As of 2013, Spotify boasted an active user database of over 50m people (Spotify Press, 2013). The platform has experienced resounding success and is a key driver behind a movement towards music consumption online Saari and Eerola, 2014). There is, however, a problem. 37.5m of Spotify’s users are free users, who fail to act as a source of direct revenue for the platform. Despite failing to provide revenue to Spotify, these users incur variable costs with each song played due to royalties owed. Spotify must, therefore, find a way to increase their conversion rate of premium customers in order to reach profitability.
In response to this scenario I propose a social commerce solution, named “Spotify Tribes”. Literature has found that people are increasingly “eager to recompose their social universe” (Cova and Cova, 2002: Pp.596) and hold a primal need to interact and exchange knowledge with like-minded others (Faraj and Johnson, 2011). Whilst these needs have been shown empirically, Music-as-a-Service (MaaS) providers have failed to satisfy the market. I propose an example of this case through the following question: How do you share music with your friends?
The answer, most likely, involves you sending a URL link to digital media via a social platform. This is a very basic level of sharing, and fails to incorporate the social and self-esteem needs (Maslow, 1943) that should be incorporated within the experience.
My platform, therefore, operates as a built in functionality within the desktop’s current platform, alongside a stand-alone mobile application – very similar to that of Fb Messenger – which can be seen in the below wireframes. The development establishes groups of users that can share music within their communities – or “tribes” – and subsequently manage this content from within. Online community literature states that communities should be formed in three states, which is mirrored in my innovations functionality: open, closed (approval required) and secret (by invitation only) (Kietzmann et al., 2011). Within these groups, there are the standard social communication features such as expressing approval via likes, commenting and reposting to other groups / streams. Drawing inspiration from Netflix, community members are able to anonymously rate submissions, enabling taste profiles to be developed for individual users and the community as a whole. Social media literature suggests that the inclusion of social tagging for content aids in user engagement (Tan et al., 2011). Communities will, therefore, be able to tag each post and produce custom playlists from community content. Recommendations are then made to the individual based on community content, in either its whole or select form.
This solution resolves Spotify’s problem of conversion by increasing the value its premium offering whilst also complementing its fit with the free service, which is empirically recognised to increase conversions (Wagner et al., 2014). Whilst the web functionality will be available to all user categories, the mobile application and ability to create a community is included in the premium membership only. Whilst this sounds like a simple, if not ambitious, solution to Spotify’s problems, an increase of just 0.5% in conversion rate would result in millions of dollars of revenue based on previous annual revenue of €747m (Sisario, 2014), reported subscription ratio (Dredge, 2014) and published user figures (Spotify Press, 2013).
Cova, B. and Cova, V., 2002. Tribal marketing. European Journal of Marketing, 36(5/6),
Dredge, S., 2014. Spotify’s UK revenues rose 42% in 2013 as music service turned a profit. [online] the Guardian. Available from: http://www.theguardian.com/technology/2014/oct/07/spotify-uk-revenues-2013- profit-music [Accessed 5 Jan. 2015].
Faraj, S. and Johnson, S., 2011. Network Exchange Patterns in Online Communities. Organization Science, 22(6), pp.1464-1480.
Kietzmann, J., Hermkens, K., McCarthy, I. and Silvestre, B., 2011. Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), pp.241-251.
Maslow, A., 1943. A theory of human motivation. Psychological Review, 50(4), pp.370- 396.
Saari, P. and Eerola, T., 2014. Semantic Computing of Moods Based on Tags in Social Media of Music. IEEE Transactions on Knowledge and Data Engineering, 26(10), pp.2548-2560.
Sisario, B., 2014. As Music Streaming Grows, Spotify Reports Rising Revenue and a Loss. [online] Nytimes.com. Available from: http://www.nytimes.com/2014/11/26/business/spotify-discloses-revenue-but-not- its-future-plans.html?_r=0 [Accessed 5 Jan. 2015].
Spotify Press, 2013. Information. [online] Available from: https://press.spotify.com/uk/information/ [Accessed 5 Jan. 2015].
Tan, S., Bu, J., Chen, C., Xu, B., Wang, C. and He, X., 2011. Using rich social media information for music recommendation via hypergraph model. TOMCCAP, 7S(1), pp.1- 22.
Wagner, T., Benlian, A. and Hess, T., 2014. Converting freemium customers from free to premium—the role of the perceived premium fit in the case of music as a service. Electronic Markets, 24(4), pp.259-268.