How do I track cost per business outcome in Cognocient?
Link AI spend to business outcomes — ticket resolutions, conversions, documents processed — and calculate true cost-per-outcome ROI with the X-Cost-Outcome header.
The X-Cost-Outcome header links each AI call to the business result it produced — a resolved ticket, a signed contract, a generated report. Cognocient aggregates these into cost-per-outcome metrics that answer the CFO's core question: what did we get for this AI spend?
Why cost per outcome changes the conversation
Without outcome tracking, you have AI spend. With it, you have AI ROI.
Without outcomes: "We spent $12,000 on AI last month. Is that a lot?" — No answer. You have a cost with no context.
With outcomes: "We spent $12,000 on AI and resolved 28,500 support tickets at $0.42 each. Our human agents cost $8.40 per ticket." — AI delivers 20× cost efficiency per ticket. ROI is obvious.
Quick setup — X-Cost-Outcome header
The fastest way to track outcomes is the X-Cost-Outcome attribution header. Add it alongside your existing X-Cost-Feature header:
The X-Cost-Outcome tag feeds simple call-level analytics (visible alongside your other attribution tags), separate from the outcome-definition system in Step 1 below. If you want the dedicated cost-per-outcome dashboard, define an outcome and record events for it as described next.
Use lowercase hyphen-separated names that describe what happened: ticket-resolved, document-processed, lead-qualified. Avoid generic names like chatbot-call or llm-used.
Step 1 — Define an outcome
Go to Executive → Outcomes → Define Outcome. An outcome definition has four fields:
Name — What you're measuring. Examples: "Support ticket resolved," "Contract drafted," "Document processed."
Description (optional) — Free-text notes for your team.
Correlation Method — By Session or By User. This determines what correlation_key you'll send when recording an event: a session ID or a user ID.
Window (minutes) — How far back Cognocient looks to match AI calls to the outcome event, correlated on the matching session or user tag. Default 30 minutes.
Step 2 — Record outcome events
Call this endpoint from your application when a positive outcome occurs — ticket resolved, contract signed, document approved. Authenticate with your Cognocient proxy key.
The endpoint returns 202 Accepted. outcome_name must match an outcome you've already defined in Step 1 — it isn't created implicitly on first event.
If you already have session IDs in your existing application, you can add outcome tracking by calling this endpoint from your downstream business logic — the webhook that fires when a ticket closes, the callback when a contract is signed. No changes to AI call code needed.
Step 3 — Reading outcome data in the dashboard
The Outcomes list (Executive → Outcomes) shows your outcome definitions — name, correlation method, window, and status. Click into one to see its metrics for the selected period:
| Field | What it shows |
|---|---|
| Total Outcomes | Count of outcome events recorded |
| Total AI Investment | Sum of AI spend from calls matched to those events (by session or user, within the correlation window) |
| Avg Cost per Outcome | Total AI Investment ÷ Total Outcomes — your primary ROI metric |
There's no success-rate, cost-per-failed-outcome, or trend line on this view today — it reports cost against recorded events only, not against a denominator of attempted-but-abandoned sessions.
Industry benchmarks for cost per outcome
Use these benchmarks to calibrate your own metrics and frame the ROI conversation for your board:
| Outcome type | Typical AI cost | Human cost equivalent | AI advantage |
|---|---|---|---|
| Support ticket (L1) | $0.15–$0.50 | $6–$12 | 12–80× |
| Support ticket (L2) | $0.80–$2.00 | $20–$35 | 10–40× |
| Contract first draft | $1.50–$4.00 | $150–$400 | 50–100× |
| Document classification | $0.01–$0.05 | $1.50–$3.00 | 30–300× |
| Code PR review | $0.40–$1.20 | $40–$80 | 35–200× |
| Sales outreach email | $0.05–$0.20 | $5–$15 | 25–300× |
Benchmarks are illustrative ranges. Actual costs vary by model, prompt complexity, and task definition. Your Cognocient data will give you the precise figure for your implementation.
Related articles