Update
Sep 28, 2025
Giga Team
Contents
Every support call contains two hidden signals:
What the customer was trying to do
Why they couldn’t do it
Most teams can’t track either one reliably. Reason codes are inconsistent, categories like “Delivery Issue” or “Payment Problem” are too broad, and “Other” buckets grow until they’re meaningless.
Intents & Tags fix that.
Intents as goals, Tags as reasons
Intents capture the customer’s goal.
Examples: “Can’t log in”, “Payment failed”, “Return item”, “Reschedule booking”
Tags explain why the goal failed.
Examples: “Password reset not working”, “Card declined”, “No return label”, “Change fee issue”
How it works
Create Intents: Add categories in the dashboard that reflect common customer goals.
Add Tags: Define the reasons those goals fail, with clear descriptions of when they apply.
Automatic analysis: Giga processes conversations in real time and applies intents and tags automatically.
Integrated data: Insights flow into dashboards, analytics, post-conversation code, and webhook payloads.
Why it matters
Clarity: No more guessing why customers call - the goals and blockers are explicit.
Smarter iteration: Train agents or adjust policies where failures actually occur.
Product feedback: Spot product flaws or design gaps driving support volume.
Operational focus: Track which goals fail most often and prioritize fixes.
Measurement: See if changes reduce failure rates over time.
Examples across industries
Industry | Intent (Goal) | Tags (Reasons for failure) |
Fintech | Transfer funds | 2FA failed · Insufficient funds · Blocked account |
Retail | Return item | Wrong size · No return label · Policy confusion |
Travel | Reschedule booking | Change fee issue · No availability · System error |
Food delivery | Order not received | Driver delayed · Wrong address · Restaurant closed |
The framework is flexible. Whatever the industry, every support interaction can be mapped as a goal plus a reason it failed.
Intents & Tags are live in Giga. Teams can log in today, define their own categories, and immediately see structured breakdowns of customer conversations.
