In this project, we will explore all the ways in which we can use Excel to help us analyze and compare a large dataset that holds information for over 4000 crowdfunding campaigns. We will learn and apply different functions and tools within this program that allows us to create charts and pivoting tables.
With this analysis we will be able to present the statistics of successful campaigns, failed, and otherwise, for an up-and-coming playwright named Louise; who is looking to get a clearer picture of what her budget should be to be able finance her play.
We will present different types of graphics to be able to easily visualize all the information that this dataset contains, so that Louise can make informed decisions moving forward. It is important to see the trends that are happening with past campaigns that have been able to reach their goal, and if not, what were the factors or variables that affected the outcomes.
The following line chart shows how many theater campaigns were launched successfully, failed or canceled from 2009 to 2017. We have labeled and grouped the data to see which months of the year had better results than others.
If Louise is trying to launch a campaign that she estimates will need a budget of $10,000, this is a helpful way to identify when would be the best time during the year to do so. According to our available data, 111 theater campaigns were successful during the month of May, with June also having one of the highest successful numbers. Similarly, the number of theater campaigns that failed is higher during those same months. Meaning that Summer is one of the most popular times of the year to launch theater campaigns; however, would these be successful? The total of theater campaigns launched during the month of May is 166, including successful, failed and canceled. Out of all those, only 31% of the campaigns failed. We can conclude that May and June, (Summer time) is one of the most popular seasons for launching theater campaigns and that there is a 69% chance to have it be a success.
Furthermore, we will also offer an insight to Louise based on the goals set for past campaigns that were specifically launched for plays, and how many of them reached that goal, failed, or canceled the campaign altogether. We will also show her the range of those goals, and identify whether there is a good range to choose to be able to allocate the necessary funds for her campaign; or whether there are certain goals that may not be achievable.
As seen above, there were 186 campaigns that had a fundraising goal of less than $1,000; of these 141 were successful in reaching that goal, while 45 failed. That is 76% of them; being the highest range with a successful outcome for all the goals shown above. Next, the fundraising goals go up to a range of $1,000-$4,999. 534 projects set this range as a goal for their campaigns, having 388 of them be successful, and 146 fail. Even though more campaigns set out to reach for these amounts, 73% of those campaigns were successful in reaching those funds. However, we know that she wants to raise $10,000 for her campaign. So if we move to the chart and look at the campaigns that set out a goal of $5,000-$9,999; we see that out of 169 projects, only 93 were successful. That is 55% of them being able to achieve their goal. In other words, if Louise wants to set out her crowdfunding campaign to reach a goal of $10,000 she has 55% chance of achieving this goal; but if this seems too risky, she could set out her campaign to raise a goal of $4,999, increasing her chances at succeeding, and secure the funds for her play. It is also important to note that no campaigns canceled their projects.
One of the challenges when analyzing large spreadsheets of data, is to narrow down all that information to what would be most useful for us. We can do so with Excel, when applying filters and visually creating representations of those narrower results. Another challenge is to accurately inspect the results of our analysis. It is up to us to be able to study the distribution of the dataset and get back to Louise with a valuable examination of this data.
After applying all the learned skills in class, I can confidently say that I now see Excel like a tool and not a strange program. It has great features that shows us when there are mistakes and where it could be. It shows us suggestions as to what function may work best for what we are doing and overall it is a fantastic way to analyze large sets of data and visualize what our trends are, and make an informed decision based on the analysis of it all. Louise can now take into account when would be the best time of the year to launch her campaign, whether she should set out for a lower goal that can be statistically more likely to be achieved.