Synonym Management

Here’s a problem every webshop faces: your customers call things by different names than you do. They search for “sofa” but your products say “couch”. They look for “sneakers” while you stock “trainers”. They type “tshirt” but your catalog says “t-shirt”.

Each of these vocabulary mismatches is a failed search, a customer who can’t find what they’re looking for, even though you sell exactly what they want. This is where synonym management comes in.

Synonym management is teaching your search that different words can mean the same thing. When someone searches for “sofa”, the search should also show products labeled “couch” because they’re equivalent terms.

Why vocabulary mismatches happen

The vocabulary mismatch problem is surprisingly common. Studies show that customers and businesses use different terminology for the same products about 30% of the time. That’s almost a third of searches potentially failing due to simple word choice differences.

This happens for several reasons:

Regional differences cause confusion. In the US, people say “sneakers”. In the UK, they say “trainers”. In Australia, “runners” is common. Your catalog uses one term, but customers from different regions use others.

Industry terminology differs from consumer language. You might call something “therapeutic footwear” while customers search for “comfortable shoes for feet pain”. Technically different terms, but customers want the same thing.

Spelling variations create problems. Is it “gray” or “grey”? “T-shirt”, “tshirt”, or “t shirt”? All refer to the same product, but traditional search sees them as completely different.

Brand names become generic terms. People search for “Kleenex” when they mean tissues, “Bandaid” for adhesive bandages. If you sell these products under generic names, customers might not find them.

Each of these is a synonym problem waiting to be solved.

How AI learns synonyms automatically

Here’s where modern search gets interesting: you don’t need to manually program all these equivalent terms. AI-powered search figures them out automatically.

The AI watches customer behavior. It notices that searches for “sofa” and searches for “couch” lead to the same products being clicked and purchased. Without anyone teaching it, the system learns these words must mean the same thing in your catalog’s context.

When hundreds of customers search for “running shoes” and “jogging sneakers” and consistently click on the same products, the AI understands these are synonymous phrases. The pattern is clear from behavior.

This automatic learning is powerful because it catches synonyms you’d never think to add manually. Maybe customers in your region use slang terms or abbreviations you’re not aware of. The AI picks up on any terms that lead to the same products.

It also adapts to your specific catalog. “Boot” means footwear in a fashion store but might mean car trunk or protective covers in an automotive store. The AI learns the correct meaning for your specific business context.

When you still need manual synonyms

Despite AI’s power, some synonyms still benefit from manual addition:

Industry-specific technical terms might not have enough search volume for the AI to learn automatically. If you sell specialized equipment, you might need to manually define that “oscilloscope” and “scope” are equivalent.

Brand new products or categories won’t have behavioral data yet. When you launch a new product line, manually adding relevant synonyms ensures customers can find it immediately.

Business-critical terms deserve explicit control. Maybe you want absolute certainty that “phone” shows “smartphones” and “mobile phones”. A manual synonym rule guarantees this.

Common misspellings can be added proactively. If you know customers often type “recieve” instead of “receive”, add it as a synonym before the AI learns it naturally.

The best approach combines AI learning with strategic manual additions, letting automation handle the majority while you focus on high-impact cases.

The danger of over-synonyming

Here’s an important caution: being too aggressive with synonyms causes problems.

If you make “bag” and “purse” complete synonyms, searches for “trash bags” or “shopping bags” will show purses. That’s not helpful. These words overlap in meaning sometimes but not always.

Making “boot” synonymous with “shoe” means searches for “hiking boots” will show all shoes. Too broad, boots are specific types of shoes, not interchangeable.

Good synonym management is precise. Only make terms synonymous if they’re truly interchangeable in 95%+ of contexts. For broader relationships, let semantic search handle the connection, it’s more nuanced.

One-way versus two-way synonyms

Sometimes synonyms should work in both directions, sometimes only one:

“Sofa” ↔ “couch” should be bidirectional. Completely interchangeable terms.

“Sneakers” ↔ “trainers” should be bidirectional. Regional variations for the same thing.

But consider this case: “iPhone” → “smartphone” might be one-way. When someone searches “iPhone”, showing all smartphones is reasonable, iPhones are smartphones. But when someone searches “smartphone”, they probably don’t want exclusively iPhones. That would be too narrow.

“Shoes” → “sneakers” could be one-way. Searching for “shoes” could include sneakers among other shoe types. But searching for “sneakers” should stay specific, not show all shoes.

These one-way relationships (sometimes called “expansions”) require careful thought but can be very powerful.

Context-specific synonyms

The tricky part about synonyms: context matters enormously.

“Monitor” in an electronics store means computer screen. In a hospital supplies store, it means patient monitoring equipment. In a finance context, it means tracking investments. Same word, completely different meanings.

Good synonym management considers context. Maybe “monitor” = “screen” only in the electronics category, not site-wide. Category-specific synonyms prevent false matches.

This is another reason AI learning is valuable, it learns context-specific equivalences from actual usage patterns in your store.

What this means for your webshop

Without good synonym management, you lose sales every day to vocabulary mismatches. Customers can’t find products you definitely stock, simply because they call things by different names than you do.

Traditional search required maintaining massive synonym dictionaries manually, tedious work that’s never complete. Modern AI-powered search like TextAtlas learns synonyms automatically from customer behavior, eliminating most of this maintenance.

You still maintain strategic control, adding important synonyms manually when needed, overriding incorrect AI-learned connections if necessary. But the day-to-day synonym discovery happens automatically, continuously improving as more customers shop.

The result is search that understands all the different ways customers express what they want, regardless of the exact terminology your catalog uses. Fewer failed searches, happier customers, more sales, all from making sure different words that mean the same thing are treated as equivalent.