kickstarter_analysis

An Analysis on Kickstarter Campaigns

Kickstarter Analysis

Overview of Project

Purpose:

An up-and-coming playwright Louise started a crowd funding campaign to fund her play Fever, she meet 86% of her goal of $2885 in 28 days with an average donation of $248.5 backed by 10 donors. Louise wants to know what the outcomes of other campaigns are in relation to the launch dates and funding goals. The purpose of our analysis is to use the Kickstarter data of different campaigns and provide insights with visual representation in Excel to Louise. The Kickstarter data we are looking at contains information of 4,114 campaigns that launched from 2009-2017.

Resources Used:

Kickstarter_Challenge _Data

Microsoft Excel for Mac Version 16.55

Analysis and Challenges

Analysis of Outcomes Based on Launch Date:

The following steps were used to analyze different outcomes based on Launch Dates:

1.) A year column was created in the Kickstarter Worksheet to extract the year from the “Date Created Conversion” column using the YEAR() formula.

2.) A pivot table was created using the Kickstarter Worksheet data, below is a screen shot of the field list:

3.) The Parent Category was filtered to show only data for the “theater” since Louise's play Fever is in the same Parent Category , column labels were filtered to show data for “successful”, “failed” and “canceled” campaigns. The campaign outcomes were sorted in descending order.

4.) A line chart was then created from the pivot table to visualize the relationship between theater outcomes and launch months. Below is the image of the line chart:

Analysis of Outcomes Based on Goals:

1.) A new worksheet was created to analyze the percentage of successful, failed and canceled campaigns based on different goal dollar ranges. The COUNTIF () function was used to populate data in column’s B,C and D, based on the following columns in the Kickstarter Workbook, the “goal” amount column, the outcome column and the subcategory column using “plays” as a criteria since Fever falls in this subcategory.

2.) These were the results by each dollar amount range.

3.) A line chart was created to see the visual representation of the relationship between percentage of successful, failed, or canceled campaigns and the different ranges in goal amounts.See image below.

Challenges and Difficulties Encountered:

Listed are possible challenges that one could face:

1.) Not anchoring the array for the COUNT-IF formula could result in incorrect outputs

Results:

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

  1.) The two most successful champaigns were launched in in May and June.

  2.) The least successful time to launch a champaign would be in December where the number of failed champaigns is almost equal to the number of successful champaigns.

• What can you conclude about the Outcomes based on Goals?

  1.) The goal ranges with highest success is less than $1000 and $1000 to $4999, there are 529 successful campaigns out of a total of 720 in these ranges. The goal ranges between $30,000- $39,999 shows a 67% successful but the sample size is much lower with 6 out a total of 9.

• What are some limitations of this dataset?

  1.) More parameters could be analyzed to determine what makes a successful campaign:

  • The genre's of plays that have had the most successful outcomes could be analyzed by subcategory.
  • The average donation and amount of backers supporting successful outcomes.
  • Using statisical measures to get rid of outliers that do not make sense to create a more realistic representation of outcomes.

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

  1.) Successful campaigns based on Geograhic regions.

  2.) Graphs of how other parent categories fared.