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Project Description

Fortune 500 Global Travel Call Center

Global Travel Company Optimizes Call Center Headcount through Workforce Planning

Client:  Fortune 500, global business travel services provider

Employees:  62,500

Client Goals & Challenges:

To effectively serve customers in more than 140 countries on six continents, the client operates call centers in regions across the globe. Having outgrown their spreadsheet-based planning process the client looked to Workforce Insight to create one standard model that can be used globally at call centers in Europe, the Asia-Pacific region, and the Americas. Previously each region developed independent versions of their forecast using spreadsheet models. The desire is to have a unified approach that will offer visibility to senior leaders on workload and workforce KPIs. The unified approach will also allow for variations in forecasting methods by region, depending on practices in place today. Workforce Insight helped design and implement a unified solution that consolidated the disparate regional approaches and data into one global cloud-based model.

Optimized Global Workforce Planning & Forecasting 

The solution provided the client with one global model for workforce planning at all of their call centers.    It increased the consistency of the planning process, drove better analysis, improved performance of the model, and provided much-needed scalability.

The solution provided benefits to all key stakeholders:  Planners benefit from detailed views, improved forecasting, and the ability to easily make adjustments. Operations managers can run what-if scenarios without changing the published forecast.

Senior Leaders can see a common set of KPIs for the first time across all regions and can drill down into lower level details if desired.


Workforce Insight Solution:
Workforce Planning & Optimization (Analytics Solution)

Workforce Insight designed and developed a robust call center workforce planning solution that included:

  • Historical data inputs, including customer data and supply (headcount) data, factors in forward-looking events and adjustments, and provides suggested full-time equivalent employee numbers (FTEs) up to 3 years into the future.
  • A self-correcting process using multiple forecast snapshotting and comparison methods to further improve the accuracy with each iteration.
  • Multiple trending methodologies are used for each forecast element including moving average, linear regression on top of seasonal patterns for churn, migration, average handling time, shrinkage and attrition.
  • The Erlang C formula takes forecasted volume, average handling time, and specified SLA to provide total FTEs required as well as an over/under value.

Project Details