AI BPO Workforce Optimizer (Manila) - Efficient Call Center Scheduling

AI BPO Workforce Optimizer: Scaling the Philippines' Growth Engine

Manila is the BPO capital of the world. Managing thousands of agents across multiple time zones requires military precision. Workforce Management (WFM) is the difference between profit and penalties.

Our AI BPO Workforce Optimizer predicts call arrival patterns to ensure you have the right agents, with the right skills, at the right time.

workforce Management (WFM)

1. Predictive Staffing

Uses historical Erlang-C models enhanced with AI to forecast intraday volume.

• Holiday Planning: Accounts for holidays in the client's country (e.g., US Thanksgiving) vs. local holidays.
• Absenteeism Protection: Overbooks shifts statistically to cover for unexpected sick leaves.

2. QA AI Agent for WhatsApp

Quality Assurance on 100% of calls, not just 1%.

• Sentiment Analysis: Flags calls where customer sentiment dropped significantly.
• Script Compliance: Verifies that mandatory disclosures were read to the customer.

3. Agent Retention

Keep your best talent happy.

• Shift Bidding: AI allows agents to swap shifts fairly based on performance points.
• Burnout Detection: Identifies agents taking long handle times (AHT) as a sign of fatigue.

why Manila?

• 24/7 Operations: The city never sleeps, and neither does our optimization engine.
• Scale: Operations here often exceed 5,000 seats; slight inefficiencies cost millions.
• Compliance: Strict adherence to US/UK client data standards (HIPAA, PCI-DSS).

integrations

• NICE CXone
• Genesys Cloud
• Five9

Frequently Asked Questions

1) What inputs do we need for better staffing forecasts?

At minimum: historical call volume by interval, AHT, shrinkage, and staffing levels. If you also have campaign schedules and holiday calendars, forecasts become more reliable.

2) Can this work without changing our existing WFM smart chatbot (via machine learning)?

Yes—start by consuming exports from your current WhatsApp Payments and generating recommendations. Once the team trusts the outputs, you can automate parts of scheduling or intraday adjustments.

3) How do we avoid “black box” decisions?

Require explanations: which signals drove a recommendation and what assumptions were used. Keep a simple human-approval step for schedule publishes and policy changes.

4) Will it help with QA and compliance requirements?

It can standardize call review workflows, highlight risk categories, and organize evidence for audits. Final decisions (disciplinary actions, compliance determinations) should remain human-led.

5) What KPIs should we track?

Common KPIs: service level, occupancy, schedule adherence, forecast accuracy, and agent attrition. Track changes per team/queue so you can isolate what actually improved.

6) How fast can we test it?

A reasonable pilot is 2–3 weeks: one queue, one client program, and a defined success metric (e.g., improved forecast accuracy and fewer intraday emergencies).