Client: Global Financial Services Provider

Industry: Financial Services

Objective: Develop an advanced staff capacity planning system to optimize workforce management for both contact centre and back-office operations, while improving operational efficiency and maintaining service-level agreements (SLAs).

Background and Challenges

The client was a global financial services provider operating contact centres and back-office teams across multiple geographies in US, UK, Europe, India and Australia. These operations handled millions of customer interactions monthly through various channels, including phone, email, chat, and back-office processes such as claims processing, fraud investigations, and account management.

Key Challenges:

1.  1. High Variability in Workload:

• Contact centres experienced fluctuating demand due to seasonality (e.g., tax season, regulatory deadlines), product launches, and promotions.
• Back-office operations had complex workflows with inconsistent processing times, causing bottlenecks during peak workloads.

1.  2. Inefficient Workforce Allocation:

• Manual workforce planning led to overstaffing during off-peak periods and understaffing during surges, increasing costs and jeopardizing SLAs.

1.  3. Lack of Integration Between Contact Centre and Back-Office Planning:

• Workforce management for the two divisions was handled separately, leading to inefficient resource sharing and missed optimization opportunities.

1.  4. Limited Forecasting Capabilities:

• The organization relied on outdated tools that provided basic historical forecasting, lacking the sophistication to predict dynamic workload changes or incorporate external factors (e.g., economic trends or regulatory changes).

1.  5. Service Quality Requirements:

• Specific client requirements needed adherence to SLAs, including response times, case closure deadlines, and customer satisfaction metrics.

1.  6. Attrition and Workforce Turnover:

• High attrition rates in contact centres required frequent onboarding and training, further complicating capacity planning.

Solution: A Comprehensive Workforce Capacity Planning System

The organization partnered with a data analytics and workforce optimization team to implement a scalable and data-driven staff capacity planning system. The solution focused on integrating demand forecasting, workforce optimization, and resource allocation across both contact centre and back-office operations.

Key Components of the Solution:

1. Demand Forecasting

• a. Integrated forecasting models for contact centres and back office:

• Developed predictive models to forecast workload demand across channels (e.g., call volume, email tickets, and claims).
• Incorporated factors such as:
• Historical workload data.
• Seasonal trends and holidays.
• Product launches and marketing campaigns.
• Macroeconomic indicators (e.g., interest rate changes).
• External events (e.g., regulatory deadlines, natural disasters).

b. Real-Time monitoring for contact centres:

• Used API to ingest real-time data (e.g., incoming calls, emails and chats) to provide real time insights making short-term forecasting updates achievable.

2. Integrated Workforce Management

• a. Unified Workforce Planning Tool:

• Integrated workforce management for both contact centre and back-office operations, allowing resource sharing during off-peak times.
• Deployed Verint Workforce Management for contact centre scheduling and Blue Prism RPA to manage task prioritization in back-office workflows.
• Cross-trained employees to handle both front-office and back-office tasks, maximizing workforce flexibility.

3. Optimization Algorithms for Capacity Planning

• a. Scenario Planning:

• Implemented optimization algorithms using Python to test different staffing scenarios (e.g., varying attrition rates, surge demand).
• Modelled "what-if" scenarios to prepare for unexpected events like system outages, demand surge or unforeseen staffing shortages. Leadership and planning/analytics teams could test these scenarios using a dashboard interface during planning sessions. 

• b. Resource Allocation Models:

• Built linear programming models to optimize staffing levels by balancing:
• Workload volume.
• SLA adherence.
• Cost efficiency (e.g., minimizing overtime costs).

4. Real-Time Dashboards and Insights

• a. Power BI Dashboards:
  • Provided live dashboards for:
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• Workload metrics: Call volumes, case processing rates, backlog levels.
• Workforce metrics: Staffing levels, agent utilization, adherence to schedules.
• SLA tracking: Performance against service-level targets.
• Agent-Level Analytics: Enabled supervisors to monitor individual performance metrics (e.g., average handle time, first-call resolution) and identify training needs.

5. Workforce Flexibility

• a. Cross-Functional Resource Sharing:

• Established a pool of cross-trained staff who could switch between contact centre and back-office tasks based on real-time demand.

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• b. Dynamic Shift Scheduling:

• Implemented auto-scheduling that adjusted agent shifts based on real-time forecast updates, reducing reliance on overtime.

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• c. Automation for Back-Office Tasks:

• Deployed RPA bots to automate repetitive back-office tasks (e.g., data entry, compliance checks), freeing up human staff for higher-value activities.

Results and Benefits

1. Improved Forecast Accuracy:

• Forecasting accuracy improved by 30%, enabling better staffing decisions and reducing both overstaffing and understaffing.

2. SLA Compliance:

• SLA adherence rates increased to 82% (from 60%), reducing customer complaints and penalties.

4. Workforce Productivity:

• Cross-training and dynamic scheduling improved workforce utilization by 25%, ensuring more efficient use of staff during peak and off-peak periods.

5. Customer Satisfaction:

• Enhanced SLA adherence and faster response times led to a 15% increase in CSAT scores.