Project 7: Marketing Campaign ROI Analysis(R/ChatGPT)
Science & Technology
Introduction
Welcome back to the K Channel! Today we're discussing the results from the recent general election in the US, and while I won’t get into political matters, it's fascinating to see how data analytics can inform us about such events. I can share that the average voter turnout for presidential elections from 1900 to 2020 was just around 57 to 58%.
However, our primary focus today is the furniture market data set. As always, feel free to jump in with any comments or suggestions throughout the session. All relevant resources, including this dataset, are available in the repository linked in the video description.
Let’s kick things off by exploring our data. A heads up: set your alarms for 9:00 PM this Friday! I will be launching a series on SAS (Statistical Analysis System), where we will learn together, as I’m beginning my journey with it.
Now, let’s start analyzing the data. I’m currently using an updated data set which reflects the order and ship dates through late November.
An interesting point to consider is how we define the advertising campaign, particularly when calculating total sales generated by each campaign. In SAS, we have a few methods for accessing data, including a point-and-click interface and SQL code, which many times we will generate behind the scenes.
Today, we intend to answer three main questions, keeping it concise and returning to complete the rest on Sunday night. The first task involves identifying distinct market segments. The second task looks at calculating total sales generated by each campaign, and the final task will involve reviewing total sales by quarter.
Using queries, we can explore which market segments are most profitable. For example, we see that the Central division is underperforming, demonstrating a negative return on investment. In contrast, the South shows strong profitability, reflecting more efficient marketing campaign strategies.
The visualizations we produce can tell a compelling story. For example, plotting sales by quarter presents clear data trends, and bar plots facilitate easy comparison across market segments or periods.
Though this session is brief, I am happy with the progress we’ve made. Remember to prepare for Friday night as we embark on our SAS journey, starting with setting up accounts and working with existing data sets.
To ensure clarity, let's share a final review before concluding this session. I recommend capturing screens as we finalize results for our analyses.
Thank you for being a part of this analytical journey. I appreciate your engagement and look forward to seeing you again this Friday!
Keywords
- Marketing Campaign
- ROI
- Data Analysis
- Voter Turnout
- SAS
- SQL
- Market Segments
- Visualization
- Profitability
FAQ
Q: What is the focus of the article?
A: The article focuses on analyzing the return on investment (ROI) of marketing campaigns using a furniture market data set.
Q: What tools will you be using for data analysis?
A: The primary tools discussed include SAS and SQL for data manipulation and analysis.
Q: When will the next session take place?
A: The next session will occur at 9:00 PM this Friday, where we will start our journey into SAS.
Q: How long is the duration of the sessions planned?
A: The sessions are intended to last around 30 minutes, focusing on concise and effective analysis.
Q: Where can I find the data sets and resources mentioned?
A: All resources, including data sets, are linked in the repository available in the video description.
