1. The State of Online Music Stores A feature and content analysis of online music stores and a review of music information-seeking behavior.

2. Why Analyze Online Music Stores? In addition to their catalogue, online music stores succeed and fail by supporting users’ information-seeking behaviors. Music stores in general depend on discovery of new materials as well as fast access to known items to increase sales. Online music stores must support the broadest range of users to increase their market share and profit.

3. Literature on Music Information- Seeking Behavior Information needs are usually defined with bibliographic information. (Downie & Cunningham 2002; Cunningham et al. 2003; Bainbridge et. al. 2003; Lee & Downie, 2004) “People search for music as an auditory experience” (Lee & Downie, 2004). (Downie & Cunningham, 2002; Cunningham et al., 2003) Contextual Information is important. (Downie & Cunningham, 2002; Lee & Downie, 2003) Relational Information is important. (Downie & Cunningham, 2002; Lee & Downie, 2004)

4. Literature on Music Information- Seeking Behavior Music information-seeking is social. (Cunningham et al. 2003; Lee & Downie, 2004) Music information-seekers are less likely to consult an ‘expert’ than a friend. (Cunningham et al., 2003; Lee & Downie, 2004)

5. Literature on Music Information- Seeking Behavior Music information needs are often roughly defined. (Cunningham et al., 2003; Bainbridge et al., 2003; Kim & Belkin, 2002) Browsing is a significant activity. (Cunningham et al., 2003) Searching and Browsing are interleaved. (Cunningham et al., 2003)

6. Observations Based on the Literature Population and environment effect information-seeking activities and desired outcomes. Music information needs are ill defined and MIR systems (i.e. music stores & music digital libraries must support exploratory search)

7. The Sample Requests for site recommendations were sent out to three music oriented listservs. –I received approximately 15 response yielding 23 unique sites. Submitted sites were reviewed and selected in an attempt to represent the breadth of features. –20 out of 23 sites were included in the analysis

8. Method: Pre-analysis Sites were briefly explored to determine the breadth of features available. –Browsing, Searching –Created accounts when available –Looked for services Features were listed for creation of analysis matrix. Features in matrix assigned to four categories: 1.Contextual Information 2.Relational Information 3.Persistent interactivity 4.Information-seeking activity

9. Method: Data Collection Binary: Does site have a feature? –Ex: Search Numeric: How many/How often. –Ex: how many items displayed on the front page? Loose observations of site. –Ex. The site appears to want people to only browse.

10. Method: Data Analysis Compared quantitative data collected.

11. Findings: Site Descriptions Most of the sites are built using html –1/5 of sites with audio surrogates used an embedded player build with flash. –21% used Flash; 30% used RA; 16% used Mp3; 35% used no audio. 21% had “collections” 47% sell other stuff Avg. 52 items on front page –About1/3 of sites had no items –10% had >300

12. Contextual Information

13. The Sites http://www.turntablelab.com http://www.boomkat.com http://www.bleep.com http://karmadownload.com http://www.cduniverse.com http://forcedexposure.com http://www.midheaven.com/front.html http://www.juno.co.uk http://www.vibrantsound.com/music/home.php http://www.breakbeatscience.com http://www.chemical-records.com http://www.redeyerecords.co.uk http://www.dustygroove.com http://planetxusa.com http://www.othermusic.com www.deepfixrecords.com www.primalrecords.com www.htfr.com beatport.com

14. Findings: Relational Info.

15. Findings: Social Info-Seeking

16. Findings: Activity Support

17. Findings: Int. Persistence

18. How the Sites Measure Up

19. What Online Music Stores should Learn from the Literature Provide better relational data Improve social aspects of sites

20. What the Researchers can Learn from Online music stores Some of the most often recommended sites have some of the worst scores on relational information, interactivity persistence, or social support. These sites could serve as stimulous materials for experiments and user studies.

21. Limitations This study only tells us what the population of online music stores looks like. –We need measures of “success” to make inferences about value of features. In this case we should control for usability, catalogue and price competitiveness.

22. What’s Next Usability Study Operationalize these variables for an experimental study Build a better system!