The purpose of this project was to summarize campaign data and identify trends related to theater and play outcomes. Visualizations were used to present theater results based on launch dates over a period of time. Finally, we filtered the campaigns to understand how funding goals were related to the outcome of plays.
Analysis was done by creating two separate visuals, a pivot table, and series of formulas based on the kickstarter dataset.
The challenge with Outcomes vs Launch is difficulty analyzing how total campaigns may or may not impact the outcome. The above graph illustrates a declining trend in success during later months; however, a correlation between total number of campaigns and success rate could debate the launch date by itself is not as relevant.
The analysis of Outcomes vs Goals revealed there were no cancelations for play campaigns. By counting outcomes, totaling projects, and calculating percentages the graph showed the increase in goal amount negatively impacted the success rate of the campaign except for the range between 35,000 and 45,000.
The difficulty with this assumption is all dollar amounts were valued as USD when the dataset clearly differentiated currency. This graph would have been more accurate if all amounts were converted to USD and then plotted.
Successful theater campaigns was the first conclusion made about theater outcomes. The line chart shows successful theater campaigns are much higher during spring months compared to the rest of the year. The total number of theater campaigns is also higher in summer months.
The second observation includes the failed and canceled campaigns. Despite the time period and total number of campaigns in a month, the canceled and failed numbers remain relatively constant.
There is no consistent relationship between success rates and amount of campaign funding goals for plays. For goals ranging from zero (0) to thirty thousand (30,000), as the goal amount increases the percentage of success decreases. The exception to this trend is campaigns with funding goals between thirty-five thousand (35,000) and forty-five thousand (45,000).
The first limitation of the dataset is currency. Outcome analysis is performed on dollar ranges despite different currencies included in the dataset (USD, GBP, EUR, etc.). This could negatively impact the perceived trends.
Another limitation is the relationship between campaign content and culture. Culture could play a major role in how campaigns are received and therefore funded. Different cultures may prefer different genres (campaign content).
There are a couple other graphs that could prove to be valuable.
- The number of backers and compared to goal and how that impacts the success rate of campaign goals.
- The amount of time elapsed between campaign start and launch and how that duration impacts success or failure.
- Use a bar graph and add total number of campaigns per month; this would potentially show a relationship between the total number of campaigns and success rate.