Developed a series of econometric time series models to forecast the expected monthly inbound call volume for the business benefits division of a leading investment brokerage, by client, contract type and relationship history length. Models took into account contract renewal events, publicly-traded client equity share prices, macroeconomic indicators and seasonal events. Compared to business as usual, our models reduced overall portfolio forecast error from 15% to 6% for the 3-month planning horizon, and reduced operational risks via the estimated annual cost set-aside for projected call center agent hiring and training costs by over $2 million.