Using natural search click events to optimize online advertising campaigns

A method is described for augmenting sponsored search results in a search engine, which includes extracting attribute data from a plurality of natural searches for a search term linked to a plurality of uniform resource locators (URLs), analyzing the attribute data of one or more attributes for clickers and non-clickers to determine at least one greatest distinguishing factor between the clickers and non-clickers, and integrating the at least one greatest distinguishing factor into a matching algorithm used by the search engine to rank order and display a plurality of the most relevant ads corresponding to the plurality of URLs in response to a search for the term. The method may also integrate the at least one greatest distinguishing factor into a marketer algorithm to enable a marketer of a URL to strategically choose a search term, along with the at least one greatest distinguishing factor, on which to bid.

BACKGROUND

1. Technical Field

The disclosed embodiments relate to a system and methods for augmenting, and thus enhancing, sponsored search results in a search engine with natural search click events.

2. Related Art

Pay-per-click sponsored search terms are typically listed at the top of a list of search engine results when users search on such terms. Attached to the search terms are the Uniform Resource Locators (URL) links, which a searcher may click to go to marketers' web pages to further search a given result. The history of those who search for pay-per-click terms is usually well tracked because advertisers or marketers who pay for the terms want to be able to track click-through rates of these terms according to their target markets. This is known as optimizing online campaigns based on past success rates of sponsored search terms.

Click-through rates may be tracked for not only sponsored terms, but also for non-sponsored terms (e.g., unpaid listings), in which the latter creates natural search click events. While clicks on URLs within a set of search results can be directly tracked by a search engine, clicks after a searcher is at a marketer's website may further be tracked by using an invisible pixel tag or “web beacon” on a marketer's web page. A web beacon (also referred to as a web bug) is a piece of software code, which is invisible to a web searcher that pings back to a search engine server to close the loop and report on the extent to which a searcher uses a web page, up to and including purchasing activity. Information gathered may also include who is reading the web page and from what computer. Though many search engine or search term sponsoring companies collect natural search event information, they have done very little with the information in terms of enhancing marketing campaigns or providing searchers with more relevant results. One reason for this outcome is that the information is not directly linked to paid-for search terms. Also, the amount of natural search event information is so voluminous, it is often difficult to process or make sense of the information in a useful, cost-effective way.

SUMMARY

By way of introduction, the embodiments described below include a system and methods for augmenting and enhancing sponsored search results in a search engine with natural search click events.

In a first aspect, a method is disclosed for augmenting sponsored search results in a search engine, which includes extracting attribute data from a plurality of natural searches for a search term linked to a plurality of uniform resource locators (URLs), analyzing the attribute data of one or more attributes for both clickers and non-clickers to determine at least one greatest distinguishing factor between the clickers and non-clickers for the one or more attributes, and integrating the at least one greatest distinguishing factor into a matching algorithm used by the search engine to rank order and display a plurality of the most relevant ads corresponding to the plurality of URLs in response to a search for the term. The method may further include integrating the at least one greatest distinguishing factor into a marketer algorithm used by the search engine to enable a marketer of a URL to strategically choose a search term, along with the at least one greatest distinguishing factor, on which to bid.

In a second aspect, a method is disclosed for augmenting sponsored search results in a search engine, which includes extracting attribute data from a plurality of natural searches for a search term associated with a plurality of URLs, analyzing the data of a plurality of attributes for both clickers and non-clickers to determine at least one attribute that differentiates clickers from non-clickers, grouping the clickers into segments based on the at least one differentiating attribute, and calculating relative click-through rates of each segment to be associated therewith.

In a third aspect, a method is disclosed for augmenting sponsored search results in a search engine, which includes extracting attribute data from a plurality of natural searches for search terms linked to a plurality of URLS, associating the attribute data in memory with the specific users that conducted the natural searches, executing a targeting algorithm that incorporates the associated attribute data for a user performing a search for a search term, and re-ranking a set of URL results based on the executed targeting algorithm before being displayed to the user.

In a fourth aspect, a system is disclosed for augmenting sponsored search results in a search engine, which includes an index manager to extract attribute data from a plurality of natural searches performed by a search engine for a search term linked to a plurality of URLs. A database is in communication with the index manager to store the extracted attribute data, in which the database comprises user attributes associated in memory with specific users that performed the natural searches and the aggregates of the user attributes grouped by type of attribute. A processor is in communication with the database to perform a method, which includes analyzing the aggregates of the user attributes for both clickers and non-clickers to determine at least one attribute that differentiates clickers from non-clickers, grouping the clickers into one or more segments based on the at least one differentiating attribute, and storing the grouped segments in the database.

A matching algorithm in memory of the search engine may integrate the at least one differentiating attribute to rank order and display the most relevant URL or ad results in response to a search for the term. A marketer algorithm in memory of the search engine may integrate the at least one differentiating attribute to enable a marketer of at least one URL to choose a strategic search term, along with the at least one differentiating attribute, on which to bid. A targeting algorithm in memory of the search engine may integrate the associated attribute data for an identified user performing a search for a search term, wherein when the targeting algorithm is executed, the targeting algorithm re-ranks a set of URL results before being displayed to the identified user. In the various embodiments, attribute data may include user geography, a user technographic, a user demographic, and a user purchasing history, among others.

DETAILED DESCRIPTION

In the following description, numerous specific details of programming, software modules, user selections, network transactions, database queries, database structures, etc., are provided for a thorough understanding of various embodiments of the systems and methods disclosed herein. However, the system and methods disclosed can be practiced without one or more of the specific details, or can be practiced with other methods, components, materials, etc.

In some cases, well-known structures, materials, or operations are not shown or described in detail. Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. The components of the embodiments as generally described and illustrated in the Figures herein could be arranged and designed in a wide variety of different configurations.

The order of the steps or actions of the methods described in connection with the embodiments disclosed may be changed as would be apparent to those skilled in the art. Thus, any order in the Figures or Detailed Description is for illustrative purposes only and is not meant to imply a required order.

In certain embodiments, a particular software module may include disparate instructions stored in different locations of a memory device, which together implement the described functionality of the module. Indeed, a module may include a single instruction or many instructions, and may be distributed over several different code segments, among different programs, and across several memory devices. Some embodiments may be practiced in a distributed computing environment where tasks are performed by a remote processing device linked through a communications network. In a distributed computing environment, software modules may be located in local and/or remote memory storage devices.

FIG. 1is a diagram of an exemplary system100for augmenting marketing campaigns with natural search events. As discussed above, a plurality of users102(variably referred to herein as searchers102) include millions of people throughout the world who use the Internet, or World Wide Web, to browse for information, many of whom also shop online. As users102browse, their activity may be tracked through search engine queries and web beacons or the like as discussed above, which activity generates a large volume of natural search event information, in addition to sponsored search term activity. The natural search event information may be broken down into user attributes104or group of attributes106by categories, such as geography, technographics (the technical specifications of the computer systems used by users102), demographics, and purchasing history, to name just a few.

Marketers have difficulty determining which targeting attributes104to use to achieve any particular marketing goal, especially where they have relied solely on paid-for terms to attract visitors. If they have a given publisher's target market information from the past, marketers can analyze the results of the permutations and then decide which targeting attributes104(e.g., location, user attributes104, context, etc.) worked best for any given advertising content. If they have not used a given targeting attribute104of that particular publisher, marketers are unable to predict if the price-premium is worth using that attribute104. Moreover, focusing solely on the attributes104of past campaigns leads to reinforcement of inefficient choices. For example, if a marketer has only targeted males in the past, no analysis would be able to predict the expected performance of targeting females.

The attributes104may be gathered in various contexts108by conducting search queries112, by browsing web pages (or websites)114, or by any other method known in the art for gathering natural search event information of searchers102. User attributes104may be independent or in either overlapping or non-overlapping combinations. When a search query112is conducted, the user102obtains a result set118of marketer listings from a publisher's search engine. The system and methods of the present disclosure may then be used to produce a modified result set122of sponsored listings incorporating the natural search event information collected from search queries112and browsed web pages114, etc.

The modified result set122will likely include re-ranking the search results listed due to modifications made by a sponsored-listing search engine to a matching algorithm. Other algorithms, such as a targeting algorithm, may also be modified to incorporate natural search information stored and tracked for specific users that may be used to match such users with a modified set of results in which the user would be more likely to be interested. Yet further algorithms, such as a marketer algorithm, may be modified to incorporate general pools of natural search information that can be used to suggest or recommend to marketers various keywords on which to bid, e.g. keywords that best match their target market and/or advertising campaign strategies. Helping marketers on the one hand and modifying result sets122on the other allows this disclosure to be implemented into existing technologies in a way that augments marketers' campaign strategies and enhances the relevancy of user results.

FIG. 2is an exemplary block diagram200of a search engine202and related storage for implementing the system ofFIG. 1within the larger context of a network206such as the Internet. The search engine200of system100provides for automatically identifying which attributes104an online marketer210should use by analyzing the attributes104of users102engaged in natural search activity, otherwise known as algorithmic search activity. To do this analysis, the attributes104are first be extracted (or otherwise collected) and stored as discussed below.

When the marketer210accesses the search engine202to decide on a marketing campaign, and optionally bid on pay-per-click search terms, it is through a marketer campaign booking interface212, which is typically set up as a web page of the publisher. Pay-per-click search terms are associated with relevant Uniform Resource Locators (URLs), or website links, of the marketer's various web pages114. Pay-per-click listings are stored in a database214, which usually also include ad copy related to the web page114to provide relevant, yet enticing or interesting information about the web page114. Users102access the search engine202through a user interface216such as a web browser. Clicking on a URL link on a page of search results will take the user102to a marketer's web page114, and if it is an unpaid listing, will begin to create a history of collectable natural search click events as described above. As will be seen, this history can be collected, stored, and tracked according to individual users, or grouped together and analyzed as a group for various markets or segments defined by one or more attributes.

The search engine202includes a processor202, a memory224containing, inter alia, main algorithms226such as matching, targeting, and marketer algorithms226, and modified algorithms228of each if they exist, and an index manager234. The index manager234manages the listings of search results, including those sponsored by marketers210, and includes an attribute extractor238to extract the attributes104from natural search activity, such as by communicating with a web beacon of a web page114of the marketer210. Once attributes104are extracted or collected from users102, whether for clickers or for non-clickers, they are stored in a user attribute database242. The difference between a clicker and a non-clicker is whether a URL or other web page link in a set of search engine results was actually clicked by the user102(“a clicker”) to drill down to the URL of a marketer210listed therein. This initial web page access is also referred to as an “impression,” which is used in tracking advertising success metrics such as cost per impression (CPM) and click-through rates. These and other success matrices can be further tracked as the user102browses to access further sub-pages of a URL.

The index manager234farther includes an attribute grouper246to group attributes104according to whether combinations of attributes104are deemed overlapping or non-overlapping, and some are left in individual attribute104categories. Grouped user attributes106are then stored in a database250of grouped natural search event history. One of skill in the art will appreciate that databases214,242, and250may be a single database, may be distributed across the network206, and may optionally be local to the search engine202.

The index manager234further includes a listings and ranking module254to list and rank search results for display to a user102searching for a particular keyword search term. The listings include URLs that are linked to the search terms in the paid listings database214, which also include ads for the URLs so that the searchers102know the general subject matter of the destination web page114. The listings and rank module254by default uses the main algorithms226in memory224to produce a ranked list of search results for a search term. Ranked highest in the search results include the highest bidding sponsored ads and/or URLs from the paid listings database214, in addition to the most relevant unpaid listings as determined by the main and modified algorithms226,228. The main algorithm226includes a matching algorithm that bases its relevancy, and thus ranked order, on at least the content of the web page114connected to the URL and/or to the associated ad displayed with the search results in a query results page.

Reference to “ads” or URL content as referred to herein may include the relevancy of META tags that are known to be used in search engine optimization, and thus affect the ranking of web pages114having different keywords or description therein. Indeed, the META tag description field of a web page will often be used as the ad content displayed in a search engine results page for unpaid websites114, e.g. to provide a description of natural search results. Otherwise, META tags are usually invisible to the searcher102, but still often used to rank web pages114. The main algorithms226may also include a targeting algorithm to target any natural search history information acquired, for instance during the current session, to further match the ranking of the listing results to the specific user102.

If, however, user attributes exist in the database242or stored groups of user attributes exist in the database250that relate to the keyword search term, a modified algorithm228may be executed by the listings and ranking module254to perform URL content and/or ad search term matching. Thus, the search engine202may use a modified matching algorithm that incorporates overlapping or non-overlapping grouped attributes from the database250, discussed in detail with reference toFIG. 4. And, further, the search engine202may use a modified targeting algorithm that incorporates stored history of a specific user102doing the searching as found in the user attribute database242, discussed in detail with reference toFIG. 3. The targeting algorithm, therefore, seeks to incorporate the specific tastes or preferences of a user102as determined by past natural search history of that user102.

One of skill in the art will appreciate that main and modified matching and targeting algorithms226,288may be integrated into respective main and modified matching algorithms226,228. In addition, there may be only a single algorithm226or228used that has the option to incorporate various aspects of stored user attributes found in databases242and250if relevant to a keyword term being search for by a user102. One of skill in the art will also appreciate that the processor220may be combined with the index manager234, and may process portions of software code to implement functions of both.

The processor220analyzes the stored one or more attributes104for both clickers and non-clickers for a search term to determine at least one factor between the clickers and non-clickers of the searchers102for that term that distinguishes them the most. The listings and ranking module254may then incorporate this at least one greatest distinguishing factor by use of a modified algorithm228to adjust (or re-rank) the order in which the listings are displayed to the searcher102. The manner in which a modified algorithm228is employed to enhance the relevancy of listings displayed by the listings and rank module254based on stored attributes104or groups of attributes106is discussed in more detail inFIGS. 5 to 7.

Furthermore, the attribute grouper246uses the at least one greatest distinguishing factor to come up with segments within the clickers that would be useful to a marketer210to augment and enhance an online optimization campaign. Click-through rates or other success metrics may be collected on these various segments to be presented to the marketer210or otherwise used in optimizing the marketer's campaign. For example, success metrics related to grouped segments may help the marketer210to make a more informed choice in regards to bidding on search terms to which the segments relate if those segments hold special significance to the target market of the marketer210, or may allow the marketer210to restrict the scope of coverage of a marketing campaign to specific segments or portions thereof.

FIG. 3is an exemplary block diagram300showing one aspect of the search engine ofFIG. 2, which is capable of extracting and storing attribute data from natural search events created by the users102. The users102access304marketer web pages114through marketer web servers308over the network206, such as the Internet. The search engine202connects to the web pages114and extracts312or otherwise collects312attribute data from natural search activity conducted by users102, both clickers and non-clickers. As discussed, this may be accomplished by a web beacon that communicates user browser activity and information, together with attribute data316composed of attributes104, back to the search engine202for storage in the user attribute database242. The attribute data316in the database242, as shown, may be stored under a person and/or Internet Protocol (I.P.) address. Common natural search attributes104may thus also be collected through cookies and through I.P. address resolution, among other methods.

Examples of attributes104thus extracted from searchers102, as shown, include geography, such as the country, state, city, and/or zip code where the searcher102is browsing from. This type of information is often attainable through resolution of the user's I.P. address. Further examples of attributes104include a technographic of a user's system used to access a marketer's web page114. Technographics include, for example, connection speed, type of browser, size or quality of a monitor, an operating system, and a plug-in of a connection device (not shown) used by the searcher102. This type of information would be indicative of, inter alia, how serious of a computer or online user the searcher102is based on how up-to-date or expensive his or her computer system and connection are. Further examples of attributes104include demographics, such as age, gender, race, and disability. Attributes104may also include a purchasing history of a searcher102, which includes such information as a quantity purchased, a price paid per item, and a total price paid, either per visit or over a time period of multiple visits to a web page114. As collected in mass, and stored in the database242, attributes104become attribute data316.

FIG. 4is an exemplary block diagram400showing another aspect of the search engine202, which is capable of grouping the extracted attributes104into segments of attribute categories or types of attributes. For instance, natural search user attributes104are collected in the user attribute database242, which are then grouped404by the attribute grouper246of the search engine202into segments based on at least one greatest distinguishing factor between the clickers and non-clickers of searchers102. These segments of information, along with independent attribute data316aggregated from database242and categorized into separate types of attributes, are stored in the grouped natural search event history database250. As shown, at least two sets of segments are delineated by combinations of attributes that are overlapping420and those that are non-overlapping424.

Examples of overlapping attributes420include all users102in a state having a particular gender, as there will always be females and males in all states. Another segment that may be added to the latter includes a sport that users102are interested in, despite the fact that some sports attract much more attention from one gender than another. Another example includes a large United States (U.S.) city and a race because most races are usually represented in large U.S. cities. Because there are so many searchers102using the Internet throughout the world, most categories of attribute data316will have some overlap with another category.

There are some categories of attribute data316, however, that are non-overlapping424, and may include all males in California together with all females in California. Another example may include all Hispanics in Alaska together with all Hispanics in Florida, because of the distance and varied geography, creating a very strong mutually exclusive group. Under a similar theory, another non-overlapping segment424may include all males in Europe together with all males in the U.S. These are non-exhaustive examples of segments, and many more may be thought of or created for the purposes of enhancing online marketing campaigns for marketers210. The segments may be tracked in terms of click-through rates (or other success metrics) affiliated therewith.

FIG. 5is a flow chart500of an exemplary method for augmenting marketing campaigns with natural search events. First, attribute data316may be extracted at504by the search engine202from a plurality of natural searches for a search term that is linked to a plurality of URLs. A search engine202analyzes, at step508, the attribute data316of both clickers and non-clickers. The search engine202then determines, at step512, at least one greatest distinguishing factor between the clickers and the non-clickers. The at least one greatest distinguishing factor is then integrated, at step516, into a matching algorithm228used by the search engine202to rank order and display a plurality of the most relevant ads corresponding to the plurality of URLs in response to a search for the term. Furthermore, the at least one distinguishing factor may be integrated, at step520, into a marketer algorithm228used by the search engine202to enable a marketer210of at least one URL to strategically choose the search term, along with the at least one greatest distinguishing factor, on which to bid. In addition, the URLs themselves may be used in being ranked according to relevancy the content of the web pages114linked from the URLs.

In addition to the above integration steps at steps516and520, the search engine202may group, at step524, the one or more attributes104having at least one greatest distinguishing factor to identify one or more overlapping segments420of clickers. Likewise, the search engine202may group, at step528, the one or more attributes104of the at least one distinguishing factor into non-overlapping424segments of clickers. The search engine202may then determine, at step532, the click-through rates (or other success metrics) of overlapping and non-overlapping segments420,424. Finally, the search engine202may present the click-through rates, at step536, to a marketer210to help the marketer210to optimize an online campaign as discussed. The search engine202may also integrate segment data from steps524and528into the matching algorithm228to help automatically match relevant ads to a specific searching user102based on the at least one distinguishing factors. The later is possible because the search engine202is in communication with the user attributes database242in which specific attributes remain associated with specific users102, and which may be mapped to strongly associated segments from steps524to532.

FIG. 6is a flow chart600of another exemplary method for augmenting marketing campaigns with natural search events. At step604, a search engine202extracts or otherwise collects attribute data316from a plurality of natural searches for a search term associated with a plurality of URLs. The search engine202then analyzes, at step608, the data316of a plurality of attributes104for both clickers and non-clickers. At step612, the search engine202determines at least one differentiating attribute as between the clickers and the non-clickers. At step616, the search engine202then groups the clickers into segments based on the at least one differentiating attribute. The search engine202calculates click-through rates of each segment to be associated therewith. Additionally, the search engine202may integrate, at step624, the at least one differentiating attribute into a matching algorithm228used by the search engine202to order rank and display at least one of the most relevant URL results and the most relevant ads corresponding to the plurality of URLs in response to a search for the term. Finally, at step628, the click-through rates may be presented to a marketer210to improve campaign optimization when the marketer210decides whether to bid on a term having included therewith a related segment.

FIG. 7is a flow chart700of an exemplary method for augmenting marketing campaigns with natural search events. First, attribute data may be extracted at704by the search engine202from a plurality of natural searches for a search term that is linked to a plurality of URLs. A search engine202associates, at step708, the attribute data in memory (such as in user attributes database242) with specific users102that conducted the natural searches. The search engine202executes, at step712, a targeting algorithm228that incorporates the associated attribute data for a user102performing a search for a search term. And, at step716, the search engine202re-ranks a set of URL results based on the executed targeting algorithm228before being displayed to the user102. The search engine may also, at step720, re-rank the set of URL results based on the relevancy of a plurality of ads corresponding to the URLs as determined by the targeting algorithm. Note, as indicated before, that the targeting and matching algorithms may be combined into a single algorithm, so that althoughFIGS. 5 and 6mention matching algorithms andFIG. 7mentions a targeting algorithm does not mean that the two algorithms are mutually exclusive.

While specific embodiments and applications of various methods and systems for conducting experiments over the Internet have been illustrated and described, it is to be understood that the disclosure claimed hereinafter is not limited to the precise configuration and components disclosed. Various modifications, changes, and variations apparent to those of skill in the art may be made in the arrangement, operation, and details of the methods and systems disclosed.

The embodiments may include various steps, which may be embodied in machine-executable instructions to be executed by a general-purpose or special-purpose computer (or other electronic device). Alternatively, the steps may be performed by hardware components that contain specific logic for performing the steps, or by any combination of hardware, software, and/or firmware.

Embodiments may also be provided as a computer program product including a machine-readable medium having stored thereon instructions that may be used to program a computer (or other electronic device) to perform processes described herein. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of media/machine-readable medium suitable for storing electronic instructions. For example, instructions for performing described processes may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., network connection).