Luca Pacioli, considered the father of double entry bookkeeping with debits and credits, would be fascinated with Facebook’s first ever quarterly earnings report, particularly this footnote with all kinds of interesting metrics and modern day shenanigans:
“The numbers of our MAUs and DAUs (monthly and daily average users) and average revenue per user (ARPU) are calculated using internal company data. While these numbers are based on what we believe to be reasonable estimates of our user base for the applicable period of measurement, there are inherent challenges in measuring usage of our products across large online and mobile populations around the world. For example, there may be individuals who maintain one or more Facebook accounts in violation of our terms of service, despite our efforts to detect and suppress such behavior. We estimate that “duplicate” accounts (an account that a user maintains in addition to his or her principal account) may have represented approximately 4.8% of our worldwide MAUs as of June 30, 2012. We also seek to identify “false” accounts, which we divide into two categories: (1) user-misclassified accounts, where users have created personal profiles for a business, organization, or non-human entity such as a pet (such entities are permitted on Facebook using a Page rather than a personal profile under our terms of service); and (2) undesirable accounts, which represent user profiles that we determine are intended to be used for purposes that violate our terms of service, such as spamming. As of June 30, 2012, we estimate user-misclassified accounts may have represented approximately 2.4% of our worldwide MAUs and undesirable accounts may have represented approximately 1.5% of our worldwide MAUs. We believe the percentage of accounts that are duplicate or false is meaningfully lower in developed markets such as the United States or Australia and higher in developing markets such as Indonesia and Turkey. However, these estimates are based on an internal review of a limited sample of accounts and we apply significant judgment in making this determination, such as identifying names that appear to be fake or other behavior that appears inauthentic to the reviewers. As such, our estimation of duplicate or false accounts may not accurately represent the actual number of such accounts. We are continually seeking to improve our ability to identify duplicate or false accounts and estimate the total number of such accounts, and such estimates may be affected by improvements or changes in our methodology.”
