Do I need embeddings for clustering support tickets?

Not always—but often yes, if your tickets are mostly free-text and you want meaningful clusters.

When embeddings help

Use embeddings if tickets are:

• Unstructured text: “Login fails on iPhone after update”
• Semantically similar but lexically different: “can’t sign in” vs “authentication error”
• Longer than a few words
• Need clustering by issue meaning, not just shared keywords

Embeddings turn each ticket into a vector that captures semantic similarity, so clustering methods like K-means, HDBSCAN, or hierarchical clustering work much better on support text.

When you may not need embeddings

You might skip embeddings if:

• Tickets are already structured with labels/categories
• You’re clustering on metadata only (product, region, severity, etc.)
• You just need keyword-based grouping
• You have very short, repetitive tickets and simple TF-IDF works well enough

Common alternatives

• TF-IDF + clustering: simpler, cheaper, good baseline
• Embeddings + clustering: better for semantic grouping
• Rule-based / taxonomy-based routing: best if you already know the categories

Practical recommendation

For support tickets, a strong default is:

1. Clean ticket text
2. Create embeddings
3. Cluster with HDBSCAN or K-means
4. Review clusters with sample tickets
5. Optionally add metadata features

If you want, I can suggest the best clustering approach for your ticket volume and data format.