Transforming customer experience at a large call center
Frontline.ai deployed across 20k+ agents
Transforming customer experience at a large call center
Frontline.ai deployed across 20k+ agents
context
Existing processes were highly inefficient & manual
Forecasts were inaccurate and required significant manual effort to update on a monthly basis; driving operational inefficiencies and negatively impacting customer experience
outcome
Capacity Planning solution drove operational efficiencies to improve experience and reduce costs
10-15%
NPS improvement
4-7%
Cost savings
50%
WFM task automation
solution
Deployed Capacity Planning solution
Improving demand and supply forecasts to enable better workforce management decisions
92% accuracy
increased from 85%
Automatic updates
every 2-4 hours versus 1 month
Rolling forecasts
18 month and 60 day forecasts in 15 min increments
1% improvement in forecasting accuracy translated to 0.5-1% cost savings through improved business decisioning across capacity planning processes
Capacity planning tool optimized schedules across five categories
AI technology was leveraged to build upon supply and demand forecasts to create agent schedules to best meet customer demand.
Employee skill & proficiency
across different call types
Shift types
e.g., duration, contiguous, days of week
Flex shift availability
to manage spikes in demand
Schedule change frequency
to adjust daily schedules with prior notices e.g., 2 weeks in advance
Employee mix
e.g., internal vs. external, full-time vs. part time
One interface embedded in user workflow
Enabling workforce planning teams to easily access demand and supply forecasts and optimized schedules to improve business decisions, drive productivity, lower costs, and improve customer experience.
Granular outputs
Customizable forecast categories and schedule optimization constraints, with dynamic filtering by geographic region and time horizon.
One Tool
All forecasting and scheduling outputs accessible in one easy-to-use dashboard.
Rich functionality
Situational analysis enables user to interact with model outputs and provides model explainability by detailing the reasoning behind outputs.
See Frontline.ai solutions in action