    I  also  predicted  Stanford  would  favor  iTunes,  and  ASU  would   have   a   balanced   distribution   between   the   three   services.     As   it   turned   out,  respondents’   schools   had   a   substantial   impact   on   their   preference   between   iTunes,  Pandora  and  YouTube.         37  
  • 39. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose    Figure  10   Partially  due  to  ASU’s  sample  being  too  small,  music  technology  preference  varied  widely  between  the  three  targeted  colleges.    iTunes  easily  won  the  position  of  ASU’s  favorite  service,  while  Stanford’s  favorite  service  was  split  between  iTunes  and  Pandora.  Furthermore,  the  correlation  between  preferences  for  iTunes  and  respondent’s  school  was  positive  and  statistically  significant,  meaning  that  students  from  Stanford  and  ASU  were  more  likely  to  prefer  iTunes.  One  possible  explanation  for  iTunes’  strong  performance  in  these  two  colleges  is  Apple’s  strong  brand  presence  on  both  campuses.  Apple  has  large  offices  near  both  Stanford  and  ASU,  and  tends  to  hire  students  from  both  schools.  On  the  other  hand,  GCC’s  favorite  service  was  balanced  between  Pandora  and  YouTube.  As  a  community  college,  GCC  most  likely  has  a  greater  percentage  of  students  living  on  a  budget,  which  may  explain  why  the  two  free  services  rank  highest  among  them.  Whether  these       38  
  • 40. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  companies  care  about  how  they  stack  up  among  these  colleges,  these  findings  present  unique  insights  into  the  diffusion  of  music  technology  in  different  schools.     The  fourth  potential  factor  in  music  service  preference  that  I  analyzed  was  the  respondents’  perceived  competence  with  computers.  When  asked  to  categorize  their  competence  using  computers,  the  largest  group  of  respondents  indicated  they  were  “Average”,  totaling  67.  Another  38  indicated  their  computer  competence  was  “Advanced”,  16  chose  “Expert”,  and  4  chose  “Basic”.  “None/Very  Little”  was  also  among  the  possible  categories  of  competence  with  computers,  but  unsurprisingly  no  respondents  selected  it.  Admittedly  I  didn’t  expect  respondents’  competence  using  computers  to  correlate  with  their  favorite  music  service  as  closely  as  other  factors,  but  I  did  expect  Pandora  and  YouTube  to  perform  strongest  in  the  Basic  and  Average  groups  due  to  their  simple  interfaces.  I  also  predicted  Advanced  and  Expert  computer  users  to  prefer  the  playlisting  and  organizational  features  of  iTunes.  As  it  turned  out,  the  relationship  between  competence  with  computers  and  favorite  music  service  wasn’t  statistically  significant.       39  
  • 41. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose    Figure  11   In  terms  of  respondents’  1st  Favorite  music  service,  Pandora  ranked  highest  among  Average  and  Advanced  computer  users,  while  YouTube  performed  best  in  the  Basic  and  Expert  groups.  But  beyond  that,  it’s  difficult  to  see  any  patterns  between  competence  using  computers  and  favorite  music  service.  The  spreadsheet  of  Spearman  correlations  from  SPSS  confirms  this,  showing  no  statistically  significant  correlations  between  the  two  variables.  However,  stronger  preferences  for  Pandora  correlated  with  higher  computer  skills,  just  above  the  .05  level.  In  this  case,  the  concentration  of  responses  in  the  middle  two  groups  suggests  an  inadequacy  in  the  wording  of  the  question  for  computer  skills.  Not  only  is  the  wording  general  and  vague,  responses  are  self-­‐reported  and  may  not  be  accurate  for  this  variable.  Musical  Experience  and  Music  Technology  Preference   I  used  two  questions  on  the  survey  to  address  the  music  listening  habits  of  respondents.  The  first  asked  respondents  how  many  hours  per  week  they  spent  listening  to       40  
  • 42. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  music  with  an  open-­‐ended  response  format.  On  average,  respondents  indicated  they  listen  to  music  18.9  hours  per  week  with  a  standard  deviation  of  16.5  and  a  range  of  1  to  98  hours.  For  this  variable,  I  decided  to  analyze  only  respondents’  favorite  music  service  and  determine  the  average  user’s  listening  hours  per  week  for  each  service.  Though  all  three  services  have  ways  to  track  their  users’  listening  hours  more  accurately  and  on  a  larger  scale,  my  methods  provide  the  college  student  perspective  on  the  relative  strengths  and  weaknesses  of  each  service  in  comparison  to  the  other  two.  I  predicted  that  the  more  engaged  listeners  would  favor  iTunes  and  Pandora  as  their  top  choice,  since  these  services  are  geared  more  towards  lean-­‐back  listening  experiences.  In  my  experience,  YouTube  is  the  search  engine  of  choice  for  recalling  or  discovering  a  particular  song,  or  for  sharing  DJ  responsibilities  with  several  friends.  However,  as  it  is  an  on-­‐demand  and  therefore  user-­‐controlled  experience  I  would  expect  the  respondents  who  primarily  use  YouTube  to  spend  less  time  listening  to  music.   The  second  listening  habits  question  asked  students  to  categorize  how  often  they  listen  to  music  online,  choosing  from  “Never”,  “Rarely”,  “Sometimes”,  “Often”,  and  “All  the  Time”.  Besides  the  difference  that  the  first  question  addresses  music  listening  in  general  and  the  second  addresses  online  music  listening,  the  former  is  a  scale  variable  and  the  second  is  ordinal.  I  expected  having  both  scale  and  ordinal  variables  would  prove  useful  for  visualizing  listening  habits  and  provide  a  second  measure  to  verify  interesting  differences.         41  
  • 43. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose    Figure  12     Average   listening   hours   per   week   turned   out   to   be   a   much   better   metric   than  frequency  of  online  music  listening,  but  both  provide  visual  evidence  of  Pandora’s  tendency  to  attract  heavy  users.  Users  who  selected  Pandora  as  their  favorite  music  service  tended  to  listen   to   music   more   often   both   in   general   and   online.   Furthermore,   the   correlation  between   stronger   preferences   for   Pandora   and   greater   frequency   of   online   listening   was  statistically   significant   at   the   .05   level.   This   makes   sense   because   Pandora   requires   very  little   effort   to   start,   continues   playing   similar   music   for   several   hours,   and   continues  indefinitely   if   the   user   gives   occasional   feedback.   YouTube   also   offers   somewhat   similar  playlisting   but   this   is   a   secondary   feature   and   lesser   known   among   respondents.   iTunes       42  
  • 44. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  supports  long  listening  sessions  but  requires  more  effort  by  the  user  to  manage  playlists,  not  to  mention  buy  or  own  the  music  in  the  first  place.     The  second  portion  of  musical  experience  that  I  expected  to  influence  music  technology  preference  was  musical  education.  Three  similar  questions  measured  respondents’  levels  of  musical  education  in  school,  years  of  private  lessons,  and  years  spent  playing  an  instrument.  Though  my  only  expectation  regarding  the  first  measure  was  that  Pandora  would  attract  those  with  more  music  education  in  school,  I  was  uncertain  how  music  technology  preference  would  correlate  with  private  music  lessons.  I  expected  respondents  who  played  an  instrument  to  favor  YouTube  with  its  wide  selection,  tendency  to  include  lyrics  in  songs,  and  because  so  many  musicians  use  it  as  a  platform  to  showcase  their  music.  I  also  hypothesized  that  preference  for  Pandora  would  be  stronger  among  experienced  musicians  because  of  its  personalized  song  recommendation  and  implementation  of  professional  musicians’  ratings.  Results  for  music  education  in  school,  private  lessons,  and  years  playing  an  instrument  are  displayed  in  Figures  13,  14  and  15,  respectively.   Choice  of  Music  Service  vs.  Music  Education  Levels   35   30   25   20   iTunes   15   Pandora   10   YouTube   5   0   0   1  Level   2  Levels   3  Levels    Figure  13       43  
  • 45. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose     To   measure   music   education   in   schools,   surveys   asked   respondents   to   select   any  school  levels  during  which  they  took  at  least  one  music  class,  choosing  from  “Elementary  (K  –   8th)”,   “High   School”,   and   “College”.   The   number   of   checked   boxes   for   each   respondent  became  their  number  of  music  education  levels,  totaling  between  0  and  3.  My  hypothesis  about   musicians   tending   to   prefer   YouTube   was   incorrect,   possibly   because   of   the  distinction   between   using   YouTube   to   listen   to   music   and   using   it   to   upload   their   own  material.   The   spreadsheet   of   bivariate   correlations   revealed   that   stronger   preference   for  iTunes  was  correlated  with  more  years  of  music  education,  while  stronger  preference  for  YouTube  was  correlated  with  fewer,  both  at  the  .05  level.  A  possible  explanation  for  these  trends  is  that  musicians  prefer  greater  control  over  their  music  and  enjoy  making  playlists.  More   experienced   musicians   also   might   be   more   inclined   to   pay   for   their   music   because  they   appreciate   it   more.   Regardless   of   the   reasons   why,   it’s   clear   that   music   experience  plays  a  substantial  role  in  determining  which  music  applications  college  students  prefer.   Favorite  Music  Technology  vs.  Years  of  Private  Music  Lessons   10   8   6   Years   4   +1  σ   3.69   2   2.02   2.55   Mean   0   -­‐1  σ   -­‐2   iTunes   Pandora   YouTube    Figure  14     Using  the  compare  means  function  in  SPSS,  I  calculated  the  averages  and  standard  deviations   of   respondents’   years   of   private   music   lessons   in   salutation   to   which   music       44  
  • 46. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  service   was   their   favorite.   Respondents   choosing   iTunes   as   their   favorite   showed  significantly   higher   levels   of   experience   with   private   music   lessons,   and   this   correlation  was  statistically  significant  at  the  .05  level.  Although  a  larger,  random  sample  is  required  to  substantiate  these  findings,  the  similar  results  of  music  education  in  school  add  a  degree  of  confidence  in  this  case.   Favorite  Music  Technology  vs.  Years  of  Playing  an  Instrument   4   3   +1  σ   Years   2.22   2   1.76   1.54   Mean   1   -­‐1  σ   0   iTunes   Pandora   YouTube    Figure  15     I   distinguished   between   years   of   private   music   lessons   and   years   playing   an  instrument   because   I   personally   have   played   guitar   for   over   ten   years   and   only   took  lessons  for  about  two  of  them.  I  wondered  how  my  choice  in  music  service  might  have  been  affected  by  continuing  lessons  throughout  the  ten  years,  or  if  I  hadn’t  played  an  instrument  at  all.  Once  again,  iTunes  was  especially  popular  among  the  more  experienced  musicians,  while   preference   for   YouTube   was   correlated   with   fewer   years   of   playing   an   instrument,  both   significant   at   the   .05   level.     I   found   this   interesting   because   I   used   iTunes   almost  exclusively   throughout   high   school   and   started   using   Pandora   and   YouTube   more   when   I  wanted  to  explore  music  that  was  similar  or  that  my  friends  had  shared  with  me.         45  
  • 47. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose    Music  Preference  and  Music  Technology  Preference     The   final   group   of   variables   I   predicted   would   influence   music   technology  preference   included   genre   preference,   feeling   toward   popular   music,   and   determining  factors  of  song  preference.  I  was  curious  which  preferred  genres  would  correlate  with  each  music   application   and   guessed   that   niche   genres   would   be   rated   higher   among   heavy  iTunes   users,   and   that   preferences   for   YouTube   and   Pandora   would   correlate   with   more  popular  genres.  I  based  these  predictions  on  my  observations  of  friends  using  each  service;  iTunes  was  the  first  choice  for  creating  playlists  of  songs  that  had  personal  significance  and  not  necessarily  musical  similarity,  as  with  Pandora.  YouTube  seemed  to  be  the  social  music  discovery  application  of  choice  and  my  friends  used  Pandora  to  listen  to  stations  based  on  their  favorite  hit  artists  or  songs.  I  used  the  compare  means  function  in  SPSS  to  determine  each  genre’s  mean  rating  depending  on  respondents’  favorite  music  service  (Figure  16).       46  
  • 48. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose         Favorite  Music  Technology  vs.  Genre  Rating  (Mean)   1.6   1.4   -­‐1  =  Dislike          0  =  Neutral        1  =  Like        2  =  Love   1.2   iTunes   1   Pandora   YouTube   0.8   0.6   0.4   0.2   0    Figure  16     As  it  turned  out,  only  a  limited  number  of  genres  correlated  significantly  with  music  service  preference.  Respondents  who  rated  dance  higher  were  less  likely  to  choose  iTunes  as  their  favorite,  and  more  likely  to  choose  YouTube.  On  the  other  hand,  fans  of  rock  were  much   more   likely   to   prefer   iTunes   and   not   YouTube.   Interestingly,   only   one   genre  (R&B/Soul)   correlated   with   Pandora   and   it   was   barely   significant   at   the   .05   level.  Additionally,  YouTube  was  correlated  with  stronger  preference  for  hip  hop/rap  and  Latin  music.   Given   that   all   three   music   services   offer   a   wide   range   of   genres,   the   lack   of   more  significant  correlations  is  understandable.     The  other  two  topics  within  music  preference  that  I  hypothesized  would  influence  choice   of   music   technology   turned   out   to   be   almost   completely   unrelated.   The   first   was  feeling   toward   music   from   top   40   charts.   Once   again   I   expected   Pandora   and   YouTube   to       47  
  • 49. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  correlate  with  positive  feelings  toward  top  40  charts.  While  these  predictions  turned  out  to  be  accurate,  the  correlations  weren’t  statistically  significant.  The  second  topic  was  factors  in   song   preference,   measured   by   the   six   variables   from   Figure   2.   The   single   variable   that  was  correlated  with  any  of  the  music  technologies  was  popularity,  associated  with  weaker  preference  for  iTunes  and  significant  at  the  .01  level.  The  collection  of  these  three  measures  of  music  preference  did  align  with  my  expectations,  but  the  lack  of  statistically  significant  correlations  suggests  that  a  larger  sample  should  be  tested.  Music  Technology  Influencing  Preference  and  Spending     Although   the   questions   addressing   how   the   three   music   services   affect   music  preferences   and   spending   were   placed   at   the   end   of   the   survey,   I   view   them   as   the   most  significant  to  the  future  of  the  music  industry.  The  first  statistical  procedure  I  carried  out  to  analyze   the   results   was   a   simple   descriptive   function   in   SPSS,   which   produced   the   mean  responses  and  the  standard  deviations  for  all  125  respondents.  To  facilitate  visualization  of  the   results,   responses   were   coded   as   follows:   “strongly   disagree”   =   1,   “disagree”   =   2,  “neutral”   =   3,   “agree”   =   4,   and   “strongly   agree”   =   5.   Since   the   question   format   was   identical  for  the  three  services,  comparing  the  results  by  service  revealed  interesting  trends.       48  
  • 50. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose    Figure  17   On   average,   respondents   indicated   that   all   three   services   make   them   listen   to   music  more  often  and  listen  to  more  music  within  genres  they  like.  Pandora  and  YouTube  users  also  indicated  they  listen  to  a  wider  range  of  genres,  share  music  with  their  friends  more,  and  that  music  plays  a  bigger  role  in  their  life.  Although  the  other  average  responses  don’t  rise   above   Neutral   (=   3),   their   standard   deviations   show   that   many   respondents   agree   that  they   buy   more   music,   buy   different   music,   and   even   buy   concert   tickets   as   a   result   of   using  iTunes,  Pandora  and  YouTube.  The  differences  between  each  music  service’s  ratings  along  these  metrics  present  interesting  considerations  regarding  contributing  factors.    Effects  on  Preference       49  
  • 51. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  More  Listening     The  first  measure  of  impact  on  music  preferences  was  the  most  basic;  respondents  indicated  whether  they  agreed  or  disagreed  with  the  statement  “I  listen  to  music  more”  as  a  result   of   using   each   music   service.   Pandora   ranks   the   highest   on   this   metric,   with   “strongly  agree”   almost   within   one   standard   deviation.   YouTube   and   iTunes   then   follow   Pandora,  and   all   three   services   rank   between   “agree”   and   “strongly   agree”   with   one   standard  deviation  above  the  mean.     I  listen  to  music  more   Disagree            Neutral              Agree   5   4   3.76   3.47   3.21   3   +1  σ   2   Mean   1   -­‐1  σ   0   iTunes   Pandora   YouTube    Figure  18   This  order  may  be  explained  by  the  relative  ease  of  use  of  each  music  service.  First  time   Pandora   users   enter   an   artist,   song,   or   genre   to   start   listening   to   a   station,   and   the  music   automatically   starts   for   returning   users   upon   revisiting   the   site.   Similarly,   using  YouTube   simply   requires   knowing   the   artist   or   song   name   and   one   more   click   starts   the  music.   While   iTunes   has   plenty   of   the   same   tricks   to   speed   up   the   time   it   takes   to   find   a  song,   artist   or   playlist,   users   may   perceive   iTunes   as   requiring   more   time   and   effort.  However,   the   discrepancy   between   services   along   this   metric   is   likely   influenced   by   a       50  
  • 52. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  number   of   other   factors.   Most   importantly,   these   findings   confirm   that   all   three   services  facilitate  and  increase  music  listening  to  a  greater  extent  than  other  music  technologies.    Wider  Range  of  Genres   Next,  respondents  indicated  their  level  of  agreement  with  the  statement  “I  listen  to  a  wider   range   of   genres”   as   a   result   of   using   iTunes,   Pandora,   and   YouTube.   This   metric   is  significant   because   services   that   expand   their   users’   range   of   genres   are   shifting   both  listening   hours   and   revenue   toward   songs   and   artists   that   would   have   remained  undiscovered   otherwise.   Again,   Pandora   ranked   highest   with   68%   of   respondents  indicating  either  “agree”  or  “strongly  agree.”  YouTube  was  ranked  second  with  an  average  score   just   over   “neutral”   and   iTunes   scored   a   full   point   below   Pandora   with   an   average  score  leaning  toward  “disagree”.   I  listen  to  a  wider  range  of  genres   Disagree            Neutral              Agree   5   4   3.89   3   3.16   2.76   +1  σ   2   Mean   1   -­‐1  σ   0   iTunes   Pandora   YouTube    Figure  19   Pandora’s  higher  rank  in  this  area  isn’t  particularly  surprising  given  that  the  Music  Genome  Project,  its  core  technology,  recommends  new  songs  based  on  400  attributes  and  occasionally  serves  up  songs  from  neighboring  genres.  Similarly,  YouTube’s  personalized       51  
  • 53. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  “Recommended  Videos”  and  “Suggestions”  both  surface  songs  that  may  fit  within  other  genres  than  the  original  song.  Furthermore,  the  sheer  size  of  YouTube  Music  provides  the  widest  selection  and  its  heavily  used  social  functionality  increases  cross-­‐genre  exposure  on  Facebook  and  elsewhere.  While  iTunes  has  almost  as  wide  a  music  selection  as  YouTube,  songs  on  iTunes  must  be  purchased  to  hear  more  than  a  preview  and  users  must  be  slightly  more  proactive  to  find  unfamiliar  music.  The  two  most  likely  explanations  for  these  rankings  are  the  differences  in  primary  mechanisms  for  discovering  new  music  and  the  relative  emphasis  on  musical  “horizon  widening”  by  iTunes,  Pandora  and  YouTube.  Deeper  Within  Familiar  Genres   The  next  measure  of  impact  on  music  preferences  flows  logically  from  the  previous  metric.  Respondents  chose  their  level  of  agreement  with  the  statement  “I  listen  to  more  music  within  genres  I  like”  as  a  result  of  using  each  music  service.  While  expansion  across  genres  certainly  benefits  both  the  user  and  the  industry,  exposure  to  new  music  within  familiar  genres  has  a  stronger  impact  and  is  more  likely  to  inspire  the  user  to  purchase.  On  average,  the  surveyed  college  students  indicated  that  all  three  services  deepen  their  familiarity  with  music  genres  they  like.  Pandora  won  its  third  category  in  a  row  with  73%  of  respondents  selecting  “agree”  or  “strongly  agree”.  YouTube  ranked  second  with  a  mean  of  3.53,  followed  closely  by  iTunes  averaging  just  above  “neutral”  at  3.18.         52  
  • 54. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose   I  listen  to  more  music  within  genres  I  like   5   Disagree            Neutral              Agree   4   3.99   3.53   3   3.18   +1  σ   2   Mean   -­‐1  σ   1   0   iTunes   Pandora   YouTube    Figure  20   Pandora’s  Music  Genome  Project  uses  a  combination  of  musicologists’  ratings  and  user  feedback  to  create  personalized  radio  stations  themed  by  artist,  song,  or  genre.  As  a  result,  this  technology  deepens  users’  knowledge  and  appreciation  of  familiar  genres  by  definition,  so  it’s  understandable  Pandora  ranks  highest.  Again,  YouTube  ranks  second  in  this  measure,  which  is  likely  a  byproduct  of  its  personalized  recommendations,  similar  video  suggestions  bar,  and  social  features.  However,  it’s  surprising  respondents  didn’t  rank  iTunes  as  high  with  its  Genius  playlisting,  iTunes  Essentials  mixes,  and  purchase  history-­‐based  song  recommendation.    While  it’s  possible  these  features  are  lesser  known  compared  to  YouTube’s  prominent  next  videos,  a  more  likely  explanation  is  that  respondents  associate  iTunes  with  music  they  already  own.  In  this  way,  these  users  are  more  likely  to  explore  and  discover  music  on  a  free  streaming  platform  and  perhaps  switch  to  iTunes  to  buy  tracks  they  particularly  like.  Regardless,  it’s  clear  all  three  music  technologies  are  helping  users  find  good  music  and  artists  find  new  fans.         53  
  • 55. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  More  Sharing  Music   Historically,  music  discovery  has  been  a  profoundly  social  experience  and  I  wanted  to  measure  and  compare  college  students’  opinions  on  the  social  features  of  iTunes,  Pandora  and  YouTube.  For  this  metric,  respondents  indicated  their  level  of  agreement  with  the  statement  “I  share  music  with  my  friends  more”  as  a  result  of  using  the  three  services.  YouTube  ranked  highest  among  respondents  in  this  area,  with  over  62%  of  respondents  agreeing  or  strongly  agreeing.  Pandora  had  the  second  highest  rating  along  the  sharing  dimension,  with  an  average  score  just  above  neutral  and  “agree”  within  one  standard  deviation.  On  the  other  hand,  the  average  respondent  said  they  didn’t  share  more  as  a  result  of  using  iTunes  and  only  26%  indicated  otherwise.   I  share  music  with  my  friends  more   5   Disagree            Neutral              Agree   4   3.64   3   3.07   2.69   +1  σ   2   Mean   -­‐1  σ   1   0   iTunes   Pandora   YouTube    Figure  21   According   to   YouTube’s   press   page,   Facebook   users   watch   over   500   years   of  YouTube  videos  everyday,  and  over  500  YouTube  links  are  tweeted  every  minute.  YouTube  Music   makes   up   approximately   31%   of   all   videos,   and   it’s   likely   a   greater   percentage   of  shared   videos   are   music   related.     Sharing   music   from   YouTube   is   both   easy   and   popular,       54  
  • 56. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  especially   for   the   college   student   demographic.   Last   summer   Pandora   underwent   a  complete   site   redesign   into   HTML5   and   introduced   a   number   of   social   features   including  the   ability   to   follow   friends’   music   activity,   view   a   personalized   stream   of   music   activity,  and   share   songs   or   stations   through   Pandora,   Facebook,   or   Twitter.   As   respondents  showed,   these   features   put   Pandora   ahead   of   iTunes   on   social   functionality,   but   still   well  behind   YouTube.   iTunes   also   introduced   a   social   feature   called   Ping   in   late   2010   but   it  seems   to   have   been   fairly   ineffective,   at   least   with   college   students.   As   these   music  applications’  social  integration  becomes  more  intuitive  and  familiar  to  users,  the  process  of  sharing  music  will  continue  to  scale  and  improve  both  engagement  and  spending.  Effects  on  Spending  More  Buying   The   first   measure   of   the   three   music   technologies’   impact   on   spending   was   perhaps  the   most   important   question   of   the   survey,   at   least   to   the   music   industry.   The   proliferation  of   illegal   filesharing   applications   like   Napster,   Kazaa,   and   Limewire   has   caused   upheaval  among  record  labels  and  artists,  and  for  good  reason.  While  there  are  important  benefits  to  free   exchange   and   many   artists   are   experimenting   with   free   mixtapes   and   similar  promotions,  most  musicians  struggle  to  make  a  living  even  without  having  to  worry  about  digital   piracy.   This   portion   of   the   survey   was   intended   to   measure   the   impacts   of   legal  music   applications   on   individual   users’   spending.   Respondents   indicated   their   level   of  agreement   with   the   statement   “I   buy   more   music”   as   a   result   of   using   each   application.  Consistent   with   the   industry-­‐wide   trend   of   declining   sales,   all   three   services’   averages  ranked   between   disagree   and   neutral,   with   iTunes   ranking   highest.   Pandora   followed       55  
  • 57. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  iTunes  with  17%  of  respondents  buying  more  music  and  YouTube  ranked  third  with  only  15%  in  agreement.   I  buy  more  music   Disagree            Neutral              Agree   5   4   3   2.61   +1  σ   2.46   2.38   2   Mean   1   -­‐1  σ   0   iTunes   Pandora   YouTube    Figure  22   As   the   only   music   service   of   three   with   a   fully   integrated   music   store,   it’s  understandable   iTunes   ranks   highest   in   this   metric.   A   likely   contributing   factor,   iTunes’  personalized   “Genius”   song   recommendations   are   shown   prominently   in   both   the   offline  application   and   the   home   page   of   the   iTunes   store.   All   songs,   artists   and   albums   within   the  offline  library  link  to  the  iTunes  store  to  facilitate  the  music  shopping  process,  and  iTunes  also   recently   increased   song   preview   time   limits,   presumably   for   the   same   purpose.  Despite  these  features,  the  average  college-­‐aged  respondent  doesn’t  buy  more  music  as  a  result  of  using  iTunes.  While  the  wording  of  the  question  prevents  us  from  knowing  if  users  are   buying   less,   it’s   reasonable   to   assume   that   the   30%   of   respondents   who   strongly  disagreed   are   buying   less.   Pandora   and   YouTube   both   feature   links   to   purchase   music   in  iTunes  and  elsewhere,  but  the  findings  of  this  survey  show  that  only  a  small  percentage  of  users  actually  click  through  to  music  stores.       56  
  • 58. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  Buying  Different  Music   Second  only  to  buying  more  music,  this  metric  has  tremendous  implications  for  the  music   industry   and   directly   addresses   the   primary   inquiry   of   this   study.   In   an   effort   to  explore   the   consequences   of   transitioning  from  AM/FM  radio  to  digital  music  applications,  the   survey   asked   respondents   whether   they   bought   different   music   as   a   result   of   using  iTunes,  Pandora  and  YouTube.  Although  iTunes  ranked  highest  on  the  buying  more  music  metric,   Pandora   ranked   highest   for   buying   different   music,   and   YouTube   slightly   edged   out  iTunes   for   second   place.   “Agree”   fell   within   one   standard   deviation   of   Pandora’s   average  response,   with   over   35%   of   respondents   agreeing   or   strongly   agreeing,   compared   to  YouTube’s  16%  and  iTunes’  18%.   I  buy  different  music   5   Disagree            Neutral              Agree   4   3   2.88   +1  σ   2.31   2.41   2   Mean   -­‐1  σ   1   0   iTunes   Pandora   YouTube    Figure  23   The  most  intriguing  aspect  of  these  findings  is  not  that  Pandora  ranks  higher  than  iTunes,  but  that  Pandora’s  average  response  for  the  “buy  different  music”  metric  is  higher  than   its   average   response   for   “buy   more   music”.   Though   the   majority   of   users   don’t  perceive  Pandora  as  increasing  their  music  purchases,  a  significant  portion  believe  Pandora       57  
  • 59. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  is   redirecting   their   music   spending.   It’s   also   important   to   note   that   Pandora   pays   artists  both   performance   and   composition   royalties   every   time   a   song   is   played,   regardless   of  whether  the  user  skips  it.  In  this  way,  users  indirectly  support  unfamiliar  music  simply  by  using   Pandora,   whether   they   end   up   buying   or   not.   As   mentioned   earlier,   iTunes   Genius  and  YouTube’s  recommended  videos,  social  functionality,  and  links  to  iTunes  all  contribute  to  the  portion  of  respondents  who  say  they  are  buying  different  music,  though  the  majority  indicate  otherwise.    Buying  Concert  Tickets   The   third   possible   effect   on   spending   that   the   survey   measured   was   users   buying  concert   tickets   that   they   otherwise   wouldn’t   have,   as   a   result   of   new   music   technology.    Based  on  my  preliminary  study’s  findings  for  a  Communication  course  at  Stanford,  I  didn’t  expect   more   than   a   handful   of   respondents   to   agree   for   any   of   the   services.   While   this  metric   did   measure   the   lowest   on   average   for   all   three,   both   Pandora   and   YouTube  performed   much   better   than   expected.   Respondents   ranked   Pandora   highest   once   again  with  over  17%  agreeing  or  strongly  agreeing.  YouTube’s  agree  and  strongly  agree  groups  were  even  at  6%  each,  and  iTunes  had  5%  indicating  agree  and  none  for  strongly  agree.       58  
  • 60. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose   Disagree        Neutral            Agree   I  buy  more  concert  tickets   5   4   3   +1  σ   2.43   2.25   2   1.82   Mean   1   -­‐1  σ   0   iTunes   Pandora   YouTube    Figure  24   There  are  a  number  of  possible  explanations  for  Pandora  and  YouTube’s  albeit  slight  victory   over   iTunes   in   this   measure.   First,   both   services   monetize   through   advertising   on  pages   with   content   that   is   either   mostly   or   completely   music-­‐based.   This   makes   both  services   prime   locations   for   concert   advertising.   Second,   two   of   the   primary   motives   for  attending   concerts   include   discovering   new   music   and   sharing   the   experience   with   friends,  both   of   which   Pandora   and   YouTube   users   enjoy   online.     iTunes’   lower   ranking   may   be  explained  by  the  fact  it  now  sells  live  albums  in  the  iTunes  Store,  reducing  some  users’  need  to  attend  in  person.  Music  is  Bigger   The   final   measure   of   impact   on   spending   was   admittedly   vague,   asking   respondents  to  indicate  their  level  of  agreement  with  the  statement  “music  plays  a  bigger  role  in  my  life”  as   a   result   of   using   the   three   music   apps.   Although   its   implications   are   difficult   to   quantify,  it  addresses  the  idea  that  new  music  technology  brings  users  closer  to  their  favorite  songs  and   artists,   which   benefits   everyone   in   the   industry.   While   Pandora   ranked   highest   on       59  
  • 61. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  average,  respondents  had  more  polarized  opinions  of  YouTube,  bringing  its  upper  standard  deviation   above   Pandora’s.   iTunes   scored   an   average   just   under   neutral,   with   over   33%  agreeing  or  strongly  agreeing.     Music  plays  a  bigger  role  in  my  life   5   Disagree            Neutral              Agree   4   3.35   3.30   3   2.96   +1  σ   2   Mean   -­‐1  σ   1   0   iTunes   Pandora   YouTube    Figure  25   Due   to   the   abstract   nature   of   the   question   and   the   closeness   between   the   three  services,   analyzing   any   causes   for   difference   would   be   purely   conjecture.     However,   this  particular  metric  suggests  that  at  least  half  of  college  students  are  more  engaged  in  music  as   a   result   of   using   the   three   music   apps   in   question.   In   constructing   this   portion   of   the  survey,  the  previous  metrics  followed  a  general  order  of  increasing  levels  of  engagement.  By  placing  this  metric  at  the  end,  it’s  possible  I  primed  some  respondents  to  select  lower  levels   of   agreement   than   if   I   had   placed   at   the   beginning.   One   could   argue   that   the   first  metric,   listening   to   music   more,   would   qualify   as   music   playing   a   bigger   role   in  respondents’  lives,  despite  the  latter  scoring  lower  overall.         60  
  • 62. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose   Discussion   My  love  for  music  and  new  recommendation  technologies  drove  me  to  explore  how  other  college  students  navigate  the  unlimited  choice  of  digital  music.  Growing  up  I  noticed  how  often  people  complained  that  the  radio  only  played  the  same  ten  songs.  But  after  a  few  failed  attempts  at  DJ-­‐ing  parties  with  family  and  friends,  I  understood  that  radio  stations  could  only  take  a  limited  number  of  risks  in  terms  of  unfamiliar  or  irrelevant  music.  I  also  quickly  learned  that  my  parents  and  their  friends  didn’t  enjoy  hard  rock  the  same  way  that  my  garage  band-­‐mates  and  I  did.    Witnessing  the  fundamental  power  of  music  in  my  own  life,  I  became  especially  curious  about  how  music  preferences  are  formed  and  transformed.  Through  this  study,  I  was  able  to  answer  many  of  the  questions  I  had  pondered  about  individuals’  tastes  in  music,  and  also  came  up  with  several  new  questions  to  explore  in  the  future.     In  my  analysis  of  the  six  factors  impacting  song  preference,  I  was  fairly  surprised  by  how  low  respondents’  ranked  the  importance  of  popularity  and  friends’  tastes.  These  two  factors  arguably  played  the  biggest  roles  in  determining  song  preference  prior  to  the  Internet,  and  now  seem  to  be  of  secondary  importance.  The  two  most  important  factors  in  song  preference  turned  out  to  be  “fitting  the  mood”  and  “artistic  talent,”  the  former  of  which  may  be  served  by  emotion-­‐based  music  recommendation,  user-­‐generated  tags,  or  Pandora’s  themed  radio  stations.  The  much  greater  perceived  importance  of  “artistic  talent”  compared  to  “popularity”  seems  to  represent  a  growing  trend  among  younger  generations.  Adolescents  subscribing  to  this  “hipster”  attitude  assume  that  popular  songs  are  rarely  made  by  artistically  talented  artists,  and  also  that  the  fewer  people  that  have       61  
  • 63. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  heard  of  their  preferred  artists  or  songs,  the  better.  Those  with  a  strong  desire  for  unfamiliar  music  tend  to  spend  hours  searching  various  obscure  music  sites  and  blogs,  while  others  are  content  with  the  occasional  unfamiliar  song  on  Pandora  or  Genius  recommendation  on  iTunes.    New  music  technologies  that  account  for  varying  degrees  of  the  hipster  mindset  will  serve  college  users  better  at  the  very  least,  and  may  also  widen  the  musical  horizons  of  older  generations  accustomed  to  AM/FM  radio  and  other  traditional  music  media.   The  vast  majority  of  my  examination  of  music  preferences  focused  on  variables’  correlations  with  genre  preferences.  I  found  that  users  who  liked  at  least  one  niche  genre  were  much  more  likely  to  enjoy  several  more  genres,  whereas  more  popular  genres  like  hip  hop/rap,  dance,  and  pop  were  associated  with  much  narrower  tastes  in  music.  There  were  also  a  number  of  significant  correlations  between  genre  preferences  and  demographic  information  like  age,  gender,  and  computer  skill  level.  While  music  technologies  currently  track  user  demographics,  this  study’s  findings  suggest  that  their  recommendation  algorithms  should  alter  suggestions  for  different  demographical  groups.  Additionally,  further  research  should  explore  correlations  with  artist  and  song  preferences,  and  also  examine  how  personality-­‐based  questions  relate  to  music  tastes.  For  example,  users  who  play  baseball  may  be  especially  likely  to  enjoy  Jimmy  Buffet’s  music.   Both  musical  experience  and  listening  environment  also  impacted  respondents’  music  preferences,  revealing  a  need  for  further  inquiry  and  possibly  the  incorporation  of  this  data  in  recommendation  systems.  Musical  experience  generally  showed  a  predictable  trend  of  being  positively  correlated  preference  for  niche  genres,  which  almost  undoubtedly       62  
  • 64. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  applies  to  lesser  known  artists  and  songs  as  well.  Despite  the  brevity  of  the  survey’s  section  on  music  listening  environment,  it  uncovered  interesting  relationships  between  genre  preferences  and  the  location,  state,  and  activity  of  music  technology  users.  Once  again,  the  width  of  topics  that  the  survey  addresses  and  non-­‐random  sampling  prevent  definitive  conclusions  in  these  areas,  warranting  additional  study.   I  personally  use  at  least  seven  different  music  sites  and  services  on  a  weekly  basis,  and  I’ve  always  loved  hearing  how  others  find  music  online.  In  this  study  I  selected  the  most  popular  and  purely  legal  music  applications,  narrowing  down  the  list  to  my  three  favorites  for  more  focused  analysis.  Though  Pandora  turned  out  to  be  respondents’  favorite,  most  students  indicated  that  they  use  all  three  services  and  probably  use  several  more.  The  most  relevant  question  on  the  survey  regarding  the  reasons  to  choose  one  service  over  another  asked  respondents  their  favorite  feature.  Results  showed  that  users  favor  YouTube  for  its  wide  selection  of  music,  Pandora  for  its  song  recommendation  and  personalization,  and  iTunes  for  its  interface  and  range  of  features.  Although  iTunes’  apparent  balanced  effort  between  features  appealed  to  many  respondents,  Pandora  and  YouTube’s  domination  of  one  or  two  core  features  seemed  to  elicit  more  passionate  responses  from  users.   Besides  preference  for  core  features,  respondents’  demographics  played  an  important  role  in  the  determination  of  music  technology  preference.  First,  younger  respondents  favored  YouTube  while  older  respondents  favored  iTunes.  These  correlations  may  be  due  to  differences  between  age  groups’  size  of  music  collections,  budgets  for  music,  music  preferences,  or  other  causes.  Next,  females  were  significantly  more  likely  to  rank       63  
  • 65. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose  Pandora  as  their  favorite,  while  males  were  slightly  more  inclined  to  favor  iTunes.    The  surveyed  college  students  from  Stanford  and  Arizona  State  University  were  significantly  more  likely  to  prefer  iTunes,  while  students  from  Glendale  Community  College  tended  to  prefer  Pandora  and  YouTube.  Stanford  and  ASU  students’  preference  for  iTunes  is  likely  a  result  of  Apple’s  strong  brand  awareness  on  both  campuses,  while  GCC  students’  preference  for  Pandora  and  YouTube  may  be  a  result  of  these  being  free  sources  of  new  music.  Three  out  of  the  four  demographic  variables  that  I  analyzed  were  significantly  correlated  with  preference  for  one  or  more  music  technologies.   Musical  experience  and  musical  education  both  influenced  choice  of  music  technology  as  well,  to  varying  degrees.  Respondents  who  chose  Pandora  tended  to  listen  to  music  more  often  both  in  general  and  online.  In  terms  of  music  education,  respondents  who  had  taken  more  music  classes  in  school  or  more  private  instrument  lessons  were  more  likely  to  use  iTunes  and  less  likely  to  use  YouTube.  This  was  surprising  because  I  expected  musicians  to  use  YouTube  to  showcase  their  work,  but  it’s  likely  that  experienced  musicians  have  a  greater  appreciation  for  music  and  are  therefore  more  willing  to  pay  for  it.  Additionally,  musically  experienced  individuals  listen  to  a  wider  range  of  genres  and  appear  to  prefer  iTunes’  interface  that  provides  greater  control  over  their  listening  experience.  Keeping  these  trends  in  mind,  music  services  like  Pandora  and  YouTube  might  test  and  implement  new  subscription-­‐based  features  that  acknowledge  users’  musical  experience  and  education,  whether  this  means  tweaking  recommendation  algorithms  or  using  an  alternative  interface  for  improved  control.       64  
  • 66. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose   While  knowing  the  factors  that  influence  college  students’  choice  of  music  and  music  technology  is  valuable,  determining  the  effect  of  these  technologies  on  music  preference  and  spending  was  the  primary  goal  of  this  research.  I  predicted  that  the  shift  from  AM/FM  radio  to  digital  music  would  reshape  demand  in  the  music  industry  to  lessen  the  domination  of  hit  artists  and  open  up  opportunities  for  lesser-­‐known  artists.  As  one  might  expect,  the  simple  answer  is  that  it’s  complicated.  All  three  new  technologies  that  I  studied  benefit  the  music  industry  by  increasing  users’  engagement  and  expanding  their  preferences  both  within  and  across  genres.  Pandora  was  the  clear  winner  in  terms  of  causing  users  to  consume  music  more  often,  listen  to  a  wider  range  of  genres,  listen  to  more  music  within  familiar  genres,  and  buy  different  music.  In  this  way,  Pandora  does  the  most  to  support  artists  further  down  “the  long  tail”.  On  the  other  hand,  YouTube  appears  to  enhance  social  music  sharing  better  than  Pandora  or  iTunes,  and  iTunes  understandably  facilitates  music  purchase  more  than  the  other  two.  Future  research  may  examine  each  service’s  effect  in  greater  detail,  and  include  other  new  music  technologies  as  well.   Despite  my  best  attempts  to  balance  comprehensiveness  and  manageability,  the  survey  may  have  missed  some  factors  that  influence  music  tastes  and  choice  of  technology.  On  the  other  hand,  with  a  completion  rate  under  16%,  the  survey’s  appearance  or  length  clearly  dissuaded  most  potential  respondents  from  completing  it.  After  I  finished  designing  the  survey,  several  new  music  applications  like  Spotify  and  8tracks  became  prime  candidates  for  similar  research,  making  me  wish  I  could  start  over  again.  However,  this  study’s  multivariate  approach  contributed  original  and  significant  findings  that  demonstrate  the  potential  of  new  music  technology  to  benefit  college  students,  artists  along  “the  long  tail,”  and  the  music  industry  as  a  whole.       65  
  • 67. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose   References  Anderson,  Chris.  The  Long  Tail:  Why  the  Future  of  Business  is  Selling  Less  of  More.  New  York:   Hyperion  Books,  2006.  Print.  Beer,  David.  "The  Pop-­‐Pickers  Have  Picked  Decentralised  Media:  The  Fall  of  Top  of  the  Pops   and  the  Rise  of  the  Second  Media  Age."  Sociological  Research  Online  11,  no.  3  (2006).    Bourreau,  Marc,  Francois  Moreau,  and  Michel  Gensollen.  "The  Digitization  of  the  Recorded   Music  Industry:  Impact  on  Business  Models  and  Scenarios  of  Evolution."  SSRN   eLibrary  (2008).  Bryson,  Bethany.  ""Anything  But  Heavy  Metal":  Symbolic  Exclusion  and  Musical   Dislikes."  American  Sociological  Review.  61.5  (1996):  884-­‐899.  Print.   <>.  Christenson,  P.,  &  Peterson,  J.  (1988).  Genre  and  gender  in  the  structure  of  music   preferences.Communication  Research,  15(3),  282-­‐301.  Retrieved  from   http://  html  David,  Shay,  and  Pinch,  Trevor.  "Six  degrees  of  reputation:  The  use  and  abuse  of  online   review  and  recommendation  systems  (originally  published  in  March  2006)"  First   Monday  [Online],  (17  March  2011).  Gaffney,  Michael,  and  Rafferty,  Pauline.  "Making  the  Long  Tail  visible:  social  networking   sites  and  independent  music  discovery."  Program:  Electronic  Library  &  Information   Systems  43.4  (2009):  375-­‐391.  Academic  Search  Premier.  EBSCO.  Web.  17  Mar.   2011.  LeBlanc,  A.,  Sims,  W.,  Siivola,  C.,  &  Obert,  M.  (1996).  Music  style  preferences  of  different  age   listeners.  Journal  of  Research  in  Music  Education,  44(1),  49-­‐59.  Retrieved  from   http://  html  Lessig,  Lawrence.  Remix:  Making  Art  and  Commerce  Thrive  in  the  Hybrid  Economy.  New   York:  Penguin  Pr,  2008.  Print.  McGinn,  Robert  E.  Science,  Technology,  and  Society.  Upper  Saddle  River,  New  Jersey:   Prentice  Hall,  1991.  Print.  Monroe,  Don.  "Just  For  You."  Communications  of  the  ACM  52.8  (2009):  15-­‐17.  Business   Source  Complete.  EBSCO.  Web.  18  Mar.  2011.  Rentfrow,  Peter  J.,  Gosling,  Samuel  D.The  do  re  mis  of  everyday  life:  The  structure  and   personality  correlates  of  music  preferences.  Journal  of  Personality  and  Social   Psychology,  Vol  84(6),  Jun  2003,  1236-­‐1256.  doi:  10.1037/0022-­‐3514.84.6.1236  Surowiecki,  James.  The  Wisdom  of  Crowds.  Anchor,  2005.         66  
  • 68. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose       Appendix  1.  Full  Survey           67  
  • 69. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose           68  
  • 70. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose         69  
  • 71. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose       70  
  • 72. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose       71  
  • 73. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose       72  
  • 74. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose       73  
  • 75. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose       74  
  • 76. The  Longer  Tails  of  iTunes,  Pandora  and  YouTube                                                                                                                                Penrose    2.  Survey  Recruitment  Email  From: Andrew Penrose apenrose@stanford.eduBCC: Stanford email lists, my parents’ students at Glendale Community College, ASU friendsSubject: Andrews Brief Music Technology Survey (could change your life)Hi Everyone,Im writing my honors thesis on digital music technologies and Im surveying college students to better understandthe usage and effect of iTunes, Pandora, and YouTube. Please do me a favor and take 15 minutes to share yourexperience with online music and support my research. ahead and forward this if you love good music and want to make it easier to find. :)I really appreciate it!Thanks,Andrew--Andrew PenroseScience, Technology and Society, B.A. HonorsStanford Class of | (602) 451-0150 | @apenrose3       75