Crowdfunding platforms like Kickstarter and Indiegogo have been growing in success and popularity since the late 2000s. From independent content creators to famous celebrities, more and more people are using crowdfunding to launch new products and generate buzz, but not every project has found success.
To receive funding, the project must meet or exceed an initial goal, so many organisations dedicate considerable resources looking through old projects to discover “the trick” to finding success. For this Challenge, I'll organise and analyse a database of 1,000 sample projects to uncover any hidden trends.
- Conditional Formatting
- Column Creation
- percent funded
- average donation
- category
- sub-category
- Date Created Conversion
- Date Ended Conversion
- Pivot Tables Stacked Column Charts and Line Graphs
Given the provided data, what are three conclusions that we can draw about crowdfunding campaigns?
• Based on the Category Pivot table, Theater Category and Sub-Category Plays, have the most Successful campaign compare to the others • Based on Pivot table 3, Campaigns launched during Jun and July had the most success rate. • The Theater-Plays Campaign, had bigger Backers counts therefore there were bigger number in success and fail .But the campaign was mostly successful.
What are some limitations of this dataset?
• We don’t have enough data about the precise location of the campaigns. • We don’t have enough data about the size and the quality of the campaigns.
What are some other possible tables and/or graphs that we could create, and what additional value would they provide
• A Scatter plot graph showing the relationship between funding goal and success rate maybe can help to understand better if having a higher funding goal had anything to do with the success rate. • A bar chart showing the relationship between location and success rate.
Use your data to determine whether the mean or the median better summarises the data.
• The mean best summarises the data for both successful and failed campaigns.
Use your data to determine if there is more variability with successful or unsuccessful campaigns. Does this make sense? Why or why not?
• There is more variability in the successful campaigns as the number of successful campaigns is bigger compare to the unsuccessful campaigns.
- Computed calculations of percentages for projects that were successful, failed, or were cancelled per goal range
- Created a line chart showing the relationship between the goal’s amount and its chances at success, failure, or cancellation
- Computed calculations of the mean, median, min, max, variance, and stdev using Excel formulas
- A brief and compelling justification of whether the mean or median better summarises the data