This project has the objective of supporting a playwright launching a crowdfunding campaign to sponsor her next play titled “Fever”.

The purpose of this analysis is to help the playwright identify and understand trends based on previous campaigns to support her decision of launching a crowdfunding campaign. Her goal is to raise $ 10,000 for her next play.

This analysis considered multiples factors to support the stakeholder’s decision. Factors that were included in this analysis were the number of successful campaigns, the increase numbers of blurbs in the recent years and the number of investors.

The challenges encountered within this analysis were related to the lack of more recent data (2007 to 2017), the variety of countries included and the target audience for this campaign.

Due to recent changes caused by the pandemic, more recent data would provide a better understanding of the impact it made on the entertainment industry. The variety of countries involved would indicate the cultural support for theater plays, as Great Britain and United States are known for their Broadway shows, a campaign based is these countries might have a greater chance of being successful.

The lack of demographics data can be an extra challenge for this analysis, reports from the Broadway League reveals that > “the demographic of the Broadway audience 2018-2019 indicates that the average age of the theatergoer in North America is in between 40 to 45 years older in the last two decades”[1]. Having access to this information could be able a touchstone for the campaign and a possible deal-breaker for a potential sponsorship.

[1] Research Reports | The Broadway League. (n.d.). (https://www.broadwayleague.com/research/research-reports/)

The analysis shows that the number of successful campaigns for theater are higher when launched in April, May and June when compared with the remaining months of the year. In average, 60% of the campaigns for the theater segment are successful, however, in May it increases to 67% followed by June with 65% chances to have a successful campaign.

The analysis shows that overall campaigns between $ 5,000 and $, 9,999 are 55% successful and campaigns between $ 10,000 and $ 14,999 are 54% successful. As the proposed campaign has a goal of $10,000, a second analysis was created based on the value of the campaign. Data shows the average success for plays pledging $ 10,000 is 53%, the chances of success increase for the months of May and June. Although the months of August and September present a high percentage of success the data observed in other parameters does not support this conclusion (ex. bakers count)

There are other variables that can be considered when analysing this data, and questions as the public target, type of play (comedy, musical, drama) and which country the campaign is starting are important for the campaign strategy. The lack of information on these variables brings some challenges and difficulties when presenting this data to Louise.

Another point of discussion is the location where this play will be launched. For example, launching this play in New York at Broadway can increase the chances of having a good return in investment in a short term as the location presents an advantage due to its popularity and high traffic of possible consumers (local population plus tourists). This information would be valuable when presenting the play to possible sponsors.

What are two conclusions you can draw about the Outcomes based on Launch Date?

• The average success rate for theater campaigns is 61% based on the last 8 years.

• The most successful months to launch a campaign are April, May and June.

What can you conclude about the Outcomes based on Goals?

The success rate for theater campaigns is 54% (within the range of $10,000 and $14,999), campaigns under $5,000 have almost a 25% chance of being successful.

What are some limitations of this dataset?

The dataset had the following limitations:

• Audience demographics – knowing the audience demographics would help in understand the potential for success which can support potential sponsorship.

• Genre of play – Further break down in genre can help in determining the success of other plays within the same genre as the intended campaign.

• Dataset ends in 2017 and there is no recent data – Considering the current climate more recent data would increase accuracy of the analysis.

What are some other possible tables and/or graphs that we could create?

Number of backers per category, number of campaigns by year, number of plays by year, number of backers per country, and number of plays per country.