Zero to Data Analyst with ChatGPT

Data is everywhere! From your monthly spending reports from your bank to customer reviews of your business’s latest products. Being able to derive meaningful insights from this data has long been a skill only reserved for data scientists. Due to this skill gap, most people do not use any of the data available to them personally or their business. Enter, ChatGPT Advanced Data Analysis - a game-changing tool that can give you the powers of a data analyst!

Today, I'll introduce you to this tool and explain how you can use it. We will also go through a short case study of a small coffee shop using ChatGPT to analyze sales data and implement recommendations. Whether you're a business owner looking to understand customer trends or an individual trying to make sense of personal finances, this will be an invaluable tool if you wield and use it correctly. Let's dive in!

Why should you care?

In today's data-driven world, analyzing and interpreting information is as crucial as basic literacy. Whether you're a business owner, a student, or simply someone who wants to make more informed decisions, understanding data analysis can give you a significant edge.

Now, ask yourself:

- Do you ever feel overwhelmed by the amount of data you encounter in your work or personal life?

- Have you ever wished you could quickly extract meaningful insights from complex datasets?

- Are you curious about how AI could help you become more data-savvy?

If you answered yes to any of these questions, then learning how to effectively use ChatGPT for data analysis could be invaluable to you!

How to use ChatGPT Advanced Data Analytics

It’s helpful to think of ChatGPT Advanced Data Analytics as a feature that gets activated once you upload a data file to your chat either from your computer, Google Drive, or Microsoft One Drive.

The most important consideration is to ensure you have selected the latest model, ChatGPT 4o in this case, before starting your analysis. This will ensure you are getting the best performance and accuracy.

Understanding ChatGPT's Capabilities in Data Analysis

1. Data Interpretation: ChatGPT can help you understand complex datasets by explaining trends, patterns, and outliers in plain language.

2. Realtime Statistical Analysis: ChatGPT can guide you through statistical concepts and help you interpret results. It can also perform relatively complex statistical analysis/calculations and use machine learning to derive in-depth meaning from your data.

3. Visualization: ChatGPT can visualize your data in different chart types using different visualization techniques based on your data and goals.

Limitations and Considerations

It would be irresponsible to talk about using ChatGPT to make decisions without highlighting some limitations:

1. Data privacy concerns: Be cautious about sharing sensitive data with ChatGPT. Always anonymize your data (where possible) before sharing it.

2. Potential for biased or incorrect information: Always apply critical thinking and verify important information from reliable sources. As we have discussed before, ChatGPT is prone to hallucinations and can present very convincing but incorrect data.

Case Study: Sunshine Café's Data-Driven Transformation

Let's look at a real-world example of how a small business can use ChatGPT Advanced Data Analytics to gain valuable insights and drive growth.

Sunshine Café, a local coffee shop, has been struggling to optimize its operations and increase profitability. The owner, Sarah, decided to analyze their sales data using ChatGPT Advanced Data Analytics. Here's how she did it:

1. Data Collection

Sarah exported a sample of sales data from her point-of-sale system into a CSV file. The data included date, time, item sold, category, price, whether the customer was a regular, day of the week, temperature, precipitation, total sales, number of transactions, average transaction value, marketing spend, and staff on duty.

Date,Time,Item,Category,Price,Regular Customer,Day of Week,Temperature,Precipitation,Total Sales,Num Transactions,Avg Transaction,Marketing Spend,Staff on Duty
2023-04-01,06:06,Espresso,Coffee,3.5,Yes,Saturday,64,0.44,78.5,56,1.4,0,4
2023-04-01,06:07,Cappuccino,Coffee,4.0,No,Saturday,62,0.4,88.75,53,1.67,50,3
2023-04-01,06:09,Turkey Sandwich,Lunch,7.5,Yes,Saturday,64,0.44,25.25,56,0.45,0,4
2023-04-01,06:09,Espresso,Coffee,3.5,Yes,Saturday,64,0.44,53.5,56,0.96,0,4
2023-04-01,06:13,Blueberry Muffin,Pastry,2.75,Yes,Saturday,64,0.44,10.25,56,0.18,0,4

2. Data Upload

Sarah uploaded the CSV file to ChatGPT Advanced Data Analytics.

3. Initial Exploration

She started with a simple query: "Can you provide a summary of this dataset?"

4. Deeper Analysis

Based on the summary, Sarah asked more specific questions:

• "What are the bestselling items?"

• "Is there a correlation between time of day and sales volume?"

• "What's the average spend of regular customers versus non-regular customers?"

• "How does weather affect our sales?"

5. Visualization

Sarah requested visualizations to better understand the data: "Can you create a bar chart of sales by category?"

Insights and Recommendations

Finally, she asked ChatGPT to summarize key insights and provide recommendations based on the analysis.

By following this process, Sarah uncovered several valuable insights:

• Peak Sales Hours and Days:

  • Peak Hours: 9 AM, 11 AM, and 6 AM are the top three hours for sales.

  • Peak Days: Wednesday and Saturday have the highest sales volumes.

• Marketing Impact:

  • Marketing Spend: There is no clear trend indicating a direct impact of marketing spend on total sales

• Sales by Staff on Duty:

  • The number of staff on duty shows varying total sales, with higher sales observed when more staff are on duty, although this is not a strong trend.

• Additional Visualizations:

  • Sales by Day of Week: Confirms that Wednesday and Saturday are peak sales days.

  • Average Transaction Value by Time of Day: Indicates fluctuations in average transaction value throughout the day.

  • Sales Volume by Temperature Range: Shows no strong trend in sales volumes across different temperature ranges.

Based on these insights, Sarah implemented several changes:

1. Optimize Staffing Levels:

   Since higher staff numbers slightly correlate with higher sales, ensure adequate staffing during peak hours (9 AM, 11 AM) and peak days (Wednesday, Saturday) to maintain service quality and customer satisfaction.

2. Targeted Marketing Campaigns:

   Develop targeted marketing campaigns for slower days (Thursday, Sunday) to balance sales throughout the week.

3. Enhance Customer Experience:

   Even though the spending between regular and non-regular customers is similar, focusing on customer loyalty programs could help in retaining regular customers and converting new customers into regulars.

This case study demonstrates the power of ChatGPT Advanced Data Analytics for a small business. By asking the right questions and interpreting the data correctly, even small datasets can yield powerful insights.

Conclusion

As with the other AI tools and techniques we've discussed in previous issues, consider incorporating ChatGPT into your AI toolkit. Reflect on our discussions about AI Thinking, and imagine how this new tool could complement your existing strategies, helping you make more informed decisions and drive growth, personally and for your business. Until next time, happy prompting!

— Fauzi