The goal of this project is to provide the playwright, Louis, with recommendations regarding crowdfunding for her play ‘Fever’ and plan it to be set up for success. These recommendations are based on insights drawn from analyzing data of previous several thousand crowdfunding projects. be mirrored in hers.
The purpose of this analysis is to understand campaigns from start to finish. Therefore, a dive in these data was needed to uncover any hidden trends and understand if her goal of reaching $10000 is possible in the timeline she has in mind. Also, to identify any factors which played role in making a successful crowdfunding that matches the same targeted category which could
As requested in the deliverables, the length of fundraising campaigns is a valuable piece of data that will be needed to plan the upcoming crowdfunding, so identifying the existing of a correlation between the length of a campaign and its success was vital. Therefore, a closer look at how campaign length might be tied to its outcome was done. (see img 2)
(img 2, theater outcome by launch date)
The second deliverable was to identify the ‘Outcomes based on goals’ which analyze the correlation between the amount of the goal and the highest chances achieving it. COUNTIFS, ROUND and AVARAGE were used to identify the numbers which offer the required deliverables. (see img 3)
(Img 3, Outcomes based on goals)
First the data needed to be organized in the Kickstarter sheet as a table then sort it in a way that would make it more readable and searchable and therefore easier to analyze, taking a general overlook on the data was helpful to plan the next step.
A deep look into the data was helpful to understand what type of data was offered. The next step was to create the change the formatting of some columns, one of the challenges we faced was the format of the timestamp and the dates, it was solved by changing the format. Also, filtering, sorting and conditional formatting were performed to customize the worksheet and refine the visual presentation of the data to provide Louise with information at a glance. Later, pivot table and pivot chart were created to add more customization to the data in order to provide a visual feedback. (see img 1)
(img1. A full data overlook table)
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In order to visualize the Theater outcome by launch date, a pivot table was created relying on the sorted and filtered data in the main ‘Kickstarter’ sheet, which holds all the data we have. The goal was to understand the relation between campaign launch dates and its time of the year, the results show that crowdfunding for theater tends to be more successful between April to August, and failed ones are between Dec to Mar.
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Another key finding was to identify the chance for success in relation to the overall campaign funding goal, in short, the findings suggested that Louise has 54% chance to reach her 10K goal, and the chances of her getting fully funded means that she needs to reduce the goal to under 5K which will raise the possibility to more than ~ %70.
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The offered data was good, the limitation that could’ve improved the results knowing the means of crowding and the what platforms were used, and the age groups, income..etc for the targeted doners.
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More work need to be done on finding the relation between successful campaigns and their launch dates, and the overall goal of the crowdfunding campaign, which means, maybe during summer doners are willing to pay more, which means 10K goal can be achieved only during summer, this is just a possibility that needs to be explored.