Q1 planning. Your support team needs OKRs. The temptation is to write something like:

Objective: Improve customer support quality. KR1: Increase CSAT from 4.2 to 4.5. KR2: Reduce average first response time from 2 hours to 1 hour. KR3: Resolve 90% of tickets within 24 hours.

These look measurable. They'll get approved. And they'll drive exactly the wrong behavior.

KR1 will lead to agents being extra nice while not necessarily solving problems better. KR2 will lead to faster auto-acknowledgments that don't help anyone. KR3 will lead to premature ticket closures so the 24-hour clock stops.

Good support OKRs are harder to write but lead to actual improvement.

The Problem With Standard Metrics as OKRs

Most support metrics measure activity, not outcomes. Tickets closed, response time, handle time. These are useful for operational monitoring. They're terrible as goals because they can all be gamed without improving the customer experience.

An agent who closes 50 tickets per day might be rushing through them. An agent who closes 30 might be resolving them properly, including follow-up questions and root cause investigation. The second agent is better. The first agent meets the OKR.

Good OKRs measure outcomes that matter to the business and can't be easily gamed.

OKRs That Actually Work

Objective: Reduce customer effort in support interactions.

KR1: Reduce messages-per-resolution from 4.2 to 3.0.

Why this works: fewer messages means the agent solved the problem faster, asked the right questions upfront, and didn't require the customer to repeat information. You can't game this by closing tickets early because unresolved issues reopen, which increases messages.

KR2: Reduce repeat contacts (same customer, same issue, within 30 days) from 18% to 12%.

Why this works: repeat contacts mean the first resolution didn't stick. Reducing them requires actually solving problems, not just closing tickets. This metric rewards thorough resolution over fast resolution.

KR3: Increase self-service deflection from 25% to 40%.

Why this works: it pushes the team to improve documentation, proactive communication, and self-service tools. Every deflected ticket is a customer who got help without waiting.

Objective: Make support a product improvement engine.

KR1: File 15 actionable bug reports from support data (accepted by engineering).

Why this works: it pushes support to not just handle tickets but identify root causes. "Accepted by engineering" means the bug reports are good enough to act on, not just ticket dumps.

KR2: Reduce ticket volume in the top 3 ticket categories by 20%.

Why this works: it forces the team to look upstream. If "how do I export?" is your top category, reducing it by 20% means fixing the export UX, writing better docs, or adding in-app guidance. You can't reduce volume in a category without addressing the root cause.

KR3: Deliver a monthly product insights report to the product team.

Why this works: it formalizes the support-to-product feedback loop. The report includes ticket trends, customer quotes, feature request aggregation, and bug frequency. Product gets data. Support gets heard.

Objective: Increase support team efficiency without sacrificing quality.

KR1: Increase automation rate from 30% to 50%.

Why this works: it encourages the team to identify and automate repeatable responses. Supp's AI classification makes this trackable: what percentage of classified intents get fully automated responses?

KR2: Maintain or improve CES (customer effort score) while increasing automation.

Why this works: it's the quality check on KR1. You can't automate your way to 50% if the automated responses are bad, because CES will drop. This forces thoughtful automation, not indiscriminate automation.

KR3: Reduce cost per resolution by 15%.

Why this works: it combines speed, automation, and efficiency into one financial metric. You can reduce cost per resolution by automating simple tickets, improving agent efficiency, or reducing repeat contacts. All three are good outcomes.

How to Measure These

Messages-per-resolution: most help desks track this. If yours doesn't, count total messages in resolved tickets divided by number of resolved tickets.

Repeat contacts: tag customers who contact within 30 days about the same category. Supp's classification makes this automatic: if the same customer gets the same intent classification twice in 30 days, that's a repeat contact.

Self-service deflection: measure users who view a help article after initiating the support flow and don't submit a ticket.

Automation rate: Supp tracks this directly. The percentage of classified messages that get a fully automated response without human involvement.

CES: survey question after resolution. "How easy was it to get your issue resolved? 1-7." Aim for 70%+ scoring 5 or above.

Bug reports accepted: count in your project tracker (Linear, Jira). Only count bugs that engineering acknowledges and prioritizes.

The Quarterly Review

At the end of the quarter, the OKR review should answer two questions:

Did the numbers improve? If KR targets were hit, great. If not, why not?

Did the customer experience improve? This is the meta-question. Numbers can improve while experience stays flat (or gets worse) if you're measuring the wrong things. Use qualitative data (ticket samples, customer quotes, agent feedback) alongside the metrics.

If the numbers improved and the experience improved, your OKRs were good. If the numbers improved and the experience didn't, your OKRs are measuring the wrong things. Revise them for next quarter.

Support OKRs should make your team better at helping customers, not better at hitting arbitrary targets. The distinction matters, and it starts with choosing what you measure.