Spotify online music service. We conduct preliminary tests for cognitive, attitudinal and conative effects. Following from the research on background music above ...
A STUDY OF THE POTENTIAL ROLE OF MUSIC CLASSIFICATION TECHNOLOGIES IN VIDEO ADVERTISING
Theo Lynn, Dublin City University; Ireland Michael Lynham, Dublin City University, Ireland David Kenny, Irish Centre for Cloud Computing and Commerce, Dublin City University, Ireland Artemisa Jaramillo, Dublin City University, Ireland ABSTRACT Video advertising continues to be a mainstay of the marketing arsenal. The increased targeting capabilities of digital television broadcast networks, consumer adoption of broadband and the accompanying changes in consumption is driving greater interest in digital video advertising by consumers, broadcasters and advertisers. Online digital video advertising is growing rapidly driven by greater capacity to target audiences, drive engagement and measure impact. Advances in customer profiling technologies and music classification technologies provide the basis for personalised background music in advertising. Extant research is based on the traditional notion of an advertisement having static non-personalised music, a ‘one size fits all’ approach. This paper extends existing research to examine the impact of personalised background advertising in video advertisements on cognitive, affective and conative outcomes. The findings of this preliminary study suggest that the personalisation of background music can result significantly higher results for advertisement recall, attitudes towards the advertisement and emotional effects, and also purchase intention. The results also suggest there is no impact on perceived fit or music congruence where the background music is selected using music classification technologies.
INTRODUCTION There is an extensive and well-developed literature on the use of background music in television advertising (Kellaris and Cox 1993; Park, Park and Jeon, 2014). Studies have consistently shown that presence (versus absence), familiarity, and fit of background music impact the cognitive (Hoyer and Brown. 1990; Hahn and Hwang 1999), attitudinal (Park and Young, 1986; Ali and Peynircioglu 2010) and conative outcomes of advertising (Alpert and Alpert 1989; Alpert, Alpert and Maltz 2005). More recently, Park, Park and Jeon (2014) have shown that consumer involvement has a significant impact on effectiveness of the familiarity and fit of background music in advertising.
Unsurprisingly, background music selection is a major
consideration in advertising production. Despite the large body of empirical results and managerial interest in the area of background music in advertising, the academic literature has not addressed how advances in digital technologies might renovate existing theory or present new avenues for research. The introduction of digital file formats for music and pervasiveness of devices and streaming services for digital music has resulted in a substantial body of computer science research in the area of music classification including content- and acousticbased methods. Our paper explores the potential role of music classification technologies in advertising. We posit that music classification technologies can reduce risk associated with background music selection in advertising through greater predictability of familiarity, fit and induced mood thus positively influencing the contribution of background music to cognitive, affective and conative outcomes of video-based advertising.
We organize this paper as follows. The next section introduces music classification technologies and how they are used to discover and select music. This is followed by a review of the literature on the impact of background music on marketing outcomes and a number of research hypotheses relating to the use of personalised background music in advertising are presented. Then an outline of the research methodology and the specifics of the research design utilised to explore the research hypotheses are presented. Finally, there is a discussion of the findings from the study, and the limitations encountered while undertaking this research.
Music Classification Technologies Advertisers and brands alike must respect their overall marketing objectives before they choose background music that will complement their advertisement activities (Ding and Lin, 2012). Brands and advertisers are making used of new technologies and sources to gather and analyse data that will help them understand and target consumers more effectively that will help attract the right consumers. For example, Kaye (2013) cites Ford’s collaboration with Live Nation and Ticketmaster to help discover what music their target audience was consuming based on the purchasing patterns of concert tickets. However, this is only one side of the coin in terms of music congruence or fit in advertising. Music identification and selection is equally as important if not more. Ducrot (2013) suggests that brands regularly neglect the impact of music fit on achieving advertising objectives with an over-emphasis on using music due to popularity or familiarity in the marketplace. This can be expensive and result in unexpected consequences depending on consumer personality type and indeed consumer involvement (Furnham and Bradley 1997; Park, Park and Jeon 2014). Recent research suggests that brands looking for unique pieces of music either from
past or new independent artists can add value to their advertising efforts and ignite greater attention from consumers (Graakjaer, 2014; Barry, 2014). Yet, the technologies being used to discover music remains, for the most part, a manual process.
Conklin (2013) defined music classification as a method of presenting class labels based around the piece of content, in this case, music. The majority of music classification technologies comprise two processing elements - feature extraction followed by classification (McKinney and Breebaart 2003). Ullrich et al (2014) further categorises classification technologies in two approaches - content filtering and collaborative filtering.
A content filtering approach analyses a music file based on a set of criteria which may include external data. The ensuing information connected with music can be used to identify and recommend other music that is similar and potentially of interest to the end user (Casey et al., 2008). Commonly cited examples are Apple’s iTunes Genius service and Pandora.
Content-based methods seek to autonomically capture the
properties of music, typically through metadata or acoustic methods (Cai et al, 2011). The metadata is often provided by the publishers and may be descriptive (e.g. title, artist name, genre etc.), structured (e,g. duration, file sequence identifiers etc.) and/or administrative (copyright status, year of release etc.) in nature. Metadata may be augmented using expert tagging resources (e.g. Music Genome Project / Pandora), and/or non-expert tagging (e.g. MagnaTagaTune) however manual tagging is extremely human-intensive and as such is both slow and costly, particularly where expert musicologists are used. As such, there has been significant investment in auto-
tagging research whereby tags are placed on a piece of content (including music) using a programmed tagging algorithm (Bertin-Mahieux et al. 2008). Advances in machine learning and semantic auto-tagging have improved the scalability of contentbased classification although limitations in accuracy remain (Tingle et al. 2010).
In contrast, acoustic-based methods seek to extract properties within music, such as tempo, meter and rhythm, from audio signals in the music and then construct distance measurements or statistical models to estimate the similarity or dissimilarity of music (Cai et al. 2007). Orio (2006) suggests that the most important properties within music for classification purposes are the structure of music, rhythm, melody, timbre, and the acoustics. Again advances in machine learning and other computing methods have facilitated acoustic-based auto-tagging (Marques et al. 2011). For example, RANSAC, a recently developed automatic classification technology uses similar criteria – pitch, tempo, amplitude variation patterns (e.g. loud and soft) and periodicity (how or to what extent the music repeats phrases) – to classify music. Comparative studies suggest higher levels of music classification accuracy using RANSAC (Moss, 2015).
Militaru and Zaharia (2011) defined collaborative filtering as a method of utilising a large dataset of consumers’ preferences to recommend other potential tangibles which users may wish to consume. The collaborative filtering approach analyses users’ past consumption behaviours and makes recommendations to others who have similar behavioural patterns (Forbes and Zhu, 2011). Commonly cited examples are Netflix and Amazon. Collaborative filtering utilises two key elements - (i) latent factor modelling to evaluate the user and the item of content they have consumed, and (ii)
neighbouring to evaluate the behaviour of a user who consumes similar types of content to that of another user. Collaborative filtering classification systems often augment this data with user profile and feedback from CRM systems and/or music file metadata as described earlier (Sahoo et al. 2012). These additional data sources further refine the recommendations made to end users.
A variety of researchers have suggested extensions to these approaches and methods. For example, Maxwell (2007) proposes the analysis of lyrics data as a rich source of information for song similarity particularly with advances in natural language processing technology and the text-based properties of lyrics. Rho et al (2013) introduced a context based music classification methodology which clustered the properties of music along with properties of the situation itself such as consumers’ mood. While the integration of these three elements – the music properties, the individual preferences and context – using both content and collaborative filtering represent an ideal scenario, this represents a significant logistical, economic, and technical challenge. Metadata extracted from experts or non-expert classification is time consuming and expensive (Zhang, 2003). Cultural metadata extracted from user ratings and other human authored text including social networks can contribute to music classification however this type of data has limitations such as unconstrained vocabularies, fan bias and non-acoustic tagging (Tingle et al. 2010). In both approaches, genre classification of music has proven problematic as interpreting musical genre is fraught with misinterpretation, whether by human or computer (Strum 2013). For example, the popular song, ‘Four Five Seconds’ by Rihanna, Kanye West and Paul McCartney can be classified as at least four genres - Rap, Hip Hop, Pop and Rock.
Aizenberg et al (2012) pointed out that the mass of online content has afforded many organisations the opportunity to give customers recommendations based on their interests or set criteria. Jones and Pu (2009) claimed that classification technologies can be regarded as a benefit to consumers based on the relevance of the content provided and the ease of use of such technology to the end user. Classification technologies are used widely in many online services to disrupt the established broadcast media sector including television and film (YouTube, Netflix), radio (Pandora) and music (iTunes, Pandora). Similar technologies are transforming the advertising sector providing contextual or profile based advertising, whether searchor social media based. The music licensing sector has not been oblivious to change. Online services such as SoundCloud and Getty Images, now provide access to brands and advertisers to search databases of hundreds of thousands of music and audio files by a variety of content filters including copyright status, genre, subgenre, lyric/instrumental, vocals, lyrical themes, mood, duration, speed, instruments etc. However, background music selection for advertising remains a manual and unscientific process on the whole and video-based display advertising remains relatively unchanged, targeting mass audiences with static creative elements. One size still fits all. As media consumption becomes increasingly digital and autonomic music classification technologies mature, the opportunity to transform online videobased display advertising using a personalised dynamic background music advertising system becomes real. However, is it worthwhile? There is a lack of evidence to suggest that the use of personalised dynamic background music can contribute to enhanced marketing outcomes. The next section of this paper discusses research on
the impact of background music on marketing outcomes and proposes a number of research hypotheses to be explored in this study.
Background Music and Marketing Outcomes Music serves a variety of functions in advertising including entertainment, structure and continuity, memorability, lyrical language, targeting and authority establishment (Huron, 1989). It attracts consumer interest, communicates information and acts as a memory mechanism (Hecker, 1984; Park and Young, 1986; Heaton and Paris 2006). Brands seek to connect advertisements with music to facilitate the recruitment of favourable brand attitudes, awareness and positively influence purchase behaviours among consumers (Oakes and North, 2013). Research on the use of background music in television advertising is not new with a well-established literature base on the impact that music has on consumer responses including cognitive, attitudinal and conative outcomes.
Cognitive Outcomes Most studies suggest that the presence of music in advertising increases cognitive outcomes, and specifically memorability (Hunt, 1988; Huron, 1989; Dunbar, 1990; Apaolaza-Ibanez et al., 2010). In particular, research suggests that music increases recall significantly after an advertisement has been processed (Roehm, 2001). Studies have identified a number of factors that impact recall including familiarity, preference and fit (Hoyer et al. 1984; Hahn and Hwang 1999). Consumer music preferences have also been shown to impact cognitive behaviours relating to advertising including genre (Smith and Morris, 1977; Furnham and Bradley, 1997; Zander, 2006; Dolegui, 2013).
Studies on the impact of familiarity largely suggest that brand advertising featuring popular music that consumers are familiar with (through radio play and otherwise) have higher levels of consumer attention and recall (Huron, 1989; Wallace, 1994; Hahn and Hwang, 1999; Allan, 2006; Walker, 2009; Roehm, 2011). Notwithstanding this, a study by Furnham and Bradley (1997) suggests that pop music can disrupt cognitive behaviours with both introvert and extrovert consumers. Studies on fit between background music and advertising suggest that background music that compliments an advertisement requires less cognitive attributes from the consumer and therefore positively impacts cognitive outcomes (Pomerantz, 1981; MacInnis and Park, 1991). North et al (1999) identified through their research that music that is liked by consumers has an important impact on their behaviours. Based on this research, personalised background music should be more familiar to consumers and therefore result in higher recall. However, as personalised background music caters for individual preference, it is not the music selected by the brand or advertising professional and therefore may have less brand congruence or music fit resulting in lower recall.
Attitudinal Outcomes Meyer et al (2011) stated that affective behaviours of a consumer is an important consideration within advertising and consumer behaviour.
Desmet et al (2012)
defined affective behaviours as those involving emotions and moods (which span over short and long periods of time and are intentional in nature). Music can play a critical role by developing the emotions within consumers resulting in a positive outcome for brands (Alpert and Alpert, 1989; MacInnis and Park, 1991; Pekkila 1997; Kotler,
1999; Alexomanolaki et al. 2007; Jessen & Graakjaer, 2009; Brodsky, 2010). Studies have found that music within an advertisement has a positive relationship on influencing the development of a consumer’s attitude toward a brand when compared to the nonexistence of music but more significantly that music can facilitate an advertisement by influencing consumer attitudes where there is low involvement with a brand or advertisement (Park and Young, 1986; Alpert and Alpert, 2005).
Zhu and Meyers-levy (2005) indicated that background music can provide two forms of meaning; embodied meaning (e.g. emotions/feelings) that is created through the music used in the advertisement and referential meaning (the meaning which is generated through the interlinking of the music with the video advertisement). Scherer (1995) suggests that music can elicit three elements of emotional reactions within consumers; personal experience and behaviours (elicit or implicit), and their physical responses. Music fit comes from the lyrical content present within the music and how it relates to the main advertising message that is been communicated by the brand through various mediums. If a brand can achieve music fit or musical congruity, it can assist consumers to easily consume the information within a video advertisement and gain meaning from the content presented with the advertisement resulting in a desired outcome (Oakes, 2007).
Music fit has been found to contribute to the
production of likeability attributes from consumers (Goodstein, 1993). As such, the careful selection of music for advertisements is critical so that the specific meaning, whether embodied and/or referential, and associated emotional reaction is achieved with the target audience (Barry, 2014).
Unsurprisingly, research on background music has found that a variety of factors contribute to affective outcomes including consumer music preference, emotional theme, lyrics and tempo of the music. Kellaris and Rice (1993) outlined that consumers are more attracted to music they like and/or digest more information from the music that they prefer. Stout and Leckenby (1988) also supported Kellaris and Rice (1993) findings of a positive relationship with the level of information consumers consumed from an advertisement and the degree to which they liked the music featured within the advertisement. For example, Alpert et al (2005) found that sad music contributed to the highest level of intention to purchase. Tempo can be related to emotional theme. Ding and Lin (2012)found that increased pleasure and value perceptions are direct consumer outputs when exposed to fast tempo music in advertising. Fast tempo music is normally related to positive emotions such as; happiness, positivity etc. Gorn et al (2001) claimed that music can impact consumer affective behaviours in a positive or negative manner when used in advertising.
The emotional manipulation of consumers through the use of music is not without criticism. Waldman (2000) criticised brands who integrate music into their commercial advertisements as it can distort consumers key memories associated with a particular piece of music. Scott (1990) felt that consumers are becoming more knowledgeable about the purpose of advertising and as a result feel negatively towards commercial advertising. Lusensky (2011) explained that some brands try to counteract these market criticisms by implementing the 4 E’s – Emotions, Experience, Engagement and Exclusivity. For example, music can help brands create a positive experience with existing and future customers which in turn can help facilitate brand
engagement and exclusivity as consumers feel part of a community through the creation of a common identity.
While personalised background music caters for individual preference, it is not the music selected by the brand or advertising professional. This may negatively impact brand/advertiser control of execution factors and resulting in less brand congruence or music fit and consequent attitudinal effects e.g. less positive feelings towards the brand or advertisement.
Conative Outcomes The use of music within advertisements aims to enhance the potential of influencing conative behaviours within consumers such as purchase intention. Spears and Singh (2004) define purchase intention as an “individual’s conscious plan to make an effort to purchase a brand.”
Lindenberg (2012) highlighted that environment cues are
attributes that can help to create a positive outcome from the consumer (such as a purchase).
Allan (2006) argues that music is a form of manipulation which brands utilise as part of their marketing arsenal to influence future consumers purchase intention to advance profit levels. There are numerous retail and service environment studies that highlight the positive impact of consumer music preference and genre on purchasing behaviour (Gorn, 1982; Herrington, 1996; Jacob et al. 2007; Dube and Martin, 2010). Alpert et al. (2005) found a relationship between music congruence with the product being advertised and increased likelihood of purchase. It is not unreasonable to propose that a personalised background music advertising system would result in
greater conation as the background music is likely to reflect music and genre preference.
HYPOTHESES The design for the present study involves exposing subjects to advertisements featuring non-personalised and personalised background music. Personalised background music is selected using music classification technology deployed by the Spotify online music service. We conduct preliminary tests for cognitive, attitudinal and conative effects. Following from the research on background music above, the following hypotheses are proposed:
H1: Background music recall of video advertisements featuring personalised background music is higher when compared to background music recall with video advertisements featuring non-personalised background music.
H2: Advertisement recall is higher with video advertisements featuring personalised background music compared to advertisement recall with video advertisements featuring non-personalised background music.
H3: Video advertisements featuring personalised background music are perceived to have less brand congruence than video advertisements featuring non-personalised background music.
H4: Prior familiarity with personalised background music featured in video advertisements results in more positive emotional feelings when compared to video advertisements featuring non-personalised background music.
H5: Preference for video advertisements featuring personalised background music will be greater than video advertisements featuring non-personalised background music.
H6: Intention to purchase is greater with video advertisements featuring personalised background music compared to intention to purchase levels with video advertisements featuring non-personalised background music.
METHODOLOGY Research design The study followed a quasi-experimental within-groups design (Lazar et al 2010). Subjects were first exposed to a video advertisement featuring non-personalised background music, and then where exposed to a video advertisement featuring personalised background music selected using music classification technology. Participants were exposed to both stimuli and their results were compared.
Procedure In Stage 1, subjects received an online pre-questionnaire. Participants selected their top five most liked tracks from a selection of thirty tracks, and their most liked genre from a selection of five. This data was utilised to create the video advertisement featuring personalised background music for Stage 3. Participants also answered
questions related to demographics and general attitudes towards video advertising. The duration of this stage averaged one week. During Stage 2, participants received an online hyperlink to watch a video advertisement featuring non-personalised background music and answered a questionnaire based on cognitive, affective and conative behaviours. The duration of this stage averaged one week. In Stage 3, participants received a hyperlink to watch a video advertisement featuring personalised background music, and answered a questionnaire based on cognitive, affective and conative behaviours. Finally, during Stage 4, one-week after exposure, participants received an online questionnaire to measure recall and recognition.
Stimulus The stimulus was customised according to participants’ music profile. The customised track selection criteria included: i) Participant’s most liked genre of music, music (Rock, Pop, Hip Hop, Rock, R&B/Soul) in line with Clarke et al (2012); and ii) Participant’s top five most liked tracks from the top thirty of the Irish Top 100 Singles for the week of 25th of May 2015 as sourced from IRMA (2015) in line with Areni and Kim (1993) and Kellaris and Rice (1993).
The music profile for each participant was mapped. A selected music piece based on a similar tempo, as measured by beats per minute (BPM), to the original music within the video advertisement was then used as the basis for selecting appropriate personalised
background
music
using
Spotify.
The
nearest
music
piece
recommendation generated by Spotify that featured a similar BPM to the participants liked music selection and that of the original music was then used as personalised
background music in the video advertisement presented to each individual participant. The new video advertisement featuring the personalised background music was then tested accordingly. The selected advertisement video was ‘The Big Pony Collection’ by Ralph Lauren featuring original background music from the band One Republic and their music piece entitled ‘Secrets’.
Participants The sample consisted of 61 participants consistent with similar research (Stout and Leckenby 1986; Tauchnitz 1990; Alexomanolaki, et al. 2010). Respondents were between 14 and 54 years old (M = 33.5, SD=43.1), 56 per cent were female and 44 per cent were male. These characteristics match the characteristics of the two highest groups who use the Internet and make online purchases (Nielsen 2014).
Subjects were selected using a nonprobability sampling technique (Fink 2006), specifically snowball sampling (i.e. participants who agreed to participate in the study were encouraged to recruit other participants) and judgemental sampling (within the age group of 18-54 and living in Ireland).
Measures Prior familiarity Familiarity with the brand, advertisement video and background music was measured by asking participants to rate their prior familiarity on a scale from 1 (extremely unfamiliar) to 7 (extremely familiar) (Becker-Olson and Karen 2003).
Recall and recognition
For the recall test, participants were asked to correctly select from a list provided the following: brand, name of collection featured in the advertisement, advertised product type, advertisement position within the plot, the name of the background music featured in video one (original advertisement) and video two (personalised advertisement) following Park and Young (1986). Distractors items included in all cases. For the recognition test, participants had to identify which video they were exposed to during Stage 2, and which video they were exposed to during Stage 3.
Attitudes towards advertisement in general This study utilised bi-polar adjectives over the four questionnaires to enable the gathering of data based on participants’ complete responses (e.g. Good/Bad, Pleasant/Unpleasant, Favourable/Unfavourable, Positive/Negative) (Rifon et al. 2004).
Affective and conative behaviour Affective behaviour before and after stimulus exposure was measured using items such as ‘I felt sad/happy, negative/positive, good/bad’, in accordance with Cohen et al (2004) and Allen et al (1989). A seven point Likert scale based on Happy/Sad items was utilised to assess participants’ emotions before and after stimuli exposure, adapted from Sloboda and O’Neill (2001). Finally, affective and conative behaviour was measured following Cobb-Walgreen et al (1995). The statements were: ‘The video ad was appealing’, ‘The video ad has influenced me to make a purchase’, ‘The video ad has created positive feelings after I viewed it’, and ‘I will remember this video ad in the future’.
RESULTS During the recall test, 96.72 per cent of participants (N=61) were able to identify the advertised brand. Because of the brand portrayed was the same for video one and video two, participants were also asked to recall the background music for both stimuli. Background music for video two (personalised background music) was recalled by 68.9 per cent of participants (N=61), while only 50.8 per cent correctly identified the background music for video one (non-personalised background music (N=31). H1 was then supported.
The video advertisement featuring personalised background music was correctly identified by 65.6 per cent (N= 40) of participants, while only 50.8 per cent (N=31) identified the non-personalised advertisement. As a result, H2 was supported.
The results from the analysis also indicated that there was no significant difference between video one (non-personalised video advertisement) and video two (personalised video advertisement) in relation to music fit (congruence) and participant’s perception of the brand (p >.05). Thus, H3 was not supported.
Prior familiarity of the background music featured in video one (non-personalised advertisement) and video two (personalised advertisement) and feelings/emotions (Good/Bad,
Happy/Sad,
Positive/Negative)
had
no
statistically
relationship, as showed by the Spearman’s rank correlation. supported.
significant
Thus, H4 was not
A Wilcoxon signed rank test was conducted to compare attitudes towards each video advertisements. The results from the analysis showed a significant difference in the level of favourable attitudes towards video two (personalised video) (Z=-4.679, P
