"The true sign of intelligence is not knowledge, but imagination" - Albert Einstein 💎
The purpose of this project is to assist Louise create a successful theater plays campaign using Microsoft Excel data gathered across various countries from 2009 to 2017.
Microsoft Excel was utilized to analyze the dataset, using pivot charts and pivot tables to visualize specific subsets that are of interest. For example, the graph below shows the U.S. theater campaign outcomes categorized by the three different subcategories: musical, plays, and spaces.
One challenge of analysis is the enormity of information within this dataset. When a new column is used to apply a function, the cells that are populated are limited by the filter of the data. Confirmation that the dataset is clear of any filters prior to populating new cells and double-checking any and all work done on the data is crucial.
The Theater Outcomes by Launch Date graph shows that campaigns tended to have a the most success during the month of May. Their success then tapered off towards the end of the year. Overall, more theater campaigns reached their goal than those that did not regardless of which month they started the campaign, with successful campaigns outnumbered failed ones only slightly in December. Less than ten campaigns canceled every month.
If the Outcomes Based on Goals graph included data for campaign goal of 50,000 dollars and the various currencies were converted into a single uniform currency, then some conclusions may be drawn from the graph. For example, with goals ranging from less than 1,000 to 14,999 and between 35,000 to 44,999, more campaigns succeeded than failed. However, no conclusion can be drawn from the data as it stands.
One limitation of the Theater Outcomes by Launch Date graph is that the dataset includes campaigns created between 2009 through 2017, which is not adequately described by the list of months within the x-axis of the graph. Should there be any variation in campaign outcomes between the years due to unforeseeable circumstances, national or global economic crisis for instance, the graph would change in accordance and therefore not necessarily represent a trend that is of interest.
Another limitation of this graph is that the outcome of the canceled campaigns for October is missing, which implies that there is either missing or incorrectly input data within the cells that corresponds to the data of October. If there were no canceled campaigns during that time, it would be better to enter “0” than leaving the cell(s) blank.
One limitation of the Outcomes Based on Goals graph is that the graph excluded the campaigns with a goal of exactly $50,000 USD, which there are four in total. Therefore, the percentage of projects shown within the graph are not accurate even though they seem to add up to 100%.
Another limitation of the graph is that multiple currencies were used directly without conversion to a single currency, which invalidates the goal numbers since different currencies hold different value.
One limitation of both Theater Outcomes by Launch Date and Outcomes Based on Goals graphs is that live campaigns are not included, which may or may not provide additional insight to various campaign trends.
A graph that may be of interest to Louise is the comparison of subcategory plays campaigns across different countries. If one country had an especially high percentage of successful campaigns with high goals, she may benefit from starting her campaign there.