Any public-sector agency that directly interacts with large numbers of citizens often finds that demand for its services overwhelms the limited resources available to provide them. A government can’t prioritize its citizen “customers” by using metrics like how valuable they are or how costly to serve—common practices in the private sector under similar circumstances. This was the problem facing a US federal agency seeking to make its call centers and paper-processing facilities more efficient. Rising budget pressure and demand for the agency’s services had had the effect of compromising them. In fact, during times of peak demand, agents answered less than three-quarters of phone calls to the agency, which also processed less than half of all paper applications within its target response time.
Optimizing the resources allocated to the two channels proved difficult. The agency relied on a common pool of employees who switched between fielding calls and processing forms as necessary to meet spikes in demand. Yet the channels operated independently, with separate managers; the agency relied on personal relationships to exchange important information. What’s more, pressure to respond immediately to phone calls often diverted agents from paper applications, unbalancing the two channels’ service levels.
The agency designed a labor allocation model around customer satisfaction ratings, creating “satisfaction curves” that revealed service breakpoints—levels at which delays made customer satisfaction drop significantly. In a mathematical model correlating the influence of a range of inputs on these satisfaction benchmarks, the agency set performance targets that reflected the breakpoints. That process helped managers to identify the trade-offs between staffing either channel optimally and satisfying customers in the other channel in real time, and therefore helped to improve the service balance between the two channels while also raising overall customer satisfaction. For the paper channel, the optimal allocation of resources called for a 20 to 30 percent increase in service delivery levels during periods of peak demand. Meanwhile, channel managers shared responsibility for generating and vetting the model’s inputs—for instance, weekly projections of demand and operational assumptions about expected staffing levels.
With demand for services rising and budgets falling, governments around the world are under pressure to raise their game in service operations. By linking daily work to uncontroversial metrics (such as customer satisfaction ratings), public-sector organizations can improve their service levels and save money while honoring their universal-service obligation to treat all citizens equally.