How to use GPT-4 to Revolutionize Customer Profiling

An Experiment showing how to use GPT-4 for Enhanced Customer Profiling and Personalization

 

Open AI’s ChatGPT took the whole world by surprise when it was launched last November, attracting immediately the attention of AI and tech enthusiasts, and gradually of everyone else. Since then, it has evolved and reached this GPT-4 iteration with significantly improved capabilities, exhibiting almost human-level performance on some scenarios.

 

Harnessing the power of AI has become increasingly significant for businesses, particularly in the e-commerce landscape where understanding the customer is key to success. OpenAI's GPT-4 presents a remarkable solution, providing insightful analysis of customer behavior based on purchase history.

 

This experiment breaks down the steps to how to use GPT-4 and leverage its capabilities to full potential, transforming customer profiling, intelligence, and personalization in e-commerce.

• Step 1 - Presenting Shopping History to GPT-4
• Step 2 - Extracting Preferences
• Step 3 - Constructing a Demographic Profile
• Step 4 - Stepping up a notch: Using a bank statement

 

 

Step 1 - Presenting Shopping History to GPT-4

 

The process begins by feeding GPT-4 with the customer's shopping history. In the  experiment, my Amazon shopping history was presented to the model, including purchase dates, order totals, product descriptions, and delivery information.

 

After I uploaded the data, I asked the question: “How many orders did I do this year?”. The answer was very accurate.

Step 2 - Extracting Preferences

 

Next, I asked GPT-4 about my shopping preferences based on his buying patterns. Through analyzing the order history, the model discerned my frequent purchases around business, sales, and persuasion, indicating a professional interest or involvement in these fields.

 

Further analysis revealed my fondness for the Disney "Frozen" franchise. By piecing together these elements, GPT-4 created a detailed picture of my buying behavior and personal interests.

Step 3 - Constructing a Demographic Profile

 

I then asked it to construct a demographic profile. While it is primarily a large language model, GPT-4 can make informed inferences based on product purchases. For me, it was suggested that I’m likely an adult male with potential professional ties to business or sales. 

 

However, it's crucial to remember these conclusions are conjectural and serve to supplement rather than replace direct demographic data.

Finally, I asked the model to create a profile in a tabular format. It created this:

 

 

Astonishing isn’t it? If the model can do this by looking at a few months of purchase history on Amazon, what can you do with the entire history? 

 

 

Step 4 - Stepping up a notch: Using a bank statement 

 

Next, I thought… let’s try feeding it my bank statement. And as you can see below, the results were, yet again, astonishing! GPT-4 produced a very accurate profile. There may be one or two exceptions, but overall it’s close enough.

Transforming Customer Profiling with Large Language Models

 

Using large language models, companies can unlock a wealth of insights, transforming their customer profiling and intelligence strategies. Knowing how to use GPT-4 to dissect shopping history, consumer patterns and infer preferences opens up a new door for businesses to create highly detailed customer profiles.

 

Moreover, this depth of understanding fosters an environment for improved personalization. Businesses can design highly targeted marketing strategies and make better product recommendations that align with the customer's unique preferences. This can enhance the customer experience significantly. In my case, for example, it means being targeted with relevant business literature or the latest products from the “Frozen” franchise.

 

Beyond improving personalization, knowing how to use GPT-4 for customer profiling empowers businesses to leverage its insights and ultimately make more informed decisions about their product offerings. By identifying trending preferences or buying behaviors, businesses can proactively adjust their offerings to cater to customer demand.

 

The Large Language Model revolution is going to transform the e-commerce landscape, providing businesses with an advanced tool to understand and cater to their customers more effectively. 

 

It's a clear demonstration of how AI can significantly contribute to creating a personalized and enhanced shopping experience for every customer.