Struggling with word recognition in large data set

Optimizing Word Recognition in Large Customer Inquiry Datasets: Challenges and Strategies

Handling extensive customer inquiry data sets can be a complex task, especially when aiming to accurately associate customer requests with products listed in your catalog. Recently, I encountered such a challenge while working with a dataset comprising approximately 14,000 customer inquiries, seeking to streamline product matching using AI language models like ChatGPT. This experience highlights common pitfalls and offers insights into improving accuracy in large-scale data matching projects.

The Objective

The goal was to leverage ChatGPT to identify and match product requests within customer inquiries against a catalog of around 2,000 products. While initial results showed promising precision—achieving roughly 60% accurate matches—the remaining inquiries presented significant hurdles, despite implementing specific rules and prompts.

Key Challenges Encountered

1. Variations and Similarity in Product Listings

One notable issue was the presence of numerous product variations. For example, the catalog included multiple related products such as “Scissor Lift 15m,” “Scissor Lift 13.1m,” along with several other similar entries. These subtle differences often complicated AI recognition, especially when customer inquiries referenced generic terms like “scissor lift” without specifying dimensions or model details.

1. Vagueness in Customer Requests

Many inquiries were vague or lacked detailed specifications. Customers often requested a certain category, such as “scissor lift,” without indicating preferences or specific models. In such cases, the primary goal shifted from precise matching to simple identification of the product category or type.

1. False Positives and Misclassification

An additional challenge was the occurrence of false positives, where GPT would flag products not mentioned in the inquiry as relevant matches. This misclassification can be problematic, especially in customer service or sales contexts where accuracy is critical.

Potential Strategies for Improvement

Based on these experiences, here are some suggestions to enhance product matching accuracy:

• Data Preprocessing and Standardization

• Normalize product names in your catalog to reduce variations. For example, standardize all “Scissor Lift” entries to a common format, possibly including SKU codes or attribute tags.

• Clean customer inquiries to remove extraneous information, ensuring the AI focuses on relevant request details.

• Use of Hierarchical or Categorical Matching

• Implement a two-stage approach: first identify the broader product category (e.g., “scissor lift”), then narrow down to specific models if details are provided.

• This approach aligns with the goal of matching vague requests to relevant categories