Background

Over $2 billion has been raised using the massively successful crowdfunding service, Kickstarter, but not every project has found success. Of the more than 300,000 projects launched on Kickstarter, only a third have made it through the funding process with a positive outcome.

Getting funded on Kickstarter requires meeting or exceeding the project's initial goal, so many organizations spend months looking through past projects in an attempt to discover some trick for finding success. I this project I have organized and analyzed a database of 4,000 past projects in order to uncover any hidden trends.

Step by Step

• Used conditional formatting to fill each cell in the state column with a different color, depending on whether the associated campaign was successful, failed, or canceled, or is currently live.

• Created a new column O called Percent Funded that uses a formula to uncover how much money a campaign made to reach its initial goal.

  • Used conditional formatting to fill each cell in the Percent Funded column using a three-color scale. The scale starts at 0 and is a dark shade of red, transitioning to green at 100, and blue at 200.

• Created a new column P called Average Donation that uses a formula to uncover how much each backer for the project paid on average.

• Created two new columns, one called Category at Q and another called Sub-Category at R, which use formulas to split the Category and Sub-Category column into two parts.

• Created a new sheet with a pivot table that analyzes initial worksheet to count how many campaigns were successful, failed, canceled, or are currently live per category.

  • Created a stacked column pivot chart that can be filtered by country based on the table created.

• Created a new sheet with a pivot table that analyzes initial sheet to count how many campaigns were successful, failed, or canceled, or are currently live per sub-category.

  • Created a stacked column pivot chart that can be filtered by country and parent-category based on the table created.

• Created a new column named Date Created Conversion and transformed the launched_at column information from UNIX time into readable date/time format.

• Created a new column named Date Ended Conversion and transformed the launched_at column information from UNIX time into readable date/time format.

• Created a new sheet with a pivot table with a column of state, rows of Date Created Conversion, values based on the count of state, and filters based on parent category and Years.

  • Created a pivot chart line graph that visualizes this new table.

• Created a new sheet with 8 columns:

  • Goal

  • Number Successful

  • Number Failed

  • Number Canceled

  • Total Projects

  • Percentage Successful

  • Percentage Failed

  • Percentage Canceled

    • In the Goal column, created 12 rows with the following headers:

      • Less than 1000
      • 1000 to 4999
      • 5000 to 9999
      • 10000 to 14999
      • 15000 to 19999
      • 20000 to 24999
      • 25000 to 29999
      • 30000 to 34999
      • 35000 to 39999
      • 40000 to 44999
      • 45000 to 49999
      • Greater than or equal to 50000

    • Used the COUNTIFS() formula to count how many successful, failed, and canceled projects were created with goals within the ranges listed above. Populated the Number Successful, Number Failed, and Number Canceled columns with this data.

    • Added up each of the values in the Number Successful, Number Failed, and Number Canceled columns to populate the Total Projects column. Then, used a mathematical formula to find the percentage of projects that were successful, failed, or canceled per goal range.

    • Created a line chart that graphs the relationship between a goal's amount and its chances at success, failure, or cancellation.

• Created a report addressing the following questions:

1. Given the provided data, what are three conclusions we can draw about Kickstarter campaigns?
2. What are some limitations of this dataset?
3. What are some other possible tables and/or graphs that we could create?