Apache Corp. is one of the world’s top independent oil and gas exploration and production companies with more than $60 billion in assets and operations in the U.S., Canada, United Kingdom, Australia, Egypt and Argentina. Apache uses thousands of Electrical Submersible Pumps (ESPs) to pull oil from reservoirs worldwide, onshore and offshore. Many ESPs fail unpredictably resulting in missed production targets. Large oil and gas companies lose tens of thousands of barrels of oil daily due to ESP failures. Foresight into the performance of ESPs is critical to preventing unscheduled downtime or performance degradation that can impact oil and gas production. Even a modest increase in ESP performance can amount to significant gains in production and revenue.
When Microsoft launched its cloud-based Business Productivity Office Suite (BPOS), now known as Office 365, the company needed to better forecast customer adoption and likely impact on service requests.
Cisco needed to gain better insights into several key performance measures (e.g., ease of doing business, customer satisfaction) and how these measures would perform in the future so that Cisco could make appropriate resource decisions. But without in-depth modeling of metrics, drivers, and various interrelationships, Cisco had limited knowledge on which to base resourcing decisions.
Dell’s Global Customer Service organization has tens of thousands of agents spread across multiple centers responsible for ensuring customer satisfaction with Dell products and services. Two of Dell’s key performance indicators are customer satisfaction score and first contact resolution—both of which needed to be improved. But in a challenging economy, Dell needed to improve these metrics without additional resources.