Faced with stiffening competition, increasingly demanding customers, high labor costs, and, in some markets, slowing growth, service businesses around the world are trying to boost their productivity. But whereas manufacturing businesses can raise it by monitoring and reducing waste and variance in their relatively homogeneous production and distribution processes, service businesses find that improving performance is trickier: their customers, activities, and deals vary too widely. Moreover, services are highly customizable, and people — the basic unit of productivity in services — bring unpredictable differences in experience, skills, and motivation to the job.
Such seemingly uncontrollable factors cause many executives to accept a high level of variance — and a great deal of waste and inefficiency — in service costs. Executives may be hiring more staff than they need to support the widest degree of variance and also forgoing opportunities to write and price service contracts more effectively and to deliver services more productively.
As with any task or operation, to improve the productivity of services, you must apply the lessons of experience. Consequently, measuring and monitoring performance (and its variance) is a fundamental prerequisite for identifying efficiencies and best practices and for spreading them throughout the organization. Although some variance in services is inescapable, much of what executives consider unmanageable can be controlled if companies properly account for differences in the size and type of customers they serve and in the service agreements they reach with those customers and then define and collect data uniformly across different service environments. To do so, it is necessary to bear in mind a few essential principles of service measurement.
• First, service companies need to compare themselves against their own performance rather than against poorly defined external measures. Using external benchmarks only compounds the difficulties that service companies face in getting comparable measurements from different parts of the organization.
• Service companies must look deeper than their financial costs in order to discover and monitor the root causes of those expenses. This point may seem self-evident, yet many companies fail to understand these causes fully.
• Finally, service companies must set up broad cost-measurement systems to report and compare all expenses across the functional silos common to service delivery organizations. The goal is to improve the service companies’ grasp of the cross-functional trade-offs that must be made to rein in total costs.
None of these principles is easy to implement. Top executives are likely to face resistance from managers and frontline personnel who insist that services are inherently random and that service situations are unique. Managers who have grown used to the protection that lax measurement affords may be reluctant to view their operations through a more powerful lens. But only by adopting these principles and implementing rigorous measurement systems throughout the organization can service executives begin to identify reducible variance and take the first steps toward bringing down costs and improving the pricing and delivery of services.
Why Variance Is Difficult to Measure
Executives who launch variance-measurement programs in a service business are often surprised at the level of difference they discover among similar sites and groups within their own organization, let alone when they compare one company with another. In general, a company’s metrics are not uniform across its business units, so that, for example, one group in a call center may regard all calls on a given issue as a single case, while another logs every call separately. A top executive with a background in consumer goods (where items are similar and thus comparable) assumed control of a service business and was shocked to find that the variance of key metrics among similar sites ranged from a factor of 2 to 30. Site managers explained this vast range by asserting that every site was different — and, according to their metrics, they were right.