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Internet Radio


“Radio Connected” Research
Project
Paul Dwyer
CAMRI, University of Westminster
Internet & Radio

 Being the gatekeeper was the most profitable
  place to be, but now we‟re in a world half
  without gates. Digital distribution gives everyone
  worldwide, instant access to music. And filters
  are replacing gatekeepers Courtney Love (2000)

 “radio in the digital era will produce new forms,
  battles over gateways and increased
  interactivity on the part of listeners … rather than
  a fundamental reorientation: a limited increase
  in choice for the listener, and greater industry
  concentration among producers” David Hendy
  (2000)
• substitution mass
  audiences/advertis
  ers switched from
  radio to TV as main
  medium
• Radio delivers a
  continuous linear
  stream of live (real-
  time) or pre-
  recorded music
  audio
• differentiation Costs
  of operation
  enables tailoring
  services to niche
  audiences/advertis
  ers
Audience
                              Research

   Advertiser
   s



Record           Selection      Playlist          Niche
                                (format    Call
Company                                           Audience
(Catalogue)                     )




                             Music
                             Knowledg
         Music
                             e
         Sales
         Data
Problem: Playlist Selection

 Music Knowledge - Recommendation

1. personal taste/musicology – genre experts
   search and recommend new/quality music

2. „professional‟ – search & recommend on tastes
   of „typical listener‟ Ahlkvist (2001)
Problem: Standard Formats

 economies of scale (share marketing, audience
  research and advertising sales) and scope (developing
  standardized music formats and playlists (for stations in
  different markets) favour networks over stations

 Target most profitable demographic groups

 networks succeed vs independents/small groups

 standard formats succeed vs diverse or niche content.

 Many case studies Canada (Berland, 1990, 1993;
  Grenier, 1993), Sweden (Wallis and Maim, 1993),
  Britain (Bamard, 1989), France (Miller, 1992), and
  Australia (Tumer,1993)
Playlist (chart)

 Established music selection was responsibility of network
   management

 music selection based on audience research/advertising sales
   not music knowledge

 DJs/presenters (at best) responsible for the non-music elements

 reduce diversity by reducing the number of different songs
   played

 Select songs with big sales (hits) or airplay or industry promotion

 Tendency for playlists to become shorterand more similar to
   other stations/the charts (Alkhvist and Fisher 2000)
Internet & Digitisation

 Reduce economies of scale and scope

 technical and 'on air' staff costs are low, presenter costs
   can be nil

 cost efficient to serve very small niche musical tastes

 Server logs provide instant, free 100% accurate
   audience research is (e.g. songs/playlists with biggest
   audience )

 enables the listener to interactwith music databases to
   select their own playlist

 reduces radio networks power as gatekeepers between
   record companies, advertisers and audiences
Niche
                                         Audience




                     Music Meta

Record                                                         Advertiser
                       data
Company       Pull                Pull   Playlist   Audience
(Catalogue)                                         Data
                                                               Music
                                                               Listenin
                                                               g Data


                       Music
                       Knowledge
Long Tail and Internet Radio

 From selecting ‘hit’ songsfor playlists to making all
  music available for listeners to create customized
  continuous music playlists of almost unlimited diversity

 A niche of one – last.fm

 internet radio stations compete on:

 ‘findability’/discovery

 control/customisation

 cost (amount of ads/subscriptions)

 size of songs database
Findability& Customisation

 Music metadata (e.g. pandora 400 music genes
  – find „genetically similar‟ songs)

 Fan metadata (e.g. folksonomies “love/ban”)

 Audience metadata (e.g.
  last.fm‟saudioscrobbler which tracks your music
  habits across devices – PC, ipod etc.)
Supplier Power

 Big Four record companies want new royalty
  stream but don‟t want „radio‟ to compete with
  „music download‟

 Only cooperate with internet stations which
  mimic functionality of radio – live streaming not
  free „on demand‟

 Limits to skip and pause functions
• Live radio provides
communication
and company
which music stream
cannot
•
entertainment, com
fort,security
•Mood
management
– Getting you up or
calming you down
• Information – esp.
music

Ofcom (2004) iPod
generation
DJ Chat (Monologue)                              Twitter
1. “fun atmosphere” (mood         1. “playful self”
    management)
                                  2. daily chatter
2. “what I‟m doing now”
    (company, communication)      3. reporting news – leaders and
                                      followers
3. comments on topical issues,
   pop music, sex and
   relationships (information)    4. Conversations (peer to peer)

4. Phone-ins (interact with
    celebs)

5. Dave says hi to Maggie (peer
   to peer)

 Brand &Scannell (1992)           Java et al. (2007)

                                   Ferguson & Greer (2011)

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1. paul dwyer internet radio emma

  • 1. Internet Radio “Radio Connected” Research Project Paul Dwyer CAMRI, University of Westminster
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  • 3. Internet & Radio  Being the gatekeeper was the most profitable place to be, but now we‟re in a world half without gates. Digital distribution gives everyone worldwide, instant access to music. And filters are replacing gatekeepers Courtney Love (2000)  “radio in the digital era will produce new forms, battles over gateways and increased interactivity on the part of listeners … rather than a fundamental reorientation: a limited increase in choice for the listener, and greater industry concentration among producers” David Hendy (2000)
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  • 5. • substitution mass audiences/advertis ers switched from radio to TV as main medium • Radio delivers a continuous linear stream of live (real- time) or pre- recorded music audio • differentiation Costs of operation enables tailoring services to niche audiences/advertis ers
  • 6. Audience Research Advertiser s Record Selection Playlist Niche (format Call Company Audience (Catalogue) ) Music Knowledg Music e Sales Data
  • 7. Problem: Playlist Selection  Music Knowledge - Recommendation 1. personal taste/musicology – genre experts search and recommend new/quality music 2. „professional‟ – search & recommend on tastes of „typical listener‟ Ahlkvist (2001)
  • 8. Problem: Standard Formats  economies of scale (share marketing, audience research and advertising sales) and scope (developing standardized music formats and playlists (for stations in different markets) favour networks over stations  Target most profitable demographic groups  networks succeed vs independents/small groups  standard formats succeed vs diverse or niche content.  Many case studies Canada (Berland, 1990, 1993; Grenier, 1993), Sweden (Wallis and Maim, 1993), Britain (Bamard, 1989), France (Miller, 1992), and Australia (Tumer,1993)
  • 9. Playlist (chart)  Established music selection was responsibility of network management  music selection based on audience research/advertising sales not music knowledge  DJs/presenters (at best) responsible for the non-music elements  reduce diversity by reducing the number of different songs played  Select songs with big sales (hits) or airplay or industry promotion  Tendency for playlists to become shorterand more similar to other stations/the charts (Alkhvist and Fisher 2000)
  • 10. Internet & Digitisation  Reduce economies of scale and scope  technical and 'on air' staff costs are low, presenter costs can be nil  cost efficient to serve very small niche musical tastes  Server logs provide instant, free 100% accurate audience research is (e.g. songs/playlists with biggest audience )  enables the listener to interactwith music databases to select their own playlist  reduces radio networks power as gatekeepers between record companies, advertisers and audiences
  • 11. Niche Audience Music Meta Record Advertiser data Company Pull Pull Playlist Audience (Catalogue) Data Music Listenin g Data Music Knowledge
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  • 13. Long Tail and Internet Radio  From selecting ‘hit’ songsfor playlists to making all music available for listeners to create customized continuous music playlists of almost unlimited diversity  A niche of one – last.fm  internet radio stations compete on:  ‘findability’/discovery  control/customisation  cost (amount of ads/subscriptions)  size of songs database
  • 14. Findability& Customisation  Music metadata (e.g. pandora 400 music genes – find „genetically similar‟ songs)  Fan metadata (e.g. folksonomies “love/ban”)  Audience metadata (e.g. last.fm‟saudioscrobbler which tracks your music habits across devices – PC, ipod etc.)
  • 15. Supplier Power  Big Four record companies want new royalty stream but don‟t want „radio‟ to compete with „music download‟  Only cooperate with internet stations which mimic functionality of radio – live streaming not free „on demand‟  Limits to skip and pause functions
  • 16. • Live radio provides communication and company which music stream cannot • entertainment, com fort,security •Mood management – Getting you up or calming you down • Information – esp. music Ofcom (2004) iPod generation
  • 17. DJ Chat (Monologue) Twitter 1. “fun atmosphere” (mood 1. “playful self” management) 2. daily chatter 2. “what I‟m doing now” (company, communication) 3. reporting news – leaders and followers 3. comments on topical issues, pop music, sex and relationships (information) 4. Conversations (peer to peer) 4. Phone-ins (interact with celebs) 5. Dave says hi to Maggie (peer to peer)  Brand &Scannell (1992)  Java et al. (2007)  Ferguson & Greer (2011)