How to Use AI to Analyze Your Shopify Store Data

Photo by Solen Feyissa on Unsplash

Artificial intelligence has well-known uses like chatbots, fraud detection, or text correction. But today I want to talk about another use case that isn’t mentioned as often: data analysis.

To test it, I ran an experiment with DeepSeek and a file containing all the sales from my Shopify store. The goal was to evaluate how well it can analyze information and whether it can actually help merchants extract valuable insights without spending hours on spreadsheets.

The Experiment: Analyzing Sales with AI

The process was simple:

1. I uploaded an Excel file with Shopify sales data to DeepSeek.
2. I asked it to identify the best-selling products based on total quantity sold.
3. I compared the results with a manual Excel analysis to evaluate accuracy.

Results and Advantages

Doing this task manually in Excel would have taken me hours. Instead, DeepSeek solved it in seconds. Automation reduces analysis time and allows you to focus on decision-making.

The initial results were accurate when I used a small dataset. However, as the number of orders increased, the AI started showing inconsistencies in data interpretation.

Limitations and Challenges

Despite its speed, accuracy still isn’t perfect when working with large data volumes. I noticed that when processing more orders, the AI lost context and generated erratic responses.

Some challenges found:

• Errors in data aggregation when there were too many rows.
• Difficulty interpreting repeated transactions within the same order.
• Lack of consistency in selecting best-selling products as the dataset grew.

This shows that the technology is still developing for handling large-scale data analysis, but the path is promising.

What I Learned About AI Reasoning

The most interesting thing wasn’t just the result, but the reasoning process the AI displayed. R1 (DeepSeek’s model) followed a clear logical structure:

1. It identified the key columns in the sales file:

   “The ‘Lineitem quantity’ column seems to indicate the quantity sold of each product in a transaction, and ‘Lineitem name’ would be the product name.”

2. It reasoned about how to group the data:

   “I observe that some rows have repeated information for the same order number (like #1001, #1002, etc.), which probably indicates multiple products in the same order.”

3. It defined the aggregation logic:

   “I need to sum all ‘Lineitem quantity’ grouped by ‘Lineitem name’.”

4. It identified edge cases that could affect the answer:

   • Null or zero values: “Check if there are products with zero quantities or null values, but it seems all quantities are positive.”
   • Filter only paid transactions: “Consider only transactions marked as ‘paid’.”
   • What to do if there are few products: “If there are fewer than 10 products, I’ll list all available ones.”

This structured, methodical approach taught me a great lesson: humans can learn to reason like AI to improve our own analytical capabilities.

Conclusion: Is It Worth Using AI for Data Analysis?

Despite its limitations, this experiment made it clear that artificial intelligence is already a useful tool for processing small amounts of data and speeding up tedious tasks. It not only allows extracting insights faster but is also a tool for learning to think more logically and in a structured manner.

What’s Next?

I’ll continue exploring new ways to apply GPT, Mistral, and DeepSeek to improve my workflows. AI still has a way to go in large-volume data analysis, but it can certainly help us optimize processes and learn better ways to reason.

Want to try something similar in your business? Contact me to share ideas about how to apply AI in eCommerce.