Patent Application
20140122221
The present disclosure is directed towards internet advertising, more particularly to optimizing bidding with multiple campaign types during prosecution of advertising campaigns.
Advertisers may run multiple campaigns as part of their marketing strategy. The objectives of each of these campaigns may differ. For instance, some campaigns may have branding goals, whereas others may have specific quantitative performance goals. In addition, the advertisers may specify budgets over sets of campaigns. For the purposes of optimizing the campaign spend relative to campaign budgets, this presents a difficult multi-objective optimization problem. Legacy systems fail to optimize in multi-campaign/multi-objective optimization situations, thus what is needed are methods for jointly optimizing advertising campaigns over disparate objectives given a total budget constraint covering a set of campaigns while still observing individual campaign budget constraints.
Other features and advantages will be apparent from the accompanying drawings, and from the detailed description that follows below.
Disclosed herein are computer implemented methods, and systems for optimizing bidding over multiple advertising campaigns having a least two campaign types. The optimization of the multiple advertising campaigns is performed subject to at least one constraint quantifying a multiple campaign spending limit amount.
The method commences by identifying a collection of the multiple advertising campaigns, where each constituent member advertising campaign has at least a respective optimization function and a respective spending limit amount. Then, the method proceeds by evaluating each of the constituent member advertising campaigns to determine a respective optimized spending amount (for that constituent member advertising campaign). The optimized spending amount is determined using the respective optimization function.
Once all of the constituent member advertising campaigns have been evaluated, then the method calculates (e.g., sums) an aggregate spending amount by aggregating the respective optimized spending amount across the constituent member advertising campaigns. A Pareto-optimal allocation is determined as follows:
The time period under which the spending is optimized can be weeks or days, or any convenient time suited for forming bids to bid on available inventory of advertising impressions that can be monetized (e.g., by placing an advertisement).
The novel features are set forth in the appended claims. However, for purpose of explanation, several embodiments are set forth in the following figures.
Like reference characters refer to corresponding parts throughout the drawings.
In modern advertising systems, advertisers may elect to prosecute multiple advertising campaigns as part of their overall marketing and advertising strategy. The objectives of each of these campaigns may differ. For instance, some campaigns may have branding goals (e.g., maximize impressions), whereas others may have specific quantitative performance goals (e.g. total return on investment for a given budget, etc.). In addition to specifying goals and constraints, the advertisers may specify budgets over sets of campaigns. Optimizing a total, multi-campaign budget relative to individual campaign budgets presents a difficult multi-objective optimization problem. What is needed are methods for optimizing advertising campaigns over disparate objectives given a total budget constraint covering a set of campaigns while still observing individual campaign budget constraints.
Herein-described are techniques that heuristically determine the remaining spend (e.g. advertising budget) apportioned to each campaign from among a collection of advertising campaigns. The heuristics observe both individual campaign budget constraints as well as a multi-campaign budget over all the constituent campaigns within the collection. The techniques consider a set of possible campaign objectives, each campaign having one or more constraints against which an optimizer seeks to optimize for a particular objective result. For example, a particular objective result might be to maximize the number of clicks, or to maximize a click-through rate. Or, a particular objective result might be to maximize the return on investment (e.g. return on advertising budget spent). Or a particular objective result might be to optimize the representativeness of the allocation won, etc. In some cases, calculating and adhering to or performing to the optimized variables during a time period in an effort to achieve a particular objective result might in an optimized spending amount for that time period. Of course it is possible that in a given time period, it is not feasible to perform to the optimized variables. For example, an optimal calculation might indicate a particular spending amount (e.g. to place some optimal number of advertisements), but such an optimal calculation might be infeasible to perform when, for example, there is not enough inventory (e.g. ad calls) in which to place the optimal number of advertisements.
In exemplary embodiments, for each individual campaign, the individual campaign has a particular quantitative objective, and for each quantitative objective there is an algorithm (e.g. a function, a subroutine, a method, etc.) that serves to calculate an optimal bid given a budget constraint and possibly other constraints as well. Of course, it follows that once spent, a bid that is optimal for one individual campaign will reduce the overall budget for the collection of advertising campaigns. Moreover, it might be possible that the same amount of spend applied to achieve a particular quantitative objective in one individual campaign might be more effective to achieve using a different quantitative objective in a different campaign within the same collection of advertising campaigns. Therefore, techniques are needed so as to calculate an optimal set of bids across multiple campaigns given the multiplicity of objectives and the multiplicity of campaign constraints and given a multi-campaign budget constraint (e.g. a multiple-campaign spending limit amount).
Of course, a campaign is conducted during some particular time period, and that time period is used by the aforementioned objective functions and by the optimizers discussed herein. Similarly, a campaign that is conducted during some particular time period has a spending limit covering the respective time period. For example, a particular advertising campaign might have a “daily limit” of some particular amount. Or, for example, a particular advertising campaign might have a “monthly limit” of some particular amount.
Some embodiments use two passes. Strictly as an example, a two-phase optimizer might commence a first phase in which each individual campaign from among the collection of individual advertising campaigns is considered separately, and the optimal bids and resulting spend for each individual campaign among the collection of advertising campaigns is calculated considering the respective individual campaign budget and respective individual campaign objectives, and using the respective objective function of each respective individual campaign.
In a second phase, the resulting aggregate spending (e.g. sum of spending of each respective individual campaign) is compared to see if the calculated aggregate spending exceeds a multi-campaign budget. If not, the technique uses the optimal bid policies for each campaign as calculated in the first phase. However, if the calculated aggregate spending does exceed the multi-campaign budget, then a processor will apportion to each campaign a fraction of the multi-campaign budget.
In one embodiment, the fraction of the multi-campaign budget apportioned to the respective campaigns is in proportion to the contribution (e.g. percentage) of each respective campaign to the aggregate resulting spend. Other apportioning (e.g. non-linear apportioning) is reasonable and envisioned, including apportioning based on advertiser-specified importance of one campaign or another (e.g. a branding campaign versus a search-term campaign, etc.), or based on advertiser-specified importance of one campaign variable or another (e.g. a total impressions presented versus return on investment, etc.). Given the apportioned individual campaign budgets (e.g. as apportioned according to the aforementioned techniques), it is possible to calculate the optimal bid for each individual campaign using the respective function or subroutine or other individual campaign optimizer (e.g. the results of which campaign optimizer are determined by the campaign objectives and constraints). Having apportioned fractions of the multi-campaign budget to the individual budgets of the individual campaigns, then each individual campaign is optimized (using the apportioned fractions of the multi-campaign budget) and the bid policies for each individual campaign are calculated and spent, accordingly.
Note that the spending constraints for any individual campaign is observed. As well, the spending constraints for the multi-campaign (e.g. a multi-campaign budget) is also observed.
An algorithmic description of the above two-phase solution is given in Algorithm 1, below.
Note that the allocations made in accordance with Algorithm 1 are Pareto-optimal. Algorithm 1 might be implemented in an advertising network, using one or more computers.
Reference will now be made in detail to various embodiments, some examples of which are illustrated in the accompanying drawings. It will be understood that the described embodiments are not intended to limit the scope of the claims. On the contrary, the embodiments are intended to cover alternatives, modifications and equivalents, which may be included within the scope of the appended claims. Furthermore, in the following detailed descriptions, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments. However, one of ordinary skill in the art can recognize that the present advances may be practiced without these specific details. In other instances, well known methods, procedures, components, and circuits have not been described in detail so as avoid unnecessarily obscuring aspects of the described embodiments.
Throughout the specification and claims, terms may have nuanced meanings suggested or implied in context beyond an explicitly stated meaning. Likewise, the phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment and the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment. It is intended, for example, that claimed subject matter include combinations of example embodiments in whole or in part.
In general, terminology may be understood at least in part from usage in context. For example, terms such as “and”, “or”, or “and/or” as used herein may include a variety of meanings that may depend at least in part upon the context in which such terms are used. Typically, “or” if used to associate a list; for example, a list such as A, B or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B or C, here used in the exclusive sense. In addition, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures, or characteristics in a plural sense. Similarly, terms such as “a,” “an,” or “the” may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for the existence of additional factors not necessarily expressly described, depending at least in part on context.
Some portions of the detailed descriptions which follow are presented in terms of procedures, steps, logic blocks, processing, and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. A procedure, computer executed step, logic block, process, etc. as may be used herein is generally conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present disclosure, discussions using terms such as “processing” or “computing” or “communicating” or “instantiating” or “registering” or “selecting”, or “displaying” or the like, refer to the action and processes of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
Subject matter will now be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific example embodiments. Subject matter may, however, be embodied in a variety of different forms and, therefore, covered or claimed subject matter is intended to be construed as not being limited to any example embodiments set forth herein; example embodiments are provided merely to be illustrative. Likewise, a reasonably broad scope for claimed or covered subject matter is intended. Among other things, for example, subject matter may be embodied as methods, devices, components, or systems. Accordingly, embodiments may, for example, take the form of hardware, software, firmware or any combination thereof (other than software per se). The following detailed description is, therefore, not intended to be taken in a limiting sense.
Some of the terms used in this description are defined below (in alphabetical order) for easy reference. These terms are not rigidly restricted to these definitions. A term may be further defined by the term's use in other sections of this description.
“Ad” (e.g. ad, item and/or message) means a paid announcement, as of goods or services for sale, preferably on a network such as the internet. An ad may also be referred to as an item and/or a message.
“Ad call” means a message sent by a computer to an ad server for requesting an ad to be displayed.
“Ad click-through rate” (e.g. click-through rate) means a measurement of ad clicks per a period of time.
“Advertiser” (e.g. messenger and/or messaging customer, etc.) means an entity that is in the business of marketing a product and/or a service to users. An advertiser may include, without limitation, a seller and/or a third-party agent for the seller. An advertiser may also be referred to as a messenger and/or a messaging customer. Advertising may also be referred to as messaging.
“Advertising” means marketing a product and/or service to one or more potential consumers by using an ad. One example of advertising is publishing a sponsored search ad on a website.
“Ad targeting” or “targeting” refers to web portals like Yahoo!, where advertisements may be displayed on web pages resulting from a user-defined search based at least in part upon one or more search terms. Advertising may be beneficial to users, advertisers or web portals if displayed advertisements are relevant to interests of one or more users. Thus, a variety of techniques have been developed to infer user interest, user intent or to subsequently target relevant advertising to users. One approach to presenting targeted advertisements includes employing demographic characteristics (e.g. age, income, sex, occupation, etc.) for predicting user behavior, such as by group. Advertisements may be presented to users in a targeted audience based at least in part upon predicted user behavior(s). Another approach includes profile-type ad targeting. In this approach, user profiles specific to a user may be generated to model user behavior, for example, by tracking a user's path through a web site or network of sites, and compiling a profile based at least in part on pages or advertisements ultimately delivered. A correlation may be identified, such as for user purchases, for example. An identified correlation may be used to target potential purchasers by targeting content or advertisements to particular users.
“Application server” is a server that is configured for running one or more devices loaded on the application server. For example, an application server may run a device configured for deducing shadow profiles.
“Click” (e.g. ad click) means a selection of an ad impression by using a selection device such as, for example, a computer mouse or a touch-sensitive display.
“Client” means the client part of a client-server architecture. A client is typically a user device and/or an application that runs on a user device. A client typically relies on a server to perform some operations. For example, an email client is an application that enables a user to send and receive email via an email server. In this example, the computer running such an email client may also be referred to as a client.
A “Computing device” means a computing device that may be capable of sending or receiving signals, such as via a wired or wireless network, or may be capable of processing or storing signals, such as in memory as physical memory states, and may, therefore, operate as a server. Thus, devices capable of operating as a server may include, as examples, dedicated rack-mounted servers, desktop computers, laptop computers, set top boxes, integrated devices combining various features, such as two or more features of the foregoing devices, or the like.
A “Content server” is a server that may include a device that includes a configuration to provide content via a network to another device. A content server may, for example, host a site, such as a social networking site, examples of which may include, without limitation, Flicker, Twitter, Facebook, LinkedIn, or a personal user site (such as a blog, vlog, online dating site, etc.). A content server may also host a variety of other sites, including, but not limited to business sites, educational sites, dictionary sites, encyclopedia sites, wikis, financial sites, government sites, etc.
A content server may further provide a variety of services that include, but are not limited to, web services, third-party services, audio services, video services, email services, instant messaging (IM) services, SMS services, MMS services, FTP services, voice over IP (VOIP) services, calendaring services, photo services, or the like. Examples of content may include text, images, audio, video, or the like, which may be processed in the form of physical signals, such as electrical signals, for example, or may be stored in memory, as physical states, for example.
Examples of devices that may operate as a content server include desktop computers, multiprocessor systems, microprocessor-type or programmable consumer electronics, etc.
“Conversion” (e.g. ad conversion) means a purchase of a product/service that happens as a result of a user responding to an ad and/or a coupon.
A “Content delivery network” or “Content distribution network” (CDN) or “base content server” refers to a distributed content delivery system that comprises a collection of computers or computing devices linked by a network or networks. A CDN may employ software, systems, protocols or techniques to facilitate various services, such as storage, caching, communication of content, or streaming media or applications. Services may also make use of ancillary technologies including, but not limited to, “cloud computing,” distributed storage, DNS request handling, provisioning, signal monitoring and reporting, content targeting, personalization, or business intelligence. A CDN may also enable an entity to operate or manage another's site infrastructure, in whole or in part.
“Database” (e.g. database system, etc.) means a collection of data organized in such a way that a computer program may quickly select desired pieces of the data. A database is an electronic filing system. In some instances, the term “database” is used as shorthand for a “database management system”. A database may be implemented as any type of data storage structure capable of providing for the retrieval and storage of a variety of data types. For instance, a database may comprise one or more accessible memory structures such as a CD-ROM, tape, digital storage library, flash drive, floppy disk, optical disk, magnetic-optical disk, erasable programmable read-only memory (EPROM), random access memory (RAM), magnetic or optical cards, etc.
“Device” means hardware, software or a combination thereof. A device may sometimes be referred to as an apparatus. Examples of a device include, without limitation, a software application such as Microsoft Word™ or a database; or hardware such as a laptop computer, a server, a display; or a computer mouse and/or a hard disk.
“Impression” (e.g. ad impression) means a delivery of an ad to a user device for viewing by a user.
The “Internet” refers to a decentralized global network of networks. The Internet includes local area networks (LANs), wide area networks (WANs), wireless networks, or long haul public networks that, for example, allow signal packets to be communicated between LANs. Signal packets may be communicated between nodes of a network, such as, for example, to one or more sites employing a local network address. A signal packet may, for example, be communicated over the Internet from a user site via an access node coupled to the Internet. Likewise, a signal packet may be forwarded via network nodes to a target site coupled to the network via a network access node, for example. A signal packet communicated via the Internet may, for example, be routed via a path of gateways, servers, etc. that may route the signal packet in accordance with a target address and availability of a network path to the target address.
“Monetization techniques” or “Monetization models” refers to techniques used in connection with sponsored search advertising, including advertising associated with user search queries, or non-sponsored search advertising, including graphical or display advertising. In an auction-type online advertising marketplace, advertisers may bid in connection with placement of advertisements, although other factors may also be included in determining advertisement selection or ranking. Bids may be associated with amounts advertisers pay for certain specified occurrences, such as for placed or clicked-on advertisements, for example. Advertiser payment for online advertising may be divided between parties including one or more publishers or publisher networks, one or more marketplace facilitators or providers, or potentially among other parties.
Some models may include guaranteed delivery advertising, in which advertisers may pay based at least in part on an agreement guaranteeing or providing some measure of assurance that the advertiser will receive a certain agreed upon amount of suitable advertising, or non-guaranteed delivery advertising, which may include individual serving opportunities or spot market(s), for example. In various models, advertisers may pay based at least in part on any of various metrics associated with advertisement delivery or performance, or associated with measurement or approximation of particular advertiser goal(s). For example, models may include, among other things, payment based at least in part on cost per impression or number of impressions, cost per click or number of clicks, cost per action for some specified action(s), cost per conversion or purchase, or cost based at least in part on some combination of metrics, which may include online or offline metrics, for example. A process of buying or selling online advertisements may involve a number of different entities, including advertisers, publishers, agencies, networks, or developers. To simplify this process, organization systems called “ad exchanges” may associate advertisers or publishers, such as via a platform to facilitate buying or selling of online advertisement inventory from multiple ad networks. “Ad networks” refers to aggregation of ad space supply from publishers, such as for provision en masse to advertisers.
“Network” means a connection, between any two or more computers, that permits the transmission of data. A network may be any combination of networks including, without limitation, the internet, a local area network, a wide area network, a wireless network, and/or a cellular network.
A “Network” means a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), or other forms of computer or machine readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wire-line type connections, wireless type connections, or any combination thereof. Likewise, sub-networks, such as may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network. Various types of devices may, for example, be made available to provide an interoperable capability for differing architectures or protocols. As one illustrative example, a router may provide a link between otherwise separate and independent LANs.
A communication link or channel may include, for example, analog telephone lines, such as a twisted wire pair, a coaxial cable, full or fractional digital lines including T1, T2, T3, or T4 type lines, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, or other communication links or channels, such as may be known to those skilled in the art. Furthermore, a computing device or other related electronic devices may be remotely coupled to a network, such as via a telephone line or link, for example.
A wireless network may couple client devices with a network. A wireless network may employ stand-alone ad-hoc networks, mesh networks, Wireless LAN (WLAN) networks, cellular networks, or the like. A wireless network may further include a system of terminals, gateways, routers, or the like coupled by wireless radio links, or the like, which may move freely, randomly or organize themselves arbitrarily, such that network topology may change, at times even rapidly. A wireless network may further employ a plurality of network access technologies, including Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G) cellular technology, or the like. Network access technologies may enable wide area coverage for devices, such as client devices with varying degrees of mobility, for example.
For example, a network may enable RF or wireless type communication via one or more network access technologies, such as Global System for Mobile communication (GSM), Universal Mobile Telecommunications System (UMTS), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced, Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n, or the like. A wireless network may include virtually any type of wireless communication mechanism by which signals may be communicated between devices, such as a client device or a computing device, between or within a network, or the like.
Signal packets communicated via a network, such as a network of participating digital communication networks, may be compatible with or compliant with one or more protocols. Signaling formats or protocols employed may include, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX, Appletalk, or the like. Versions of the Internet Protocol (IP) may include IPv4 or IPv6.
“Publisher” means an entity that publishes, on a network, a web page having content and/or ads, etc. ad/or publishes, on a network, a downloadable application and/or other digital context having digital content and/or digital ads, etc.
“Server” means a software application that provides services to other computer programs (and their users) on the same computer or on another computer or computers. A server may also refer to the physical computer that has been set aside to run a specific server application. For example, when the software Apache HTTP Server is used as the web server for a company's website, the computer running Apache may also be called the web server. Server applications may be divided among server computers over an extreme range, depending upon the workload. Servers may vary widely in configuration or capabilities, but generally a server may include one or more central processing units and memory. A server may also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.
“Social network” or “social networking website” refers generally to a network of individuals, such as acquaintances, friends, family, colleagues, or co-workers, coupled via a communications network or via a variety of sub-networks. Potentially, additional relationships may subsequently be formed as a result of social interaction via the communications network or sub-networks. A social network may be employed, for example, to identify additional connections for a variety of activities, including, but not limited to, dating, job networking, receiving or providing service referrals, content sharing, creating new associations, maintaining existing associations, identifying potential activity partners, performing or supporting commercial transactions, or the like.
A social network may include individuals with similar experiences, opinions, education levels or backgrounds. Subgroups may exist or be generated according to user profiles of individuals, for example, in which a subgroup member may belong to multiple subgroups. An individual may also have multiple “1:few” associations within a social network, such as for family, college classmates, or co-workers.
An individual's social network may refer to a set of direct personal relationships or a set of indirect personal relationships. A direct personal relationship refers to a relationship for an individual in which communications may be individual to individual, such as with family members, friends, colleagues, co-workers, or the like. An indirect personal relationship refers to a relationship that may be available to an individual with another individual although no form of individual to individual communication may have taken place, such as a friend of a friend, or the like. Different privileges or permissions may be associated with relationships in a social network. A social network also may generate relationships or connections with entities other than a person, such as companies, brands, or so-called ‘virtual persons.’ An individual's social network may be represented in a variety of forms, such as visually, electronically or functionally. For example, a “social graph” or “socio-gram” may represent an entity in a social network as a node and a relationship as an edge or a link.
Individuals within one or more social networks may interact or communication with other members of a social network via a variety of devices. Multi-modal communication technologies refers to a set of technologies that permit interoperable communication across multiple devices or platforms, such as cell phones, smart phones, tablet computing devices, personal computers, televisions, SMS/MMS, email, instant messenger clients, forums, social networking sites (such as Facebook, Twitter, or Google+), or the like.
“Software” means a computer program that is written in a programming language that may be used by one of ordinary skill in the art. The programming language chosen should be compatible with the computer on which the software application is to be executed and, in particular, with the operating system of that computer. Examples of suitable programming languages include, without limitation, Object Pascal, C, C++ and/or Java. Further, the functions of some embodiments, when described as a series of steps for a method, could be implemented as a series of software instructions for being operated by a processor such that the embodiments could be implemented as software, hardware, or a combination thereof. Computer-readable media are discussed in more detail in a separate section below.
“System” means a device or multiple coupled devices. A device is defined above.
“User” (e.g. consumer, etc.) means an operator of a user device. A user is typically a person who seeks to acquire a product and/or service. For example, a user may be a woman who is browsing Yahoo!™ Shopping for a new cell phone to replace her current cell phone. The term “user” may also refer to a user device, depending on the context.
“User device” (e.g. computer, user computer, client and/or server, etc.) means a single computer or a network of interacting computers. A user device is a computer that a user may use to communicate with other devices over a network, such as the internet. A user device is a combination of a hardware system, a software operating system, and perhaps one or more software application programs. Examples of a user device include, without limitation, a laptop computer, a palmtop computer, a smart phone, a cell phone, a mobile phone, an IBM-type personal computer (PC) having an operating system such as Microsoft Windows™, an Apple™ computer having an operating system such as MAC-OS, hardware having a JAVA-OS operating system, and/or a Sun Microsystems™ workstation having a UNIX operating system.
“Web browser” means a software program that may display text or graphics or both, from web pages on websites. Examples of a web browser include, without limitation, Mozilla Firefox™ and Microsoft Internet Explorer™.
“Web page” means documents written in a mark-up language including, without limitation, HTML (hypertext mark-up language), VRML (virtual reality modeling language), dynamic HTML, XML (extensible mark-up language), and/or other related computer languages. A web page may also refer to a collection of such documents reachable through one specific internet address and/or through one specific website. A web page may also refer to any document obtainable through a particular URL (uniform resource locator).
“Web server” is a server configured for serving at least one web page to a web browser. An example of a web server is a Yahoo!™ web server. A server is defined above.
Again referring to Algorithm 1, the system implements a two-phase approach to multi-type campaign optimization. As shown, the system of
The system operates on an identified collection of multiple advertising campaigns (e.g. set of campaigns 122, as shown). In exemplary embodiments, the set of campaigns comprises constituent member advertising campaigns including at least two campaign types (e.g. selected from among the first type campaign 142, the second type campaign 143, and the third type campaign 144). Each constituent member advertising campaign has a respective individual campaign optimizer (e.g. embodied within an individual campaign optimizer 1261, individual campaign optimizer 1262, individual campaign optimizer 126N, etc.) and a constituent member advertising campaign has a respective budget (e.g. individual campaign budget 1251, individual campaign budget 1252, individual campaign budget 125N). Similarly, each constituent member advertising campaign has a respective objective function (e.g. individual campaign optimization function 1271, individual campaign optimization function 1272, individual campaign optimization function 127N).
In a first phase, the system of
In a second phase, the system of
The multiple campaign type optimizer module 121 aggregates or sums (e.g. using the integrator Σ) the individual optimized spending amounts. An over budget test (see decision 180) is performed, and when the aggregate spending amount 182 (e.g. the output of the integrator Σ) is less than or equal to the multiple campaign spending limit amount 131, then the system commences bidding (e.g. using the bid preparation unit 135) in order to spend the respective optimized spending amounts (e.g. optimized individual campaign spend 1291, optimized individual campaign spend 1292, optimized individual campaign spend 129N, etc.). However, when the aggregate spending amount 182 is greater than the multiple campaign spending limit amount 131, then an individual campaign apportioner 130 serves to apportion the multiple campaign spending limit amount to the constituent member advertising campaigns. The apportioned campaign spend for each campaign is recast as a temporarily-reduced spending limit and is prepared (e.g. by possibly using an individual campaign budget override unit 132) as a temporary override value, and sent to the individual campaign optimizer module 120 (see re-optimization data path 145), which in turn serves to use the reduced spending limits for each individual campaign (e.g. reduced spending limit 1331, reduced spending limit 1332, reduced spending limit 133N, etc.) to calculate an optimized spending amount in each of the constituent member advertising campaigns. The flow continues, as earlier described, however this time, the decision 180 (the over budget test) will take the “No” branch, and the flow exits by returning a set of bids corresponding to the respective individual campaigns (see line 8 of Algorithm 1).
The description of the algorithm to generate bid policies includes the aspect of the re-optimization data path 145 in line 7 of Algorithm 1.
Again referring to
A campaign parameter tuning module 211 might use any such available data for operations for campaign parameter tuning. A performance-driven advertising campaign parameter may include performance metrics 216. By definition, performance metrics 216 may comprise one or more instances of a performance metric 317.
The aforementioned bids are provided to an advertisement bidding module 209, and bids are submitted to an auction engine server 204. When a bid wins at auction for the opportunity to present an advertisement in an impression, then an advertisement serving module 218 composites the ad in real-time.
In various embodiments, advertising is placed by a variety of different types of advertisers (e.g. large advertisers, small advertisers, advertisers seeking brand advertising, advertisers seeking conversions, etc.), where each type of advertiser is holding interests in a variety of campaign objectives and constraints. Therefore, embodiments allow advertisers to express a diverse set of objectives/constraints (e.g. using the form for collecting information regarding objectives and constraints). At the same time, the interface (e.g. form, screen device, etc.) of such an embodiment might be sufficiently rich so as to assist the advertiser toward expressing only the most economically sensible constraints. Moreover, the interface (e.g. form, screen device, etc.) of such an embodiment might be sufficiently rich so as to show estimated the impact of various settings and/or changes in the campaign objectives and constraints.
Of course, there exists a wide range of possible campaign objectives and constraints. Strictly as an example, an objective function for an advertiser's advertising campaign might be a net utility value, expressed as the sum of the values of clicks received during some time period minus the cost of operating the campaign over that same time period. Another objective function for an advertiser's advertising campaign might comprise a return on investment value (ROI, also known as the return on advertising spend—ROAS). ROI is equal to the total value received divided by the cost. For example, the combination of this objective function with a minimum spend constraint might be useful for advertisers who only know their advertising budget as well as the relative (and not the absolute) value of a click from each keyword.
In addition to the above, any one of the constraints described below might be used within a calculation of the objective function of the optimization calculations (e.g. the calculation using an optimization function). For example, an advertiser might try to maximize the number of clicks received subject to the maximum cost-per-click constraints.
As shown, the data layout might comprise any one or more campaign collection descriptors (e.g. campaign collection descriptor 6100, campaign collection descriptor 6101, campaign collection descriptor 6102, campaign collection descriptor 610N, etc.) which in turn comprises one or more individual campaign descriptors (e.g. individual campaign descriptor 6200, individual campaign descriptor 620N, etc.), one or more individual campaign budget descriptors (e.g. individual campaign budget descriptor 6300, individual campaign budget descriptor 630N, etc.), one or more individual campaign time period descriptors (e.g. individual campaign time period descriptor 6400, individual campaign time period descriptor 640N, etc.), and any one or more individual campaign optimizer module descriptors (e.g. individual campaign optimizer module descriptor 6500, individual campaign optimizer module descriptor 650N, etc.).
The values stored in any descriptor might be provided to modules as described above. For example, the value stored in individual campaign time period descriptor 640N might be provided to the individual campaign optimizer 126N. In some embodiments, an individual campaign optimizer module descriptor describes advertiser-specified importance of one individual campaign or another (e.g. importance of an ROI campaign versus the importance of a search-term campaign, etc.).
As shown, the flow chart 700 commences by assembling a collection individual campaigns (see operation 710), and establishing a time period for optimization (see operation 720). Optimization might be performed in arbitrarily small time slices. For example, if a daily spending limit were established at say one thousand dollars, the daily spending limit could be broken down into smaller amounts, corresponding to the smaller time slices. In a linear break-down, a one thousand dollar daily limit could be broken down into say ten hours per day, the ten hours corresponding to the core hours (e.g. 7am-5 pm) in that time zone, and each of those ten hours could have a budget of one hundred dollars. Continuing, each individual campaign might have its own campaign bidding policy (and again refer to the aforementioned forms of
In operation, an optimized amount to spend for each individual campaign is calculated (see operation 750), and the aggregate spending across all of the collection of individual campaigns is calculated (see operation 760). In some embodiments, the aggregate spending across all of the collection of individual campaigns is compared with the multiple campaign spending limit amount 131. If the aggregate spend is not over budget (see decision 765), then spend according to the optimized spend for each individual campaign in the collection (see operation 780). On the other hand, if the aggregate spend is over budget (see decision 765), then apportion each individual campaign's campaign budget for the time period see operation 770), and re-optimize (see re-optimization flow path 785).
Other embodiments that may vary, for example, in terms of arrangement or in terms of type of components, are also intended to be included within the claimed subject matter. As shown,
A client device may vary in terms of capabilities or features. Claimed subject matter is intended to cover a wide range of potential variations. For example, a cell phone may include a numeric or other instance of a keypad 1056 or a display 1054 of limited functionality, such as a monochrome liquid crystal display (LCD) for displaying text and symbols. In contrast, however, as another example, a web-enabled client device may include one or more physical or virtual keyboards, mass storage, one or more accelerometers, one or more gyroscopes, a global positioning system (e.g. GPS 1064) or other location-identifying type capability, or a display with a high degree of functionality, such as a touch-sensitive color 2D or 3D display, possibly with backlighting or other illuminator 1058, or a haptic interface 1062, or another variety of input/output interface 1060, for example.
A client device may include memory 1030, possibly with a section of RAM 1032 or other memory (e.g. data storage 1044) or forms of non-volatile memory (e.g. ROM 1034) that can be accessed by a processor (e.g. CPU 1022) and which may execute a variety of basic I/O functions (e.g. BOS 1040) and/or may execute a variety operating systems 1041, including a personal computer operating system such as a Windows, iOS, or Linux, or a mobile operating system such as iOS, Android, or Windows Mobile, or the like. A client device may include or may execute a variety of possible applications 1042, such as a client software application enabling communication with other devices, such as communicating one or more messages using a messenger application 1043, or such as via email, short message service (SMS), or multimedia message service (MMS), including via a network such as a social network including, for example, Facebook, LinkedIn, Twitter, Flickr, or Google+, to provide only a few possible examples. A client device may also include or execute an application to communicate content, such as, for example, textual content, multimedia content, or the like. A client device may also include or execute an application to perform a variety of possible tasks, such as browsing using a browser 1045, searching, playing various forms of content, including locally stored or streamed video, audio (e.g. using audio interface 1052) or games (such as fantasy sports leagues), possibly including networked games or other applications using one or more instances of network interface 1050. Any of the foregoing functional blocks comprising the client device 1000 can be powered by one or more instances of power supply 1026, and any of the foregoing functional blocks comprising the client device 1000 can communicate with any other of the foregoing functional blocks over a communication bus 1024. The foregoing is provided to illustrate that claimed subject matter is intended to include a wide range of possible features or capabilities.
Any node of the network may comprise a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof capable to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g. a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration, etc.).
In alternative embodiments, a node may comprise a machine in the form of a virtual machine (VM), a virtual server, a virtual client, a virtual desktop, a virtual volume, a network router, a network switch, a network bridge, a personal digital assistant (PDA), a cellular telephone, a web appliance, or any machine capable of executing a sequence of instructions that specify actions to be taken by that machine. Any node of the network may communicate cooperatively with another node on the network. In some embodiments, any node of the network may communicate cooperatively with every other node of the network. Further, any node or group of nodes on the network may comprise one or more computer systems (e.g. a client computer system, a server computer system) and/or may comprise one or more embedded computer systems (including a processor and memory), a massively parallel computer system, and/or a cloud computer system.
The computer system (e.g. computer 1150) includes a processor 1108 (e.g. a processor core, a microprocessor, a computing device, etc.), a main memory (e.g. computer memory 1110), and a static memory 1112, which communicate with each other via a system bus 1114. The computer 1150 may further include a display unit (e.g. computer display 1116) that may comprise a touch-screen, or a liquid crystal display (LCD), or a light emitting diode (LED) display, or a cathode ray tube (CRT). As shown, the computer system also includes a human input/output (I/O) device 1118 (e.g. a keyboard, an alphanumeric keypad, etc.), a pointing device 1120 (e.g. a mouse, a touch screen, etc.), a drive unit 1122 (e.g. a disk drive unit, a CD/DVD drive, a tangible computer readable removable media drive, an SSD storage device, etc.), a signal generation device 1128 (e.g. a speaker, an audio output, etc.), and a network interface device 1130 (e.g. an Ethernet interface, a wired network interface, a wireless network interface, a propagated signal interface, etc.).
The drive unit 1122 includes a machine-readable medium 1124 on which is stored a set of instructions (i.e. software, firmware, middleware, etc.) 1126 embodying any one, or all, of the methodologies described above. The set of instructions 1126 is also shown to reside, completely or at least partially, within the main memory and/or within the processor 1108. The set of instructions 1126 may further be transmitted or received via the network interface device 1130 over the system bus 1114.
It is to be understood that embodiments of this disclosure may be used as, or to support, a set of instructions executed upon some form of processing core (such as the CPU of a computer) or otherwise implemented or realized upon or within a machine- or computer-readable medium. A machine-readable medium includes any mechanism for storing non-transitory information in a form readable by a machine (e.g. a computer). For example, a machine-readable medium includes read-only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; and electrical, optical or acoustical or any other type of media suitable for storing non-transitory information.
While the figures and description have been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the claimed embodiments can be differently embodied in other specific forms without departing from the scope of the claims.
