Method of Optimizing Internet Advertising

• US 20090099904A1
• Filed: 09/02/2008
• Published: 04/16/2009
• Est. Priority Date: 08/31/2007
• Status: Abandoned Application

• Current Assignee: Numeric Analytics, LLC
• Original Assignee: Numeric Analytics, LLC
• Inventors: Carpenter, David, Affeld, Peter Douglas
• Application Number:
  US12/203,071
• Publication Number:
  US 20090099904A1
• Time in Patent Office: Days
• US Class Current: 705/14.43
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0264 based upon schedule

Abstract

A method of maximizing profits based on internet advertisement frequency modeling is provided herein. In the method, user data, and reach and frequency data are subjected to a modeling equation, and then coordinated and used to determine a rate at which an advertisement produces a sale of a product advertised in the advertisement for predicting an optimal advertisement frequency at which profits can be maximized in order to minimize wasted investment costs in diminishing returns from internet advertising.

Claims

1. A method of optimizing a benefit from an advertisement served on an internet web page using an advertisment serving frequency, comprising:

collecting page view data relating to at least one visitor, wherein each visitor has at least one view of a web page, wherein the web page has an advertisement served thereon at an advertisement serving frequency;

preparing reach frequency data based on the page view data collected from the at least one visitor, wherein the reach frequency data comprises page reach frequency data and advertisement reach frequency data;

using a predictive methodology and the reach frequency data to predict (i) a number of unique visitors that will view the web page for specified numbers of viewing occurrences as a percentage of a total of unique visitors during a selected time period and (ii) a number of unique visitors that will view the advertisement for a specified number of view occurrences of the web page for the advertisement serving frequency;

collecting conversion data for the at least one visitor;

combining the page view data and the conversion data;

determining a number of conversions and a number of non-conversions for each of the at least one visitor that viewed the advertisement for specified numbers of viewing occurrences;

calculating a conversion rate for each of the specified numbers of viewing occurrences by dividing the number of conversions for each of the at least one visitors that viewed the advertisement for each of the specified numbers of viewing occurrences by the total of the number of conversions and the number of non-conversions for each of the at least one visitors that viewed the advertisement for each of the specified numbers of viewing occurrences;

expressing the conversion rates as a function of the specified numbers of viewing occurrences of the advertisement;

determining a number of expected conversions using the reach frequency data and the conversion rates for the advertisement;

determining expected benefit from the advertisement using the number of expected conversions and the frequency of serving of the advertisement, wherein the benefit can be evaluated from the number of expected conversions; and

selecting the frequency of serving of the advertisement at which the expected benefit has a desired value.

2. The method according to claim 1, wherein the page view data comprises a visitor identification, a date and time stamp of the view of the web page, and a page view identification.

3. The method according to claim 2, wherein the page reach frequency data is prepared by

aggregating the page view data into a first data set including the page view identification, the visitor identification and a count of a number of times the visitor viewed the web page;

further aggregating the first data set into a reach frequency table wherein a row in the reach frequency table represents the page identification and a count of unique visitors that viewed the identified page for each of the specified numbers of viewing occurrences during the selected time period; and

calculating a percentage of each of the counts by dividing each of the counts for each of the page identifications by a total of the counts for each web page identified.

4. The method according to claim 3, wherein a page reach frequency histogram is prepared demonstrating a relationship of the calculated percentages for each of the counts for each of the specified number of viewing occurrences with the specified number of viewing occurrences.

5. The method according to claim 2, further comprising calculating an advertisement reach frequency data distribution using equation (V):

6. The method according to claim 5, wherein the predictive methodology for determining the distribution of page views, g(x), is selected from the group consisting of a Poisson distribution method, a cumulative gamma distribution method, and an empirical values method

7. The method according to claim 1, wherein the predictive methodology for predicting the number of unique visitors that will view the web page for the specified numbers of viewing occurrences as a percentage of the total of unique visitors during a selected time period and for predicting (ii) the number of unique visitors that will view the advertisement for the specified number of view occurrences of the web page for the advertisement serving frequency is selected from the group consisting of a Poisson distribution method, a cumulative gamma distribution method, and an empirical values method.

8. The method according ot claim 7, wherein the predictive methodology is the Poisson distribution, and the methodology comprises employing equation (I):

9. The method according to claim 8, wherein λ

is estimated using a statistical technique selected from the group consisting of maximum likelihood and least squared errors.

10. The method according ot claim 7, wherein the predictive methodology is the gamma distribution and a probability density function is calculated using a gamma distribution equation (II):

11. The method according to claim 10, wherein α

and β

are estimated using a method selected from a maximum likelihood or least squared errors.

12. The method according to claim 7, wherein the predictive methodology is the empirical values method and the method further comprises calculating actual reach frequency percentages from the reach frequency data.

13. The method according to claim 1, wherein a conversion is one of revenue generated by a purchase, profit generated by a purchase, revenue generated by a subscription, a free subscription sign-up, a purchase quantity generated, a purchase indicator selected, and a click-through generated by a selection.

14. The method according to claim 1, wherein the conversion data includes a visitor identification column, a conversion date and a time stamp column, and a column including information selected from a revenue generated by a purchase, a purchase quantity, and an indicator flag indicating a conversion.

15. The method according to claim 1, wherein the step of determining the number of expected conversions further comprises

estimating a conversion rate (c_A) for the advertisement a using equation (VII), wherein all values of ν

are greater than 0, and 0;