System and method for measuring the relative and absolute effects of advertising on behavior based events over time

A method comprising:

• collecting, using a monitoring application executed by at least one client system, information about user activity on the Internet;
• storing the information as metric data, the metric data comprising time stamps associated with the user activity for a time period;
• accessing the metric data;
• creating, based on the time stamps, a record for each discrete time unit of the time period;
• parsing variables from each record, each variable being associated with a metric in each record;
• determining types of the parsed variables;
• determining, based on the types of the parsed variables, a first regression modeling function to be applied to a first variable of the parsed variables and a second regression modeling function to be applied to a second variable of the parsed variables;
• applying the first regressive modeling function to each metric of the first variable and the second regressive modeling function to each metric of the second variable;
• determining, based on the application of the first regressive modeling function and the second regressive modeling function, lift for each discrete time unit of the time period as a difference in an expected outcome of those exposed to at least one impression from those not exposed to the at least one impression;
• and
• generating and storing a report based on the lift for each discrete time unit of the time period.

A non-transitory computer-readable medium comprising instructions executable by at least one processor to perform a method, the method comprising:

• collecting, using a monitoring application executed by at least one client system, information about user activity on the Internet;
• storing the information as metric data, the metric data comprising time stamps associated with the user activity for a time period;
• accessing the metric data;
• creating, based on the time stamps, a record for each discrete time unit of the time period;
• parsing variables from each record, each variable being associated with a metric in each record;
• determining types of the parsed variables;
• determining, based on the types of the parsed variables, a first regression modeling function to be applied to a first variable of the parsed variables and a second regression modeling function to be applied to a second variable of the parsed variables;
• applying the first regressive modeling function to each metric of the first variable and the second regressive modeling function to each metric of the second variable;
• determining, based on the application of the first regressive modeling function and the second regressive modeling function, lift for each discrete time unit of the time period as a difference in an expected outcome of those exposed to at least one impression from those not exposed to the at least one impression;
• and
• generating and storing a report based on the lift for each discrete time unit of the time period.