A
S R E N E T S I L F O M SPECTRU
viour a h e B r e m u s n o C in rences fe if D g in d n a t s r e d n U ces n e r fe e r P e r n e G n o d of Music Fans Base Report and Analysis by
Matthew Chisling
BCom, McGill University MSc, London School of Economics (In Progress)
This is an academic paper originally published for submission and review to a Professor in the Information Systems department at McGill University during my undergraduate career. It was later presented at the SCECR 2013 conference in Lisbon, Portugal. All photos here are used for academic purposes, and are not the property of the writer.
N O I T C U D INTRO Many industries have struggled to adapt to contemporary marketing practices as the rules of the marketing game change drastically with the rise of Web 2.0. However, the music industry has received a darker fate: not only has the promotional system changed for marketers in the music industry, but also the entire value chain has been revolutionized. The industry’s panic as to how to re-monetize the once-mammoth industry in the new millennia has been at the heart of many research questions for contemporary scholars. The result of limited amount of research focusing on the business development side of the music industry has left many questions for music marketers to understand The first trend to address is the rising number of sources where and how music can be discovered. The increase of diverse portals through which word-of-mouth marketing can generate significant purchasing power has fractured the industry’s marketers in assessing what is the best way to reach audiences with a particular record or album. The second trend to address is in the proliferation of accessibility to genres of music outside the most popular. The ability to foster online communities of devoted fans across geographical boundaries, as well as the ability to distribute music digitally, has called for a surge of popularity in genres unlikely to claim Top 40 status. Genres of music such as Country, Metal, Jazz and Christian have been able to grow in terms of number of established artists and number of established records. Because of these two growing trends, marketers need to be able to understand how to handle this growing number of potential points of influence as the market for particular genres of music expand. The industry’s growing amount of consumer choice in terms of music means that marketers can no longer provide template push campaigns to attract consumers to listen to any record. They must create specific marketing campaigns that reach different listeners in the optimal ways. Current research, such as publications by Dewan and Ramaprasad (2012), Dhar and Chang (2007), and Molteni (2003), on similar topics has been conducted – however, little research has successfully tackled the same issues as the ones mentioned above. For example, work has effectively proven that music markets should be segmented; however, the research has suggested that shopping habits, not listening preferences, should create the segments. Other research has proven that certain marketing efforts, including Word of mouth (WOM) marketing are positively related to music sales – however these works have always considered the impact of a solitary marketing effort – such as sampling or “buzzmarketing” – in isolation from each other. There is still a need to understand if consumers who have different listening preferences can be targeted using different marketing methods more successfully. This paper seeks to lay the groundwork in understanding how listeners of different genres of music discover their music differently, and whether the marketing efforts that encourage them to purchase their music are different. In other words, this paper seeks to understand the effectiveness of certain marketing efforts from a segmented genre point of view: Are Pop music listeners discovering their music from somewhere different from Hip-Hop listeners? Might radio be the optimal marketing effort to attract country music consumers, or should we focus on digital marketing efforts? The implications of this paper may serve as the basis of marketing models for the new music industry, and may be the first in many works that look at, more specifically, how to customize marketing efforts to reach different segments. In the past, campaigns segmented by listening tastes were not relevant: now, they must be undertaken to break through the clutter and reach consumers. This remainder of this paper is outlined as follows. First, we will analyze the current body of research related to music discovery and music influencers to see what questions remain. Next, we will examine these questions using secondary data from Nielsen. The results of this data analysis will be discussed in the remainder of this paper.
W E I V E R . LIT The literature surrounding the influencing powers of music consumption, as a topic to be widely debated and analyzed from different angles, is a relatively new field of study: Largely, this is because the music industry was a mainstay of topdown communication for a large time – influencing a consumer to buy a physical recording, traditionally came from a radio push, corporate advertising from a record label, or perhaps through the influence of a referent or tastemaker in a local community (record shop, social circle, etc.). However, with the recent influx of not only quantity of music available to us, but also the rising variety of sources that can hold influencing power in the music purchasing decision, critical attention is beginning to look at the segmentation of the consumer market for music, the variety of sources’ impact on music consumption behavior, and the power of a particular genre on socio-political identity.
Frame Concepts and Contextual Theories Consumer behavior theorists have linked the idea of “The Fashion System” to explain how cultural goods are adopted in communities over time (Solomon et. al, 2011). Georg Simmel’s “trickle-down effect” (1904), superseded by Polhemus’s “Bubble-Up Theory” (2007), discusses the idea of cultural goods beings passed from one social circle to the next. Because goods are appropriated by a social class as a marker of status at a point in time, they become attractive to other social groups, who then appropriate the good as a status symbol of their own. These sociological theories have explained why cultural goods are passed along in audiences, which has become the basis of much understanding about word-ofmouth marketing and how goods can become widely adopted. However, these theories are rarely applied specifically to the field of music, from a sociological, ethnographic, perspective. It is important to understand how music transfers from circle to circle, as music is a good that is highly personal, yet equally impersonal due to its ease in reproduction in the digital age. Music is a digital good, which means that it is both cheap and easy to duplicate. As such, just because one person can enjoy it does not mean another person cannot enjoy it at the same time elsewhere. Theorists additionally have identified music in its new media formats are being a low involvement good, largely because of a decrease in costs to acquire it (Solomon et. al, 2011). As such, it should be analyzed based on the low-involvement hierarchy communication model of consumer behaviour. In the past, consumers would hear a track, develop an attitude towards it, and then decide whether to purchase it. Now, consumers can access music so easily and so cheaply that the mentality has switched: consumers hear (or blindly purchase) a track first, then subsequently develop an attitude towards it. The importance of identifying music as being low-involvement suggests that the marketing efforts that will be used to persuade people to buy it may now be different from what they were before. An important concept that highlights why music marketing must change, and must be considered in this body of work, is the Long-Tail theory developed by Anderson (2006). He explains that online businesses selling downloadable or streaming consumer goods have a unique advantage: to be able to offer a much wider catalogue to consumers – including the lesser-known and lesser-sold titles or products further down the tail of a sales/number-of-product graph – while not incurring the costs that normally would be applied to a traditional, brick-and-mortar store, should they choose such a strategy. The implication of this will be important in some genres more than others: Some genres are quite niche, and the increased accessibility to more artists within the genre opens up new opportunities to attract consumers to those particular genres of music.
One last consideration that must be taken into effect for this research is the reversion of consumers paying for music as opposed to pirating it. Though this is a hotly debated topic, recent evidence is suggesting that this is potentially a development that will impact the marketing side of the music industry. A Canadian study determined that in 2011, consumers were increasingly paying for paid downloads of music (Ipsos Reid, 2011). Additional studies regarding the HADOPI, or “three-strikes,” law in France have suggested that in France, the amount of illegal downloading of music has reduced in favor of paid downloads: A research study by French research company BVA revealed that 53% of French citizens reported cutting back or stopping piracy altogether because of the HADOPI laws (IFPI, 2011). Danaher et. al (2012) reported a 25% increase in iTunes album purchases following the implementation of HADOPI. An important finding by Danaher et al. (2012) in this same study was the difference in some genre audiences, particularly the Hip-Hop genre, to shift to purchasing music more so than others. This is important because it identifies consumers of different genres to have different buying behavior, an important assumption for the remainder of this paper.
Reports and Findings Much of the literature that focuses on how new media marketing efforts influence purchasing decision has been streamlined so that it focuses on one particular effort. For example, Peitz and Waelbrock (2005) identified that a sampling of a recording will generate more interest in the recorded piece. Chen et al. (2011) found that fan-generated content on websites like MySpace positively impact music sales. Dhar and Chang (2007) identified that as an album is discussed more online via user-generated content, there is a greater volume of sales of the album. Thus, online “chatter” positively correlates to greater sales. Furthermore, they discovered that positive reviews from major press sources will result in an even higher amount of sales – both these discoveries sync with the Nielsen data. Dewan and Ramaprasad (2012), however, recently found that social media buzz, namely buzz on web “blogs”, has little effect on sales of albums, and a negative effect on single sales. However, they acknowledge that users are still influenced to consume music due to positive blog buzz – whether they pay for it or not. Riegner’s data (2007) concurs with much of the earlier work on buzz-marketing, arguing that word-of-mouth marketing increases the likelihood of purchases. Recent research on the importance of traditional media has supported earlier claims that traditional media, such as radio, still have significant impact on the sales of music (see Dewan and Ramaprasad 2012, and Nielsen 2012). Furthermore, Peoples (2012) in collaboration with Billboard and National Association of Recording Merchandisers identified television as a new major source of music discovery, due to the popularization of music television programs. Other research has in the past considered the impact of one genre in the consumption of music. Bennett (1997) focuses on the importance of “pub rock,” a low-grade, stripped back style of aggressive rock music played in small venues, as a mode of discovering music in the United Kingdom. Further research in this area, the discovery of music in a particular genre’s context, is limited. Finally, another branch of research has looked at music consumption patterns and audience segmentation, however this is largely done from a socio-economic perspective: Molteni (2003) segmented the market for music into five categories based on their listening habits and interests in downloading music online, and considers how these people are influenced to buy their music. Tu and Lu (2012) segmented music consumers with online presence to understand their reactions to online sampling and online recommendations relative to their shopping habits. However, these analyses do not consider a further segmentation of the market explicitly by genre. The underlying issue surrounding much of this analysis is that it does not handle the intersections of these strands of research: for example, while there is research on markets for consumption and how we should reach them, the segments are based on shopping habits, not preferences. Furthermore, while authors look at consumer preferences as a way of discovering or establishing an identity, they do not at the same time look at how these segmented consumers buy their music, or discover it. In addition, while tools to discover music/influence people to buy music are beginning to be analyzed, they are not done in comparison to one another. Therefore, it is hard to tell whether the measured results would hold true if a consumer had to pick amongst a variety of options instead of just the one researched. The framework theories and traits identified point to many opportunities to address important questions for the discovery methods and marketing techniques of recorded music, yet the literature suggests more explicit work related to how to reach smaller segments needs to be conducted.
Y G O L O D O H T DATA AND ME This analysis has been conducted using secondary data from the Nielsen Music 360 survey conducted by Nielsen in May, 2012 using their ePanel. The Nielsen Music 360 was conducted to develop comprehensive research on the music industry from a variety of different angles. Users were asked about information on their consumption habits, discovery habits, acquisition behavior, as well as their preferences and demographics. 3,002 consumers from the United States answered the online survey. The data collected by Nielsen was then further analyzed on a crosstab desktop application: SPSS was used to generate descriptive frequency statistics based on select cases that were categorized by the user’s preferred genre of music. Further analysis was done using independent two-variable T-tests to determine the significance of the differences in patterns of behavior of users across different genres. Table 1 profiles the eight major genres that were considered for this analysis, and includes a brief sampling of artists that, according to respondents’ preferences, were representative of that genre.
Table 1 - Preferred Genres and the Artists that Compose Them
Summary statistics are reported in Table 2 and indicate the demographics of survey respondents. There were 3002 participants, all of whom live in the United States with a near-perfect split in male to female participants. A significant majority of the participants were over the age of 55, which might not be representative of the American population of music listeners. Nearly 70% of all participants had at least some college education.
Table 2A - Gender of Partcipants
Table 2B - Age of Participants
Table 2C - Education Level of Partcipants
RESULTS Tables have been included to summarize the findings of the research. Table 3 summarizes the results of the question related to whether marketing efforts would increase one’s likelihood to purchase a song. Table 4 summarizes the results of the question related to how consumers discovered the most recent song they purchased. Table 5 is summarizes the T-tests used on this data to prove its statistical significance, and is heat-sensitive to the data that is more significant.
Table 3 - Genre-Specific Likelihood to Purchase Music Because of a Marketing Effort Music Discovery (%)
Top 40 (n=308)
Christian (n=140)
Country (n=367)
Hip Hop (n=127)
Oldies (n=186)
Pop (n=198)
58.1
62.9
51.5
54.3
38.7
64.6
62
61
56.6375
31.5
27.1
20.2
18.1
17.2
28.8
30.4
30.5
25.475
24
20
19.1
16
17.7
25.3
23.9
28.4
21.8
26
20
19.9
13.8
12.9
27.8
23.9
29.8
21.7625
31.2
23.6
23.4
16
14.5
32.8
29.3
30.5
25.1625
19.2
12.9
11.7
9.6
8.6
16.7
20.7
19.9
14.9125
39.9
30
24
16
16.7
41.9
38
35.5
30.25
9.7
2.9
7.1
9.6
3.2
9.1
5.2
14.2
7.625
8.4
1.4
4.1
7.4
1.6
6.1
6.5
11.3
5.85
6.2
1.4
4.6
6.4
2.7
5.1
5.4
9.2
5.125
5.5
1.4
3.8
4.3
1.1
6.1
6.5
9.2
4.7375
8.1
1.4
5.2
5.3
1.6
5.1
7.6
11.3
5.7
7.5
2.1
3.8
4.3
1.6
5.1
5.4
9.9
4.9625
8.8
1.4
4.1
3.2
2.2
5.6
6.5
10.6
5.3
A music recommendation engine (Genius)
22.4
10.7
10.9
9.6
8.1
19.7
25
22.7
16.1375
When an artist wins an award (Grammy)
41.6
27.1
33.5
24.5
20.4
41.4
31.5
33.3
31.6625
19.2
9.3
14.2
13.8
7.5
18.2
19.6
21.3
15.3875
Positive Recommendation from a friend Positive review from a music review website
Positive Review from a music critic Positive review from a music magazine positive review on music blogs
Positive review from Metacritic High popularity on iTunes Negative recommendation from a friend Negative review from a music review website Negative review from a music critic negative review from a music magazine
Negative feedback on music blogs Negative review from metacritic Low popularity rating on iTunes
Â
An endorsement from a brand
Rap (n=92)
R&B (n=141)
Average
Table 4 - Genre-Specific Breakdown of How Consumers Discovered Their Last Song Purchased (%) Friends/relatives - via conversation
Friends/relatives - via texts/IM/email/posts Music recommendation engines (such as Genius)
Music identification services (such as Shazam, SoundHound) Other music apps/services iTunes
Top 40 (N=267)
31 13
3
Christian (N=116)
28
8 2
Country (N=274)
27
9 1
Hip Hop (n=120)
39 17
3
Oldies (n=107)
19
7 2
Pop (N=185)
42 18
2
Rap (N=82)
38 18
1
Avg.
Sum
St. Dev
35
32.375
259
7.595816518
3
2.125
17
0.83452296
R&B (n=120)
11
12.625
101
4.56500665
4
3
2
3
2
3
5
3
3.125
25
0.991031209
3 24
3 16
1 16
1 28
2 9
2 30
1 33
4 18
2.125 21.75
17 174
1.125991626 8.293715349
Rhapsody
2
1
1
3
4
2
1
7
2.625
21
2.065879266
Online music stores/portals (other than iTunes or Rhapsody)
3
0
2
2
2
2
4
7
2.75
22
2.052872552
Pandora
14
16
13
9
13
19
15
30
16.125
129
6.289163015
Online music/radio streaming services (other than Pandora)
5
3
4
7
1
6
4
12
5.25
42
3.284161124
Radio - AM/FM (over the air) Radio - satellite (such as SiriusXM) Radio - free streaming from the AM/FM radio station's website
54 8 13
61 6 14
62 13 11
41 8 7
53 8 12
55 7 14
44 10 13
58 8 12
53.5 8.5 12
428 68 96
7.540367555 2.138089935 2.267786838
4
1
3
2
4
3
6
5
3.5
28
1.603567451
1
0
1
1
1
1
1
2
1
8
0.534522484
18
14
12
23
7
22
24
18
17.25
138
5.922113522
4
0
2
6
2
6
4
5
3.625
29
2.133909892
iTunes Ping
3
1
1
5
2
3
2
3
2.5
20
1.309307341
Google+ (Google Plus)
1
0
2
2
3
3
1
2
1.75
14
1.035098339
Social networking sites (other than Facebook, Twitter, iTunes Ping) YouTube
3
1
3
5
5
5
7
8
4.625
37
2.263846285
Peer-to-peer (P2P) music/audio file sharing programs Network music players (such as Roku SoundBridge, Logitech Squeezebox, etc) Facebook
24
23
14
36
16
35
44
30
27.75
222
10.37510757
VEVO
4
4
3
5
3
5
5
9
4.75
38
1.908627031
Video websites (other than YouTube and VEVO)
3
3
1
3
2
1
2
7
2.75
22
1.908627031
Official website of the band/artist Official website of the music label Online reviews
5 3 2
10 1 1
5 1 3
8 5 3
8 4 3
8 3 4
2 1 1
5 5 5
6.375 2.875 2.75
51 23 22
2.55999442 1.726888201 1.38873015
Blogs, forums, message boards, chat rooms
2
3
2
8
2
4
1
9
3.875
31
2.997022332
Other music websites
4
2
2
6
1
6
4
7
4
32
2.20389266
Video games
3
3
2
3
2
2
4
2
2.625
21
0.744023809
Video game portals (such as XBOX Live, PlayStation Network, etc) TV shows
1
3
2
4
2
1
6
3
2.75
22
1.669045921
16
16
13
10
23
23
12
16
16.125
129
4.764076886
6
3
7
7
6
9
7
12
7.125
57
2.587745848
Music-specific TV channels TV ads/commercials
10
7
8
8
12
7
10
15
9.625
77
2.774243784
Movies
14
14
15
13
18
23
15
18
16.25
130
3.284161124
Movie soundtracks
12
12
12
10
20
19
17
17
14.875
119
3.796144661
Magazines
2
2
3
4
6
5
2
5
3.625
29
1.597989809
Newspapers
1
2
1
1
4
1
1
3
1.75
14
1.164964745
In-store - at the shelf
2
6
9
7
14
6
10
8
7.75
62
3.494894235
In-store - posters or signs
2
2
4
3
7
4
0
6
3.5
28
2.267786838
Music store employee
1
1
1
3
3
1
2
3
1.875
15
0.991031209
Live events/performances/concerts
3
7
5
3
11
6
2
10
5.875
47
3.313931631
Other
2
7
4
2
8
2
1
2
3.5
28
2.618614683
333
310
303
364
333
420
381
448
Total (%)
Â
Table 5 - T-Tests for Significance of Differences in Averages Between Two Genres Country Music Listeners VS. Hip Hop Listeners Friends/relatives - via conversation
Friends/relatives - via texts/IM/email/posts
Music recommendation engines (such as Genius)
Music identification services (such as Shazam, SoundHound, etc)
Other music apps/services
iTunes
Rhapsody
Online music stores/portals (other than iTunes or Rhapsody)
Pandora
Online music/radio streaming services (other than Pandora)
Radio - AM/FM (over the air)
Radio - satellite (such as SiriusXM)
Radio - free streaming from the AM/FM radio station's website
Rap Vs. Hip-Hop Listeners
Pop Vs. Top 40 Listeners
0.02
0.846
0.027
0.025
0.846
0.012
0.886
0.026 0.052
Country Vs. Christian
Top 40 Vs. Oldies
Hip Hop vs. R&B
0.829
0.013
0.028
0.83
0.008
0.099
0.746
0.084
0.886
0.109
0.741
0.345
0.451
0.374
0.303
0.433
0.508
0.583
0.544
0.541
0.596
0.812
0.787
0.802 0.006
Top 40 Vs. Country
0.506 0.506
Country Vs. Oldies
Pop Vs. Hip Hop
Christian vs. Rap
0.167
0.298
0.079
0.139 0.148 0.215
0.173
0.298
0.063
0.14
0.136
0.478
0.051
0.14
0.137
0.453
0.239
0.036
0.439
0.703
0.03
0.328
0.985
0.776
0.452 0.812
0.383
0.703
0.031
0.421
0.985
0.77
0.353
0.703
0.286
0.845
0.841
0.392
0.532
0.818
0.286
0.703
0.84
0.838
0.352
0.275
0.543
0.118
0.552
0.193
0.417 0.503
0.794
0.327
0.355
0.503
0.099 0.1
0.12
0.596
0.136
0.825
0.001
0.046
0.029
0.078
0.107 0.363
0.477
0.487
0.012
0.491
0.142
0.823
0
0.046
0.029
0.051
0.373
0.006
0.296
0.524
0.641
0.835
0.421
0.124
0.296
0.085
0.879
0.806
0.497
0.631
0.469
0.124
0.298
0.177
0.882
0.811
0.373
0.478
0.828 0.144
0.669
0.053
0.53
0.977
0.565
0.038
0.133
0.37
0.913 0.912
0.407
0.46
0.025
0.647
0.053
0.264
0.232
0.149
0.535
0.844
0
0.236
0.25
0.158
0.547
0.843
0.188
0.357
0.749
0.594
0.055
0.181
0.239
0.332
0.751
0.569
0.01
0 0
0.666
0.862
0.877
0.856
0.667
0.862
0.172
0.729
0.74
0.877 0.041
0.141
0.733
0.25
0.169
0.738 0.773
0.288
0.531
0.025
0.004
0.977
0.533
0.083
0.807
0.989
0.196
0.865
0.807
0.989
0.865
0.369
0.156
0.167 0.577
0.181
0.37
0.066
0.594
0.676
0.007
0.068
0.117
0.014
0.016
0.857
0.007
0.068
0.123
0.014
0.016
0.861
1
0.046
0.2
0.876
0.332
0.019
0.863
1
0.164
0.877
0.493
0.803
0.275
0.525
0.819
0.108
0.351 0.939
0.079 0.276
0.939
0
0.045
0.667
0.217
0.19
0.774
0.509
0.8
0.275
0.525
0.822
0.588
0.092
0.544
0.282
0.997
0.152
0.428
0.536
0.558
0.13
0.532
0.189
0.152
0.429
0.569
0.209
0.064
0.812
0.787
0.712
0.259
0.997 0.856
0.315
0.675
0.891
0.931
0.235
0.802
0.794
0.721
0.863
0.316
0.674
0.932
0.32
0.011
0.756
0.337
0.083 0.709
0.01
0.425
0.168
0.299
0.057
0.02
0.758
0.343
0.715
0.003
0.425
0.071 0.072
0.129
0.315
0.033
0.487
0.184
0.144
0.353
0.174
0.977
0.355
0.067 0.038
0.083
0.469
0.205
0.025
0.286
0.777 0.777
0.176
0.977
0.362
0.958
0.892
0.669
0.31
0.086
0.328
0.394 0.231
0.083
0.006 0.04
0.332
0.958
0.895
0.647
0.31
0.088
0.421
0.287
0.413
0.736 0.722
0.798
0.211
0.109
0.244
1
0.333
0.724
0.663
0.235
0.792
0.246
0.014
0.333
1
0.331
0.738
0.686
0.32
0.306
0.496
0.199
0.425
0.303
0.958
0.398
0.33
0.015
0.355
0.511
0.222
0.124
0.467
0.303
0
0.251
0.01
0.03
0.087
0.338
0
0.254
0.011
0.045
0.068
0.306
0.969
0.524
0.486
0.355
0.969
0.534
0.519
0.546 0.514
0.226
0.715
0.121
0.445
0.299
0.707
0.089
0.44
0.121
0.223
0.467
0.089
0.04
0.151
0.24 0.855
Peer-to-peer (P2P) music/audio file sharing programs
Network music players (such as Roku SoundBridge, Logitech Squeezebox, etc)
iTunes Ping
Google+ (Google Plus)
Social networking sites (other than Facebook, Twitter, iTunes Ping)
YouTube
VEVO
Video websites (other than YouTube and VEVO)
Official website of the band/artist
Official website of the music label
0.098
Online reviews
Blogs, forums, message boards, chat rooms
Other music websites
0.217
0.958
0.887
0.034
0.373
0.445
0.375
0.035
0.004
0.683
0.016
0.002
0.004
0.689
0.003
0.21
0.449
0.952
0.021 0.697
0.21
0.45
0.707
0.853
0.439
0.238
0.147
0.951 0.774
0.985
0.948
0.495
0.383
0.238
0.149
0.786
0.985
0.948
0.084
0.25
0.426
0.905
0.29
0.386
0.033
0.125
0.296
0.426
0.905
0.333
0.417
0.018
0.632
0.714
1
0.226
0.164
0.33
0.806
0.375 0.535
0.811
0.338
1
0.851
0.108 0.345
0.853
0.596
0.727
0.228
0.252
0.338
0.282
0.752
0.52
0.816
0.892
0.683
0.303
0.36
0.189
0.763
0.52
0.816
0.894
0.564
0.811
0.002
0.029
0.211
0.629
0.82
0.82
0.728
0.977
0.005
0.503
0.016
0.012
0.237
0.652
0.813
0.82
0.729
0.977
0.025
0.477
0.063
0.487
0.491
0.768
0.09
0.791
0.136
0.414
0.574
0.395
0.119
0.469
0.501
0.757
0.025
0.791
0.137
0.33
0.592
0.424
0.667
0.806
(Table 5 Continued) Video games
Video game portals (such as XBOX Live, PlayStation Network, etc)
TV shows
Music-specific TV channels
TV ads/commercials
Movies
Movie soundtracks
Magazines
Newspapers
In-store - at the shelf
In-store - posters or signs
Music store employee
Live events/performances/concerts
Other
0.508
0.902
0.541
0.903
0.175
0.537
0.451
0.812
0.439
0.703
0.404
0.845
0.985
0.667
0.433
0.818
0.383
0.703
0.405
0.84
0.985
0.676
0.34
0.629
0.797
0.735
0.767
0.977
0.977
0.244
0.551
0.299
0.652
0.806
0.735
0.767
0.436
0.625
0.078
0.347
0.1
0.179
0.271
0.417
0.631
0.085
0.368
0.123
0.179
0.272
0.923
0.859
0.36
0.182
0.783
0.181
0.923
0.86
0.373
0.13
0.778
0.984
0.729
0.314
0.618
0.984
0.733 0.794
0.301
0.606
0.01
0.836
0.738 0.558
0.796
0.013
0.835
0.142
0.055
0.994
0.546
0.16
0.063
0.664
0.512
0.679
0.108
0.218
0.179
0.251
0.112
0.415
0.022
0.087
0.405
0.792
0.639
0.912
0.181
0.792
0.625
0.913
0.223 0.251
0.492
0.109
0.587
0.261
0.661
0.47
0.51
0.109
0.587
0.297
0.653
0.482
0.341
0.374
0.806
0.445
0.89
0.868
0.374
0.806
0.889
0.869
0.071
0.13
0.911
0.462 0.057
0.504
0.322
0.994
0.097
0.13
0.082
0.49
0.334
0.071
0.396
0.054
0.759
0.911 0.302
0.296
0.189
0.726
0.492
0.096
0.337
0.12
0.759
0.3
0.351
0.256
0.812
0.787
0.966
0.615
0.092
0.177
0.975
0.085
0.795
0.802
0.794
0.966
0.646
0.183
0.177
0.975
0.177
0.783
0.77
0.021
0.364
0 0.001
0.802
0
0.129
0.015
0.332
0.166 0.385
0.051
0.351
0.535
0.234
0.741
0.485
0.427
0.363
0.011
0.734 0.776
0.464 0.629
0.442
0.036
0.332
0.802
0
0.096
0.211
0.199
0.041
0.357
0.167
0.612
0.045
0.237
0.127
0.097
0.357
0.425
0.564
0.158
0.296
0.978
0.516
0.835
0.244
0.703
0.166 0.975
0.23
0.312
0.37
0.978
0.489
0.828
0.333
0.703
0.975
0.323
0.382
0.373 0.413
0.363
0.715
0.191
0.587
0.003
0.039
0.236
0.05
0.985
0.16
0.319
0.707
0.213
0.604
0.017
0.039
0.234
0.09
0.985
0.128
0.293
0.798
0.843
0.163
0.003
1
0.209
0.055
0.889
0.059
0.225
0.792
0.841
0.217
0.022
1
0.207
0.106
0.886
0.034
Results of Marketing Efforts Participants were asked, of a variety of options, “how would each of the following impact your decision to purchase music?� While there were differences between results in terms of how impactful one medium was compared to another, there were few noticeable differences between the genres, specifically. The most prominent differences came from participants who responded that the medium was either much more likely to make them purchase the song, or a little more likely to make them purchase the song. Across all genres, a positive recommendation from a friend was the marketing effort most likely to convince a respondent to purchase a record. However, while, on average, 56% of respondents claimed they were influenced by their peers to purchase a record, people who preferred Oldies music lowered the average; only 38% of them claimed that those around them influenced them. There was visible variance in the level of impact a positive review from a music review website had on participants. A positive review from a music review website was a much stronger influencer on those who preferred pop music (31.5%), rap music (30.4%), and R&B music (30.5%), compared to the average of 25%. In two genres, positive reviews from music review websites had much less impact: Those who listened to hip-hop and oldies music were not as impacted, given than only 18.1% and 17.2% of respondents acknowledged it as a source of impact, respectively. There were other categories were the only two noticeable differences were those who preferred oldies and hip-hop music. Those who preferred oldies or hip-hop music were less likely to attribute their purchases to positive review from blogs. Furthermore, they were the two categories that were the least affected by a high popularity rating on iTunes: While an average of 30.3% of respondents in the overall sample acknowledged that a high popularity rating on iTunes would make them likely to buy a record, only 16% of hip-hop fans, and 16.7% of oldies fans, agreed with that statement. The samples that were most positively influenced by iTunes popularity were top 40 listeners (39.9%), pop listeners (41.9%), and rap listeners (38%).
One category that appeared to impact listeners across genres was “when an artist wins an award, such as a Grammy.” The average percentage of respondents who felt this made them more likely to purchase a song was 31.7%. This number was significantly higher for respondents who preferred pop or top 40 music: 41.6% of top 40 listeners claimed that awards influenced them, and 41.4% of pop listeners said that an award would influence them. A media that had a surprisingly low amount of influence was “an endorsement from a brand”: An average of only 15.4% of respondents claimed to be positively influenced by brand endorsements: Outliers to this set on the low end were those who preferred country music (9.3%) and oldies music (7.5%). The respondents who were most influenced by brand endorsements were those who preferred R&B music: 21.3% of them claimed that a brand endorsement would make them more likely to purchase a song.
Results of Discovery Habits Participants were asked, of a variety of options, “Thinking about the last song you purchased, how did you discover it?” Participants were allowed to answer multiple responses. The responses to this question produced interesting results: in certain categories, where few differences were expected, there were more variances in responses across genres. In other categories, there were few differences in the amounts that participants used the media to discover music. These results will be interpreted later in the analysis. The two discovery tools that had the greatest amount of statistically significant differences between the amount of listeners who discover music through them were also the largest two points of discovery: AM/FM radio, and YouTube. Over half of all listeners discover new music through radio; more than a quarter of them discover their new music through YouTube. There is a large amount of variance in these numbers, though: for radio, the lowest amount of discovery is for HipHop and Rap fans. Only 41% of hip-hop fans will discover music through radio, and only 44% of rap fans will find their music through the traditional medium. On the upper end of the list, 61% and 62% of Christian and Country music fans will discover their music through this medium, respectively. The other four genres considered in this analysis – Top 40, Pop, R&B and Oldies – were all close to the mean in terms of how they used different types of discovery mechanisms to discover music. While there was significant variance in the amount of impact radio has for discovery, YouTube had a higher amount of variance: It’s standard deviation was over 10, which was much higher than the majority of categories, whose standard deviations were between 1 and 4. An average of 27% of all listeners discover their music through Google’s video platform. However, for some genres of music, this number is much lower, or higher. The genre least impacted by YouTube appears to be country, where only 14% of listeners discover music through YouTube, followed by Oldies, where that number is 16%. On the other end of the spectrum is rap, where 44% of listeners use YouTube to discover new music. Other significantly higher percentages were for pop and hip-hop listeners, where 35% and 36% of listeners respectively claim to discover music through YouTube. Several genres that surprisingly had little impact on discovery patterns include some of the new digital sources that focus on recommendations. The music recommendation services and identification services, such as Shazam, iTunes Genius, Soundhound, and Rhapsody, all had very little impact. There were no statistical differences in the way that listeners of different genres used them to discover music: Furthermore, the numbers were quite small, ranging from 1% to 5%. There was one digital service, fueled by recommendations, that does have some traction in terms of discovery patterns. Pandora radio is used by, on average, 16% of listeners to discover music. However, though there is some variance in the numbers of listeners who use Pandora to discover music, the differences were not statistically significant. The only outlier to this particular set was with R&B fans: 30% of all R&B listeners actually use Pandora to discover music, which is 11% higher than the next highest category, pop music.
Another digital service that is used by many to discover music, but not in a significant way according to genre segmentation, is Facebook. An average of 17% of listeners discover a new song through Facebook, which is much higher than many other social networking sites, blogs, and web forums. However, there are little significant differences between the ways that listeners of different genres of music use Facebook to discover tunes. The significantly different numbers in the set were the low numbers of Country and Oldies music fans who use the global social networking site: less than 7% of Oldies listeners are using Facebook to discover music, while 12% of country music fans have adopted the site as a source for music discovery. Streamlining the data reveals some trends in the way that consumers discover music in store. 9% of consumers who listen to country music, 10% of those who listen to rap music, and 14% of consumers who listen to oldies music claim they discover new music from in-store displays, which is significantly more than listeners in other genres. On the opposite end, a significantly low amount of listeners of Top 40 music claim to discover music through in-store displays. Depending on the genre, only 6% of listeners, on average, discover music through a live event. The only significant different amount was with listeners who prefer Oldies music: over 11% of them claim to discover new music through live performances and concerts. The number of people who claim to discover new music from blogs or online forums is low, with only an average of 3.9% of listeners discovering their music through this new media. However, listeners of Hip-Hop and R&B music were over twice as likely to discover music from blogs than listeners who prefer other genres. Certain listeners discover music from more sources than their peers. For example, R&B listeners, on average, discover a new song from approximately 4.5 sources, while Country and Top 40 fans are likely to discover the song from 3.0 and 3.3 sources, respectively.
N O I S S U C DIS
The main motivation of behind this research is to understand the differences between individuals who prefer different genres of music, so as to build better marketing models to establish relationships with them. Specifically, because music has become a low-involvement good, it is important to think about ways to build strong relationships with consumers, so that they get more involved with music while consuming it. The discussion here will revolve around the major findings of each genre specifically, so as to understand what should now become the best course of action for marketers looking to reach consumers in any specific genre. Listeners who identify Top 40 music as their favorite genre are more influenced than their peers on outside, authoritative sources. They were more likely than other groups to agree that positive recommendations from a music review site, high popularity on iTunes, or an artist winning an award (such as a Grammy), were likely to make them purchase a record. As such, it can be argued that more marketing efforts should be driven towards major publications, particularly online ones, as they hold more influence over these listeners. Where Top 40 listeners were below the curve of their peers was in their use of in-store displays for discovering music. Given the lower age demographic of a traditional Top 40 listener, this is likely true because these consumers are shopping online more often for their records than listeners who prefer other genres. The only major findings specifically related to users who identify Christian music as their favorite genre is that they were significantly less influenced by major, authoritative sources. Contemporary Christian listeners were much less likely to be influenced by brand endorsements than their peers. Furthermore, they were much less likely to use iTunes as a source of discovery of their music than listeners of other genres. While listeners who identify Contemporary Christian music can come from a variety of demographics, the nature of the music inhibits the genre from being popular in the mainstream. As such, brands are less likely to endorse Christian music. Furthermore, because the genre is relatively traditional in terms of its production and distribution, it can be argued that iTunes is not as big a player in that market; at least not in early 2013. The major interpretation related to country music listeners in particular is that they are the most representative of the overall studies related to marketing and discovery patterns in the industry. They were never significantly more or less likely to be influenced by a marketing effort than their peers, and the only major difference between them and their peers for discovery habits was in their balance of discovery via radio or YouTube. Country music listeners scored the highest on using radio to still discover new music (63%), and the lowest on using YouTube for the same goal (14%). While there are a variety of potential explanations for this – including the demographic of country music listeners and the variety of “traditional media” that make of the infrastructure of the country music scene – the major findings of this research is that they appear to be the curve flatteners of the data, as they are the largest demographic of listeners in the United States. Listeners who identified Hip-Hop as their favorite genre were interesting in the ways that they were significantly less likely to be influenced by a variety of media than their peers. Hip-Hop listeners scored lower than their peers in three major categories of the marketing efforts question: They were less likely to be influenced by positive reviews from a music review website, high popularity on iTunes, and by a positive review on a music blog. Interestingly, though, hip-hop listeners were significantly more likely to discover new music through message boards, forums, and blogs. This finding may be attributed to the discovery culture of hip-hop listeners: Perhaps those who prefer hip-hop music would like to discover their music on their own, and not consider authority sources as the ones who pointed them to the music. As such, they discover the music on blogs, but are not necessarily influenced by other bloggers pushing them to buy a new record. In the future, hip-hop music should not necessarily be distributed to the major publicity sources, as one might do for the Top-40 genre. Similar to Polhemus’ “trickle-down/bubble-up” theory, perhaps the hip-hop community is one that would rather styles be moved from the bottom upwards, as opposed to the top down, as we see in Top 40 communities.
The interesting finding related to those who prefer Oldies music is that they were most similar to those who identified Hip-Hop as their favorite genre. Oldies listeners, in this data, were often the lowest scorers in categories: They were much less likely than their peers to be influenced to purchase because of a positive recommendation from a friend, because of a positive review from a music review website, a positive review on a blog, high popularity on iTunes, or an endorsement from a brand. They also scored significantly lower than their peers in both iTunes and YouTube as a discovery tool. There were no categories in the question about marketing where they scored higher than their peers. However, they were significantly more likely than their peers to use in-store displays as a point of discovery. Furthermore, they were the only genre that was significantly different, and in this case, more likely, in the amount that they used live events as a point of discovery. This finding is interesting because it reveals that there are still listeners who particularly prefer “old media” and marketing efforts when engaging with music. Those who identified Pop music as their favorite genre were not significantly more or less likely than their peers to use a particular media as a discovery source, aside from them being slightly more likely to rely on friends and relatives to discover music. They were also a touch more likely to use YouTube as a discovery source. However, where Pop listeners stood out from their peers were in the ways that they were influenced. Pop listeners were the most likely to be influenced to purchase music because of a high popularity rating on iTunes, and they were the most likely to be influenced by an artist winning an award, such as a Grammy. An interesting finding of the study was the ways that rap listeners and hip-hop listeners actually scored quite differently in many categories. While hip-hop listeners’ answers suggested that they were less likely to be influenced by marketing efforts, rap listeners were actually more influenced than their peers to purchase a record by positive reviews from a music review website and a high popularity rating on iTunes. Furthermore, Rap listeners were significantly more likely than their peers to discover music through in-store displays. The most interesting finding, related to rap listeners, however, was in their usage levels of radio and YouTube. There was no genre in which listeners used YouTube more than radio. However, those who listen to rap discovered music through YouTube and Radio 44% of the time: This “tie” score suggests that the industry is indeed converging towards the point where YouTube will eclipse radio as the number one source of discovery, at least in this genre. Given this particular data set’s higher weighting on older participants, those who listen to Rap are towards the lower end of the age bracket. This may stand as an illustration that, if the data were analyzed by age as well, one could see that younger respondents would be more adept with new technologies, and more likely to use new technologies, such as YouTube, to discover new music. The final group studied was those who identified R&B music as their favorite genre. R&B listeners were significantly more influenced than their peers by two media: They were more likely to buy a song because of a positive review on a music review website, as well as by an endorsement from a brand. In addition, they were part of only one of two genres that were significantly more likely than their peers to discover music on blogs or chat rooms. Interestingly, R&B listeners were 1.5-2 times as likely to use Pandora as a discovery tool than any other genre. This was one of the most visible outliers in all the data. While it is hard to explain why this might be the case, this is an interesting finding to promote to marketers. One finding that is important to consider is that not all genres prefer the same communication methods: The two largest differences have been the types of media used for communication, and the flow of the messages. There is a clear divide in genres between whether they prefer old media or new media: Genres like Hip-Hop, Rap, Pop, and Top 40 are all more likely to adopt and be influenced by new media entering the music industry. However, there are some genres, such as Country, Oldies, and Christian, that are still invested in old media and are better influenced by it. The other major difference between these samples was their preference for top-down or bottom-up communication. There was a clear spectrum for this – HipHop listeners, for example, were much more invested in media that offered democratized communication as opposed to traditional media that favored top-down messages, which was something preferred by Top 40 listeners.
Figure 1, below, summarizes the spectrum between the genres that prefer old media to new media. Figure 2, further below, illustrates the spectrum between the genres that prefer top-down communication to bottom-up communication.
Figure 1 - Spectrum of Genres: Preference for New Media vs. Old Media
Figure 2 – Spectrum of Genres: Preference for Top-Down vs. Bottom-Up Communication
S N O I S U L C CON A key takeaway from this study has been that no two genres are the same in their consumption behavior patterns of the music listeners. Though many marketers have looked at the industry with other segmentation strategies in mind, it is important that we include genre preference in there, simply because the data is so illustrative of differences across the genres. While there is aggregate data suggesting that radio is the most important discovery source, and that word of mouth marketing is the most effective, a further separation of data cases reveals that these blanket truths are less true for some genres than others. It is important to keep these differences in mind as marketers attempt to build relationships with listeners, either to encourage them to discover new music, or to influence them to buy something they are familiar with. There were some limitations to this study: First, the Nielsen data accounted for more genres than the ones analyzed in this study. More genres were not included in the study either because the sample size was questionably too small, or the discrepancies between genres is murky: For example, Rock as a genre was not considered in the scope of the research because Nielsen’s data accounted for several different types of rock music, and the genre’s expansive nature may have clouded the differences between listeners. Furthermore, there may be limitations in the way that participants classified their favorite genre. While participants were required to list their preferred genre of music, many of them identified their favorite artist to be someone not in that category. A notable example of this was when at least one participant in nearly all genres studied had listed Adele as their favorite artist. Further studies that are more specifically tailored to genre preferences should be conducted to ensure that participants are clearer about their tastes in music.
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