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Quality leaderboard: read the ranking and act on it

Quality → Leaderboard ranks every agent on the points they earned during a window, computed on demand from the QA scorecards, handled calls, and CSAT responses the platform already captures. The ranking is not a spreadsheet export or a score that waits for a weekly job — the page re-computes when you open it, so the board always reflects the data you actually have. This guide covers the reading part: what the numbers are made of, how to compare agents fairly, and where to go next once a rank has your attention. The setup side — authoring the form these scores come from, the sampler that decides what gets reviewed, and the AI auto-scorer that feeds the same ledger — lives in the sibling guides linked at the bottom.

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1. What the leaderboard shows

Each row is one agent in the selected window, ranked by total points with the underlying metrics beside the rank: Points are a composed score, not a source metric. The default ruleset weights QA alongside volume and CSAT so an agent cannot win the board on calls alone. The Scoring rules tab on the same page shows the exact rules and badge definitions behind the current board, and lets you apply custom weights that re-rank the rows on the spot — the board announces that with a Custom weights applied chip, so nobody reads a tuned ranking as the default one. The board answers rules_source: "default" on a normal read; when custom weights are applied it answers rules_source: "custom". Treat the two as different rankings — a custom-weight preview reorders agents and is meant for evaluating a rewards scheme before you adopt it, not for the standing supervision record.

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2. Open the board against the right window and team

  1. Navigate to Quality → Leaderboard in the dashboard. Access is role-scoped: owner, admin, and supervisor roles see the board; other roles get a permission notice instead of the table. Agents see their own card under the same page, computed against the same default board — their rank always matches what the supervisor view shows for them, which prevents “your dashboard says one thing” arguments.
  2. Pick the window with the Today / This week / This month selector. The board re-ranks on each switch. Use Today for shift-floor goals and same-shift recognition; This week for the standard coaching view; This month for compensation and review-conversation inputs, where the sample is large enough to be fair.
  3. Narrow to a team with the queue filter. Options are the voice queues the board scopes on server-side. All queues is your full supervised roster; picking one queue turns the page into a team competition — useful for a tier-one vs. tier-two contest or for isolating a new-hire cohort.
Over the API the same reads are POST /api/v1/quality/gamification/leaderboard (body: period, optional queue_id) and GET /api/v1/quality/gamification/config for the rule and badge definitions behind the board. A supervisor requesting a queue outside their assignment gets a 403.

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3. Read the ranking fairly — normalize for sample size

Two agents’ points are comparable only when their windows and scopes match, and even then the raw rank over-credits volume. Read the columns together:
  • Points vs. Calls. A high-call agent accumulates volume-based points mechanically. Before concluding anything about quality, split the rank by the QA column: an agent sitting at rank 3 with a 94 QA average is doing different work from one at rank 3 on volume alone.
  • QA average vs. coverage. The QA column averages evaluated calls, not all calls. If your sample quota reviews three of an agent’s conversations per week, an 88 average over three calls carries more noise than an 84 over twelve from a higher-coverage peer. When a rank surprises you, widen the window — the month board normalizes what the day board exaggerates.
  • CSAT vs. AHT. Fast handle times that drag CSAT down are not a win; the badge definitions usually encode a floor on one metric to gate the other, precisely so a speed-at-all-costs style cannot farm awards.
  • Sample-size guards on badges. Badges earn out only over a minimum number of samples, but the points behind them have no such floor — treat a top-ranked agent on a thin window as an unconfirmed signal until the week or month view confirms it.
A fair comparison rule of thumb: compare within the same queue, same window, and similar call counts, and weight the QA and CSAT columns over total points when the inputs differ in coverage.

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4. Drill from a rank into the underlying evidence

A rank is a summary; supervision happens on the interactions beneath it. Two drill paths:
  • Quality → Agents → your-agent — the per-agent view carries the QA score, compliance-flag count, CSAT, and adherence for the window, plus the recent-evaluated-interactions list. This is the fastest path from “why is this agent at rank 14?” to the specific calls and scores underneath.
  • Quality → Evaluations filters the evaluation ledger by agent; Quality → Recordings holds the call recordings those evaluations grade. Work one low outlier per session — read the scorecard, listen to the recording, and check whether the score survives your own listen. If it does not, that is a calibration problem with your reviewers or the AI auto-scorer flag threshold, not an agent problem.
Auto-sampled and AI-flagged rows land in the same ledger: a score you drill into may carry an override from a reviewer who already corrected the AI’s first pass. The corrected total is what the leaderboard counts.

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5. Act on it — coaching and reporting

The board’s purpose is targeting, not decoration:
  • Coaching targeting. A sustained bottom-decline in QA or CSAT is the queue for a coaching plan — author one under Voice → Coaching → Coaching plans, attach training modules, and close it with a measured before/after report when the agent recovers. Scores below your coaching threshold also auto-assign a plan against the same surface, so a hand-authored plan and an auto-assigned one share one workflow. The full loop is in Voice coaching plans.
  • Recognition. The top of the month board, read with the sample-size fairness above, is your recognition shortlist — badges make a good secondary filter because their minimum-sample guards screen out thin-window spikes.
  • Monthly reporting. For the organization-level service view that pairs with per-agent QA, the monthly SLA and availability report covers delivery health and availability; the leaderboard supplies the per-agent quality side of the same monthly review packet.

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6. Caveats — run it without poisoning the floor

A leaderboard can improve a team or quietly wreck one, and the difference is how supervisors talk about it:
  • Never shame the bottom. Read bottom-rank agents in private and coach from the per-agent view, not from the board in a team channel. The visible use of the board should be recognition at the top and trend-checking in the middle.
  • Use the trend, not the absolute rank. A single window’s rank is noise at low sample sizes; a three-window trajectory — rising, flat, or declining — is a coaching signal. An agent climbing from rank 18 to 11 over a month is winning even though 11 never appears on anyone’s trophy line.
  • Custom weights are a preview, not a verdict. Re-ranking on tuned weights is for evaluating a scoring scheme before adoption. Switching weights mid-competition rewrites history; pin one ruleset per competition period and change it only between periods.
  • The board rewards what the ruleset measures. If agents start optimizing the measured columns at the expense of unmeasured ones — shorter handle time with colder closes, for example — the rules need attention, not the agents.

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See also