Measuring Success

Deploying AI tools is only half the job. To justify continued investment and guide optimization, you need a clear measurement framework. This page walks you through baselining, tracking per-service impact, calculating ROI, and reporting results to stakeholders.

Baseline metrics to capture before deployment

Before turning on any Simpli service, snapshot these numbers from your existing helpdesk. You will compare against them at 30, 60, and 90 days post-launch.

Metric	Where to find it	Why it matters
Average handle time (AHT)	Helpdesk reporting	Primary productivity indicator
First response time (FRT)	Helpdesk reporting	Customer experience baseline
CSAT score	Post-ticket surveys	Quality of experience baseline
QA scores (manual)	QA team spreadsheets or tool	Quality of agent work baseline
Tickets per agent per day	Helpdesk reporting	Throughput baseline
Escalation rate	Routing/escalation logs	Routing accuracy and agent capability baseline
Self-service deflection rate	KB or help center analytics	Measures how often customers solve issues without a ticket

Capture at least four weeks of data to account for weekly variation. If your volume is seasonal, note where you are in the cycle.

Per-service impact metrics

Each Simpli service moves different needles. Use this table to know what to track for each service you deploy.

Service	Key Metric	How to Measure	Typical Impact
Triage	Misrouted ticket rate	Compare before/after routing accuracy	40-60% reduction
Reply	Draft acceptance rate	Reply `/feedback` endpoint	60-80% acceptance after tuning
Reply	Handle time reduction	Helpdesk AHT reports	20-35% reduction
QA	Scoring coverage	% of conversations scored	100% (vs 5-10% manual)
Sentiment	Early escalation rate	Interventions triggered by alerts	30-50% reduction in surprise escalations
Pulse	Report generation time	Time to build weekly reports	80-90% reduction
KB	Gap closure rate	KB `/gaps` trending over time	Varies by initial KB completeness

Track each metric independently. Improvements in one area (such as Triage accuracy) often have downstream effects on others (such as handle time), but you want to attribute impact clearly.

ROI calculation framework

ROI for AI in support comes down to three factors:

Cost of AI -- LLM inference costs, infrastructure, and licensing
Time saved -- agent hours freed up, multiplied by fully loaded cost per hour
Quality improvements -- CSAT uplift, reduced churn, fewer escalations

Gathering cost data

Every Simpli service exposes a /usage endpoint that reports token consumption and API call counts. Aggregate these monthly to get your total LLM spend.

Calculating time saved

Time saved per ticket = (old AHT - new AHT)
Monthly hours saved   = Time saved per ticket * monthly ticket volume
Dollar value           = Monthly hours saved * fully loaded cost per agent hour

Worked example: 10-agent team, 500 tickets per day

Assumptions:

Old AHT: 8 minutes
New AHT with Reply + Triage: 5.5 minutes (31% reduction)
Fully loaded agent cost: $35/hour
Working days per month: 22
Monthly ticket volume: 500 * 22 = 11,000 tickets

Calculation:

Time saved per ticket: 2.5 minutes
Monthly hours saved: 11,000 * 2.5 / 60 = 458 hours
Dollar value of time saved: 458 * $35 = $16,030/month

Now subtract your AI costs:

Typical LLM spend for this volume (Reply + Triage + QA + Sentiment): approximately $1,500-3,000/month depending on model choice and prompt length
Infrastructure costs: varies by deployment model

Net monthly ROI: $13,000-14,500/month for this example team.

This does not yet account for quality improvements. If CSAT increases by even one point and you can tie that to reduced churn, the ROI grows significantly.

Quality-driven ROI

Quality improvements are harder to dollarize but often more valuable:

CSAT uplift: Higher satisfaction correlates with retention. Even a 1-2 point improvement matters at scale.
Churn reduction: If Sentiment-driven early interventions prevent even a few enterprise customers from churning, the revenue impact can dwarf all other savings.
Consistency: 100% QA coverage means every conversation is scored, not just a random 5-10%. This catches problems earlier and coaches agents faster.

Reporting cadence

Cadence	Audience	Content	Source
Weekly	Ops/team leads	Volume, SLA, AHT, draft acceptance rate, escalation alerts	Pulse `/metrics`, Reply `/feedback`, Sentiment trends
Monthly	Directors/VPs	Executive summary with trends, cost vs savings, quality scores, action items	Aggregated Pulse + QA + Sentiment + Reply data
Quarterly	Executives/board	ROI review, strategic recommendations, scaling decisions, forecast	Full ROI calculation, Pulse `/forecast`, cost analysis

Automate as much of this as possible. See the Executive Reporting workflow for templates and scripts.

Common pitfalls

Measuring too early. Give each service at least 2-4 weeks before drawing conclusions. Agents need time to learn the tools, and models improve as you tune prompts and rubrics. The first week is not representative.

Optimizing for speed over quality. A 50% reduction in handle time means nothing if CSAT drops. Always track speed and quality metrics together. If AHT drops but QA scores or CSAT decline, something is wrong.

Not accounting for the trust ramp-up. Draft acceptance rates start low because agents do not yet trust the AI. This is normal. Track the trend line, not the day-one number. Acceptance typically climbs steadily over the first 4-6 weeks.

Comparing apples to oranges. Make sure you are comparing the same ticket types, channels, and complexity levels before and after deployment. If you launched Triage and it started routing complex tickets to senior agents, their AHT might go up -- but that is the correct behavior.

Ignoring agent feedback. Metrics tell you what happened. Agents tell you why. If draft acceptance is low, talk to agents before tweaking prompts. They will tell you exactly what is wrong.

Next steps

Executive Reporting -- build leadership dashboards from Simpli data
Cost Optimization -- reduce LLM spend without sacrificing quality
Change Management -- prepare your team for AI adoption

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