NATURAL LANGUAGE PROCESSING (NLP)
Definition
Natural Language Processing (NLP) is a branch of artificial intelligence that enables machines to understand, analyze, and generate text. In pricing, NLP is used to match products with different descriptions across catalogs, analyze customer reviews, extract product features, or detect price mentions in unstructured sources.
Why it's important
- Automating product matching: NLP automatically matches "iPhone 15 Pro 256 GB Blue" with "Apple iPhone 15 Pro - 256GB - Blue Titanium" across two different catalogs.
- Enriching data: Automatically extracting the brand, size, and color from raw product descriptions speeds up the catalog organization process.
- Analyzing sentiment: Understanding customer opinions about a product or price helps shape pricing strategy (perception of expensive, fair, or cheap).
A concrete example
A cosmetics retailer is using an NLP model to match its catalog of 12,000 products against the catalogs of six competitors. The previous manual matching process covered 38% of the products. The NLP model, trained on 50,000 validated product name pairs, achieves a 94% match rate with over 99% accuracy. This allows the company to monitor 11,280 products instead of 4,560, and to enhance its competitive benchmarking without hiring additional staff.
How to measure/use it
NLP in pricing relies on several techniques: 1) text vectorization (Word2Vec, BERT, embeddings), 2) similarity calculations (cosine, Euclidean distance) for matching, 3) supervised classification to categorize product descriptions, 4) sentiment analysis to process customer reviews. Modern pricing analytics solutions incorporate pre-trained models on specific retail corpora to accelerate implementation.
Common Mistakes
- Underestimating the need for labeled data: Training a high-performance NLP model requires several thousand pairs of data validated by experts.
- Using a general-purpose model: a model trained on Wikipedia performs worse than a specialized retail/pricing model.
- Don’t skimp on accuracy: a match with 80% accuracy results in 20% of incorrect matches, which can undermine value.
Learn more
- Research & Data: Product matching using NLP and computer vision.
- Solutions: Pricing Analytics with a built-in NLP engine for large-scale matching.
- Tip: Pricing training to equip teams with the skills to use NLP.
- Resources: Check out our pricing FAQ to learn how AI fits into pricing.