A research paper proposing that commercial radio regulation in the United Kingdom should undertake radio station music playlist analysis in order to ensure diversity of content within the radio marketplace, written by Grant Goddard in January 2003.

1. TOOLS FOR RADIO CONTENT
REGULATION #1: PLAYLIST
DIVERSITY ANALYSIS
by
GRANT GODDARD
www.grantgoddard.co.uk
January 2003

2. “It is worrying that many local radio stations play the
same music all day every day.”
Rosemary McKenna MP, House of Commons
Communications Bill debate.1
“If a format is good enough for one owner, it should
be good enough for another.”
Commercial Radio Companies Association Response to
the Draft Communications Bill.2
“Examining performance of media industries ought
to be the ultimate step in media economics
analysis….. We need to foster a connection between
media economics and the longtime problems of how
to best promote diversity, how best to promote
localism, or how best to promote some other value
we might hold dear.”
Professor Douglas Gomery, University of Maryland.3
“One possibility which suggests itself is whether
there already exists a body of data within the
industry, which might be made available to
Ofcom/the Radio Authority, in order to make develop
[sic] a more useful data base, which can in turn be
used to inform regulation of the industry…. quite a
lot would be gained simply by flushing information
about station performances into the open.”
Professor Ian Hargreaves, Radio Authority Strategy
Conference.4
1
Hansard, House of Commons Debates, 3 December 2002, Column 843
“Commercial Radio Companies Association Response to the Draft Communications Bill,” CRCA, 8 February 2002
Gomery, Douglas, “Ownership Policies, Diversity & Localism,” photocopied working paper. Douglas Gomery is a
Professor of Media Economics and History at the University of Maryland, and author of eleven books, two of which
earned national book awards. "Who Owns The Media?" (with Benjamin Compaine) recently won the Association in
Education in Journalism and Mass Communication’s Picard prize for best media economics book of the year 2000,
while "Shared Pleasures" won Lincoln Center’s best television and film book of 1992. Gomery’s numerous articles
have appeared in journals around the world, and for five years he was author of the column "The Economics of
Television" for the "American Journalism Review."
4
Hargreaves, Ian, “Radio And The Quality Test,” Confidential paper presented at the Radio Authority Strategy
Conference, 11-12 April 2002
2
3
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 2

3. BACKGROUND
The Radio Authority uses ‘formats’ as a regulatory proxy for content diversity in
local markets. These are not ‘formats’ in the American sense of the word that
merely describes a radio station by its dominant programming content. For
example, Capital FM London’s ‘format’, as mandated by the Radio Authority,
is:
CAPITAL FM IS A CONTEMPORARY/CHART MUSICLED SERVICE FOR UNDER 40'S IN LONDON. Music
programming will be predominantly (up to 100%) current
chart hits, new releases, and hits less than 10 years old.
No more than 20% will be hits over ten years old.
Specialist programmes for the target audience, which
complement the main music mix, may be broadcast in
non-daytime for up to 15 hours a week. Speech must
account for at least 15% daytime weekdays (10% at
weekends) or 5% non-daytime. News bulletins containing
local/regional news must be broadcast at least during
peaktime weekdays and breakfast at weekends. National
news will feature at other times. Other information,
including entertainment news, travel news, whats-ons,
leisure activities and so on should be balanced across
each day.5
But such format definitions, however detailed, can serve only as proxies for
content diversity in a radio marketplace. Empirical tools are needed that can
measure the actual diversity achieved in a specific market comprising any
number of competing or complimentary radio stations.
Economic analysis of product diversity in radio markets was pioneered in 1952
by American economist Peter Steiner.6 He assumed a thoroughly active
listenership in which audience size was determined by the availability of a
preferred programme type. In Steiner's model, when your favourite programme
type is not on, neither is your radio.7 If there is a large enough audience for a
particular programme type, then two or more competing stations will split that
audience by offering similar programmes of that type. This process will
continue until the audience for that programme type has been divided into
small enough segments that it makes more sense for the next competitor to
counter-programme with a completely different type of programme.
Consequently, where only a few competitors exist (as is the case in most UK
local markets), similar programmes tend to be offered by stations. Only as the
number of competitors increases does stations' programming become more
differentiated.
5
from Radio Authority website http://www.radioauthority.org.uk
Steiner, Peter O, “Program Patterns & The Workability Of Competition In Radio Broadcasting,” Quarterly Journal Of
Economics, 1952, #66 (2)
7
Webster, James G & Phalen, Patricia F & Lichty, Lawrence W, "Ratings Analysis," Lawrence Erlbaum Associates,
New Jersey, 2000, p 165
6
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 3

4. Steiner asserted that a monopolist station owner would maximise product
diversity and economic welfare in a broadcast market because a monopolist
would want to capture every single listener and would therefore not duplicate
programming across its co-owned stations. Or, in plain English, “some of the
time you really get more diversity, more variety, if somebody controls more
than one channel.”8 But Steiner’s findings rely on strict assumptions about
consumer preferences that may not always hold up in the real world. If
listeners pay for programming, even implicitly by sitting through
advertisements, Steiner’s conclusions may no longer hold. Or if programmes of
the same type, broadcast by competing stations within a radio market, are
perfect substitutes to listeners, then one of those competitors may eventually
move to broadcast a different type of programming, so as not to compete for
advertising revenue solely on the basis of price.9
Because this economic theory does not provide us with a clear relationship
between diversity of station ownership and diversity of programming within a
market, the issue of content diversity analysis has increasingly attracted the
attention of empirical study in the US.
8
Federal Communication Commission, “Roundtable Discussion On Media Ownership Policies,” 29 October 2001, MM
Docket Nos. 01-235, 96-197, 92-264, 94-150, 87-514 & CS Docket Nos. 98-92 and 96-85, page 47
Becker, Gary & Murphy, Kevin, “A Simple Theory Of Advertising As A Good Or A Bad,” Quarterly Journal Of
Economics, 1993, #108 (4)
Gabszewicz, J, & Laussel, Didier & Sonnac, Nathalie, “TV Broadcasting Competition & Advertising,” CORE Discussion
Paper 2000/6, 1999
Anderson, Simon & Coate, Stephen, “Market Provision Of Public Goods: The Case For Broadcasting,” NBER Working
Paper #W7513, 2000
9
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 4

5. PLAYLIST DIVERSITY ANALYSIS IN THE U.S.
“There certainly is a lot of format diversity across the US.”
Conclusion of Wall Street report on US radio industry.10
A recent study (commissioned by the FCC) examined and compared the song
playlists of 288 radio stations with the highest ratings in the largest US
markets, as reported by Radio & Records magazine. The analysis uses a
technique proposed by Peter Alexander11 that breaks down products into a
bundle of characteristics that are given a numerical value of one if that
characteristic is present or a numerical value of zero if that characteristic is not
present. Each song played by a radio station is treated as an individual product
characteristic. The songs played on two stations are compared and a measure
of diversity is calculated according to how many of those songs are identical.12
If two stations each play 10 songs and none of those songs are the same, the
measure of diversity equals 10. If two stations play exactly the same songs,
the measure of diversity is 0. If two stations have only one song in common,
the measure of diversity equals 9. The study analysed the top ten playlisted
songs of stations within the same format, but in different markets, in March
2001 and produced the resulting measures of diversity:
TABLE ONE: AVERAGE DISTANCE WITHIN SAME FORMAT OF US RADIO STATIONS13
Format:
Distance:
Adult Contemporary
Active Rock
Adult Alternative
Alternative
CHR Pop
CHR Rhythm
Country
Hot Adult Contemporary
Jazz
Rock
Urban Adult Contemporary
Urban
5.53
6.02
7.06
6.68
6.00
6.79
5.94
6.49
6.39
6.94
7.21
6.68
where distance = 10, no songs are the same; where
distance = 0, all songs are the same (from the 10
most played songs on national sample of 288 US
radio stations).
To the UK observer, what might seem remarkable is the homogeneity of songs
between hundreds of radio stations operating the same format, but located in
markets separated by thousands of kilometres within the continental US. The
evidence that more than four out of the top ten songs are exactly the same on
adult contemporary stations in, say, New York, Nashville and Los Angeles
10
Miller, Victor B & Ensley, Christopher B & Young, Tracy B, “Format Diversity: More Or Less?”, Bear Stearns & Co
Inc, November 4 2002, research paper
11
Alexander, Peter, “Product Variety & Market Structure: A New Measure & Simple Test,” Journal Of Economic
Behavior & Organisation, 1997, #32
12
Williams, George & Brown, Keith & Alexander, Peter, “Radio Market Structure & Music Diversity,” FCC Media
Bureau Staff Research Paper #2002-9, September 2002
13
ibid., Table 2
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 5

6. seems to put the lie to the notion that commercial music radio in the US is
locally-orientated. Much of this homogeneity is due to the power that the
record companies and music trade magazines wield through their huge
promotion and advertising budgets. Stations tend to slavishly follow the
playlists of similarly formatted, larger stations in bigger markets. The smaller
the market, the less likely a station can afford to commission its own regular
music research, therefore the more likely it is simply to follow the music
playlists of bigger city stations within the same format. (National, cabledelivered music television channels such as MTV, BET and CMT also have an
important impact on tastes in local markets.)
In order to determine whether the huge amount of ownership consolidation that
has taken place in the US radio industry since the 1996 Telecommunications
Act has impacted the level of format homogeneity, the researchers compiled
the same data for March 1996. They found that station playlists had become
very slightly less diverse between March 1996 and March 2001, but only by an
almost insignificant factor of 2.4%. Playlist diversity showed a marginal decline
in nine of the twelve formats analysed, but it is impossible to attribute such
changes solely to radio industry consolidation. The declining fortunes of the
music industry have caused major record companies to release significantly
less product than they did in 1996, so there are now fewer newly issued music
titles from which stations can select their playlists.14
In addition to their analysis of stations within exactly the same format, the US
researchers also compared playlists between stations in pairs of different, but
closely related, formats. Again, the data was based upon the ten most played
songs listed in Radio & Records magazine in March 2001. The results were:
TABLE TWO: AVERAGE DISTANCE ACROSS PAIRS OF SELECTED FORMATS OF US
RADIO STATIONS15
Format One:
Format Two:
Distance:
Adult Contemporary
Active Rock
Active Rock
Adult Alternative
Adult Alternative
Adult Alternative
Alternative
Alternative
CHR Pop
CHR Pop
CHR Rhythm
CHR Rhythm
Urban AC
Hot Adult Contemporary
Alternative
Rock
Alternative
CHR Pop
Hot Adult Contemporary
CHR Pop
Rock
CHR Rhythm
Hot Adult Contemporary
Urban AC
Urban
Urban
9.06
7.40
7.30
9.37
9.47
8.96
9.44
8.33
8.49
8.11
9.41
7.85
8.61
where distance=10, no songs are the same; where distance=0, all songs are the same
(from the 10 most played songs on national sample of 288 US radio stations).
For those of us (anoraks) in the UK who have long tried to discern the subtle
differences between the 254 radio formats16 that exist in the US [see Appendix
14
ibid., page 11
ibid., Table 3
16
BIA – Investing In Radio, Fall 2001
15
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 6

7. A], these results are interesting. If, on average, two out of the ten most played
songs on CHR [Contemporary Hit Radio] Rhythm stations and Urban stations
are exactly the same, how significant are the differences between these two
formats to the average radio listener? In the US, niche media markets are
segregated by race, as much as they are by age or gender. The greatest
difference between CHR Rhythm stations and Urban stations is that the former
target white listeners whilst the latter target black listeners.17 It is likely that the
former stations employ white DJs and target mainstream advertisers, whilst the
latter employ black DJs and target black and minority advertisers. What is not
so obvious to their segregated audiences is that the stations in these two
formats overlap musically more than their two sets of listeners realise. The two
stations could even be co-owned since most black-targeted stations in the US
are white-owned.18
In a wider context, these results throw significant light on a number of other
recent research studies in the US that argue, to differing degrees, that
consolidation either has not changed or has increased the number of different
formats available in local markets since industry consolidation started in
1996.19 If many of the 254 radio formats designated in the US have some
similarities in musical content to each other, any research that simply uses
format names (rather than playlists) to demonstrate an increase in the number
of different formats available in each local market becomes a rather academic
exercise. In this post-consolidation era, it is natural that an owner of multiple
stations in the same market will move to adopt different format names, in order
to attract to each station different listeners and to sell advertising to different
sets of buyers. But the results outlined above suggest that some of the songs
on stations' playlists will be similar, even if one station labels itself "CHR
Rhythm" and the other "Urban."
Another recent research report by the Future of Music Coalition20 analysed
playlist data from US trade magazines Radio & Records and Billboard and
concluded that “formats with different names have similar playlists” with
evidence of as much as a 76% overlap. Its assertion that there exists
“considerable format homogeneity” within local markets in the US elicited a
frosty response from the radio industry. A spokesperson for Clear Channel,
which owns 1200 US radio stations, argued that radio stations offer different
listening experiences even if they share the same songs. Diane Warren, Clear
Channel VP Communications, explained:
“The music might cross over, but the combination of the
information, the community service, the commercials, the
17
The word “urban” is a US radio industry euphemism for “black”
Waldfogel, Joel, “Comments On Consolidation & Localism: Prepared For FCC Roundtable: Oct 29 2001,” 2001,
photocopied working paper
19
Miller, Victor B & Ensley, Christopher B & Young, Tracy B, “Format Diversity: More Or Less?”, Bear Stearns & Co
Inc, November 4 2002, research paper
“Has Format Diversity Continued To Increase?,” report by BIA Financial Network, 5 June 2002
Fratrik, Mark R, “State Of The Radio Industry: What Is Going On With Radio Formats?,” report by BIA Financial
Network, 2001
20
DiCola, Peter & Thomson, Kristin, “Radio Deregulation: Has It Served Citizens And Musicians?,” report by the
Future of Music Coalition, 18 November 2002
18
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 7

8. promotions, all these elements make up the total radio
station.”21
In a world where stations’ own adopted format labels may not reveal any
significant information about their musical content (relative to their
competitors'), broadcast regulators need to exercise extreme caution not to
take format information only at face value. In the UK context, the Radio
Authority's legislative ability to prescribe, for example, a CHR station, an AC
[Adult Contemporary] station and a dance music station within the same local
radio market is no guarantee that real content diversity will exist within that
market for listeners.
Analysis tools need to be designed and implemented in radio regulation that
measure ‘exposure diversity,’ as much as ‘content diversity.’ ‘Content diversity’
measures the diversity of viewpoints, diversity of programme types (formats)
and demographic diversity that exist in media content available within a given
broadcast market. Whereas ‘exposure diversity’ refers to the diversity of
sources that are actually consumed by the audience, which may be very
different from the diversity of content or sources that are made available to
them. The ‘free market’ model of broadcast regulation assumes that audiences
who are provided with a diversity of content options automatically consume a
diversity of content. This is not necessarily the case. The mere provision of a
radio service within an area is no guarantee that it will attract an audience.22
A local radio station might choose to air a minority interest programme at a
broadcast hour it knows has minimal ratings potential. The station can
(justifiably) claim to have made an effort to achieve ‘content diversity’ in its
output. But an analysis in more critical terms of ‘exposure diversity’ would
award it ‘nul points’ if the programme was hidden away in a quiet corner of the
schedule and never actively promoted.
Similarly with music content, a radio station might (justifiably) claim to achieve
‘content diversity’ by playing a wide variety of songs from an extensive music
library. But if, in its peak hours, that station is simply playing the same current
chart hits over and over again, such diversity will be lost on the average
listener. Offering such diversity at times when very few people are available to
listen does not achieve ‘exposure diversity.’
21
Sullivan, Andy, “UPDATE 1 – Radio Regulation No Hit For Listeners – Report,” Reuters/Variety news story
accessed on the internet at Yahoo! News, 19 November 2002
22
Napoli, Philip M, “Diversity & Localism: A Policy Analysis Perspective,” Graduate School of Business Administration,
Fordham University, photocopied working paper.
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 8

9. PLAYLIST DIVERSITY ANALYSIS IN THE UK
"While there can be difficulty in practice in establishing
and maintaining distinctions between various mainstream
popular music formats, the [Radio] Authority believes that
there is a continuing need to regulate for diversity in
some way, lest all services tend towards a bland centre
and a limited focus."
Radio Authority document ‘Radio Regulation For The
21st Century.’23
Music radio stations publish playlists on a weekly basis that comprise a list of
the new releases that will be played on that station in the forthcoming week.
Often these playlists are divided into categories. ‘A-list’ songs receive the most
regular and high-profile exposure on a station and represent the station’s most
popular songs and/or the biggest current hits. ‘B-list’ songs receive less
exposure on the station and represent up-and-coming artists and songs. ‘C-list’
songs receive irregular exposure in non-peak periods.
However, a station’s published playlist may not be synchronous with the songs
actually played on that radio station during the same week. This can be due to
a number of reasons. A station may add a particularly ‘hot’ song (for example,
a new Robbie Williams single) in the middle of a week, as soon as it is
supplied to the station. Or, the parameters that have been set within the
station’s music scheduling software may force the play of some B-list songs on
more occasions in a week than some songs on the A-list. Or, a station may
add a song to the playlist due to pressure from the promotion department of a
major record company to do so, but then not actually play the song on-air.24
Because of the ‘reality gap’ known to exist between the list of songs that a
station says it is playing (the playlist) and the songs it actually plays, the music
industry has long subscribed to monitoring services that provide song-by-song
lists of every programme broadcast on the largest UK radio stations. Initially,
this was achieved by employing a team of staff (mainly women) in a London
office who listened to stations’ output in real time or on time-shifted recordings,
and wrote down the song details.25 A decade ago, electronic ‘fingerprinting’
was introduced which allowed computers to monitor a station’s output and
recognise a song by analysing a tiny sample of music within it, which is then
matched against one of many thousand song samples digitally stored in its
memory.
23
"Radio Regulation For The 21st Century - Submission To The DCMS/DTI," Radio Authority, June 2000, section
7.11, p 23
24
An aside. In 1980 I was responsible for compiling the playlist at Metro Radio. When I refused to add the Queen song
“Flash Gordon” to the playlist, EMI Records boycotted the radio station and refused to supply it with free promotional
copies of its new releases. The boycott lasted several months during which time, when I needed copies of other EMI
Records releases to add to the playlist, I had to drive to the nearest record shop and purchase copies over the
counter. Life would have been easier if I had simply added the Queen song to the published playlist but not scheduled
it within programmes. EMI Records, at the other end of the country, would never have known that the song was not
being played.
25
Sham Tracking was the most successful company in London doing this painstaking monitoring work.
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 9

10. In the UK, a company called Music Control26 monitors a growing number of
radio stations across the country and analyses the information for paid
subscribers to its service. The data is also used in music industry charts
published weekly in the trade magazine ‘Music Week.’ Music Control goes
much further than merely providing a list of songs played by a particular radio
station in any given week. Their data is weighted according to the RAJAR
ratings achieved by that radio station, both in absolute terms (i.e. does the
station have two million listeners rather than two thousand) and in relative
terms (i.e. does the station’s breakfast show have twenty times the number of
listeners to the station’s late-night show).
Music Control compiles a weekly airplay chart for each of the stations it
electronically monitors, with songs ranked by their total number of ‘audience
impressions,’ rather than simply by the number of times they were played. This
is similar to the system used in radio advertising, where the success of a
campaign can be quantified according to the number of ‘gross impressions’ –
the theoretical number of listeners reached across the whole of an
advertisement’s life, taking account of scheduling across different dayparts and
different programmes.
The weekly airplay charts produced by Music Control provide us with excellent
data for examining the issue of ‘exposure diversity,’ because they already
incorporate audience weighting using the most recent RAJAR survey data. For
example, a particular song would have to be played more than 100 times
during a station’s overnight programmes to achieve equivalent weight within
that station’s airplay chart to a single play in its breakfast show (if the ratio of
breakfast to overnight listeners were 100 to 1). Such weightings produce an
airplay chart that more accurately reflects the impression that the ‘average’
listener to a station will come away with of that station’s music content. If a
station schedules a narrower selection of songs in its highly rated breakfast
show than it plays during the remainder of the day, then that narrow selection
of songs will tend to dominate the station’s Music Control airplay chart.
As an example, the top five songs in BBC Radio 1’s Music Control airplay chart
for week 48, from 24 to 30 November 2002 inclusive, were:
TABLE THREE: BBC RADIO 1 AIRPLAY CHART FOR WEEK 48 OF 200227
ranking
artist – title
no. of plays no. of points
1
Jennifer Lopez – Jenny From The Block
29
25.243
2
Christina Aguilera – Dirrty
31
25.190
3
DJ Sammy & Yanou – Heaven
30
24.843
4
Ms Dynamite – Put Him Out
31
24.689
5
Liam Lynch – United States Of Whatever
31
24.591
26
"Music Control monitors music broadcast on over 700 radio and television stations across 18 countries worldwide.
The music industry standard, we provide a wide range of reports and charts, from bespoke analyses of individual titles
to radio station profiles to company market share performance. Music Control uses a unique patented electronic
fingerprinting technology 'Medicor' developed for the sole use of Music Control and the direct specific needs of the
music industry. Reports are available via our closed user client website in real time, daily, weekly or any other period
requested. Over 1000 record companies worldwide use the Music Control services. We are IFPI recognised and are a
partner company of BDS the North American Music Monitors. Music Control is part of the Media Control Group,
Europe's leading music monitors for over 20 years."
27
2002 © Music Control, proprietary research
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 10

11. This table demonstrates the ranking by Music Control’s own system of ‘points’
rather than simply by the total number of plays achieved throughout the
station’s output during the whole week. The top song was played fewer times
than other songs, but its plays were in hours with higher audiences than those
other songs.
This renders the Music Control airplay charts a powerful tool for comparative
analysis of stations' music formats, since the charts accurately reflect:



the songs actually played on-air, rather than the songs the station includes
in its published playlist, or the songs produced by the station’s scheduling
software (for example, its RCS Selector log);
the ratings skew of a particular station, so that if its evening show is the
most popular segment of its broadcast day (as sometimes happens with
dance music formats), the airplay chart accurately factors that
characteristic;
the relative weight of stations’ audience sizes within the whole UK radio
market, since the points allocated to songs in each station’s chart reflect
the relative size of each station’s listenership.
The Music Control airplay charts of two different radio stations can be
compared quantitatively in different ways:
METHOD ONE. A simple method of comparison is to count the number of
songs that are the same within two stations’ playlists. This is the basis of the
American research referred to above. However, this methodology does not
weight the data according to a particular song’s prominence within a station’s
playlist. So, for example, two stations may have the same song on both their
playlists but, on station A, that song might be ranked #1 whilst, on Station B, it
might be ranked #20. The average listener would be unlikely to hear the
similarity between those two stations, as the song would be much less
frequently played on station B than on station A. Whereas, a listener would
definitely notice the similarity if a song were ranked #1 on both stations. In the
American research, both these situations would be analysed the same way
with the same result.
METHOD TWO. Additional data can be compiled that shows the difference
between two or more stations’ playlist systems and music scheduling. So, for
example, an analysis of airplay charts from Week 48 (24 Nov to 30 Nov 2002
inclusive) for music stations heard in the London market reveals the following
characteristics [calculations in Appendix E]:
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 11

12. TABLE FOUR: ANALYSIS OF AIRPLAY CHARTS FOR RADIO STATIONS MONITORED IN
LONDON RADIO MARKET FOR WEEK 48 200228
BBC BBC Capital KISS Heart Magic Choice Virgin XFM BBC
R1 R2 FM
FM 106.2 105.4 FM
London
474 251
690 800
449
162
300 373 540
53
aggregate no. of
plays/week of Top
20 songs
31
21
59
54
42
26
22
27 41
9
highest number of
plays/week of any
song
16
6
17
26
8
6
7
13 19
1
lowest number of
plays/week of any
song
24
13
35
40
22
8
15
19 27
3
mean plays per
week of all Top 20
songs
22
11
34
40
16
7
16
18 25
2
median plays per
week of all Top 20
songs
15
15
42
28
34
20
15
14 22
8
range of no. of
plays/week within
Top 20 songs
5
5
13
10
13
4
4
4
7
2
standard
deviation of
plays/week within
Top 20 songs
The huge differences demonstrated here between stations’ top twenty airplay
charts can be the result of many contributory factors:





some stations have no commercials (BBC);
some stations have more speech content than music (BBC London);
some stations operate a playlist during the day but have specialist music
shows at night that include almost no playlisted songs (Choice FM, BBC
Radio One);
some stations operate short playlists whose songs are played very
frequently (KISS FM);
some stations operate long playlists whose songs are played relatively
infrequently (Magic 105.4).
As a result of these varying station characteristics, it is difficult to execute any
useful analysis from the data alone of the number of plays attributed to each
song within a station’s playlist. The data only serves to offer us a vague
impression of the different playlist regimes and programming policies adopted
by different stations.
METHOD THREE. A system of playlist comparative analysis is required that
will examine the number of identical songs within the playlists of two or more
radio stations, but will weight those similarities to the comparable degree that
they impact on the typical listener. When a listener makes the oft-heard
comment that “radio station X always play the same songs,” they are
28
Appendix E, 2002 © Music Control, proprietary research
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 12

13. (unknowingly) referring to those songs at the very top of that station’s airplay
list, because these are the very songs to which they are most exposed.
Appendix B contains an Excel spreadsheet that demonstrates the detailed
mathematical basis of a new index created to measure the degree of
diversity/homogeneity between airplay charts of two radio stations. What
follows is a shorthand explanation of how that index is calculated from airplay
chart data collected and published by Music Control. (The mathematicallyminded reader will benefit from consulting Appendix B whilst reading the
following account.)
A radio station’s twenty most played songs are assigned points according to
the ranking within its airplay chart. The #1 song is allocated 20 points, the #2
song allocated 19 points, the #3 song allocated 18 points, in sequence down to
the #20 song allocated only 1 point. Where two or more songs are tied at an
equal placing within the chart, each is allocated an equal share of their total
points.
For example, taking the BBC Radio One data from Table Three as an
example, points are allocated thus:
TABLE FIVE: POINTS ALLOCATED TO RANKINGS WITHIN BBC RADIO 1 AIRPLAY
CHART FOR WEEK 48 OF 200229
ranking
artist – title
no. of points
1
Jennifer Lopez – Jenny From The Block
20
2
Christina Aguilera – Dirrty
19
3
DJ Sammy & Yanou – Heaven
18
4
Ms Dynamite – Put Him Out
17
5
Liam Lynch – United States Of Whatever
16
A quantitative comparison can now be made between two radio stations’
airplay charts according to the points allocated to each song in each station’s
Top Twenty. Where a particular song appears in both stations' playlists, the
points allocated to the song for Station B are subtracted from the points
allocated to the same song for Station A. Any resulting minus sign is ignored
(in mathematical terms, the result is expressed as an ‘absolute’), so that is
does not matter which station is selected as Station A or which station is
selected as Station B. There are three possible outcomes when comparing the
points accrued by a song within two airplay charts:



29
a song is played on Station A but not on Station B, the resultant points will
be the points allocated to the song on Station A (= points for Station A
minus zero);
a song is played on Station B but not on Station A, the resultant points will
be the points allocated to the song on Station B (= points for Station B
minus zero);
a song is played on Station A and on Station B, the resultant points will be
that song’s points on Station A minus that song’s points on Station B (or
vice versa) expressed as an absolute value.
2002 © Music Control, proprietary research
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 13

14. Proceed to sum the total number of resultant points. If Station A and Station B
have absolutely identical playlists, the total number of points will be 0. If there
were no identical songs on Station A and Station B, the total number of points
will be 210. Stations that share some songs will accrue a number of points
between 0 and 210, according to the degree of similarity of their playlists. The
number of points depends not only upon a song being on both stations’
playlists but, importantly, on the relative proximity of the song within the two
playlists. Thus, in economic terms, there are three distinct ‘distance measures
of diversity’30 simultaneously incorporated into a single ‘score’:



the number of identical songs that appear in both station’s playlists;
the ranking of a song within a station’s airplay chart;
the distance between the rankings of the same song within two stations’
playlists.
The diversity measure score (with a value between 0 and 210) is better
understood by conversion to a handy user-friendly diversity/homogeneity
INDEX (see Appendix A for the conversion process) that takes a value from 0
to 100 which signifies:



value 0 if no songs on two station playlists are the same;
value 100 if all songs on two stations’ playlists are identical in both name
and rank;
value between 0 and 100 if some songs appear on both station playlists.
30
Alexander, Peter, “Product Variety & Market Structure: A New Measure & Simple Test,” Journal Of Economic
Behavior & Organisation, 1997, #32
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 14

15. THE PLAYLIST DIVERSITY INDEX
This INDEX can be used to examine and quantify radio station playlists in a
number of different situations:
1.
Playlist Diversity Analysis – within a local market
As an example of how this tool can be used, the airplay charts of music
stations available on AM/FM radio in the London market for week 48 of 2002
(24 to 30 November inclusive) were analysed. [BBC London is excluded in all
subsequent comparisons because it plays such a minimal amount of music
that analysis yields no useful information.] [Calculations are detailed in
Appendix C.]
TABLE SIX: PLAYLIST DIVERSITY INDICES FOR LONDON MUSIC RADIO STATIONS31
BBC BBC Capital KISS
Heart
Magic
Choice Virgin XFM
R1 R2
FM
FM
106.2
105.4
FM
11
37
46
6
0
23
16
18
BBC R1
11
29
12
9
0
0
9
0
BBC R2
37
29
39
7
0
13
11
0
Capital FM
46
12
39
12
0
20
0
1
KISS FM
6
9
7
12
4
0
0
0
Heart 106.2
0
0
0
0
4
0
0
0
Magic 105.4
23
0
13
20
0
0
0
3
Choice FM
16
9
11
0
0
0
0
5
Virgin
18
0
0
1
0
0
3
5
XFM
where 0 means no identical songs and 100 means all songs and playlist rankings are identical.
(The table is identical on either side of the grey axis.)
The two stations that demonstrate the greatest similarity in playlists are BBC
Radio One and KISS FM with an index of 46. It can be inferred that the
average listener to both stations will probably come away with the impression
that about half of the music they hear is identical on both stations. This is
confirmed by a less mathematical look at their respective playlists. A
comparison of their playlists shows that 11 songs are common to both stations.
Both stations have an identical #1 song (Jennifer Lopez). 7 out of the top 8
songs on KISS FM appear on the Radio One playlist. 4 out of the top 8 songs
on Radio One appear on the KISS FM playlist.
Pairs of stations that have absolutely no similarity in their playlists (index = 0)
are:
BBC Radio One and:
BBC Radio Two and:
Capital FM and:
KISS FM and:
Heart 106.2 and:
Magic 105.4 and:
Virgin and:
Choice FM and:
XFM:
31
Magic 105.4
Magic 105.4, Choice FM, XFM
Magic 105.4, XFM (co-owned)
Magic 105.4 (co-owned), Virgin
Choice FM, Virgin, XFM
BBC Radio One, BBC Radio Two, Capital FM, KISS FM (coowned), Choice FM, Virgin, XFM
KISS FM, Heart 106.2, Magic 105.4, Choice FM
BBC Radio Two, Heart 106.2, Magic 105.4, Virgin
BBC Radio Two, Capital FM (co-owned), Heart 106.2, Magic
105.4
Appendix D
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 15

16. 2.
Playlist Diversity Analysis – between individual stations
The playlists and Table Six above can also be examined to analyse the
performance of individual stations:
CAPITAL FM
Roughly one third similarity with BBC Radio One, BBC Radio Two and KISS
FM. These indices (37, 29 and 39 respectively) are too close to each other not
to make the inference that the station’s music is being programmed
defensively against its main three competitors in the London market. This
might also explain why the station can seem to lack an effective ‘station sound’
since it is rather attempting to be a hybrid of three other stations.
KISS FM
The index of 46 with BBC Radio One speaks volumes about the latter’s
desperation to be a dance-orientated station, rather than a youth station
embracing multiple music genres. The index of 12 with BBC Radio Two is
more surprising. The appearance of Sugababes, Blue, Sophie Ellis Bextor and
Dannii Minogue within KISS FM’s Top 20 playlist demonstrates its
determination to be more than a dance music station.
HEART 106.2
Heart 106.2’s greatest similarity is with KISS (though the index is only 12)! A
look at Heart’s playlist reveals the reason. The ten most played songs (in
descending order) were by: Westlife, Nelly, Liberty X, Blue, Atomic Kitten,
Atomic Kitten (again), Liberty X, Kylie Minogue, Darren Hayes and Anastacia.
This is somewhat surprising, given that the station’s ‘format’ as agreed with the
Radio Authority states:
“The music will be melodic or soft adult contemporary
and will exclude the extremes of dance, rap, teenage
pop, indie and heavy rock.” [emphasis added]32
Heart 106.2 is moving towards teenage pop from one direction, just as KISS
FM too is edging towards teenage pop from the opposite direction.
MAGIC 105.4
The analysis shows that Magic is the most distinctive music station heard in
London by a long way, playing songs not duplicated on other stations.
32
from Radio Authority website http://www.radioauthority.org.uk. To ensure that this was not simply a one-week
aberration, data for previous weeks was checked. The ten most played records (in descending order) on Heart 106.2
for previous weeks were by these artists:
Week 47 (17 to 23 November 2002) – Liberty X, Nelly, Westlife, Blue, Atomic Kitten, Liberty X, Atomic Kitten, Shaggy,
Kylie Minogue and Emma Bunton;
Week 46 (10 to 16 November 2002) – Nelly, Blue, Liberty X, Atomic Kitten, Westlife, Atomic Kitten, Liberty X, Emma
Bunton, Enrique Iglesias and Nelly Furtado;
Week 45 (3 to 9 November 2002) – Atomic Kitten, Blue, Liberty X, Nelly, Westlife, Liberty X, Atomic Kitten, Darren
Hayes, Anastacia and Shaggy.
Week 44 (27 October to 2 November 2002) – Liberty X, Blue, Atomic Kitten, Nelly, Sophie Ellis Bextor, Westlife,
Sugababes, Liberty X, Darren Hayes and Kylie Minogue.
Week 43 (20 to 26 October 2002) – Atomic Kitten, Liberty X, Blue, Liberty X, Sugababes, Westlife, Darren Hayes,
Nelly Furtado, Shaggy, Anastacia.
Week 42 (13 to 19 October 2002) – Sugababes, Liberty X, Blue, Westlife, Liberty X, Atomic Kitten, Darren Hayes,
Anastacia, Shaggy, S Club 7.
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 16

17. CHOICE FM
Choice’s similarity with KISS FM (index = 20) is not unexpected, given that
both major in black music. Choice’s similarity with BBC Radio One (index = 23)
is another reflection of the latter’s current preoccupation with black music and
dance music.
VIRGIN and XFM
These two rock stations prove to be very distinctive from each other (which is
how it should be) and equally distinctive from other commercial stations in the
market.
3.
Playlist Diversity Analysis – within radio ownership groups
"[The Radio Authority] also believe it is important to take
into account the impact of concentration of ownership on
the recorded music industry, with central playlists
reducing the opportunities for a diversity of music to find
broadcast time, and the risk of undue influence being
exercised by a very limited number of people."
Radio Authority document ‘Radio Regulation For The
21st Century.’33
“Red Dragon FM in Wales and its sister station in
London, Capital FM, are virtually identical in terms of their
play list…. So is it not essential to make sure that we jig
the market a little bit? It will not do it all on its own.”
Chris Bryant MP for Rhondda, House of Commons
Standing Committee on Communications Bill.34
The same analysis can be made of stations with common ownership. This
helps to illuminate the issue of whether a radio group is scheduling the music
locally or whether a ‘network playlist’ is in operation. Examining the data for
stations owned by Capital Radio for the week 48 of 2002 (24 to 30 November
inclusive) gives the following results [calculations are detailed in Appendix F]:
33
"Radio Regulation For The 21st Century - Submission To The DCMS/DTI," Radio Authority, June 2000, section
7.13, p 24
34
Communications Bill, Standing Committee E, 28 January 2003, Column Number 739
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 17

18. TABLE SEVEN: PLAYLIST DIVERSITY INDICES FOR CAPITAL RADIO GROUP RADIO
STATIONS35
Century Century Capital Beat Invicta Power Southern BRMB Fox Ocean Red
100-102 105.4
FM
106 FM
FM
FM
FM
FM FM
Dragon
FM
81
28 30
27
20
17
27 29
56
25
Century
100-102
81
26 27
25
18
17
24 27
52
23
Century
105.4
28
26
31
80
80
76
90 82
22
84
Capital
FM
30
27
31
39
38
34
33 36
27
38
Beat 106
27
25
80 39
82
81
84 88
21
90
Invicta
FM
20
18
80 38
82
82
87 82
18
85
Power
FM
17
17
76 34
81
82
77 80
14
82
Southern
FM
27
24
90 33
84
87
77
84
21
88
BRMB
FM
29
27
82 36
88
82
80
84
24
85
Fox FM
56
52
22 27
21
18
14
21 24
21
Ocean
FM
25
23
84 38
90
85
82
88 85
21
Red
Dragon
FM
where 0 means no identical songs and 100 means all songs and playlist rankings are identical.
(The table is identical on either side of the grey axis.)
The conclusions that can be drawn from these data are:




Century 100-102 (NorthEast England) and Century 105.4 (NorthWest
England) operate with almost identical music scheduling;
Capital FM (London), Invicta FM (Kent), Power (Hampshire), Southern FM
(Sussex), BRMB FM (Birmingham), Fox FM (Oxfordshire) and Red Dragon
FM (Cardiff & Newport) all operate with almost identical music scheduling;
Power FM and Ocean FM, co-owned stations in the same local market,
score a common index of only 18, which makes them quite distinct from
each other in music terms;
Beat 106 appears to be a hybrid of Century and of Capital FM in equal
proportions (indices of 30 and 31 respectively). The remaining third, one
hopes, comprises the “fresh dynamic mix of new rock and dance music”
definition in its Radio Authority ‘format.’36
An analysis of stations across one radio group would also reveal whether that
group was exerting market influence unduly in the UK market. For example,
you could see if Capital Radio was using its stations to prominently promote
releases on its Wildstar record label. Or you could see if the Chrysalis Group
were using its stations to excessively promote its own music copyrights. In
such cases, Music Control’s airplay charts would easily reveal the impact not
just of a station playing a particular song a number of times per week, but
35
36
Appendix G
from Radio Authority website http://www.radioauthority.org.uk
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 18

19. whether it is scheduling those plays in peak hours, which would make a much
more substantial impression on the audience.
4.
Playlist Diversity Analysis – across a timeline
Comparisons can be made between the same stations in the same market at
different points in time. This can help illuminate such issues as whether:





a station has made a sudden music policy change;
a station has slowly moved its format toward or away from that of other
radio stations;
ownership changes in a market have effectively widened listener choice
over time, or narrowed it;
a station has responded effectively to instructions from the regulatory
authority to amend its music programming policies;
a station is simply trying to clone an existing competitor within the same
market.
For example, a comparison of the music played in the London market this year
(week 48: 24 to 30 November 2002 inclusive) and four years ago (7 June
1998) [calculations in Appendix H] produces these two tables:
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 19

20. TABLE EIGHT: COMPARISON OF PLAYLIST DIVERSITY INDICES FOR LONDON RADIO
STATIONS IN 2002 AND IN 199837
summary table of indices: 24-30 November 2002
BBC
R1
BBC R1
BBC R2
Capital
FM
KISS FM
Heart
106.2
Magic
105.4
Choice
FM
Virgin
XFM
BBC
R2
Capital
FM
11
KISS
FM
37
29
Heart
106.2
46
12
39
Magic
105.4
Choice
FM
Virgin XFM
6
9
7
0
0
0
23
0
13
16
9
11
18
0
0
12
0
4
20
0
0
0
1
0
0
0
0
0
3
11
37
29
46
6
12
9
39
7
12
0
0
0
0
4
23
0
13
20
0
0
16
18
9
0
11
0
0
1
0
0
0
0
0
3
Choice
FM
Virgin
5
5
summary table of indices: 7 June 199838
BBC R1 BBC R2 Capital
KISS
Heart
FM
FM
106.2
2
24
22
2
14
0
24
14
18
XFM
0
8
26
9
BBC R1
19
3
16
0
BBC R2
21
5
15
0
Capital
FM
22
0
18
4
29
0
0
KISS FM
0
19
21
4
0
20
0
Heart
106.2
8
3
5
29
0
0
0
Choice
FM
26
16
15
0
20
0
0
Virgin
9
0
0
0
0
0
0
XFM
where 0 means no identical songs and 100 means all songs and playlist rankings are identical.
(The tables are identical on either side of the grey axis.)
A comparison of these two tables shows evidence that:





37
38
the highest index number in 1998 was 29 (between KISS FM and Choice
FM), compared with an index of 46 (between KISS FM and Radio One) in
2002;
XFM’s playlist is as distinctive from competitors in 2002 as it was in 1998;
KISS FM is far less distinctive in 2002 than it was in 1998 (particularly from
Capital FM and Radio One);
Capital FM is less distinctive in 2002 than it was in 1998 (particularly from
KISS FM and, surprisingly, Radio Two);
Virgin Radio is far more distinctive in 2002 than it was in 1998 from all
competitors.
Magic 105.4 was not monitored by Music Control in 1998.
Appendix I
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 20

21. It can be deduced that:



Radio One, Capital FM and KISS FM are significantly closer to each other’s
music format than they were only four years ago;
Magic 105.4, XFM and Virgin extend listener choice significantly in the
London radio market by adopting distinctive music policies from all their
competitors;
KISS FM and Radio One’s music playlist already overlap to a significant
extent and are moving closer to one another.
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 21

22. CONCLUSIONS
Playlist diversity analysis provides us with useful information about the music
content available from radio stations in a specific market. Because the system
of analysis detailed in this paper is based upon empirical data of the actual
songs played by a radio station (rather than upon data supplied by the station
itself about its playlist), it offers us a regulatory tool that views the market from
the perspective of the average listener.
The factoring of the latest RAJAR ratings within the playlist data compiled by
Music Control makes playlist diversity analysis an even more powerful tool,
because no additional interpretation is required as to a particular station’s
listenership patterns. No knowledge is required as to whether a station’s peak
audience is at 8am or at 8pm, because Music Control has already incorporated
this variable into its playlist chart for each station.
Mere words are becoming increasingly redundant in trying to describe a radio
station’s music policy in relation to another radio station. Questions such as
“What is the difference between the music played on easy listening formatted
Station A and middle-of-the-road formatted Station B?” are not easy to answer
in purely descriptive terms. Playlist diversity analysis offers a tool that can
objectively quantify the similarities and differences between radio stations in
our increasingly complex radio markets.
The new regulator Ofcom will assume a much more active role in monitoring,
analysing and evaluating commercial radio content. In order to effectively
represent consumers’ interests in its policy and strategy processes, the
regulator needs to look at content diversity more directly from the viewpoint of
the radio consumer. Empirical data derived from content monitoring can prove
far more revealing about the state of diversity achieved in a marketplace than
any amount of interpretation of format names or programming descriptions.
As part of its knowledge management remit, and in order to acquire a deeper
understanding of the content dynamics that operate within the radio industry,
Ofcom should consider:





subscribing to Music Control data;
instituting a rolling schedule of market analyses to help build time series for
individual markets;
preparing individual analyses for areas where new or re-advertised licences
are pending;
preparing individual analyses relating to stations and markets affected by
pending mergers and acquisitions;
ongoing evaluation of radio groups’ performances in achieving music policy
distinctiveness and ‘localness.’
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 22

23. Grant Goddard is a media analyst / radio specialist / radio consultant with thirty years of
experience in the broadcasting industry, having held senior management and consultancy
roles within the commercial media sector in the United Kingdom, Europe and Asia. Details at
http://www.grantgoddard.co.uk
Tools For Radio Content Regulation #1: Playlist Diversity Analysis
© 2003 Grant Goddard
page 23