Data Collection

Every conversation contains valuable information — why a customer is unhappy, what product they're interested in, whether the bot handled the issue well, how long the team took to respond. Data Collection lets you automatically extract this information and turn it into structured, reportable data.

You define the questions. The AI reads every conversation and answers them for you.

This isn't a simple tagging system. The prompt field accepts natural language instructions — the same frontier AI models that power your bot will interpret them. This means you can extract almost anything: product defects, cancellation reasons, customer sentiment, SLA compliance, purchasing power, language, retention outcomes, security concerns, and patterns you haven't thought of yet.

How to think about data collection

The core idea is simple: each data collection field is an AI prompt that runs against the full conversation after it closes. You tell the AI what to look for, give it the possible values, and it classifies every conversation automatically.

This makes data collection extremely flexible. It's not limited to what the customer said — the AI can analyze the entire interaction, including how the bot or agent responded. Some examples of what this enables:

What happened — What product issue did the customer report? What was the cancellation reason?
What the customer wanted — Were they looking for a refund? A specific product category? A payment link?
How it was resolved — Was the subscription saved? Did the agent respond within SLA? Did the bot offer the right solution?
Who the customer is — What language do they speak? What's their budget range? Are they a first-time buyer?
How the bot performed — Did the bot offer partial refunds when it shouldn't have? Did it leak another customer's data?

Think of each field as a question you'd want answered about every conversation — then let the AI answer it at scale.

Setting up data collection fields

Create and manage data fields in Settings → AI & Automation → Data Collection.

Each field has three settings:

Setting	What it does
Name	How the field appears in analytics. Choose something clear and descriptive.
Prompt	Natural language instructions that tell the AI what to look for and how to classify it. This is where the power is — see writing good prompts below.
Possible values	The specific values the AI can assign. Can be detailed categories, simple Yes/No, or anything in between.
Optional field	When enabled, the AI only assigns a value if it finds relevant information. (Recommended for most fields — many won't apply to every conversation.)

How it works

After a conversation is closed, resolved, or handed off, the AI:

Reviews the full conversation — every message from the customer, bot, and agents
Evaluates each data collection field against the conversation
- Required fields are always classified
- Optional fields are only classified when the conversation contains relevant information
Assigns the most appropriate value for each field
Saves the results for reporting, filtering, and analysis

Writing good prompts

The prompt is the most important part of a data collection field. It tells the AI what to look for and how to decide between values. The AI is capable of following nuanced, multi-step logic — don't be afraid to write detailed instructions.

Be specific about when to classify

The most common mistake is not telling the AI when to skip classification. If a field only applies to certain conversations, say so explicitly:

"This categorization should only be performed if the customer has requested a subscription cancellation. If no subscription cancellation was requested, output null."

"If the customer is not reporting a technical issue, don't output a value."

Without this guidance, the AI may force-fit a value to conversations where the field doesn't apply.

Tell the AI how to handle edge cases

Good prompts anticipate ambiguity:

"If no reason was provided, output 'No reason provided'. If none of the cancellation reasons match, output 'Other'."

"If the concern does not match any known values, classify as 'Others'. If the conversation is not product-related, leave blank."

You can analyze bot and agent behavior, not just customer messages

The AI reads the full conversation — including bot responses and agent replies. This means you can use data collection to monitor your own team's performance and the bot's behavior:

"Return 'Yes' if the bot offered partial refunds, otherwise output 'No'."

"Calculate the time between the handoff and the first agent response. Within 4 hours = 'Within SLA'. Greater than 4 hours = 'SLA Violated'."

"If the bot provided information of the wrong person and the customer complained about it, output 'Yes'. Otherwise, output 'No'."

Complex logic works

The AI can follow multi-step reasoning. You can combine multiple conditions into a single field:

"Q1: Did the customer ask for money back? Q2: Did the customer ask to cancel their subscription? Q3: What was the outcome? Classify as 'SS Saved' if subscription was retained, 'Refund Saved' if refund was avoided, or the appropriate combination. IMPORTANT: A subscription being cancelled is NOT a 'refund not saved'."

Use case ideas

Data collection is underutilized by most businesses. Here are concrete ways to extract value from conversations you're already having.

Product and service issues

The most common use case — classify what went wrong so you can spot trends and fix root causes.

Field name	What to track	Example values
Product issues	The specific defect or complaint the customer reports	Damaged, Defect, Missing Items, Not as Advertised, Wrong Size, Adverse Reaction, Others
Packaging issues	Whether the product arrived in poor condition	Broken seal, Damaged container, Missing parts, Leaks
Delivery problems	What went wrong with shipping	Delay, Lost shipment, Damaged on arrival

Tailor the possible values to your specific products. A knife brand tracks "Bent Blade, Blunt Blade, Broken Tip, Chipped Blade, Rust, Loose Handle." A pillow brand tracks "Flat Pillow, Odor/Smell, Cover Size, Damaged Zipper." The more specific your values, the more actionable your data.

Cancellation and retention

Understand why customers leave — and whether your retention efforts work.

Field name	What to track	Example values
Cancellation reason	Why the customer wants to cancel their subscription	Too expensive, Not using the service, Didn't meet expectations, Technical issues, Personal reasons, No reason provided
Cancellation outcome	Whether the customer was retained	Cancelled, Paused, Customer changed their mind
Retention outcome	Whether a refund or subscription cancellation was prevented	Subscription Saved, Subscription Not Saved, Refund Saved, Refund Not Saved
Return reason	Why the customer is returning a product	Allergic reaction, Bad quality, Better alternative, Damaged, Financial reasons, No results, Too much product

Customer insights

Learn about who your customers are and what they want.

Field name	What to track	Example values
Language	What language the customer communicates in	English, Spanish, French, German, Portuguese, Italian
Purchasing power	The customer's stated budget range	Under $10k, $10k-$20k, $20k-$40k, $40k+
Product interest	What type of product the customer is looking for	Computers, Individual components, Protein powder, Multivitamin
Customer satisfaction	How the customer felt after receiving advice	Very satisfied, Neutral, Not satisfied

Operational quality

Monitor how your team and bot perform — without manual QA.

Field name	What to track	Example values
First response SLA	Time between handoff and first agent response	Within SLA, SLA Violated
Next response SLA	Time between conversation reopen and next agent reply	Within SLA, SLA Violated
Bot behavior audit	Whether the bot did something it shouldn't have	Offered partial refund, Leaked customer data, Behaved correctly
Troubleshooting outcome	Which step resolved the customer's technical issue	Restart device, Factory reset, APN change, Not resolved
Technical issue solution	What solution the agent proposed	No data left, Device is defective, Restore network settings, Other

Payment and order status

Track operational patterns in how customers interact with orders.

Field name	What to track	Example values
Payment issues	Whether the customer had trouble paying	Payment problem, No problem
Order status	Current state of the customer's order	In preparation, In transit, Delivered, Returned, Cancelled
Package opened on return	Whether the customer opened the package before returning	Yes, No
Payment method provided	Whether the customer shared bank details	Yes, No

Security and compliance

Catch concerning behavior automatically.

Field name	What to track	Example values
Bad intent detection	Whether the customer is trying to exploit the bot	Attempting to steal information, Trying to break security
Data leak detection	Whether the bot accidentally shared another customer's data	Yes, No
Refund threat tracking	What leverage the customer used to demand a refund	Dispute/Chargeback, Scam accusation, Report to authorities, Bad review

Analytics

Insights are available in Metrics → Data Collection.

You can:

See how often each value appears across all conversations
Compare trends by channel, product, or time period
Spot rising issues early (e.g., "packaging leaks" spiking this month)
Filter conversations by any collected field for deeper analysis
Combine multiple fields to find patterns (e.g., cancellation reason and retention outcome)

This turns raw conversations into clear, structured data you can use to improve products, operations, and customer experience.