A STUDY OF THE POTENTIAL ROLE OF MUSIC CLASSIFICATION TECHNOLOGIES

IN VIDEO ADVERTISING

Presented by:

Mick Lynham HND, BA (Hons.), MSc. MSc. MMII

Dublin City University

Qualifications: Higher National Diploma in Music Management & Production / BA (Hons.) Degree in Media / MSc. Marketing / MSc. Digital Marketing

Profession: International Marketing Officer with Trinity College Dublin /

Lecturer in Marketing, Business Strategy & Supply Chain Management

Mick Lynham HND, BA (Hons.), MSc, MSc. MMII

BACKGROUND

The new multi-screen world is changing consumer behaviour■ The majority of media consumption is screen-based

■ Consumers move between multiple devices to achieve their goals

■ Television no longer consumes our full attention

Source: Google, 2012

The digital transition from analogue broadcast is making significant strides

Personalisation is a common feature of the online landscape

E-commerce Personal Shopper

Advertising

Digital technologies and personalisation are transforming the consumption and engagement of broadcast media

Television Radio

Music

Despite advances in digital formats and delivery, video based advertising has remained relatively static in form and has negative impacts on consumer engagement

Audiences are largely treated as homogenous‘One size fits all’

WHAT HAPPENS IF WE CHANGE ONE VARIABLE IN ADVERTISING?

WHAT HAPPENS IF WE APPLIED THE SAME

TECHNOLOGICAL APPROACH AS SPOTIFY TO BACKGROUND

MUSIC IN ADVERTISING?

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).

Music classification is a method of classifying musical content through the presentation of labels (Conklin, 2013)

■ Analyses a music file based on a set of criteria which may include external data

■ Content-based methods use manually-tagged metadata

■ title, artist, genre, duration etc

■ costly and time-consuming

■ Acoustic-based methods used machine extracted metadata

■ e.g structure of music, rhythm, melody, timbre, and the acoustics

■ non-trivial from a computer science perspective

■ Method of utilising a large dataset of consumers’ preferences to recommend other potential tangibles which users may wish to consume (Militaru and Zaharia, 2011)

■ users’ past consumption behaviours

■ recommendations to others who have similar behavioural patterns

■ often augmented with other data

Content Filtering Collaborative Filtering

DOES PERSONALISED BACKGROUND MUSIC IN VIDEO ADVERTISING GENERATED USING MUSIC CLASSIFICATION TECHNOLOGIES INCREASE THE

EFFECTIVENESS OF THE VIDEO ADVERTISING?

RESEARCH STUDY

Hypotheses■ 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.

Research Design

Sample and Measures■ The sample consisted of 61 participants using snowball sampling

■ Respondents were between 18 and 54 years old (M = 33.5, SD=43.1), 56 per cent were female and 44 per cent were male.

Measures

■ Prior familiarity with the brand, advertisement video and background music was measured by asking participants to rate their prior familiarity using a 7-pt Likert scale (Becker-Olson and Karen 2003).

■ Recall - 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 the original and personalised advertisement (Park and Young, 1986). Distractors items included in all cases.

■ Recognition - 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 - utilised bi-polar adjectives over the four questionnaires to enable the gathering of data based on participants’ complete responses (Rifon et al. 2004).

■ Emotions - a 7-pt 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).

■ Affective and conative behaviour - measured following Cobb-Walgreen et al (1995).

Results 1. Background music for the Video 2 (Personalised) was recalled by 68.9% while only 50.8% correctly recalled the

background music for Video 1 (non-personalised).

– H1 was supported.

2. Video 2 (Personalised) was correctly identified by 65.6% while only 50.8% identified Video 1.

– H2 was supported.

3. There was no significant difference between Video 1 (Non-personalised) and Video 2 (Personalised) in relation to music fit (congruence) and participant’s perception of the brand (p >.05)

– H3 was not supported.

4. Prior familiarity of the background music featured in Video 1 (Non-personalised) and Video 2 (Personalised) and feelings/emotions had no statistically significant relationship, as showed by the Spearman’s rank correlation.

– H4 was not supported.

5. 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 2 (Personalised) (Z=-4.679, P <.001) when compared to attitudes expressed towards Video 1 (Non-personalised). Video 2 was most preferred among participants (75.4 per cent) when compared to Video 1 (24.6 per cent).

– H5 was supported.

6. Results of a Wilcoxon signed rank test suggested that there was a significant difference the intention to purchase (Z=-6.539, P<.001) with video 2 (Median = 5) having a higher level of intention to purchase than Video 1 (Non-personalised) (Median = 3).

– H6 was supported.

Discussion & Conclusion The findings of this research study:

■ Personalisation of background music can result in significantly higher results for advertisement recall and attitudes towards the advertisement but also purchase intention within consumer engagement.

■ No impact on perceived fit with brand values where the background music is selected using music classification technologies.

■ Matching of music metadata enables fit with a brands values

■ 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 based on familiarity, preference etc. and helps to increase consumer engagement.

Discussion & Conclusion

Future research

■ Study the role of involvement in impact of personalised advertising

■ Role of other media and mode

Practice implications

■ Shift from mass consumer broadcast advertising to personalised advertising not merely within a channel but within the advertising creative along with enabling an increase in consumer engagement.

■ Change in the role of the brand and advertiser in background music selection.

■ Change in the economic model for background music licensing? Familiarity need not necessarily be as significant a consideration as in the past…

■ Another change in control and influence within the advertising ecosystem towards technology enablers?

THANK YOU Questions???