Patent Application
20080004937
Conversions are essentially binary activities and, thus, metrics derived based on observing conversion activities typically do not give complete information about the effectiveness of advertising. For example, metrics derived based on observing conversion activities do not provide information about brand awareness.
Recent web analytics tools provide for observation of data regarding activities of users, other than conversion activities of users relative to a website. These tools provide information about the path of a user among the pages of a website and a perceived value for each conversion.
In accordance with an aspect, a method is provided to observe activities by a user relative to a website (typically, an advertiser's website) and to generate an aggregated indication of the desirability of the user's actions relative to the website. The aggregated indication is referred to herein as a User Activity Rating (UAR). Thus, for example, the UAR is determined in consideration of activity beyond just conversion activity (and may not even consider conversion activity). It may be considered that business decisions based on the UAR are more informed business decisions than business decisions based on a metric derived solely from conversion activity.
User activity with respect to a website, beyond merely conversion, can be complex behavior to evaluate. Encapsulating a measurement of user activity into an easy to evaluate indication eases the process of evaluating user activity and of making and acting upon decisions based on an evaluation of user activity.
The UAR is thought to be a very good measure of the quality of a click or other user action that causes a website to be displayed to that user. UAR determination may depend on instrumentation of the web site to gather data of user activity (unless other means are available to observe user activity with respect to the web site). Thus, data useable to determine UAR may not be available for many web sites. However, the instrumentation and data is provided by many web analytics solution such as available, for example, from Yahoo! (YSO Web Analytics).
Conversion flags, discussed above, only have two states—for example, “0” indicates not converting and “1” indicates converting. In accordance with an aspect, a UAR for a user, for a web site, is determined based on a continuum of activity data, such as a sequence of URLs visited by a user (including, for example, an amount of time spent viewing a page or pages associated with each URL) and/or other measurable activity with respect to the web site, perhaps including but not solely based on occurrence of a conversion.
In accordance with one example, a value is ascribed to each time unit (e.g., five minute increments) spent on the advertiser site and a value is ascribed to particular events of the user relative to the web site. For instance, each minute the user spends on the site may be ascribed a value of 15. The event of landing on the site may be ascribed a value of 50, and each $0.10 of conversion value is ascribed a value of 1. All the values associated with a particular visit are combined (e.g., in a simple example, summed). The UAR may be limited to a threshold (in one example, 1000). Using these example ascribed values, UARs shown in Table 1 may result:
In accordance with an aspect, illustrated schematically in
Having discussed some examples of determining UAR for a web site, relative to a particular user, we now discuss some applications of the UAR. In one example, the cost to an advertiser of a particular “click” (i.e., activation of a link to an advertiser web page, from a display advertisement) is based on a UAR associated with that click. As a variation, the cost to an advertiser of a collection of clicks may be based on an amalgamation of the UARs associated with the clicks.
In accordance with another aspect, a UAR is generated for a web page relative to each of multiple users, and the UARs are then considered in some manner in the aggregate.
In one particular example of this aspect broadly illustrated in
In accordance with a general example, illustrated in the
Using the click through protection filter processing example, step 504 of the
For a good filter, a filtered set of clicks predominately includes lower quality traffic, and so the average UAR is lower for the filtered set. In accordance with this example, then, small ratios (according to some measure of what is “small”) correspond to good filters.
Another application of UAR is in analysis of web traffic anomalies. Anomalies in web traffic are often evidenced by lower quality. In fact, many advertiser complaints arise because some spike in a characteristic of advertisement activation is noticed, such as a sudden increase in activations resulting from a particular query term or phrase, or a sudden increase in activations coming from outside a normally-expected geographic area. Advertisers are loathe to pay for such activations, since the advertisers suspect that the activations do not result in activity that represents a desired effect.
To analyze such web traffic anomalies, the UARs corresponding to activations having the particular characteristic are analyzed. If it is determined that at least some of the clicks in the category have relatively lower corresponding UARs, then this may be an indicator that all clicks during the anomaly may be considered to be of low quality. Another approach is to analyze UARs over time (either individually, on a rolling average, or based on some other relatively localized statistic), where a spike of low UAR values may indicate an attack.
A threshold 608 indicates a value for the average UAR below which the activations resulting in those UARs are deemed to be of low quality. Referring to the
By using an aggregated indication of the desirability of the user's actions relative to a website, more information about the effectiveness of advertising may be gleaned than from, for example, information of conversions alone. For example, business decisions such as bid price on particular keywords may be made in a more informed manner. In addition, the aggregated indications may be employed to provide a measure of how well a categorization process, of user's actions, such as click fraud detection processing, has performed.
