Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement
Reexamination Certificate
2002-08-30
2004-10-26
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Statistical measurement
C709S224000, C379S133000
Reexamination Certificate
active
06810356
ABSTRACT:
BACKGROUND
1. Technical Field
The present invention generally relates to predicting traffic volume on the Internet, and more specifically to predicting traffic volume to assist in marketing, planning, execution, and evaluation of advertising campaigns for the Internet.
2. Related Art
The number of users on the Internet continues to grow at an astounding rate while businesses continue to rapidly commercialize its use. As they surf through websites, users generate a high volume of traffic over the Internet. Increasingly, businesses take advantage of this traffic by advertising their products or services on the Internet. These advertisements may appear in the form of leased advertising space on websites, which are similar to rented billboard space in highways and cities or commercials broadcasted during television/radio programs. Experience has shown that it can be difficult to plan, execute, and/or evaluate an advertising campaign conducted over the Internet. Unlike billboards and commercials, there are very few tools (e.g., Nielson ratings, etc.) to accurately measure or predict user traffic on the Internet.
One method for measuring exposure of advertisements posted on a website may be based on daily traffic estimates. This method allows one to control the exposure of an ad and predict the traffic volume (i.e., number of impressions, viewers, actions, website hits, mouse clicks, etc.) on a given site at daily intervals. However, there is no control over how this exposure occurs within the day itself because the method assumes a constant rate of traffic throughout the day. Experience has shown that website traffic typically exhibits strong hourly patterns. Traffic may accelerate at peak-hours, and hence, so does ad exposure. Conversely, at low traffic times, ads may be viewed at a lower rate. These daily (as opposed to hourly) estimates exhibit high intra-day errors, which result in irregular or uneven ad campaigns that are not always favored by advertisers.
This situation is illustrated in
FIG. 1
, where a pattern of under-over-under estimation is evident. Traffic volume in the hours of 12:00 am to 5:00 am, 6:00 am to 2:00 pm, and 3:00 pm to 11:00 pm are overestimated, underestimated, and overestimated, respectively.
FIG. 2
shows error size for each hour relative to the traffic volume for the entire day. Note that errors tend to average out during the day. However, during times of high relative error, ad campaigns based on a daily traffic estimate tend to accelerate; while at times of low (negative) relative error, these same ad campaigns tend to dramatically decelerate. This situation yields an uneven campaign with “run-away” periods followed by “stalled” periods of exposure.
Campaign unevenness is a symptom of prediction errors (positive or negative). As illustrated in
FIG. 2
, taking the values of these hourly errors relative to a day's total traffic can give a good indication of the gravity of the campaign's failure to predict intra-day traffic patterns. By summing the absolute value of these relative hourly errors, it is clear that the hourly prediction errors can be significant. For instance,
FIG. 2
shows a site with a very accurate daily estimate, but it has a 48.32% error relative to daily traffic when prediction error is accounted for on an hourly basis. A single hour's prediction error as a percentage of that hour's actual traffic can be much more dramatic. For instance, the hour starting at 9:00 am has a predicted traffic volume of 156,604, but the actual traffic volume is only 15,583, which is an error of 905% for that hour. Similarly for the hours of 1:00 am to 4:00 am, underestimation (per hour) ranges between 40 and 50 percent relative to the actual traffic volume for each respective hour.
Because of the dynamic nature of the Internet, it is difficult to predict the amount of time it will take before advertising goals for a particular advertisement are met. Therefore, it would be beneficial to provide a mechanism to better estimate traffic volume.
SUMMARY OF EXEMPLARY EMBODIMENTS
Methods, systems, and articles of manufacture of the present invention may assist in planning, execution, and evaluation of advertising campaigns on the Internet. Particularly, methods, systems, and articles of manufacture of the present invention may help evaluate and/or predict traffic volume on the Internet.
One exemplary embodiment of the invention relates to a method for predicting traffic. The method may comprise receiving historical traffic data for a location, and computing a prediction of traffic volume for a particular time at the location based on a linear relationship in the historical traffic data.
Additional embodiments and aspects of the invention are set forth in the detailed description which follows, and in part are obvious from the description, or may be learned by practice of methods, systems, and articles of manufacture consistent with the present invention. It is understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
REFERENCES:
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F. Huebner et al., Queueing Performance Comparison of Traffic Models for Internet Traffic 1998, pp. 471-476.*
K. Chandra et al., Traffic Characteristics of On-Line Services.*
Lin Zhang et al., Adaptive Virtual Topology Reconfiguration Policy Employing Multi-stage Traffic Prediction in Optical Internet. pp. 127-131.*
Mikio Hasegawa et al., Applications of nonlinear prediction methods to the Internet traffic, pp. III-169 to III-172.*
Sane Solutions, LLC, disclose NetTracker 6.0 Professional User's Guide. (Internet Traffic Analyzer).*
Weihaw Chuang et al., Local-Area path diversity in the internet, University of Caifornia San Diego.*
f5 network SEE.IT specification sheet.*
Carey Williamson, Internet Traffic Measurement, Nov. 24, 2001, University Of Calgary, pp. 1-9.
Garcia-Franco Jose Carlos
Kamprath Michael F.
Luenberger Rob
Raines Taylor
Advertising.com
Charioui Mohamed
Finnegan Henderson Farabow Garrett & Dunner L.L.P.
Hoff Marc S.
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