Amazing Insights Unveiled: Leveraging ChatGPT Plus and OpenAI’s Code Interpreter
In this article, we delve into the incredible capabilities of ChatGPT Plus and OpenAI’s Code Interpreter add-on. However, before we embark on this journey, let’s address the giant purple elephant in the room. But hold on, whatever you do, try not to think about a giant purple elephant.
So, what is this giant purple elephant? It’s none other than data security. Specifically, we must discuss the protection of proprietary data, both yours and mine. Here’s the catch: for ChatGPT Plus to effectively analyze your data, it needs access to it.
Now, let’s pause for a moment and consider the implications. In order to showcase the techniques I’m about to share, I had to upload a dataset containing 22,797 records from my company’s servers. The question remains: what will OpenAI and ChatGPT do with this data? Frankly, I have no idea, and that’s a significant risk.
In my case, the importance lies in sharing the data analysis process with you, outweighing the need to safeguard my own data. However, this decision is personal, as it involves my own proprietary information. I am confident that I am not violating any disclosure agreements or jeopardizing my company by sharing it with ChatGPT and, consequently, with you through this article.
But for those who wish to utilize these immensely powerful techniques, it becomes crucial to consider whether you and your company are comfortable sharing such data with an AI, potentially exposing it to the entire internet.
Now that we have addressed the purple elephant and successfully diverted your thoughts, let’s move forward. The dataset I am utilizing for this analysis comprises uninstall data collected when users remove my WordPress plugins. Here’s how it works:
When a user decides to uninstall either Seamless Donations or My Private Site, they encounter the dialog shown above. The data from each uninstallation is then sent to my server and stored.
Until now, I could only view this data in a tabular format, as illustrated below:
[Insert tabular screenshot]
However, this format provided limited insights, as I lacked the time to develop detailed analytics, charts, or pivot tables. Consequently, I could only get a rough idea of recent uninstallations, without a comprehensive overview to derive meaningful insights.
But that changes now.
To prepare ChatGPT for your file upload, you will need ChatGPT Plus, which is available through a $20/month subscription. Additionally, you must enable the Code Interpreter feature in your ChatGPT settings under the Beta Features tab.
When starting a session, select GPT-4 and Code Interpreter. Once these steps are completed, you are ready to proceed.
The next step involves uploading your data. I assume that, by this point, you and your management team have thoroughly considered the implications of the giant purple elephant (yes, I couldn’t resist), and you are comfortable with uploading data to Skynet. If so, let’s proceed.
Click the plus sign at the bottom of your session screen, followed by the “Upload” button to upload your file. Once uploaded, hit return.
After completing the upload, ChatGPT will display the number of records in the file. To verify that it has successfully read the uploaded data, you can request a description of the fields.
Now, let’s dive into the magic of data analytics with ChatGPT.
When utilizing Code Interpreter, ChatGPT becomes quite talkative. It’s like having that enthusiastic geeky friend who can’t help but share every detail of their journey before finally providing you with an answer. In this case, ChatGPT tends to provide extensive information before and after the answers, which we will omit for the sake of brevity.
To demonstrate the power of ChatGPT, I will showcase screenshots of its answers, removing the additional information. Otherwise, the screenshots would be excessively long.
So, let’s start with a simple question and a clear answer.
Question: How many records are there for each product?
As you can see, ChatGPT effortlessly calculates this information in a matter of seconds. While it wouldn’t be difficult to code this calculation, it would undoubtedly be time-consuming. ChatGPT, on the other hand, delivers the answer in just 15 seconds. Impressive, isn’t it?
Next question: What percentage of records contain comments?
Most users do not leave comments, and those who do typically select “Other” rather than the predefined uninstall reasons. Yet, with just two simple questions, ChatGPT extracts valuable insights from the raw data.
Now, let’s take it a step further.
Question: Examine all relevant comments and conduct a thematic analysis to identify common trends and patterns.
Question: For each product, describe the prevalent functionality issues mentioned in the comments.
Based on my understanding of my users, this analysis is remarkably accurate. But more importantly, ChatGPT processed 22,797 records and provided an overview of the main issues in less than a minute. Can you imagine how long it would take to accomplish this manually or through coding? Days, at the very least.
To be fair, ChatGPT didn’t immediately generate the most helpful answer. I had to experiment with different prompts until I found the ones that yielded the desired results. Nevertheless, this process took less than an hour, as opposed to days.
Now, let’s explore the visual side of data analysis.
I decided to generate some charts to compare the uninstall reasons within predefined categories and observe potential changes over the years. Here’s the prompt I provided to ChatGPT:
“For each product and then for each year, draw a pie chart of uninstall reason codes. Do not include ‘Other,’ ‘nan,’ and ‘temporary-deactivation’.”
[The article ends here.]