Thursday, June 2, 2016

Social Network Assignment

YUNHUAN DING
Social Network Analysis

Background

I used to cooperate with MusicXchange in adverting class last module. MusicXChange is a non-profit organization, which established by Berkley student Federico Masetti in boston, that aims at improving healthcare in the power of music. Musicxchange emphasizes the breakthrough in traditional medicinal practices by using Ghanaian music as a therapeutic mean to heal. Since healthcare is a significant issue in Ghana today and music paly an important role in their culture, MusicXChange wants to combine these two fields to create a revolutionary treatment in Ghana. The brand character is a humanitarian exchange of music performance where healing is the result. Recently the organization launched a project by exploring the Ghana music to get the public attention and another purpose is use social network analysis to rise the donations more than $5,000. However, as a startup which is not very well known by publics, MusicXChange is struggling with the popularity even though it is located at famous music collage Berkley, but only few people might be interested in the Ghana music. they might be concerned what is music therapy about. moreover, most of the donations come from inside of the school or those whom close to the MusicXChange. So it will be tough to keep these people.


AIM: Increase the public awareness and find the best channels to reach the potential donors, and increase the donor retention rates.

The founder of MusicXChange comes up with some ideas.
First is organize a small Ghana theme concert that invite Berkley teachers and musicians to join the concert. By the help of their reputation that people will start get attention about what is music therapy about.

 It is easier by using the social network analysis (SNA) to help the organization identify the potential donors.  Data will be collected by the survey by asking for the names, contact information, school names, jobs, income level and interests.  The key question for the network data will be:

·      Are you interesting in Ghana music therapy project and will you like get involved and make your contribution?

  • ·      Do you have social media account and what is the frequency?
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  • ·      How long time do you spend on the internet?
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  • ·      How often have you made a donation and how much is acceptable?
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The attribute will include: gender, age group, occupation, income level, interests, friends or relatives who relate to Ghana, country of residence, volunteer and working experience, health condition, education degree.

Social media attributes: total visits in each of the official social account, posts, comments, likes, shares, etc. (these parts are easy to get from the internet.)


Based on the data we collected above, we can abandon some groups who don’t have income or less income based on the income level and age, it will save the time for the overload working. Then we can use SNA to identify the people who has the most important connections relate to the fields and calculate the centrality measures in order to find out who has the great interest on public activities with high donations in the past. Then by applying the method we can identify who the is the expert in the field with high intention to do contribution in the society. The beneficial from this tool not only can save time and money but also approach the right targets in order to find out the potential customers.




1 comment:

Christopher Tunnard said...

This is a nice idea, and using social network analysis could indeed be a good way to develop a donor community for the therapeutic use of Ghanaian music in Boston. Although your survey idea is fine for standard purposes, it doesn't appear to involve a network, as all the people surveyed would already be interested in the music as shown by their attendance at the concert. There's also very little on how you'd assess the network results--only one mention of centrality (which measure?) which would be hard to do on the network you describe. And what network measure would you use to help retention, for example?

Where it might start to get interesting is something you do touch on:who are the attendees friends on social media? If you could get this data, it would create a real network, and you could examine events co-attended by friends, previous participation in crowdfunding, etc.