MONEYBALLING MUSIC - MusicTank · · 2016-03-28Big data provides the answers to questions involving ‘what’, ‘how’, ... full-time marketing assistant position within weeks

MONEYBALLING MUSIC

USING BIG DATA TO GIVE CONSUMERS WHAT THEY

REALLY WANT AND ENHANCE A&R PRACTICES AT

MAJOR RECORD LABELS

PRITHWIJIT MUKERJI

MA Music Business Management

University of Westminster

FUTURE THINKING

Future Thinking

…ideas, insight and inspiration

for tomorrow’s music business

MUSICTANK.CO.UK | MONEYBALLING MUSIC 2

Contents

Executive Summary ....................................................................................................... 3

Foreword ........................................................................................................................ 6

About MusicTank ........................................................................................................... 7

Introduction .................................................................................................................... 7

Moneyballing Consumer Understanding ................................................................... 12

The Situation At Present ................................................................................................ 12

Shazam: A Case In Favour Of A Largely Untapped Resource ...................................... 15

Advanced Moneyballing: Sentiments, Streaming, Algorithms ........................................ 24

Soundtracks To Your Life: A Playlist For Every Emotion ............................................... 28

Genres Are Dead. Long Live Genres............................................................................. 30

Big Data: To Be Used Wisely In A World Governed By The Heart ................................ 32

Moneyballing Talent Discovery .................................................................................. 33

How Not To Use Data to Find Talent ............................................................................. 33

The Next Big Thing: Chosen By The People, For The People ....................................... 38

Really Big Data: Using Numbers To Make A&R More Efficient ..................................... 42

Big Data: To Be Used Wisely In A World Governed By The Gut ................................... 48

Conclusion and Recommendations ........................................................................... 49

Bibliography ................................................................................................................. 54

Figures 1 Screenshot showing the comparison between the Top 40 UK Official Singles Charts

and reoccurring songs over a ten-week period (March – May 2014), in the Shazam Top

200 UK Charts………………………………………….……………………………………...16

2 Google’s Music Timeline……………………………………………..……………………..20

3 The Echo Nest’s Music Segments…………………………………….…………………..26

4 The Genre Ladder……………………………………………………………….……….....31

5 Graphs plotting the change in preference of certain features that dictate hit-

predictability overtime…………………………………………………….…………………...36

6 Screenshots from The Next Big Sound dashboard…………………………..………….45


Executive Summary The initial intention of this paper was to look at how big data can be married with cultural

theory and semiotics in order to enhance A&R practices and methods of consumer

understanding, as neither is being sufficiently looked at currently in the industry.

Big data provides the answers to questions involving ‘what’, ‘how’, ‘where’, and ‘when’.

What it is perhaps not excellent at doing is providing the solution to ‘why’. For these

questions, the solutions offered by big data, alongside traditional methods of A&R and

consumer understanding, should be framed within the context provided by cultural

theory and semiotics.

When research began for this paper, however, both subject areas of big data and

cultural theory turned out to be too big individually to be properly covered in one essay

of this length. In the end, big data was chosen as the topic of this essay, for the reason

that the music industry is already cautiously edging toward embracing big data, with the

emergence of companies such as Next Big Sound, Musicmetric, The Echo Nest and

with partnerships such as those between Twitter and 300, Spotify and The Echo Nest,

and Warner Music Group and Shazam, forming over the last few quarters.

It seemed more exciting and more appropriate to comment on and analyse the shift in

the way A&R and consumer understanding is being carried out rather than discuss the

benefits of cultural theory for the music industry.

Moreover, whilst there has been a considerable amount written about cultural theory

with respect to the music industry, there is comparatively not so much academic writing

involving big data; which added a further level of interest and challenge to this piece.

On this basis, the research led to an article in Forbes from which the title of this paper

was derived. The term ‘Moneyball’ comes from a nonfiction book about baseball. It

tells the story of how a financially limited baseball team went on to become a success

based on the decision to put aside traditional subjective methods of consideration in

choosing players and instead opting for the statistical analysis of objective data about

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the same players. This method has now been widely accepted as an improved process

of player selection across teams.

Similarly, the music industry harbours and encourages situations where business

decisions, such as choosing to sign an artist or targeting a particular audience, are

based on subjective judgments, gut feelings and past experience. This essay looks at

the inclusion of big data in this subjective process, divided into two chapters.

The first chapter - Consumer Understanding - begins with a discussion concerning the

deals mentioned above and leads onto a section involving analysis of Shazam charts.

This section aims to show the potential value in Shazam data analysis and how it can

be used to better understand consumer tastes in comparison to sales data or radio

plays, due to the nature of the application.

This is followed by a segment on how big data is being used elsewhere, from efforts to

understanding sentiments to the advanced work of The Echo Nest in consumer

segmentation to Spotify’s use of algorithms and their method of collaborative filtering,

which also raises the question of segmentation by genre. Here, segmentation by mood

or activity is introduced and it is argued how this type of segmentation can only be

successful alongside genre segmentation, rather than as an alternative to it.

The final key section of this chapter delves deeper into the state of genre and why,

therefore, big data is even more relevant now than it was before.

The second chapter - Talent Discovery - begins with a look at previous work done on

attempting to predict or indeed produce the next big hit, such as the research around Hit

Song Science, and why it did not work. The issue was not that big data has no place in

A&R, but rather that the wrong hypothesis was proposed: that future hit songs can be

predicted by looking at musical patterns within previous hit songs.

Instead of attempting to understand the patterns in songs, this essay proposes that big

data should be used to understand patterns in the buzz around songs that are being

created. It does not claim to predict hit songs, but rather notice the future hit song first.

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To demonstrate this point, the deals between Warner Music Group and Shazam as well

as Twitter and 300 come into the discussion here.

This follows on to highlight the importance of social media and online data in talent

discovery. Given the deluge of data the industry is now facing because of this, it further

validates the importance of companies such as the Next Big Sound and Musicmetric in

analysing big data to make sense of this deluge.

The conclusion is not to suggest that big data analysis should replace existing and

traditional practices and it acknowledges the fact that those methods, based on

subjectivity and experience, are crucial. Rather, it is about augmenting those methods

with big data analysis.

This essay argues that in an era of necessary financial constraint and in the interests of

minimising exposure to risk (compared to the boom-time1990s), the inclusion and

analysis of big data can help to not only considerably reduce the risk of subjective

business decisions and strengthen those decisions, but also reveal new opportunities

for the business and make the process of A&R and consumer understanding more

efficient and effective.

Recommendations:

1. To look at how cultural theory and semiotics can tie into big data

analysis to provide a fuller picture.

2. To examine the place of big data analysis for other areas of the

industry beyond major record labels.

3. Ideally, to carry out proper statistical analysis of the data, for example

on Shazam charts, which has been discussed here but is beyond the

scope of this essay.

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Foreword Prithwijit Mukerji’s MA Music Business Management Project paper is an empirical study

of the use of social media Big Data to better anticipate consumers’ tastes and better

inform A&R processes and decision-making.

By Spring 2014, this was an extremely current subject, the stature of which developed

significantly during the course of his research including Shazam’s link-up with Warner

Music Group (Feb 2014) and the purchase of The Echo Nest by Spotify (March 2014).

This paper successfully analysed current business trends incorporating latest research

and industry-based interviews and as such offered an overview of an emerging and

exciting field of study. Prithwijit Mukerji was an outstanding participant on the course,

securing grades of the highest level for some outstanding and original work.

Additionally, he is a real social leader and was a hub for other students’ social lives,

demonstrating that committed music study can combine hard work and leisure. Offered

an internship with Universal Music Group whilst on the course, this was transferred to a

full-time marketing assistant position within weeks of ending his studies at Westminster.

On the strength of this paper and his academic achievement during his studies,

Prithwijit was awarded the MusicTank Award for Business and Innovation,

Autumn 2014.

Sally Gross, Course Leader & Graham Ball, Deputy Course Leader

MA MBM, University of Westminster

MA Music Business Management

University of Westminster’s MA MBM is truly unique, being the first and longest-running

of its kind in the UK. As such, it is regarded by the music industry as a Gold standard in

music business education, preparing and delivering consistently high-level, next-

generation music industry leaders and entrepreneurs: MA Details. Other Courses…

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http://www.westminster.ac.uk/courses/subjects/music/postgraduate-courses/full-time/p09fpmub-ma-music-business-management

http://www.westminster.ac.uk/courses/subjects/music


About MusicTank MusicTank is a pre-eminent information hub for UK music business, addressing change

and innovation through informed debate, objective analysis and industry engagement.

Established in 2003, MusicTank has built an enviable reputation for its ongoing and

unique programme of think tank debates, events and conferences, a natural extension

of which is its delivery on incisive reports commissioned from key industry figureheads.

Shortlisted for the 2012 Times Higher Education Leadership and Management

Awards - Knowledge Transfer.

Report Catalogue

Easy Money? The Definitive UK Guide

To Funding Music Projects

Remi Harris, 2013

The Dark Side Of The Tune: The Hidden

Energy Cost Of Digital Music

Consumption

Dagfinn Bach, 2012

Remake, Remodel: The Evolution of the

Record Label

Tony Wadsworth with Dr. Eamonn

Forde, 2011

Let's Sell Recorded Music

Sam Shemtob, 2009

Meet The Millennials

Terry McBride, 2008

Beyond The Soundbytes

Peter Jenner, 2006

Become a MusicTank member today: www.musictank.co.uk

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http://www.musictank.co.uk/

http://www.musictank.co.uk/resources/reports/funding-guide-2013

http://www.musictank.co.uk/resources/reports/funding-guide-2013

http://www.musictank.co.uk/resources/reports/energy-report



http://www.musictank.co.uk/resources/reports/wadsworth-remake-remodel

http://www.musictank.co.uk/resources/reports/wadsworth-remake-remodel

http://www.musictank.co.uk/resources/reports/filesharing

http://www.musictank.co.uk/resources/reports/meet-the-millennials

http://www.musictank.co.uk/resources/reports/beyond-the-soundbytes


About This Paper

Published by:

MusicTank Publishing

University of Westminster

Watford Road, Harrow,

Middlesex

HA1 3TP

If you have any comments about this paper we would love to hear from you:

musictank.co.uk/about/contacts | [email protected]

First published London, January 2015

Copyright © 2014 Prithwijit Mukerji

The copyright in this publication is held by Prithwijit Mukerji. This material may not be copied or

reproduced wholly or in part for any purpose (commercial or otherwise) except for permitted fair

dealing under the Copyright, Designs, and Patents Act 1988, without the prior written

permission of University of Westminster. The copyright owner has used reasonable endeavours

to identify the proprietors of third-party intellectual property included in this work. The author

would be grateful for notification of any material whose ownership has been misidentified herein,

so that errors and omissions as to attribution may be corrected in future editions.

ISBN: 978-1-909750-06-7

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Introduction

This thesis borrows its title from a 2013 Forbes article, Moneyball For Music: The Rise

of Next Big Sound, which referenced the 2003 nonfiction book by Michael Lewis,

Moneyball: The Art of Winning an Unfair Game. The book follows the story of Billy

Beane as the general manager of the baseball team Oakland Athletics. Despite the

team being in financial difficulty and therefore having very little chance of buying good

players, Beane applied statistical analysis and focused on non-traditional objective

indicators over traditional subjective measures of performance (Ackman, 2003) to

acquire the right players at low cost, win twenty games in a row (Thurm, 2012) and turn

the team into “one of the most successful franchises in Major League Baseball” (Lewis,

2003).

The methods used by Beane have gone on to be implemented widely by other baseball

teams and changed the way players are selected (Woolner, 2007). Currently, "if you're

not heavily invested in the statistical approach now, you've missed the boat” (Slusser,

2011).

If newer and more relevant sets of data can be used and analysed correctly to spot the

right talent in baseball, the idea is that perhaps this can also be done for music, not only

for talent discovery but also for understanding the consumer.

Spotting and developing musical talent that is able to bring in commercial gains has

been (and will no doubt continue to be in the foreseeable future) a game governed by

the gut instinct and experience of A&R individuals, backed by trusted sources of

recommendation. But to inform their decision making process in a digitised world

seemingly over-supplied with musical content and affected by reduced revenues for

A&R departments to develop artists, it is now common practice for A&R departments

already to go beyond trusted recommendations and gut instinct and back this up with

social media statistics such as Facebook Likes, Twitter followers, YouTube views and

SoundCloud plays to filter out artists.

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Data is already helping A&R in decision-making processes. The use of data to

understand consumer behaviour and inform business decisions in music is not a new

concept, with the industry continuing to draw insights from qualitative and quantitative

market research techniques – whether that is hiring an external company to run large

questionnaires or a case of forming ideas about consumer tastes based on data points

such as sales figures and radio plays. Naturally too, the same social media data sets

that are used to spot talent are also used to understand consumer tastes.

One might argue that the status quo is working for the industry, and indeed, there is not

much need to further delve deeper into the use of data: why is it necessary to analyse

dry statistics in such a seemingly subjective industry?

This thesis disagrees with maintaining this status quo. It advances the notion that if

further opportunities are available to find talent more effectively, whilst better aligning

this new talent with the consumers’ true preferences by utilising the relevant analysis of

the right data, then these opportunities should be explored. This thesis aims to

demonstrate that objective data analysis is highly valuable to subjective qualifiers and in

fact takes them further.

Whilst the music industry is using data, there is better data out there that can be put

through more advanced forms of analysis to bring out deeper insights, and eventually

have greater positive commercial impact on the business. This better data is ‘big data’,

data that is “…too big, moves too fast... [but within which] lie valuable patterns and

information, previously hidden because of the amount of work required to extract them”

(Dumbill, 2012).

By ‘Moneyballing Music’ then, this thesis proposes incorporating big data and objective

statistical analysis into the existing methods of market research and A&R techniques to

deliver enhanced results. Companies such as The Next Big Sound, Musicmetric and

The Echo Nest are making the incorporation of big data analysis into music possible,

whilst new deals between companies such as Shazam and Warner Music Group or

Twitter and Billboard provide a sense of optimism and further opportunities for big data

inclusion.

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This thesis aims to analyse these developments as well as highlight suggestions for

how existing platforms, for example Shazam, can be utilised further than they are being

now to aid consumer understanding and talent discovery.

In order to demonstrate the application’s efficacy to the industry, Shazam charts were

collected and topline statistical analysis carried out over a ten-week period and then

compared to The Official UK Charts.

Unlike the original Moneyball, processing big data for music in-house requires newer

systems that may not be supported by the existing infrastructure of the company, whilst

external and specialised help requires additional revenues. For this reason, the thesis

will focus mainly on major record labels, which have more financial capital in

comparison to independent record labels and therefore be better resourced to

implement change in their operations or bring in specialist talent.

This is not to say that these developments are purely for those with capital; rather that

at this moment, as the music industry sits on the cusp of embracing big data analytics, it

would be more valuable to look at the analysis of change implementation where it is

possible and occurring rather than carrying out a discourse on revolutionising the

industry as a whole with big data, which is not yet possible.

Keeping that in mind, employees of Universal Music Group across Digital, Market

Research and A&R were interviewed in order to capture the work currently taking place

in the record label that accounts for the dominant part of the market. To provide

balance to the thesis and represent an independent label (which can still be considered

to have capabilities to embrace big data analysis), an A&R Scout and a Digital Product

Manager from Ministry of Sound were also interviewed.

The purpose of the following thesis is not to suggest that big data analysis should

replace existing methods, but rather that it will evolve existing practices to produce

better commercial results, bring the right talent in front of the spotlight and give the

consumer what they truly want.

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Moneyballing Consumer Understanding The Situation At Present

“Music is experiential and fundamentally social – it is socially constructed, socially

embedded and its nature and value are inherently social” (Bowman, 1998) (O’Reilly,

Larsen, Kubacki, 2013).

Given this core characteristic, in a digitised environment of music consumption where

social data is more readily available about consumers, it is not difficult to see why both

the use and thirst for consumer behaviour data is growing.

Databases holding information about target audiences continue to be employed and

developed to inform marketing decisions at major labels such as Universal Music Group

(UMG). Alongside this, the analysis of social media statistics delivered through sites

such as Facebook, Twitter and YouTube has been a well-established practice to better

gauge consumer interests for some time now, too.

UMG have invested in creating their own internal analytics tools that pull social media

data together amongst other data points. This includes the use of a dashboard and a

social CRM (Customer Relationship Management) tool which allows audience analysis

using not only demographics such as age and territory, but it also indicates the type of

products the audience might have an affinity toward, which can then be used to look at

brand partnerships. So; “let’s say we notice that 95% of an artist’s fans also ‘Like’

ASOS on Facebook, we could approach ASOS with a campaign plan knowing that they

are a relevant brand for an artist campaign” (Bennett, 2014).

Whilst relatively impressive, fundamentally, UMG’s business is music and not data

analytics, which undoubtedly and correctly mean that the focus for the company lies on

the former.

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To gain additional knowledge on online consumer conversations around relevant

subjects, UMG have been utilising external social analytics companies such as Topsy,

which “can decipher how often a term is tweeted, find an influential person on a specific

subject, or measure the exposure of an event or campaign” (Macmillan, Wakabayashi,

2013).

With a searchable database of every single Tweet on its database since the launch of

Twitter (Wagner, 2013) – which stood at 300 billion in March last year (Smith, 2014),

and with a previously estimated 500 million tweets sent per day (Holt, 2013), this gives

Topsy users a staggering amount of information to work with in understanding what

consumers are talking about.

Importantly, in “2013 alone, Twitter users sent more than one billion tweets about music,

with 100 million of those tweets coming from music-related accounts. Additionally in

2014 people using music services sent more than 40 million tweets about the music

they're playing” [last updated 27.03.14] (Hernandez, 2014).

In hindsight, it was only a matter of time before Billboard, which is built on over 200

exclusive charts, announced its recent partnership with Twitter. By tracking US music

conversations, Billboard will create further charts that “will reflect the top tracks being

discussed at the moment and over an extended period of time on Twitter, as well as

surface the most talked about and shared songs by new and upcoming acts” (Billboard

Staff, 2014).

This will publicly make available and rank, in real-time, consumer music trends for the

largest music market in the world. From a music marketers’ perspective, this presents a

clear view of what kind of music the consumers are choosing to talk about online as

they discover it, thus highlighting trends that can be used to better understand shifts in

consumer tastes more dynamically without having to directly ask the audience about

their preferences, in contrast with more traditional market research methods.

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Another tool which the industry has begun to use, although mainly for A&R purposes

(which will be discussed later) but which can also be used to track changes in music

tastes over a period of time, is Shazam.

This is the smartphone application with 25 million tracks in its database that connects

over 450 million people globally (Shazam, 2014) and allows its users to discover songs

they hear in the world around them via their phone.

Its 90 million monthly active users are voluntarily tagging 17 million songs, TV shows

and ads every day to identify the tracks they like, causing Shazam-driven music sales of

more than 500,000 a day – seven per cent of all global digital track sales (Dredge,

2014).

As the “exchange and consumption of popular music are becoming automatic,

weightless and hence, more privatized, mobile and invisible” (Rojek, 2011), this

provides marketers with another possible set of data with which to understand

consumer music tastes, but with the added benefit that it is collected through a

consumption platform that is congruous with the way music is partly exchanged and

consumed at present.

Some of this data is already publicly available. Shazam delivers weekly Shazam charts

via email to anyone who requests it, which includes charts of the top 200 most tagged

tracks and most tagged new releases by key territories across America (Brazil, Canada,

Mexico, USA), Europe (France, Germany, Italy, Spain, UK) and Australia. Therefore

the industry already has access to this top-line data.

However, it is perhaps not being fully utilised to its full potential from a consumer-

understanding perspective just yet. As Jack Fryer, Head of Insight at Universal UK,

explained on the potential of such applications (2014): “the emergence of data-

generative tools like [Shazam] is extremely powerful. You absolutely can’t argue with

that kind of information – it’s unprompted.”

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Shazam: A Case In Favour Of A Largely Untapped Resource

In order to evaluate how an unprompted source of consumer data compares with

definitive data such as sales figures in terms of understanding consumer tastes, the

Shazam Top 200 UK Chart was looked at weekly over a period of ten consecutive

weeks, dating from Tuesday 4th March to Tuesday 6th May, 2014 - Fig. 1.

To align this comparison with the UK Official Singles Charts, only the top 100 were

chosen for analysis. Furthermore, on average, the life of a single on the UK Official

Singles Chart is a little more than six weeks (Hawtin et al. 2014) (Musicstats, 2013) and

generally Shazam’s charts can predict what will be on national official charts a month or

so in advance (Knopper, 2014).

This indicates that in theory and on average, a track that is popular enough for the mass

music consumer should stay in the top one hundred of the Shazam charts for at least

ten consecutive weeks. This includes the four-week lead up to the track entering the

official charts that Shazam claims it can predict plus, all other things remaining equal,

another six weeks for an average single to continue to be discovered (and therefore

tagged on Shazam) whilst it remains in enough of the public’s interest (to be purchased

and therefore stay on the official charts).

Across the ten-week period, thirty-two tracks for thirty artists were tagged every week,

showing a consistent preference in certain styles of music for Shazam users. The

overarching genres of music were, unsurprisingly and broadly, electronic and dance,

pop and modern RnB – highly representative of the dominant genres in the current UK

Official Singles Chart. On the surface, this does not provide any truly valuable insight to

a marketer than the existing data of record sales and radio airplay.

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Fig. 1. Comparison between the Top 40 UK Official Singles Charts and

reoccurring songs over a ten-week period on the Shazam Top 200 UK Charts for

the period Tuesday 4th March to Tuesday 6th May, 2014.

The advantage in using Shazam charts over sales or radio charts, however, is that they

are spontaneous, free and independent, presenting purely consumer music preference

data; and it is inherently digitised, whereas radio is not.

There are two aspects to this:

Firstly, digitisation has allowed for greater levels of control, creativity and participation

by audiences, leading to the erosion in power of “industrial, professional and institutional

cultural production [and giving way to a] more democratic and vigorous system”

(Hesmondhalgh, 2013) of communication in music.

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This has resulted in a reduction of power and influence held by traditional gatekeepers

(such as key radio stations) and marketers at major record labels to dictate which tracks

are heard by and pushed onto the consumer along with heavy advertising.

The music industry is being ever urged to employ more pull-marketing tactics (i.e.

encouraging and attracting the consumer to actively seek out the product) alongside

existing push-marketing tactics (i.e. taking the product to the consumer). Indeed, to

align their content more accurately with their target audiences and pull audiences in,

Radio 1 is now looking to Shazam to help them guide their playlist decisions over how

many people are texting in to the programme (Lowe, 2014).

Due to the relatively independent nature of the Shazam charts, it can be proposed that

these charts are more reflective of consumers’ true music preferences than radio charts,

for example, which are dictated by gatekeepers at radio stations.

Secondly, there is the composition of music consumption in the UK to consider,

whereby sales figures alone may no longer be an accurate representation of artist or

song popularity.

Looking at the previous year, the UK recorded music market grew by 1.9 per cent in

2013. This growth was driven primarily by streaming, which grew by over 41 per cent

on 2012 (Ingham, 2014).

The importance of streaming is also being acknowledged by the industry, so much so

that for the first time in history, streaming data from services such as Spotify, Deezer,

Napster, O2 Tracks, Xbox Music, Sony's Music Unlimited and rara have been included

in the UK Official Singles Charts (Lane, 2014).

Currently, therefore, it would be unwise to ignore the impact of streaming revenue on

the business. This in turn begins to provide a good argument for the industry to also

look at other non-sale metrics for music popularity, such as Shazam.

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Given these two conditions, it may be worth delving deeper into the Shazam data, which

is what Warner Music Group (WMG) chose to do by forming a strategic alliance earlier

this year (2014), in order to bolster its A&R efforts (to be discussed later in this report)

and proposed to create, as Rob Wiesenthal, COO of WMG termed it; “the first crowd-

sourced, big data record label” (Houghton, 2014). This deal also gives WMG access to

“enhanced deep data on fan behavior” (Shazam, 2014).

The application’s Terms and Conditions state that the company may sometimes ask the

user for information such as their name, telephone number and date of birth, whilst it

also automatically receives and tracks data about the mobile device and may detect the

user’s location if opted-in. Shazam also states that if the user connects through

Facebook or other social networks, the company may receive some data from the

network – such as name, gender, age, locale and email address (Shazam, 2014).

Currently, the only way to access and sync Shazam tags online is to sign in with

Facebook (Shazam Support, 2014). If WMG is receiving this sort of data, this can help

the record label to categorise these trends by demographics and thus further

understand consumer tastes on a more granular level. If used correctly, this may

indeed give WMG a strategic advantage in market research over competitors. This is

because whilst similar analysis could be performed on national official charts (through

sales data) or radio charts (through listener data), another benefit Shazam provides

over other charts is to do with timing.

If Shazam is truly able to predict what will appear in official charts, it can help deliver

foresight rather than hindsight, or at the very least, help marketers to understand

consumer trends sooner and as they happen.

Despite the lack of access to Shazam’s granular data, it may yet be hypothetically

possible to utilise what is currently available in order to bring out further insight. In the

absence of demographic information, the focus shifts toward changes in the content

itself as an indication of consumer behaviour and tastes.

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Each artist in the Shazam Top 200 UK charts could be given a genre label. These

labels should be a manageable set capable of being tracked; more descriptive than

broad genres such as electronic, rock or pop, whilst avoiding extremely specific labels

such as industrial-psych-punk.

With a particular set of genre labels decided upon, new tracks entering the charts can

also be categorised under these labels. This allows the grouping of artists into more

convenient clusters, which in turn allows for analysis of which genres are becoming

more or less popular over time.

This can be seen in two ways:

1. As songs receive more or less tags over time and travel up or down the charts, it

would be possible to track how the related genres are also moving through the

charts.

2. As new tracks enter the charts and replace existing ones, it would be possible to

analyse the popularity of genres based on the proportion of the charts they make up

and based on the sections of the charts these clusters occupy.

Moreover, by looking within each cluster, it would be possible to gauge the most popular

tracks within each genre, enabling marketers to better recognise success. This idea is

based on the concept of Google’s Music Timeline - Fig. 2 - which charts the shifts in

genres over time…

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Fig. 2. Google’s Music Timeline (each genre can be expanded to reveal sub-genres,

which then can be expanded further to reveal the timeline of specific works by artists

that make up the genre as a whole)

Thus, Shazam can not only potentially highlight short-term, dynamic shifts in consumer

tastes in advance, but also help to better demonstrate – in comparison to existing sales-

driven metrics – how consumer tastes are changing over time, or indeed, the direction

in which these tastes may be heading.

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Arguably, every data set has its limitations and Shazam is no different. Given “there’s a

world-view, then a market-view, then a Shazam-view” (Fryer, 2014) of consumer

preference, Shazam is perhaps not necessarily representative of the general

population. Despite its large and growing user base, Shazam is primarily a smartphone

and tablet application (although it is also possible to use it via desktop and laptop

devices). In January this year, smartphone penetration was at 56 per cent for all UK

adults (Nkwocha, 2014) whilst tablet penetration was at 43 per cent of the total UK

population (O’Reilly, 2014).

With regards to how much of this audience engage with Shazam – “the consumer-

insight agency Nielsen published data [in 2012] demonstrating that four out of five of

those who have a mobile phone or tablet monitor it while watching television” (Kay,

2013).

This raises three points:

1. The data does not account for approximately half of the UK population who do not

use smartphones or tablets. However it is worth questioning whether that is a

genuine limitation.

Smartphone users tend to be in the age group of fifteen to 44 year-olds, over a fairly

equal gender split (with a slight skew toward males) across the ABC1 (middle class)

socio-economic group (Weareapps, 2013). The Shazam audience follows the

general smartphone-using audience, whilst Shazam for TV is used mainly by 25 to

54 year-olds, with the second largest category being 18 to 34 year-olds (Berelowitz,

2012).

As digital sales and streaming services gain more dominance, according to the

British Recorded Music Industry (BPI), it is the younger audiences who are not only

spending more on digital music but also tend to spend the most overall – one third

more than any other segment (BPI, 2013).

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This segment falls within the smartphone and tablet using audience who engage

with Shazam. Therefore, if the main music-buying audiences that affect sales

figures reflected in the UK Official Charts are represented by Shazam users, the

“Shazam-view” may indeed be sufficiently aligned with the UK “market-view” to be a

considered as a significant data point.

2. There is an argument to suggest that Shazam usage is restricted to certain

environments and situations, such as whilst watching TV or listening to the radio,

and hearing recorded music in public places such as in restaurants or in nightclubs.

Therefore it follows that only music appropriate to these settings will be possible to

Shazam – ruling out, for example, live concerts, thereby limiting the representative

scope of the data for all types of music. A look at the UK Top 200 Charts will show a

lack of guitar-based indie or rock music, and almost no sign of certain other genres

such as classical; yet this is also characteristic of the current UK Official Charts –

certain genres are simply not commercially as popular as others.

Further, when it comes to discovering new music, irrespective of genre, a live show

for the general population is not typically the correct setting for this activity. Rather,

consumers unsurprisingly prefer to pay for tickets to experience live shows by artists

that they are already fans of (and hence would not need to use Shazam because

they already know the songs).

Since radio remains the most popular way to discover new music for the general

population (Fitz-Gerald, 2013), followed by TV, friends, YouTube and Facebook

respectively (Music Ally, 2012), Shazam becomes a relevant companion in the these

situations, making the application’s data also more pertinent for a major record label

investing mainly in popular music genres.

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3. With a feature such as AutoTag now available - which automatically tags songs in

the background, whilst the phone owner watches TV (Perez, 2013) - Shazam usage

is evolving from purely song-tagging to a second-screen experience with TV. Now it

is possible to engage more deeply with the artist or original subject (such as a Super

Bowl half-time show or the Grammys) and access and revisit exclusive digital

content through the application, and link to external sites to buy tickets and

merchandise.

As Shazam moves more towards TV activity, more of the tags will involve songs

heard on adverts, films and popular TV shows. For a track to get to this level

requires it to have obtained support from gatekeepers such as publishers and

brands. In 2012, 85% of the application’s users were already ‘Shazam-ing’ TV

shows and adverts equally (Hockenson, 2012); this has perhaps increased further

since.

Additionally, Shazam is also used whilst listening to the radio, which remains one of

the UK music industry’s key gatekeepers. Hence, it can be argued that the music

that is being ‘Shazam-ed’ is not necessarily that which consumers actively look for

and discover themselves. It is still something that is marketed to them through a

specific platform and presented to them around content they are interested it,

combined with an easy-to-use, non-intrusive mobile application.

Nevertheless, the application does help to filter out and highlight more clearly what

the audience prefers out of the multitude of songs being marketed and presented to

them via TV shows, adverts and radio. Consequently, it is important to not disregard

Shazam but perhaps adjust down the idea of Shazam charts as a wholly true

reflection of the consumers’ completely unprompted, pure and independent choice in

music preference.

If exploited correctly, and in conjunction with other market research techniques,

Shazam can become a useful tool in providing more granular, up-to-date insight into

consumer music tastes.

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Advanced Moneyballing: Sentiments, Streaming, Algorithms

Certain companies are taking the analysis of the music consumer beyond demographic

segmentation, whilst interpreting social statistics in new ways, beyond the basic

analysis of the number of Twitter mentions or Facebook Likes.

In July 2012, EMI Music in partnership with Data Science London held the Music Data

Science Hackathon, a global event where “more than 1,300 formulas and ideas were

submitted in answer to the question: ‘Can you predict if a listener will love a new song?’”

(Record of the Day, 2012).

The data used consisted of one million interviews involving topics such as level of

passion toward specific genres and favourite artists, preferred methods for music

discovery, views on music piracy, streaming and music formats (Kotenko, 2013).

Amongst various data-driven insights, basic demographics such as age and gender

were shown to be particularly weak indicators of music preference.

These events strongly support the idea that other factors beyond demographics should

be looked at in understanding consumer music tastes.

The next hackathon, for example, aims to look at the relationship between music and

emotions, whilst the “winning visualisations from Gregory Mead, CEO and co-founder of

Musicmetric, included a graph plotting the relationship between how consumers feel

about particular artists and the words they use to describe them” (Record of the Day,

2012).

More so, Mead’s Musicmetric “collects all artist data automatically from across the web

and is the only dashboard to integrate benchmarks across” social media, illegal

download site BitTorrent and online mentions ranked by influence and sentiment

(Wired, 2012).

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Now consider that 345 million tracks were downloaded via BitTorrent alone in the first

half of 2012, whilst in the following year seven million individuals in the UK used at least

one service where content was hosted illegally each month (BPI, 2013), and alleged

fans who are neither engaged nor loyal can still be bought on social networks such as

Facebook to inflate numbers...

By incorporating the study of sentiments alongside the numbers, and the unfortunate

but very prevalent illegal consumption habits alongside data from legal channels,

companies such as Musicmetric (which will be discussed in the A&R section) can help

deliver clearer insights into the actual tastes of the music consumer that go beyond

existing research – insights which could help guide marketers to give consumers the

music they truly want legally.

Particular studies showed that streaming could be the answer to curbing piracy (Knapp,

2013) and combined with the fact that streaming is becoming increasingly popular,

another impressive company capitalising on the rich data available through these

means of music consumption is The Echo Nest. This is a self-defined music

intelligence company, with currently over one trillion data points concerning

approximately 35.5 million known songs for over 2.7 million known artists, serving 432

music applications (The Echo Nest, 2014).

The company uses this data to help music services such as Rdio, Rhapsody and

Spotify - and was acquired by Spotify recently (Etherington, 2014) - to recommend new

songs based on previous song choices and playlists.

One solution (amongst several) that The Echo Nest offers is their Music Audience

Understanding capability. By applying their Taste Profile technology on anonymous

listening, the solution can gather data on each fan’s music activity to understand their

unique tastes and preferences, through artist plays, favourites, ratings, skips and song

plays. It then creates dynamic, music segments to understand music tastes across

entire audiences through the segmentation of audiences by over 710 genres and styles

of music, affinity to artists and several unique behavioural segments as shown in Fig. 3

(The Echo Nest, 2014).

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Fig. 3. The Echo Nest’s Music Segments (which change as users’ music preferences

and listening habits change over time).

Whilst the Music Audience Understanding solution is designed to improve predictive ad-

targeting and drive better targeted marketing and advertising strategies, what it

demonstrates is that with the amount of data available, there is a real opportunity to

better understand consumers on a level beyond just their music preferences based on

traditional demographic-segmentation models. This information can even be used to

“teach its algorithms what movies you'll watch - and even how you'll vote” (Vanderbilt,

2014).

This level of predictive profiling can potentially provide marketers with a wealth of

information to not only understand consumer music tastes, but also give insights into

their online personalities and behaviours, plus how and what kind of culture these

consumers are choosing to interact with.

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This in turn can feed back into their music preferences, the use of which marketers can

serve the consumer more bespoke music content - thereby better aligning what the

consumer truly wants with the talent that the A&R team have discovered and

developed. Another method of understanding consumer tastes through data is what

Spotify calls ‘collaborative filtering’, which partly drives Spotify features such as

Discover and Spotify Radio. Complex algorithms and matrix calculations are employed

in this method of grouping songs together, but the simple and core idea behind this is:

“if a lot of users listen to tracks x, y, z, then those tracks are probably similar”

(Bernhardsson, 2013).

This is based on the Netflix film recommendation model and is similar to the Amazon

purchase recommendation model of ‘users who bought x also bought y’. In comparison

to film, music has the advantage in that it allows for more niches to be formed within the

subject, which means that algorithmic grouping for music tends to be even more

accurate – and explains why this type of grouping naturally leads to recommended

tracks that happen to be in the same genre, without initially using genre to segment and

present songs to consumers.

This system of recommendations is not fool proof, of course. There are often cases of

recommendations that do not always work together. Algorithms do not pick up on

tracks that are being played as guilty pleasures rather than those that truly reflect the

listeners’ actual music tastes, nor does it pick up on other users’ listening to songs

through that profile.

As Mattie Bennett, International Digital Marketer at Universal Music points out: “Just

because you like the Vamps for example, doesn’t mean you’re going to like another

band that sounds like them or looks like them, because ultimately it’s how you feel”

(2014). Music still remains an emotional, intangible entity, governed by mood and

specific, personal experiences. Where emotions of individuals are involved,

understanding the culture around the genre or a group of similar songs, and

understanding why a consumer is drawn toward and likes an artist or group of artists,

for example, under the umbrella of a particular genre, through data, can undoubtedly

only go so far.

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Soundtracks To Your Life: A Playlist For Every Emotion

Several companies already exist which attempt to tackle this issue, moving away from

offering music purely segmented by genre and driven by algorithm, to presenting a

collection of music based on the current mood or activity of the consumer.

This is similar to the way traditional, mainstream radio stations may choose tracks

appropriate to the time of day, and as Fryer explains, radio segmentations “speak of

attitudes and sensibilities and not genre. So you know what a Radio 1 thing is, or what

a Radio 1 show is, which isn’t attached to genre” (Fryer, 2014).

One such solution is Stereomood, a music streaming service that allows the creation of

playlists based on mood and activity. Users can search for the appropriate collection of

songs to match their mood by typing into the search bar how they feel (with suggestions

coming up including ‘I feel funny’, ‘I feel meditation’ and ‘I feel sexy’), whilst the website

readily offers playlists on the home page such as ‘Happy’, ‘Aggressive’, ‘Driving’,

‘Sleepy’ and ‘Work Out’.

Another website offering a similar solution is 8tracks, an online streaming radio service

also centred on user-curated playlists. In this author’s opinion, 8tracks works better

than Stereomood in understanding consumers’ desires to choose certain types of

music, as it allows not only a three-step filtering process (Stereomood only allows one),

but it provides genre suggestions, both broad and niche (Stereomood does not).

Through the 8tracks Explore feature, users can input any mood, activity and genre, and

continue to filter through twice more, giving them a more targeted and personalised

result.

This highlights the importance of the idea that whilst there are broadly agreed-upon

concepts of moods, dividing music purely by emotion and activity will not be fully

successful, given it is so subjective – which is perhaps why mainstream radio has not

always been successful in delivering the music its (desired) target audience wants.

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How one listener might personally interpret ‘happiness’ or choose music appropriate for

‘running’ or ‘sex’ may well differ from another’s. Typically, this difference in

interpretation is dependent on what the listener is familiar with and the culture that

surrounds them and defines for them what the most appropriate soundtrack to certain

moods and activities should be.

With regards to music, culture expresses itself through the similarity of beats, melodies

and characteristics within certain types of songs, which in turn gives rise to a genre.

Hence, genre remains relevant in segmenting consumers, especially when the

consumers are segmenting themselves – and with such genre fragmentation, the

efficacy of data arises once more.

The perceived value in 8tracks’ segmentation by mood and activity, or even Spotify’s

recommendations based collaborative filtering, is to tailor content to that moment and

maximise the personalisation of music consumption of an individual. In this case,

possibly the more accurate way to understand a consumer’s online behaviour and

provide the correct music content to her, then, is to look at what the consumer herself

has consumed (or listened to) previously online.

Using cookie data, marketers can follow a consumer’s journey through the World Wide

Web. Information about this journey, collected in the form of online cookies, can then

be used by marketers to retarget consumers with relevant advertising that directs her to

content she is interested in, including a music product. This is what e-commerce

websites such as Amazon and eBay do, by offering suggestions of products or ads

related to what the user has previously shown interest in.

In this way, if a classic rock fan is searching for KISS merchandise on Google or KISS

songs on YouTube, she can then be served ads directing her to websites that sell the

latest KISS Top 40 Deluxe Edition Blu-Ray DVD, for example. She could also be

served ads on social media, directing her to Deep Purple vinyl, Genesis CDs, or in the

case of a classic rock revival, a new band could be introduced to her in this way.

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Whilst this is more toward how particular music products are marketed to consumers

(for example via companies such as RadiumOne or the Google Display Network),

barring any users deleting cookie data from their computer, and factoring for any

confusion of cookie data due to multiple users on the same computer, this data provides

an unaltered, unprompted log of online behaviour, decision-making and preference-

mapping – offering a golden opportunity to understand how consumers exercise their

tastes and how their passion points (or more importantly, their favourite types of music

and artists) are linked together.

Genres Are Dead. Long Live Genres

When it comes to audience segmentation, The Echo Nest’s methods highlight one

element worth discussing. If successful, their Music Audience Understanding solution

manages to capture the idea that genre is still important in understanding consumer

tastes whilst acknowledging the fact that genre fragmentation is very much present.

Data visualisations from The Echo Nest, such as Every Noise At Once help to show

how genres are fragmented and connected to each other, whilst the Every Noise’s A

Retromatic History of Music shows how more and more genres were created annually,

from 1950 to 2013, with over 150 genres identified from their data in 2007 alone.

As Mark Mulligan proposes in his Music Industry Blog, genre does not matter any less

than it did before; it simply matters in a different way. Mulligan discusses how

previously, music was “the defining cultural reference point” – one could identify a

consumer’s affinity to music by their fashion choices, and given that music was the core

cultural reference point, the average ‘music IQ’ was higher.

Now, however, as “music competes with a fierce array of alternative cultural identifiers

such as branded clothing, extreme sports, networked gaming... [whilst] media

consumption, time and wallet share are also competed for more intensely than ever

before” (Mulligan, 2014), the average ‘music IQ’ has dropped. In essence, now “music

is an accessory of lifestyle architecture. It is one of many codes, not necessarily the

privileged one, that represent who you are and what you do” (Rojek, 2013).

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This has resulted in mass music consumers congregating in an under-defined and

formless middle ground of popular music, whilst many new forms of music also edge

closer toward the pop end of the scale due to the same lack of definition. This does not

mean, as Mulligan states, that genre matters less, but rather, that genre now has a

varying degree of relevance to consumers dependent on their level of music

sophistication.

Whilst one could argue that this has always been the case, perhaps now more than

ever this is being further impressed upon. Mulligan’s Genre Ladder - Fig. 4 - helps to

illustrate consumer sophistication by the largest, least sophisticated group of

mainstream music fans (who like a bit of everything) to the smaller but more

sophisticated group of music fans (where things start to get tribal) to the smallest but

most sophisticated group of aficionados, who engage with and purchase music products

the most.

Mulligan states that rather than dilute the importance of genre, the digital era has led to

a ‘genre renaissance’, where artists have been able to build their own niches using the

online tools available to them.

Fig. 4. The Genre Ladder: How Consumers Interact with Music Genres

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In this sense, genre still remains relevant; rather (and as ever), it is what particular

genres stand for to people that remains relevant. In essence, genre is a segmentation

tool used to classify audience cultures, and it is by gaining an understanding of the

culture that lives in and around that genre of music that will provide better consumer

insights such as why and how the particular style of music relates to someone.

Commercially, it would be more beneficial to study micro-genres and understand the

aficionados rather than attempting to analyse the mass-market, since these not only

contain certain early adopters and trend setters which the mass market eventually

chooses to follow, but this is also the group that will emotionally invest in the music

product most highly, which then typically has higher chances than other groups of

converting to a financial investment.

Thus, with high genre fragmentation leading to higher numbers of (but less physically

visible groups of) aficionados, big data becomes an increasingly useful yet non-intrusive

method through which the consumer behaviour within these cultures can be tracked.

Big Data: To Be Used Wisely In A World Governed By The Heart

Used individually, sales and streaming data, social media statistics, cookie information,

relevant application usage figures and various forms of segmentation can only

illuminate parts of consumer behaviour. Used together and correctly, they can give

marketers a fuller view of the consumer’s identity and tastes. Of course, no amount of

data will be 100 per cent infallible. Yet data should not be expected to be flawless in the

first instance, and cannot and should not be used to replace traditional qualitative and

quantitative market research techniques. In the subjective world of music, “meeting

people is absolutely invaluable. We’re in the business of love and passion, people’s

emotions. I think where data can get you is managing expectations of an attitude or

emotion” (Fryer, 2014).

Indeed, big data should be used to augment existing methods to give marketers a

clearer picture of who their consumer is, what their consumer is doing and how they are

doing it.

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Moneyballing Talent Discovery

How Not To Use Data to Find Talent

In the early 2000s, Polyphonic Human Media Interface (PHMI) developed a product

called Hit Song Science (HSS), which used statistical analysis to predict the chance of a

song being a hit prior to release (Music Industry News Network, 2003).

Allegedly, Sony and Universal have used HSS in the past, which predicted the success

of Norah Jones against industry skepticism and picked out all Maroon 5 hits ‘This Love’

and ‘She Will Be Loved’, based on “20 aspects of song construction including melody,

harmony, chord progression, beat, tempo and pitch” (Tatchell, 2005), and then matching

and scoring new songs against 3.5 million previous hit singles.

The CEO of PHMI, Mike McCready, then went on to form Platinum Blue (now

functioning under the name Music XRay), taking this one step further. Under Platinum

Blue, songs were clustered not by genre or how they sound, but by their hit potential:

“Using a method McCready calls ‘spectral deconvolution’, the company's software can

‘listen’ to a song and, within 20 seconds, extract 40 pieces of information about its deep

structure – its ‘fullness of sound’, the instruments it uses, its chord progressions, the

cadences of its melodies and more... [finding that] about four-fifths of the songs in the

music universe were clumped together in 50 clusters of stars… 80% of all pop songs

that had ever been hits shared a relatively small number of underlying structures”

(Burkeman, 2006).

This suggests there is something measurable about a hit song, and to increase chances

of success, A&R departments should look for certain characteristics in new songs.

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There have also been other efforts similar to HSS to understand the hit potential of a

song. Engineers at Bristol University created the ‘Hit Potential Equation’, which took

into account twenty-three features such as loudness, tempo, energy, which are then

weighted by their importance for a song to be a hit based on previous chart hits over the

last fifty years.

The equation also changes over time to take into account the evolution of popular music

tastes and therefore the weighting each era gives to different features of a song

(Scoreahit.com).

Then “it’s simply a case of mining your proposed song for these exact same features…

and working out whether they correspond to the trends of the time” (Brown, 2011).

Arguably trend lines can be plotted and used to predict which features of a song are

becoming more important for the listener for it to be hit. According to the trends (Fig. 5),

A&R after 2010 should look for something that is quieter, less danceable and

harmonically simpler than previous years.

But this creates an unfavourable situation where the next potential hit is decided by

whether it fits into an equation, regardless of an A&R scout’s experience and

knowledge.

Big data should empower and support action, rather than restrict it to a set number of

features. The issue with this approach in an industry such as music is that it is a

method that is retrospective and one that encourages normative, homogenous output,

whilst evolution of creative product tends to come from the innovation (and sometimes

invention) of material that is forward-thinking, heterogeneous and one that challenges

the norm.

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Fig. 5. Graphs plotting the change in preference of certain features that dictate hit-

predictability over time.

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Arguably, the same ‘hit clusters’ do include a variety of types of songs, such as Van

Halen and Norah Jones, or U2 and Beethoven, suggesting that the same kind or genre

of music is not perpetuated. Nevertheless, it urges one to use data to look for a formula

for a hit song that can be used to manufacture success, rather than using data to spot

talent and musical product that is readily and organically emerging.

It also raises a certain question that is possibly not easily answered: does the use of

these equations cause a self-fulfilling prophecy? “Do things really become popular

because of their innate characteristics at all? If record labels pour money into songs

that computers have told them will be hits, and lo and behold they become hits, who's to

say that didn't happen simply because of the money the labels poured in?” (Burkeman,

2006)

A 2008 study carried out by Pachet and Roy successfully disputed the theory that the

popularity of music can be predicted by analysing sonic patterns in songs.

Pachet expanded on this in 2011, drawing on the work done by Salganik et al in 2006,

which demonstrated that although there are some songs that are statistically favoured

over others, because people rarely ever make decisions independently, the heavy

social influence creates what is called a ‘cumulative advantage’: “if one object happens

to be slightly more popular than another at just the right point, it will tend to become

more popular still” (Watts, 2007).

This makes the popularity of a song at early stages highly unpredictable, i.e. in a social

setting, consumers tend to opt for songs that are seen to be already popular, rather than

songs that may be considered to be of a better quality by the consumers themselves.

This puts into doubt the idea that the intrinsic qualities in a song can be independently

analysed to predict the popularity of the song.

This might be why Music XRay has now become more focussed on providing a service

that uses algorithms to answer briefs rather than help record labels find the next big

thing – for example, to find a song submission that sounds like (but is cheaper to source

than) Blur’s original Song 2 for a particular advertisement.

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A more intuitive way of using data that works more in harmony with human A&R

practices is perhaps to not extensively analyse features in a song, or how the notion of

a ‘hit’ is changing over time, but rather how songs are performing in key areas, and

plotting the predicted performance of that song.

Researchers from the School of Electrical Engineering at Tel Aviv University created an

algorithm that predicted the rise of artists Soulja Boy and Sean Kingston in 2007, two

months before they reached the top of the Billboard charts (Borel, 2008).

The algorithm pulled data from Gnutella, a peer-to-peer file sharing network and found

that the hit potential of a song depends on the artist’s level of success clustered in a

geographical area, and the speed at which this success is growing in that area.

This would be a better way to use data for talent discovery for two reasons.

Firstly, these two factors are things that A&R scouts monitor on a daily basis: looking at

(1) how ‘hot’ an artist is, and (2) how quickly she is ‘heating-up’ in the particular ‘scene’;

therefore it would work better with current A&R practices.

Secondly, it provides a better reflection of the audience’s true and current tastes, as it

pulls data directly from a crowd eager to get new music before others, from a system of

consumption that is more organic (a peer-to-peer network) than the more prompted and

controlled system of the UK Official Charts.

Rather than predict the future, data should be used to spot naturally occurring trends as

early as possible. Data should give the A&R scout a map to know where and when to

spot it, rather than what to spot.

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The Next Big Thing: Chosen By The People, For The People

The music industry has not given up on using big data in aiding talent discovery. The

focus however has shifted from the analysis of patterns in songs to methods that

inherently embrace the idea of cumulative advantage and uses it to the industry’s

benefit – allowing the listeners themselves to flag the future stars.

As previously mentioned, Warner Music Group (WMG) recently partnered with Shazam

for market research, marketing and A&R purposes. Will Mills, Vice President of Music &

Content at Shazam claims that up to 85 per cent of the songs that reach the top of the

Shazam charts go on to lead national charts.

A quick look at the Shazam charts show, unsurprisingly, how dominated the space is by

artists who are already signed, many to major labels, which feeds into Mills’ high

statistic and perpetuates the idea that Shazam charts are merely a strong and indicative

precursor to the national UK Official Charts. In fact, it is very good at doing that.

In 2012, the service predicted Lana Del Rey and A$AP Rocky as breakout artists, and

in 2013 it predicted the likes of Haim (Patrizio, 2013). Both Rocky and Del Rey went on

to top the US charts with their respective album releases, whilst Haim entered at

number 6 in the US album charts and secured a number 1 in the UK Official Album

Chart.

In December 2013, Shazam put together a list of artists to watch for 2014, which

included Sam Smith, Vance Joy and Martin Garrix, explaining that the “Shazam music

team selected the… artists by starting with qualitative industry tastemaker selections,

which is then ranked using the quantitative data of Shazam tags of those artists”

(Shazam, 2013).

This shows Shazam being mindful and aware of the importance of the subjective aspect

of music talent discovery, whilst enhancing it with their consumer data. As Ratcliff

explains in his article ‘Shazam, Big Data And The Future Of Year-End Lists’:

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“Generally people will always look to an individual expert or panel of experts for

recommendations rather than crowd-sourced opinion, which is why the Academy

Awards carries more critical weight than the People's Choice Awards… [but Shazam’s

methods are] a far more scientific approach than traditional methods, which removes

subjective opinion and puts the list in the hand of Shazam’s 400m users” (Ratcliff,

2013).

This suggests that when utilised correctly and in conjunction with existing, subjective

A&R practices, Shazam data can also become a strong asset in talent discovery.

Rich Riley, chief executive of Shazam, claims that big hits only account for a

comparatively small segment of the tags, whilst the majority of music tagged on the

application is of unsigned artists. Complementing Riley’s views, Mike Caren, president,

Worldwide A&R of WMG sees Shazam charts, with its millions of users, as an early

indicator of demonstrating the hit potential of songs (Pakinkis, 2014).

From an A&R perspective, then, it is perhaps not difficult to see why WMG decided to

create a Shazam label imprint to identify unsigned artists for development.

Understandably, WMG pays a significant amount for this exclusive partnership, which in

turn limits the access to data and methods of data analysis for talent discovery for

others, including for this report.

However, the broad principal appears to be that if an unsigned artist’s song is popular –

receiving above five thousand tags – there is a good chance of WMG offering the artist

a recording contract (Rowan, 2014), of course, with other aspects of the artist’s offering

considered.

WMG is, unsurprisingly, not the only record company to have recognised the benefits of

Shazam as a tool to spot new talent. The A&R team at Polydor Records have started

holding two meetings per week: “One is where we bring in stuff and look at what’s been

going on, on the blogs, stuff that we really like, or just things that people who we respect

have told us about. And then we have… a Shazam meeting” (Spinks, 2014).

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Using the example of Lana Del Rey, Jamie Spinks, A&R Scout at Polydor Records,

states that these meetings are for spotting things like Rey, early enough: “…it happened

without anyone really knowing… that’s the sort of thing we’re looking for. It’s that thing

where, OK there’s something that’s going on that somebody’s created for themselves,

that we don’t necessarily know that creeps up on us” (Spinks, 2014).

Shazam can indeed help A&R departments get a closer look at crowd reactions to

music more directly, without having to wait for sales data, or without having to unearth

new talent only through tastemakers and via recommendations. If anything, Shazam

provides a service that is more in line with evolving A&R techniques and facilitates it.

According to Darius Van Arman, the part-owner of independent record label triad Dead

Oceans/JagJaguwar/Secretly Canadian, “…in a lot of ways, A&R is more crowd-

sourced now… Artists are building their own audiences, so sometimes it's okay to have

the fans find an artist for you" (Rys, 2012). This is what Shazam helps record labels to

do - highlighting which artists the fans are themselves discovering without much

prompting from a tastemaker.

The service certainly does not provide the full story and the issues with Shazam were

discussed in the previous section on consumer understanding, but it becomes another

tool with which to spot appropriate unsigned artists earlier, adding another layer of

information to existing practices of talent discovery.

This is not to say that A&R has now become fully reliant on crowd-sourced numbers as

a rule, rather than trusted sources. Spinks will focus first on SoundCloud and music

blogs to find the music, and then use Facebook and Twitter to look at how consumers

are interacting with that music. In fact, Spinks does not feel Twitter is a good way to

find new talent, rather it is seen more as a way of seeing how, once that talent has been

found, consumers connect with the artist or track.

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On the contrary, Sam Lowe, A&R at Ministry Of Sound, the highly successful

independent record label with a focus on dance music, does use Twitter to find new

music. Lowe however uses the social media platform as an extension of listening to

tastemakers offline. When asked how important Twitter is to A&R, Lowe explains: “It’s

brilliant. One of the best ways to find music is what these cool, current DJs are playing,

their networks of people are very important. Once they get to that stage where they’re

known for being DJs, then all the other DJs want to be friends with them. You can see

through Twitter who the most popular ones are and people are always Tweeting those

guys. So I use Twitter a lot, because they’ll post tunes when they’re on the playlists for

example” (2014).

Taking this idea of tracking tastemakers via Twitter and expanding it vastly with the

power of big data, Lyor Cohen, the former CEO of Recorded Music at WMG, unveiled

the new partnership between Twitter and 300, his new record label at Midem 2014. The

aim of this deal is to mine the vast amount of music conversations on Twitter to spot

signs of excitement around new artists, discover and sign them.

Bob Moczydlowsky, Head of Twitter Music, explains that the purpose of the deal is to

answer questions such as: “Is there a guy in Chicago who, when he tweets about artists

it makes a meaningful impact on the growth or size or exposure of that artist. Is there a

tastemaker or a venue or a fan, a consumer in a specific location whose Tweets about

artists are more meaningful than others? Who genuinely are predicting the future

success of these artists” [sic] (Pham, 2014).

This partnership provides 300 the access to information that is also not publicly

available, such as location tags, whilst this data “could reveal flickers that might

otherwise go undetected… Imagine, for instance, a music executive getting an early

lead on a hot new rapper by tracking the most influential Twitter users in the rapper’s

local scene” (Sisario, 2014).

If successful, the Twitter-300 partnership will show the use of data for A&R not as a

separate area of study, but one that is intuitive, congruous with and enhances offline

A&R practice of following tastemakers.

[top]


Really Big Data: using numbers to make A&R more efficient

The analysis of data at this level, however, is yet to be wholeheartedly incorporated into

everyday A&R at record labels. Current data analysis is still what can be considered to

be largely manual. Facebook Likes, Twitter followers, SoundCloud listens, YouTube

plays, along with tips from tastemakers via blogs and relevant radio stations, plus sales

data on particular iTunes charts are, although valuable, not groundbreaking or novel

methods of using data in the A&R process.

These existing practices of data analysis continue to become more important and

common given the aforementioned change in the role of A&R, fuelled by the increasing

opportunities social media data offers to the A&R process.

Thus, the next step in A&R evolution is perhaps the ability to not only look at Twitter, as

per the deal between the social media platform and 300, but to tie the data points

together more quickly and more efficiently, across a larger segment of the vast and

growing social media landscape, whilst providing a way to make sense of these figures.

Universal Music Group has a service called Artist Portal, which allows the record label

to track sales figures and social media statistics of their own artists, but companies like

The Next Big Sound and Musicmetric are aiming to expand this idea for the whole of the

music industry, and labels are catching on.

Next Big Sound has already been working with major and independent labels, as well

as forming a partnership with Spotify at the end of 2013 to deliver data-driven insights

directly to artists, making it much easier than before to interpret data and providing a

stronger case for why the music industry should not ignore online data sources. In brief,

Next Big Sound “analyses social, sales and marketing signals” (Next Big Sound, 2014),

to draw insights about the changing popularity of artists. As shown in Fig. 6, the service

provides a dashboard enabling users to analyse artist popularity by tracking weekly

changes in Facebook page and Instagram likes, Twitter followers and mentions,

YouTube and Vevo video views, SoundCloud plays and even Wikipedia page views.

[top]


By upgrading to premium, paid membership, the user can access information based on

individual music tracks and specific events relevant to the artist (live shows, single or

album release dates, television appearances and mentions in the press), as well as

demographic and geographic data around the online conversation and engagement of

the artist.

When accessing the dashboard through Spotify as an artist, the service also allows the

tracking of personal sales and streaming figures, as well as the benchmarking of

performance against the success of a customisable list of artists. Along the

development process of an artist, then, it is now possible to obtain result figures much

more dynamically than before, and hence adapt strategies quicker.

The company also releases the Next Big Sound Chart in collaboration with Billboard

Magazine, which shows the fifteen “fastest accelerating artists across the Internet most

likely to become the next big sound” (Next Big Sound, 2014). There is also the

possibility to request custom charts.

Recently, a Brighton-based, psychedelic-grunge band signed to independent label

Heavenly Recordings, The Wytches, was present in the top fifteen of the Next Big Thing

Sound Chart. Also present was Wuki, a Denver-based producer of electronic music,

making this type of data particularly useful for major record labels that are able to be

more broadly spread across genres and have the infrastructure and desire to spot talent

globally.

[top]


Fig. 6. Screenshots from The Next Big Sound dashboard (showing changes in

social media statistics, changes in sales vs. streaming figures, and the relationship

between offline activity – an event – and online activity – Wikipedia page views, for an

artist near the time of the significant event)

[top]


In a blog post titled ‘What Social Media Has To Do With Record Sales’ (2012), Victor Hu

and Liv Buli of Next Big Sound analysed the impact of social media on iTunes digital

sales of tracks and albums.

The top four metrics to positively correlate to digital track sales on iTunes were radio

spins, YouTube plays, Facebook fans and Twitter fans respectively, whilst the top four

correlating metrics for album sales were, interestingly, Wikipedia page views, MySpace

plays, Rdio plays and radio spins respectively.

The analysis goes deeper by using the Granger causality test to look at whether there is

a close causal relationship between social media and digital track and album sales.

“The Granger causality test is commonly used in economics and neuroscience. This

concept focuses on whether one set of data is useful in forecasting another. With this

[they were] able to determine whether the inclusion of a particular social media metric in

fact improves the predictability of future album and track sales” (Buli, Hu, 2012).

In both cases, Facebook page views came in the top three metrics, whilst YouTube and

SoundCloud plays were in the top seven. Thus from a marketing perspective, this

demonstrates which platforms and channels artists and record labels should leverage to

maximise sales. But in showing the relationship between these digital touch points and

sales, the study also highlights the importance of using social media data as one

predictive indicator of success when discovering new talent, or when making choices for

which artist to develop.

It is important to remember that these are business decisions, specifically, in a market

where success is at some point unpredictable and affected highly by social influence -

where, although “quality is positively related to success, songs of any given quality can

experience a wide range of outcomes… the ‘best’ songs never do very badly, and the

‘worst’ songs never do extremely well, but almost any other result is possible” (Salganik,

Dodds, Watts, 2006).

[top]


The decision to develop an artist lies not only on the strong belief in their art, but also on

the belief that their art will be popular enough to be commercially successful and

profitable for the business.

As Zack O’Malley Greenburg of Forbes explains, “…according to one study, artist

discovery and development is a $4.5 billion industry, and Next Big Sound removes

some of the guesswork… [it] promises to predict album sales within 20% accuracy for

85% of artists, giving labels a clearer idea of return on investment” (Greenburg, 2014).

Another company doing similar work to Next Big Sound is UK-based Musicmetric, which

launched in 2008. Musicmetric combines the social media statistics of artists across

platforms including Facebook, SoundCloud, Twitter, YouTube and Last.fm to create an

overall social ranking and provides a dashboard through which artist-fan activity can be

tracked on a daily basis.

It also creates charts that can track not only consumption of music via download and

sales, but also consumer sentiment, as well as having the ability to do predictive

modeling.

Earlier this year, Musicmetric introduced two new features: Musicmetric Insights and

Musicmetric Explore. The former “automatically analyses performances across billions

of fan interactions online and is a quick way of seeing who’s hot and who’s not…whilst

the latter delivers an overview of any sector of the music market. For example, users

can filter down into a range of criteria, including genre, location or performance and

gauge fan reaction on a specific social network” (Sawers, 2014).

If an A&R person wants to find out who the hottest (most talked about, up and coming)

artist is in a specific geographical music scene, they can now do so more efficiently,

using already collated and standardised data, to either start the hunt or further

strengthen their beliefs and initial findings.

[top]


Providing a contrary view to the use of big data, Lowe takes the view that the Warner-

Shazam partnership will not provide WMG with a true advantage in terms of talent

discovery which the existing charts do not already provide.

Lowe will consider the artists in the last fifty tags of the Top 200 Shazam Charts to then

research their presence and reach, across DJs, blogs and social media. Radio plays by

specialist DJs as gatekeepers still remain crucial, as well as a network of trusted DJs

playing new music in selected clubs, where Lowe will look at which song is getting the

best crowd reaction.

In fact, Lowe feels that certain numbers are getting more inaccurate. For example, a

track on YouTube may have received a significant number of plays, but this may be due

to it being on a well-established music tastemaker YouTube channel with thousands of

subscribers and a history of videos with large play counts.

The numbers must of course be put in context of what else is going on in the market,

and whilst Lowe admits that there are some signings that the A&R team might not fully

like, but will sign because “it’s a massive club tune”, data will never fully dictate

decisions.

For example, in January 2014, a song called ‘#Selfie’ by The Chainsmokers was

released on Dim Mak Records and 604 Records. It peaked at number eleven on the

UK Official Singles Chart and is still in the Shazam Worldwide Top 200 Charts at

number sixty-three (as of 3rd June, 2014). The YouTube video to-date has had 129

million views, whilst the song has received over 7.7 million plays on SoundCloud after

being uploaded four months ago. With similarly impressive initial statistics, Ministry of

Sound noticed the opportunity and had the chance to sign the track, but never did,

based on the fact that the A&R team simply did not like the track.

Thus the subjective aspect of A&R, governed by gut feeling and experience will of

course remain: “It’s going to always be our gut first, once you get that information,” says

Liles [the co-founder of 300].

[top]


“I would say 20% information, 80% what we do every single day. Because no piece of

technology could ever find it, nurture it, bring it to market [and] also know when to say

‘no’ ” (Greenburg, 2014).

Indeed it can be proposed that A&R before big data was already proficient at talent

discovery and development, and this poses the question of why big data is at all

required in this process, given that gut instinct plays such a significant role.

But the conversation around the use of big data should be less about tipping the scale

toward data and away from the gut, and more about using this data to make the twenty

per cent of information richer, which in turn can considerably help to make the other

eighty per cent of A&R more effective, efficient and sturdy.

Big Data: To Be Used Wisely In A World Governed By The Gut

A&R persons are expected to be knowledgeable about their chosen field or genre of

music, and notice and understand trends early in how particular genres or scenes are

evolving so that they may remain ahead of the change and capitalise on it.

Artists with a clear vision of their project as well as strong ideas of what they want to

create may not need support in developing their sound or overall package. Artists who

are also playing several live shows and are surrounded by peers may also have their

ears close enough to the group to pick up on the latest music trends and give it their

own, original interpretation.

Other artists however, may require or desire more help from the team around them,

including their A&R, to guide them in their creation, provide them with additional

knowledge of the marketplace in which they are positioned and direct them to influences

that is relevant to them and their audiences. Whilst sales figures are a good indication

of mainstream popularity, it does not necessarily shed light on what is rising up from the

underground music scene, where there is arguably a higher level of experimentation in

the creation of new sounds occurring for an audience which is more likely to include a

higher proportion of fans, aficionados, early adopters, gatekeepers and tastemakers.

[top]


When it comes to the development of an artist within the context of the types of music

currently being enjoyed by relevant consumers, big data can also play a role in reading

these trends.

For example, Lowe spoke about a particular type of organ bass line sound originally

used heavily in drum ‘n’ bass and garage music, which is now being sampled in house

music, “on every record” that is doing well in music scenes of interest to Ministry of

Sound.

From a commercial stand point, for an artist positioned to be more under the broad

umbrella of pop music (which is inherently ‘of the moment’) that requires guidance, the

team around the artist may choose to highlight knowledge of particular sounds which

are proving to drive the popularity of songs, such as organ bass lines. A&R may well be

aware of these trends; Lowe himself certainly is.

Where data can benefit this knowledge is by providing reassurance that is more

scientific, deriving from the analysis of a much broader segment of the market, which

A&R may have missed.

By using services such as Shazam and Musicmetric, a more accurate reading can be

derived of the reception of these songs with a particular sound. It could be possible to

look at where the reception is higher with geotagging, how long the engagement with

these types of songs is lasting amongst key audiences, the sentiments attached to

these tracks, and whether it is indeed the sound which is playing a significant part in the

tracks’ successes, or whether there are other factors at play.

[top]


Conclusion and Recommendations

The music industry is just beginning to get acquainted with big data, which can be

noticed through the recent deals such a those involving Shazam, Warner Music Group,

Twitter, 300, Billboard, The Echo Nest and Spotify. This shows the growing awareness

and desire for improving methods to harness and understand online data for

commercial purposes.

Whilst there is already work going on at major record labels at varying levels to

understand this data, their capabilities and resources toward data collection and

analysis remain limited. Moreover, as technology, crucially mobile technology continues

to advance, further integrating the smartphone into the daily lives of consumers as a

constant companion, the data within these applications, such as those held by Shazam

which are more unprompted and automatic - become incredibly valuable.

The other advantage of big data is that “being able to process every item of data in

reasonable time removes the troublesome need for sampling and promotes an

investigative approach to data, in contrast to the somewhat static nature of running

predetermined reports” (Dumbill, 2012).

In essence, big data opens up possibilities which would not have been noticed before,

helps to ask questions which would not have been asked before and speeds up the

discovery of solutions to existing questions. It provides a new perspective to talent

discovery and consumer understanding and it does so efficiently.

It would seem that two groups outside of the traditional music exchange market of

producer-consumer would have greater roles to play in the near future.

The first group comprises companies that provide a digitally based service that allows

the consumption, exchange and discussion of music-products in a way that reflects and

is in harmony with the way music is being consumed – automatic, weightless and

mobile. This group includes companies such as Spotify, Shazam, Twitter, Facebook,

and YouTube.

[top]


The second group comprises those that have the expertise to capture, analyse and

successfully draw insights from the first group and deliver these to the music industry.

This group includes companies such as The Echo Nest, Musicmetric and The Next Big

Sound.

Record labels must also evolve and develop their data skillsets and technology

infrastructure in order to maximise the opportunities big data analysis can bring.

Polydor has already started to have Shazam-led A&R meetings and if big data is taken

seriously, it is not improbable to hypothesise that someday there will be a big data A&R

team sitting alongside the traditional A&R team, working together to find the next big

thing.

Similarly, although marketing and research has conventionally lent itself more toward

big data, the expansion will perhaps now be not only to understand consumer music

tastes and lifestyles better, but also to spot further commercial opportunities in the form

of providing consumers with new or improved modes of music consumption and

exchange.

Big data can provide a strong picture of the consumer and the product, however it will

not provide the full picture in a subjective industry such as music. It has the power to

more efficiently and effectively help record labels in spotting trends and understanding

what, how, where and when the changes are happening.

However, data is limited in being able to offer a holistic answer into why something is

popular. It may not be able to predict the next Sex Pistols or Adele, but it can help A&R

scouts and marketers notice trends earlier than others, giving them a commercial

advantage over competitors.

The ultimate and sustained success of songs arguably still depends on how well the

song connects with its target audience at a specific period and how relevant a song or

artist is to the environment it is brought into.

[top]


“Commercial success relies significantly on consumers’ extractions of cultural meanings

from musical offerings, and those meanings of music are shaped and constrained by

the conditions… of its production and consumption. Music is a vehicle for cultural

meaning” (O’Reilly, Larsen, Kubacki, 2013).

Since music is subjective, symbolic and social, in order to extract the most value out of

the data, it is worthwhile augmenting big data analysis with an awareness of cultural

trends and semiotics (i.e. the study of meaning-making), in order to put big data into

context and gain a fuller picture.

Ideally marketers and A&R departments should be, as Fryer suggests, “digesting

criticism every day, whether that’s Marx or Alexis Petridis” (Fryer, 2014).

This will feed into the understanding of the environment of consumers and producers,

which then helps to make better sense of their music preferences, or indeed, how their

music preference may evolve in the future.

This is easier said than done, of course, and injecting cultural theory and knowledge

into music beyond what is picked up via personal interests becomes difficult.

Currently, the core research department for the whole of Universal Music Group in the

UK officially consists of a handful of people. It is perhaps time to expand, both in terms

of big data and equally, research and insights.

Given the depth of discussion that it possible around big data, and the equally if not

larger discussion around semiotics and cultural theory, it has not been possible to look

deeper into the connection between big data and cultural theory. This would be an

interesting topic to explore.

[top]


Further, this thesis focused primarily on the largest major record label in the UK and

was supplemented with information from an independent label that behaves like a

major, due to limits in scope, time and size of thesis.

The discussion could benefit from the inclusion of all major labels and more

independents, as well as how big data may be of benefit to other players in the industry,

for example, publishers, managers, booking agents, promoters, and in fact the artist and

consumer themselves.

This thesis has also carried out and proposed some rudimentary analysis of Shazam

data, as the focus of this paper was to suggest rather than execute the analysis of big

data itself. This could be taken much further with statistical analysis similar to some of

the methods stated in the thesis and applied to data sets such as Shazam.

Music is yet to Moneyball, but it is less a tipping point and more a continuous process.

For this to happen, there needs to be an evolution in the way talent discovery and

consumer understanding is approached and thought about.

Big data is here to stay and other industries are benefitting from it already.

Now it’s the music industry’s turn…

[top]


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