Forecast, schedule, and coach at scale.
Workforce management for contact centers. AI-driven forecasting, intraday schedule adjustments, skills-based scheduling, real-time adherence, and agent coaching — all in the same platform.
WFM that doesn't need a second vendor.
Most contact centers do not lose efficiency because they lack data. They lose efficiency because their data is split across too many systems — forecasting lives in one tool, scheduling in another, adherence somewhere else, and coaching often happens weeks later. SingleComm WFM brings workforce planning into the same platform agents and supervisors already use.
What SingleComm WFM helps prevent
Understaffed queues
Forecast call, chat, SMS, and email volume so teams are not surprised by demand spikes.
Overstaffing and wasted payroll
Build schedules that match expected demand instead of guessing.
Unfair schedules
Use fairness rules for shifts, breaks, PTO, trades, and coverage.
Late coaching
Turn QA results into coaching assignments within days, not months.
Disconnected data
Use the same interaction data for forecasting, adherence, QA, and coaching.
Forecasting through coaching — one surface.
AI-driven demand models
Instead of giving one exact forecast that may be wrong, SingleComm shows a likely demand range — built from your actual interaction history, seasonality, and campaign calendar — so supervisors can plan for best-case, expected-case, and high-volume scenarios.
- ML forecasts tuned on your data
- Seasonality and campaign-driver aware
- Confidence intervals per interval
Skills-based, fairness-aware
Build schedules that match the forecast, respect skill coverage, and enforce fairness and compliance rules. What-if analysis for PTO approvals, schedule trades, and shift swaps.
- Skills- and priority-based scheduling
- Fairness and break-compliance enforcement
- What-if analysis for changes
Adjust to reality, not the plan
Real-time volume against forecast, auto-generated intraday recommendations, and one-click shift extensions or releases. Supervisors see variance as it happens, not at the end-of-day report.
- Real-time variance tracking
- Auto-recommended intraday adjustments
- One-click shift extend/release
Signal, not surveillance
Real-time adherence tracking tied to interaction state, not just presence. Agents focus on the customer; supervisors see patterns. Coaching queues surface the moments that actually matter.
- State-based adherence (not just presence)
- Coaching queue surfaces outliers
- Agent self-service adherence view
Feedback the same week, not the same quarter
Automated QA scoring across 100% of calls. Coaching assignments auto-generated from the QA rubric. Agent-facing scorecard with trend lines. Performance loops that close in days, not quarters.
- 100% automated QA coverage
- Auto-generated coaching assignments
- Agent scorecard with trend history
Real outcomes teams can measure
Fewer schedule gaps
Demand-range forecasts keep staffing matched to volume, so coverage holes show up in planning instead of in the queue.
Less manual report work
Live adherence data replaces the spreadsheet reconciliation that supervisors run between separate WFM and reporting tools.
Faster coaching
QA scores turn into coaching assignments automatically, closing performance loops in days instead of quarters.
Lower labor waste
Intraday recommendations match staffing to actual demand, trimming overstaffed intervals without hurting service levels.
Workforce planning works best when it uses live contact center data. When forecasting, scheduling, adherence, QA, and coaching live in separate systems, teams spend too much time reconciling reports and reacting late. SingleComm keeps these workflows connected so supervisors can plan, adjust, and coach from one platform.
Guides & deep dives.
Intraday management — a playbook for the day the forecast meets reality
Real-time variance tracking, the decision order for extending shifts versus moving agents versus sliding breaks, escalation rules that fire before the queue builds, and how to measure whether your intraday calls actually worked.
Read →Platform · GuideContact center forecasting — why a demand range beats a point estimate
Single-number forecasts are precisely wrong. How to forecast with ranges instead, what data feeds a good model, and how to plan best-case, expected, and high-volume scenarios at intraday granularity.
Read →Platform · GuideSupervisor self-serve reporting — getting your metrics out of the IT ticket queue
What a no-code report builder actually needs, how scheduling and sharing should work, why KPI definitions belong to teams, and the pitfalls that turn self-serve into spreadsheet chaos.
Read →