Reference no: EM131909383

Project - Analytics for Decision Support

The file emis7357sp18project1.csv contains historical ticket sales and donation data for 30,019 patrons or donors to a festival for over ten years, up to 2015.You must do the visualizations in Tableau or R and you mustuse R for the analytics.

The overarching goal of the project is to help the festival organizers understand what sort of insights, if any, they can gain from using analytics in a nonprofit setting for their patron base (donors and ticketholders).

The festival organizers would like to understand the following:

1. Descriptive analytics

a. Donors

• i. How many total donors (people who contributed a positive amount any year) are there? What about donors who contributed at least $100? $500? $1000?
• ii. How many donors were there each year in the data set? What about donors who contributed at least $100? $500? $1000?
• iii. Plot the histograms of donations for the entire time period, for 2014 and for 2015. Comment.
• iv. Plot a scatterplot with the donations for 2014 and 2015 (one dot = one row, removing [for this question only] instances with no donation either year). Comment.
• v. How many "active" donors were there at the end of 2010? (you have to define "active") How many are there at the end of 2015? Are they the same people?
• vi. Consider the donors who have donated a total of more than $100 over the past 5 years (2011 to 2015). Do they tend to donate every year? Do they donate only a few of those years? (You might want to create a table or some other summary visuals.)
• vii. Plot amount donated over the past 5 years against total amount donated. Comment.
• viii. How long on average (how many years on average) do donors keep donating money to the festival? (You might need to create a new parameter that is the first year there is a positive donation from that donor, and a new parameter about the last year.)Also include other summary statistics such as median, first/third quartile, min/max.

b. Ticketholders

• i. How many total ticketholders are there?
• ii. How many ticketholders were there each year in the data set?
• iii. Plot the histograms of ticket revenues for the entire time period, for 2014 and for 2015. Comment.
• iv. Plot a scatterplot with the ticket purchases for 2014 and 2015 (one dot = one row, removing [for this question only] instances with no ticket purchase either year). Comment.
• v. How many "active" ticket purchasers were there at the end of 2010? (you have to define "active") How many are there at the end of 2015? Are they the same people?
• vi. Consider the ticket purchasers who have bought at least$500 in tickets over the past 5 years (2011 to 2015). Do they tend to buy tickets every year? Do they buy tickets only a few of those years? (You might want to create a table or some other summary visuals.)
• vii. Plot amount of ticket purchases (in dollars, not number of tickets) over the past 5 years against total amount donated. Comment.
• viii. How long on average do ticketholders keep purchasing tickets to the festival? Also include other summary statistics such as median, first/third quartile, min/max.

c. Joint analysis

• i. Plot total donations and total ticket revenue for each patron on a graph (one dot = one record or row of the data set).
• ii. Comment on the general trend in your graph and the outliers.

2. Predictive analytics

a. Donation patterns

• i. Create a regression model to predict donations for 2014 using the previous five years of data. Because so many patrons are not donors, you might want to only consider patrons who have donated at least some amount of your choice as your data set.
• ii. Test your model on 2015 data. Comment.
• iii. Make predictions for donations in 2016.
• iv. Based on your model, who should be the top 10 donors for 2016?

b. Ticket sales patterns

• i. Create a model to predict ticket revenues or ticket revenue groups for 2014 using the previous five years of data.
• ii. Test your model on 2015 data. Comment.
• iii. Make predictions for ticket purchases in 2016.
• iv. Based on your model, who should be the top 10 ticket purchasers for 2016?

3. Conclusions
Develop a strategy for the marketing team.

a. Who are donors the festival organizers should cultivate for 2016?

• i. Donors who did not donate as much as expected in 2015,
• ii. Donors who donate a lot and whom the festival can't afford to lose.

b. Who are ticketholders the festival organizers should cultivate for 2016?

• i. Ticketholders who underperformed their 2015 estimate,
• ii. Ticketholders who play a key role in overall revenue year after year.

c. From the aggregate viewpoint of total revenue (ticket purchases + donations), who are the patrons the festival organizers should cultivate for 2016?

Attachment:- project data.rar