Archive | 2009

Detecting Unlicensed Use through IP Addresses

Scout Analytics is not the first to monitor account usage and flag unlicensed use. There are commercial and homegrown account analysis tools out there. Most of these tools try to draw correlations between time (concurrency), IP address, and browser information.  A recent set of questions from customers were: How good can these tools be? What is the difference in performance from Scout Analytics? Investigation Using real world data, we looked at different techniques to analyze the IP addresses such as: total count, average interval, frequency, smallest interval, and others.  All the techniques computed an IP-related metric for an account (such as the total count of distinct IP addresses) and compared that measure to a threshold.  Next we compared the percentile […]

Can usage data help improve real estate agent productivity?

Maybe more than you would think.  There appears to be an interesting correlation.  According the Bureau of Labor and Statistics (, the distribution of income in between agents looks like this: • The highest-paid 10 percent earned more than $111,500 a year • The middle half of all real estate agents earned between $26,790 and $65,270 a year • The lowest-paid 10 percent earned less than $20,170 a year According to a recent data analysis for session activities by real estate agents, we noticed the following: • The most active users of an MLS (upper 10%) originated 38.2% of all sessions • The average active users (middle 50%) originated 31.9% of all sessions • The least active users (bottom 10%) originated 0.3% of all sessions This […]

Does tracking frequency of access find shared accounts?

No.  At least not very accurately.  Some online services have tried to use high frequency of access as a positive indicator of account sharing.  The following example illustrates the limitations of that metric. Scout Analytics has been analyzing access patterns for one online service that has approximately 6,000 active users.  A recent regression analysis was performed where the percentage of shared accounts was correlated to the frequency of access as measured in standard deviations from the median.  The graph below shows that frequency of access was not a good indicator until more than 9 standard deviations above the median which represented < 1% of the shared accounts.

Scout Analytics SU3 is Live

Scout Analytics Service Update 3 (SU3) went live on July 2.  SU3 enhances the data collector with the following capabilities. Device Signature 46 new data elements about the machine and browser used to access a service were added to the collector.  This information is used in analytics to provide richer understanding about the number of devices accessing a service as well as the demographics associated with access. Remember Me Login The collector now tracks the “remember me” option on a login page.  The collector will register which subscribers are using “remember me” and can provide new insight into scope and revenue risk of “remember me” functionality. Mobile Device Login Page The collector has been designed to detect and accommodate mobile […]