35 best master's thesis a study of the potential role of music classification technologies in video

A STUDY OF THE POTENTIAL ROLE OF MUSIC CLASSIFICATION TECHNOLOGIES IN VIDEO ADVERTISING

By Michael Lynham 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 research 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.

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 acoustic-based methods.

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.

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.

Prior consumer behaviour research suggests that preference, familiarity, and fit of background music have significant influence on the emotional and behavioural responses of consumers to advertisements.

However extant research is based on a traditional notion of an advertisement having static nonpersonalised music, a ‘one size fits all’ approach. This research extends the discussion to begin to

examine the impact of personalised background music in advertisements on cognitive, attitudinal and conative responses among consumers.

Based on the literature review, it is proposed that personalised background music which, by definition, reflects the personal preferences of the consumer with respect to music and genre would be more familiar and therefore more likely to influence attitudes towards advertisement, recall and purchase intention. However, as personalised background music in our use scenario is selected by music classification and caters for individual preference, it may not necessarily benefit from the benefits of music congruence within the advertisement. As such, in line with Park et al. (2015) personalised background music may interfere with information processing in the advertisement and thereby impact attitudes towards the advertisement and purchase intention.

The findings of this research study suggest that the personalisation of background music can result in significantly higher results for advertisement recall, attitudes towards the advertisement and emotional effects and also purchase intention. At the same time, the results also suggest there is no impact on perceived fit or music congruence where the background music is selected using music classification technologies. It is the matching of music metadata which enables fit. By achieving high degrees of match between the original background music from a technical perspective (e.g. duration, BPM, mood etc.) a higher degree of fit is achieved. From this platform, personalisation takes over and delivers the greater effects.

The ability to personalise background music for advertising opens up a substantial realm of new research not merely validating this preliminary study but also exploring whether there is a need renovate the extensive established literature that explores the execution factors, consumer behaviour factors, processing models and effects in a much more comprehensive and systematic way. Studies on background music suggest different effects depending on the whether there is high or low involvement.

This study has focussed exclusively on video advertising through a computer. Additional studies are needed to explore the role of medium but also mode.

The implications of personalised background music are significant for practitioners. It represents a shift from mass consumer broadcast advertising to personalised advertising not merely within a channel, such as Pandora Radio, but within the advertising creative. While this has been commonplace in programmatic digital advertising for some time, it has not been a feature of video advertising or indeed radio advertising. Does the brand and advertiser no longer have a role in background music selection?

Far from it, the selection of the original music still serves as a comparison point for fit and therefore considerations on technical attributes particularly acoustic-based attributes are critical. However, familiarity need not necessarily be as significant a consideration as in the past, particularly in broadcast advertising. This has ramifications for licensing and may result in a significant change in the economics of background music licensing. This study suggests that music classification can be used as a mechanism within the background music selection process to select less expensive and more unique pieces of music to be used in advertising. From a wider perspective, as more and more consumers view and hear advertisements from digital devices, the introduction of programmatic personalised advertisement elements, whether background music, messaging or otherwise, will not only transform the creative industry but also the wider advertising ecosystem as new technology and data driven participants enter the market with enabling technologies.

The research suggests two tentative implications from this initial study of personalised background music in advertising using music classification. Firstly, the results would seem to suggest that music classification can be used as an effective mechanism for selecting less expensive and more niche pieces of music for use in advertising without impacting music congruence within the advertisement creative.

Secondly, background music need not be a ‘one size fits all’ homogenous element of an advertisement.

It has the potential to be a heterogeneous personalised experience that can affect marketing outcomes without negatively impacting fit. As such it is worthy of continued research.