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.
94% automatic matching, compared to 38% manual matching
Cosmetics retailer — matching 12,000 SKUs using NLP
automatic matching rate achieved using an NLP model trained on 50,000 pairs of labels, with an accuracy of over 99%.
Coverage of the former manual matching process
products now being monitored (up from 4,560), without additional hiring
Why it matters
- Industrializing product matching: NLP automatically bridges "iPhone 15 Pro 256 Go Bleu" and "Apple iPhone 15 Pro - 256GB - Blue Titanium" across two different catalogs.
- Enriching data: automatically extracting brand, size, and color from a raw label accelerates catalog structuring.
- Analyzing sentiment: understanding customer reviews on a product or price guides the pricing strategy (perception of expensive / fair / cheap).
Real-world example
A cosmetics retailer uses an NLP model to match its catalog of 12,000 references with 6 competitor catalogs
The former manual matching covered 38% of products
The NLP model, trained on 50,000 validated description pairs, achieves 94% matching with over 99% precision
This enables monitoring 11,280 products instead of 4,560, and enriching the competitive benchmark without additional hiring.
How to measure and 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 Optimization Software solutions incorporate pre-trained models on specific retail corpora to accelerate implementation.
Common pitfalls
- Underestimating the need for labeled data: training a high-performing NLP model requires several thousand expert-validated pairs.
- Applying a generalized model: a model trained on Wikipedia underperforms compared to a retail/pricing-specialized model.
- Failing to measure accuracy: an 80% accurate matching generates 20% erroneous comparisons, which can destroy value.
We explore this topic in greater depth in our article on agency pricing.
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