Predictive Analytics for Ticket Management at a US Logistics Leader
Staffing to tomorrow's demand, not yesterdays. How ARIMA, SARIMA and XGBoost models turned unpredictable support volumes into a forecast cutting missed SLAs by 15% and lifting workforce planning efficiency by 30%.
At a Glance
Logistics & Transportation
Forecast customer support ticket volumes accurately enough to staff against them ending the cycle of overstaffing, understaffing and missed SLAs caused by unpredictable demand.
- 15% reduction in missed SLAs through better workforce planning.
- 30% improvement in productivity from optimised workflows.
- 25% smaller ticket backlog through automated forecasting.
The Challenge
Support demand was unpredictable, and the business could only react to it. Ticket volumes fluctuated without warning, so the team was either overstaffed and paying for idle capacity or understaffed and missing service levels. Neither state was chosen; both were the result of planning without visibility.
The knock-on effects ran through the operation. Workload spikes caused inconsistent response times, unoptimized workflows created bottlenecks, and agents worked under the pressure of a queue nobody had anticipated. Without predictive insight, every staffing decision was a guess made after the fact, and the cost of guessing wrong landed on customers.
Our Approach
Cozentus built a predictive modelling solution to forecast demand before it arrived. Working from historical ticket data volume, resolution time, agent performance and customer demographics we conducted exploratory analysis to surface the underlying patterns and trends, and segmented customers by ticket behaviour to enable targeted support strategies.
We then developed forecasting models using time series analysis, regression and classification techniques, applying ARIMA, SARIMA and XGBoost to predict ticket volume, resolution time, escalation likelihood and optimal resource allocation. Every model was rigorously tested and validated before deployment, then integrated directly into the client's existing ticketing system so forecasts appear as real-time decision support in the tools the team already uses.
Predictive Support in Action:
- Demand forecasting: ARIMA, SARIMA and XGBoost models predicting ticket volume and resolution time from historical patterns
- Resource optimisation: forecasts translated into staffing recommendations, balancing capacity against predicted demand
- Integrated, real-time insight: models deployed into the existing ticketing system rather than delivered as a separate report
Business Outcomes
- 15% reduction in missed SLAs through improved workforce planning
- 25% reduction in ticket backlog via automated forecasting
- Faster response times and a measurably better customer experience
- 30% improvement in employee productivity through optimised workflows
- Balanced staffing levels, eliminating both overstaffing and understaffing
- Lower unnecessary labour costs from demand-matched resourcing
- Real-time analytics informing long-term support strategy
- Agents working with confidence against anticipated, not surprise, workloads
Client Quote
We can now accurately predict ticket volumes, optimize staffing, and ensure timely issue resolution. Our customers are happier, our teams are more efficient, and we've significantly reduced SLA breaches… We no longer struggle with last-minute staffing adjustments, and our agents feel more confident handling their workloads.
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