With web services quality of service (QoS) modeled as random variables, the accuracy of sampled values for precise service level agreements (SLAs) come into question. Samples with lower spread are more accurate for calculating contractual obligations, which is typically not the case for web services QoS. Moreover, the extreme values in case of heavy-tailed distributions (eg. 99.99 percentile) are seldom observed through limited sampling schemes. To improve the accuracy of contracts, we propose the use of variance reduction techniques such as importance sampling. We demonstrate this for contracts involving demand and refuel operations within the Dell supply chain example. Using measured values, efficient forecasting of future deviation of contracts may also be performed. A consequence of this is a more precise definition of sampling, measurement and variance tolerance in SLA declarations.