Final Project Deliverables:
1. Executive summary: summarize your findings for the board of directors of Universal Music. Since they are not all very well-versed in excel and modeling techniques, you need to formulate and communicate actionable business recommendations based upon your analysis, including its limitations, in easy-to-understand terms. Present your reasoned opinion on the efficacy and viability of your analytics-based recommendations for Universal Music and critically assess their validity. If you feel your conclusions and recommendations are not sufficiently supported by data, include your recommendations for improving them.
Executive summary preparation guidelines: should not exceed more than 4 pages
2. Dashboard: present key findings in a dashboard.
Dashboard preparation guidelines: your key findings should be presented on one page.
Final Project Objectives:
You are expected to demonstrate your knowledge and ability to
I. Explain in your own words the key ideas behind fundamental techniques in data analytics, including dashboarding, classification, clustering and AB-testing;
II. Identify new opportunities to use these techniques across business domains to guide decision making;
III. Confidently apply these techniques to novel problems using a combination of Excel and JMP;
IV. Formulate and communicate actionable business recommendations based upon your analysis, including its limitations;
V. Critically assess the validity of analytics-based recommendations in the context of specific business decisions.
Final Project Submission Instructions:
I. Final team project deliverables must be submitted via blackboard by only one team member by the due date indicated in the table above.
II. One team member submits the following items:
a. Zipped folder that contains the following deliverables by by 11:59pm (end of the day), October 11, 2019:
i. Project Executive Summary (up to 4 pages);
ii. Dashboard (1 page word file, THERE IS NO TEMPLATE);
iii. Appendix (not mandatory, only if you want to include additional information, results that are not included in the executive summary and dashboard, assumptions, etc. No page limit for the appendix. THEREIS NO TEMPLATE);
iv. Excel file with analysis. Make sure your Excel file contain analyses that justify what is included in the executive summary and dashboard. No limit on the number of sheets in the excel file.
III. You may make multiple submission. Only the last submission will be grade.
Tips for developing an effective team:
• Do team members want to participate in the team? Do team members attend all meetings?
• Are members committed to accomplishing the team mission and expected outcomes?
• Has the team defined and communicated its goals; its anticipated outcomes and contributions; its timelines; and how it will measure both the outcomes of its work and the process the team followed to accomplish their task.
• Do team members hold each other accountable for project timelines, commitments, and results?
• Do all team members understand the roles and responsibilities of team members?
• Are the team members clear about the priority of their tasks?
• Do team members communicate clearly and honestly with each other?
Case: Music in the 2010’s
An Overview of the Music Industry
The way in which people listen to music has changed throughout the years. From the cassette players of the 1970s, to the extreme market disruption brought about by iTunes in the early 2000s the way in which artists reach their fans has changed rapidly. The 2010s saw another massive disruption to the music industry: the rise of streaming services, like Spotify. These services forgo the need for listeners to own music altogether, and instead deliver large libraries of music to their subscribers for a monthly fee. They also allow artists to interact more directly with their fans, and understand their audiences through analytics.
Speaking of analytics, let’s discuss Spotify’s data assets. Spotify does a lot more than simply act as an intermediary between artists and fans. They also gather immense amounts of data on the music on their platform, and the artists that create it. For
example, Spotify’s algorithms can identify: the key of a song, its mode (major or minor), and its genre (rap, pop, country, etc.). They also attempt to quantify more abstract characteristics of songs, like how happy a song is, how easy it is to dance to it, etc. At a more aggregate level, Spotify tracks popular songs, which ultimately find their way into popular playlists, and if a song is particularly good it will find its way into a “top 10” playlist.
Because Spotify is a now major player in the music industry, music companies around the world endeavor to understand what is referred to as “the Spotify ecosystem.” They want to know how to best promote their music on Spotify, how to best support their artists, how to get their songs recommended, and of course how to make a name for themselves. In many ways, this is incredibly different from how the music industry used to operate. The Spotify ecosystem is highly dynamic, representing peoples’ tastes, and is very susceptible to shocks. For example, when an artist releases an instant hit you can bet the dynamics of this ecosystem will change because of it.
For a music company to capitalize on a market like this, it must also be dynamic. It must know the real-time preferences of listeners, the state of the ecosystem, and have an idea of where it is going. To grow any artist, this company needs to know how to best support them, from recommending up-and-coming artists to listeners to utilizing its well-made artists and providing them the support they need.
Monetize Spotify
Monetizing Spotify Spotify claims that it pays $0.006-$0.0084 to the rights holders for a song every time that song is streamed. While this might seem like a small amount of money, when you consider a song like P!nk’s “Walk me Home” has been streamed roughly 208,500,000 times, that translates into anywhere between $251000 and $1751400 for that one song alone.
Considering P!nk is a very big name in the music industry, that revenue is probably closer to the upper end of that range, and possibly even higher. This leads us to an interesting question: how do we convert the popularity of a song to revenue? There are two options for Universal Music to contract with Spotify. Note that in either of these options each stream generates the same amount of revenue for that song.
Option #1:
The following scheme is a very realistic way of modeling this. Suppose Universal Music is about to release a new Taylor Swift album, and they plan to release it on Spotify. As Taylor Swift is one of their premium artists and this is a new album, they are going to require Spotify pay them $.0084 every time a song from that album is streamed. If the album truly is as popular as Universal Music bet it would be, everyone is happy. However, what happens if the album is an unexpected flop? In that case, Spotify’s contract with Universal Music specifies that rather than paying $.0084 per stream, they now only pay Universal Music
$.002 per stream. This allows Spotify to reallocate money towards more popular artists on their platform, while allowing Universal Music to recoup revenue.
Option #2:
Another option for Universal Music is to charge a flat fee of $.006 per stream, regardless of the song being actually popular or not. Of course, if Universal Music plays it safe and only requires Spotify give them $.006 per stream for a given song that ends up being a huge it, there is an opportunity cost on the part of Universal Music. Universal Music needs to choose the one of the two options to contract for a song before the song is being released on Spotify. Universal Music’s goal, then, should be able to accurately predict the popularity of a song. That way they can make a premium off of songs that are really popular, while carefully assessing the true value of their music.
Assignment
• Despite the important role data now plays in the music industry, many companies are behind the “data curve.” To that end, Universal Music has hired you as a data analyst to help them understand how they can utilize Spotify to their benefit. The company has data on a large number of songs on Spotify. It is the hope of Universal Music that they can gain an understanding of trends in what characteristics popular songs have, they can better utilize their artists. Below are some of the questions Universal Music would like answered:Do you see any trends for this industry? Any high-level directions that Universal Music should move towards?
• Are there any factors that lead to the success of a popular song? Are the popular songs different in terms of the variables measured by Spotify or the KPIs you came up with? If not, what might this mean in terms of what makes a song popular?
• Is there a strategy to maximize the expected gain given the contract options?
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