Crowdfunding Campaign Data Analysis

Portfolio Summary: Crowdfunding Data Analysis with Excel

Background:

● Conducted a comprehensive analysis of a crowdfunding dataset with 1,000 sample projects using Excel.
● Explored market trends, success factors, and limitations in crowdfunding campaigns.

Analysis Steps:
1. Data Formatting and Visualization:
● Employed conditional formatting to categorize project outcomes (success, failure, canceled, live).
● Created the "Percent Funded" column with a three-color scale indicating funding levels.
● Calculated the "Average Donation" and introduced new columns for "Parent Category" and "Sub-Category."

2. Category and Subcategory Statistics:
● Developed pivot tables and stacked-column pivot charts for category and subcategory analysis.
● Implemented filters by country and parent category for in-depth insights.

3. Date Conversion and Outcome Trends:
● Converted Unix timestamps to Excel's date format for clearer timeline representation.
● Investigated outcomes based on launch date through a pivot table and visualized trends with a line graph.

4. Crowdfunding Goal Analysis:
● Established a goal-oriented analysis sheet with eight columns.
● Utilized COUNTIFS() formula to categorize and calculate successful, failed, and canceled projects.
● Created a line chart depicting the relationship between goal amount and project outcomes.

5. Statistical Analysis:
● Investigated the number of backers for successful and unsuccessful campaigns.
● Computed mean, median, minimum, maximum, variance, and standard deviation for both categories.
● Explored whether mean or median better summarized the data and analyzed variability in campaign outcomes.

Conclusion:
● Crowdfunding campaigns with lower funding goals have higher success rates, suggesting that smaller campaigns are easier to attract support from backers. As funding goals increase, success rates tend to decrease as well. For instance, campaigns with goals between 1000 and 4999 have an 83% success rate, compared to those with goals between 5000 and 9999 which only has a 52% success rate.
● Crowdfunding campaigns with higher funding goals that are greater than or equal to 50,000 have a lower success rate at 37% and high failure rate at 53%, indicating that it’s most likely tougher to attract enough support. With such a high goal compared to other campaigns, it’s probable that there are much more challenges and obstacles faced that led to its high failure rate.
● Lastly, when performing statistical analysis for successful campaigns, I found the mean is much larger than the median, suggesting there are outliers that skew the mean to the right, meaning the median may be a better measure to summarize the data because it's less affected by outliers. There's more variability with successful campaigns because they have a wider range of backers that support them, ultimately leading to a broader distribution of funding amounts.