1. Field of the Invention
The present invention relates to systems and methods for conducting auctions. In particular, the present invention relates to auctions that implement a function that is homogeneous of degree one. Still more particularly, the present invention relates to an auction whose function depends on a second-highest bid. The present invention also relates to systems and methods for allocating impressions or advertisements on web pages.
2. Description of the Related Art
The popularity and use of the Internet, web browsers and sites providing content has grown dramatically over the past decade. With this growth have been dramatic growth and change in advertisements on web pages or impressions, how those impressions are purchased and placed as well as different models for advertising using impressions. Advertisers used to buy guarantees, and then developed performance goals. However, more recently, the suppliers of impressions have new indirect demand sources which are mostly independent bidders (“performance advertisers”) that bid for impressions on an impression-by-impression basis. Bids by these independent bidders are sometimes referred to herein as “indirect demand.” However, other types of models have been developed. For example, traditional advertisers have hired publishers or distributors to purchase and place impressions. This is often done in the context of a campaign that includes a number of impressions, as well as certain parameters for targets and metrics for performance. The publishers or distributors act on behalf of one or more traditional advertisers, who have been offered certain guarantees about a set of impressions they will be assigned. Bids by these publishers are sometimes referred to herein as “direct demand.”
One difficulty is how to balance a marketplace between performance advertisers (indirect demand/buyers) and a publisher's own advertisers (direct demand/buyers). There is not a system that has been effective in allowing direct demand and indirect demand to compete with each other. More specifically, there are three particular problems with existing solutions that prevent them from allowing indirect demand and direct demand to compete with each other. First, there is an adverse selection problem. When direct demand and indirect demand compete, there is a concern about cherry picking where well-informed indirect demand/buyers will take all of the “good” inventory, leaving a direct buyer/demand with the “bad” inventory. Second, there is an under delivery problem. When direct demand and indirect demand compete, there is a concern that indirect demand/buyers will win too much inventory, causing the guaranteed campaigns of the direct demand/buyers to under deliver. Third, there is a need to make the marketplace strategy proof so that indirect demand will not attempt to game the system by submitting false bids to skew the award of impressions. Thus, there is a need for a system for allocating impressions that addresses these problems.
The prior art has attempted to solve this problem by controlling the manner in which the direct buyers submit bids along with the indirect buyers. More specifically, the publisher places bids at random on behalf of the indirect buyers. See Ghosh, Arpita, Preston McAfee, Kishore Papineni and Sergei Vassilvitskii (2009). “Bidding for Representative Allocations for Display Advertising.” Proceedings of the 5th international Workshop on Internet and Network Economics (WINE). However, this solution reduces the effectiveness of targeting of the direct buyers to avoid the adverse selection problem. Thus, in order to avoid adverse selection, the prior art reduces the effectiveness of other targeting.
The selling of an impression is essentially an auction involving common factors and match factors. Common factors are ones that have a similar effect on the values of ad impressions to most advertisers. For example, income is a common factor, because after controlling for other factors, higher income consumers are preferred advertising targets for most advertisers. Match factors are ones that tend to be specific to the advertiser. For example, if a consumer is known presently to be shopping for something specific—such as for a family car or for Italian designer clothing or for hotels in San Francisco—then impressions to that consumer offered now are particularly valuable to firms selling those products. Since advertisers' bids for different impressions reflect all aspects of value, including both common factors and match factors, it has been thought to be impossible to separate the common factor to produce bids for such auction that satisfy the concerns noted above. Any bid that is placed on behalf of an indirect buyer will win more often when all the value factors, including the common factors, are low, and that seems to make adverse selection inescapable.
Existing solutions do not adequately address the need for an impression allocation system that allows direct demand and indirect demand to compete, avoids the adverse selection problem, increases the efficiency of the auction (reduces the under delivery problem), and preserves the incentive for indirect bidders to bid honestly.
The present invention overcomes the deficiencies and limitations of the prior art at least in part by providing a system and methods for allocating impressions using an auction that considers losing bids. In one embodiment, the auction implements a function that is homogeneous of degree one and/or is a modified second-bid auction or another type of auction. The impression allocation system comprises a delivery engine, a statistics collector, a forecasting module, an optimization engine and a direct demand source. The delivery engine receives indirect demand, direct demand and optimization information that it uses to allocate impressions and serve ads. In particular, the delivery engine operates a modified second-bid auction that matches direct demand or indirect demand to ad calls. The statistics collector is coupled to collect information from the delivery engine and input from the forecasting module. The forecasting module provides information to the optimization to smooth the rate at which direct demand is allocated. The optimization engine receives information from statistics collector, the forecasting module and the direct demand source that it uses to produce optimization information that is used to generate bids for the direct demand. The present invention is particularly advantageous because it preserves the incentive for performance bidders to bid honestly; it eliminates adverse selection against the publisher's direct demand; and it increases the efficiency and total value produced by the auction.
The present invention also includes a number of methods including a method for allocating impressions, a method for auctioning impressions, a method for optimizing the allocation of impressions and a method for determining a plurality of multipliers.
The present invention is illustrated by way of example, and not by way of limitation in the figures of the accompanying drawings in which like reference numerals are used to refer to the same or similar elements.
A system and methods for allocating impressions using an auction that considers losing bids are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention can be practiced without some of these specific details. In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the invention. For example, the present invention is described in one embodiment below with reference to user interfaces and particular hardware.
Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic 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. An algorithm is here, and generally, conceived to be a self consistent sequence of steps 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. 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 discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” 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.
The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flash memories including USB keys with non-volatile memory or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.
The invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.
Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
Finally, the algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatuses to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein.
System Overview
The performance advertiser 102 is the computing system of an independent bidder that bids for impressions on an impression-by-impression basis. The performance advertiser 102 is coupled by signal line 112 to the network 106 for communication with the impression allocation system 108. The performance advertiser 102 is coupled to the impression allocation system 108 to send bids on impressions, ad content, advertising target information, price, or any other information related to the impression or necessary to serve the ad.
The content site 104 is a computing device for hosting a website with any type of content. The content site is a publishing website that serves web pages including ad content. The content site 104 sends ad requests to the impression allocation system 108 via network 106 and receives served ads in response. The content site 104 coupled by signal line 114 to the network 106 and thus the impression allocation system 108.
The network 106 is a conventional type, wired or wireless, and may have any number of configurations such as a star configuration, token ring configuration or other configurations known to those skilled in the art. Furthermore, the network 106 may comprise a local area network (LAN), a wide area network (WAN) (e.g., the Internet), and/or any other interconnected data path across which multiple devices may communicate. In yet another embodiment, the network 106 may be a peer-to-peer network. The network 106 may also be coupled to or includes portions of a telecommunications network for sending data in a variety of different communication protocols. In yet another embodiment, the network 106 includes Bluetooth communication networks or a cellular communications network for sending and receiving data such as via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, WAP, email, etc.
The impression allocation system 108 is coupled by signal line 118 to the network 106 for communication and interaction with the performance advertisers 102 and the content sites 104. The impression allocation system 108 receives indirect demand from the performance advertisers 102. The impression allocation system 108 receives ad requests from and serves ads to the content sites 104. The operation of the impression allocation system 108 will be described in more detail below with reference to
Impression Allocation System 108
Referring now to
The delivery engine 202 is responsible for processing impressions. The delivery engine 202 is coupled for communication with the content sites 104 via signal line 118 to receive ad calls and serve ads. The delivery engine 202 also receives indirect demand, direct demand, and optimization information. The delivery engine 202 is coupled to signal line 222 to receive the indirect demand from the performance advertisers 102, coupled to signal line 236 to receive the direct demand from the direct demand source 210, and coupled to signal line 234 to receive the optimization information from the optimization engine 208. The delivery engine 202 processes this information and selects a bid to be served as an ad. As has been noted above, the delivery engine 202 advantageously runs a modified second-bid auction to determine the winning bid. In other embodiments, the delivery engine 202 advantageously runs other types of auctions. The delivery engine 202 also uses the information from the direct demand source 210 and the optimization engine 208 to generate bids for the direct campaigns. The delivery engine 202 will be described in more detail below with reference to
The statistics collector 204 is a module for storing and processing impression information for future use. The statistics collector 204 is coupled to the delivery engine 202 by signal line 224 to receive impression information regarding auction results, ad calls received, ads served, losing bids, impression type, etc. The statistics collector 204 processes this information and stores it so that it is readily available for use by the forecasting module 206 and the optimization engine 208. The statistics collector 204 is coupled for communication with the forecasting module 206 and the optimization engine 208 by signal line 226. In one embodiment, the statistics collector 204 stores the information in a plurality of formats including a real-time format, a near-term format and a long-term format. In other embodiments, the statistics collector 204 modifies the data in these formats as additional information is received from the delivery engine 202. The statistics collector 204 will be described in more detail below with reference to
The forecasting module 206 is software and routines for providing control information to the optimization engine 208. The forecasting module 206 estimates the number of future impressions (a forecast) a campaign can expect given a set of part of the constraints and start and end times. The forecasting module 206 also estimates an available number of impressions (availability) considering competition for the same pool of impressions. By definition, the availability number is always less than or equal to the forecast number. The forecasting module 206 uses historical data received from the statistics collector 204 to predict future trends. The forecasting module 206 is coupled by signal line 226 to the statistics collector 204 to retrieve or receive any information necessary for his computations. In one embodiment, the forecasting module stores up to 30 days worth of data for nearly all targeting constraints. In another embodiment, the forecasting module 206 utilizes real-time and near-term information from the statistics collector 204. In one embodiment, the forecasting module 206 monitors for overbooking (e.g., when the availability number reaches zero). In another embodiment, the forecasting module 206 provides curves or controls to the optimization engine 208 so that the impressions are placed in the time and manner desired by the direct campaign buyer. The forecasting module 206 is also coupled by signal line 228 to the statistics collector 204 to provide updated information or corrections to the data stored by the statistic collector 204.
The optimization engine 208 is software and routines for managing campaigns and making sure that impression guarantees provided to direct demand advertisers are satisfied. More specifically, the optimization engine 208 is coupled to statistics collector 204 by signal line 226 to receive information about impressions that were won by campaigns that the optimization engine 208 is managing. The optimization engine 208 produces and sends signals that are used to generate bids by the delivery engine 202. The optimization engine 208 sends information about the priority or importance of an impression to a campaign, the type of impression for the campaign, and a multiplier for the campaign. These signals are generated from priority information, budget smoothing information, availability information, forecasts information, and historical information. The optimization engine 208 is coupled by signal line 226 to receive historical information from the statistics collector 204. The optimization engine 208 is coupled by signal line 230 to the forecasting module 206 to receive availability and forecasting information. The optimization engine 208 is coupled by signal line 232 to the direct demand source 210 to receive campaign information such but not limited to priority, media, price, competitor blocks, volume, reserve price etc. One embodiment of the optimization engine 208 will be described in more detail below with reference to
The direct demand source 210 is associated with one or more publishers and provides the information about impressions and campaigns for traditional advertisers that have a predefined relationship with the publisher. The direct demand source 210 includes ad content, campaign information, etc. The direct demand source 210 is coupled by signal line 236 to provide this information to the delivery engine 202. The direct demand source 210 is also coupled by signal line 232 to provide a smaller subset of data to the optimization engine 208. For example, the demand source 210 provides campaign information, impression information and other data to the delivery engine 202 while it only provides a more limited set of data such as priority information to the optimization engine 208.
Referring now to
The network interface module 333 is coupled to network 106 by signal line 118 and bus 320. The network interface module 333 includes ports for wired connectivity such as but not limited to USB, SD, or CAT-5, etc. The network interface module 333 links the processor 335 to the network 106 that may in turn be coupled to other processing systems. The network 106 may comprise a local area network (LAN), a wide area network (WAN) (e.g., the Internet), and/or any other interconnected data path across which multiple devices may communicate. The network interface module 333 provides other conventional connections to the network 106 using standard network protocols such as TCP/IP, HTTP, HTTPS and SMTP as will be understood to those skilled in the art. In other embodiments, the network interface module 333 includes a transceiver for sending and receiving signals using WIFI, Bluetooth® or cellular communications for wireless communication.
The processor 335 comprises an arithmetic logic unit, a microprocessor, a general purpose controller or some other processor array to perform computations and provide electronic display signals to a display device. The processor 335 is coupled to the bus 320 for communication with the other components. Processor 335 processes data signals and may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets. Although only a single processor is shown in
The memory 337 stores instructions and/or data that may be executed by processor 335. The memory 337 is coupled to the bus 320 for communication with the other components. The instructions and/or data may comprise code for performing any and/or all of the techniques described herein. The memory 337 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory or some other memory device known in the art.
In one embodiment, storage 339 stores data, information and instructions used by the delivery engine 202, the statistics collector 204, the forecasting module 206, the optimization engine 208, and the direct demand source 210. Storage 339 is a non-volatile memory or similar permanent storage device and media such as a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device known in the art for storing information on a more permanent basis. The data storage 339 is coupled by the bus 320 for communication with other components of the impression allocation system 108.
One or more of the delivery engine 202, the statistics collector 204, the forecasting module 206, the optimization engine 208, and the direct demand source 210 are software or routines executable on the processor 335. In another embodiment, one or more of the delivery engine 202, the statistics collector 204, the forecasting module 206, the optimization engine 208, and the direct demand source 210 store data that, when executed by the processor 335, causes the collectors/modules to perform the operations described below. In yet another embodiment, one or more of the delivery engine 202, the statistics collector 204, the forecasting module 206, the optimization engine 208, and the direct demand source 210 are instructions executable by the processor 335 to provide the functionality described below with reference to
Delivery Engine 202
Referring now to
The ad processor 402 handles the technical mechanics of receiving an ad call, requesting an impression to send in response, and serving up an ad in response to the received ad call. The operation of the ad processor 402 will be described for handling a single ad call; however, those skilled the art will recognize that the ad processor 402 can be scaled to handle thousands or millions of ad calls. The ad processor 402 is coupled to signal line 118 to receive an ad call. The processor 402 is also coupled to signal line 118 to serve ads. In the simplest case, the ad processor 402 receives an ad call and stores information about the ad call. For example, the information stored includes the source of the ad call, the site of the ad call, information about advertisement placement and other metrics. The ad processor 402 then issues a request to the auction module 404 for an impression to be served in response to the ad call. In response, the auction module 404 returns an impression to be served. The ad processor 404 serves the received impression and records information about the serving of the impression. The ad processor 402 also outputs on signal line 224 information about: 1) the ad call; 2) the impression that was served; 3) the serving of the ad; and 4) any error information about the serving of the ad. The ad processor 402 outputs this information to the statistics collector 204.
The auction module 404 performs an auction as will be described in more detail below and as a result of the auction identifies an impression. As has been described above, the impression identified as a result of the auction is sent to the ad processor 402 so it can be served in response to the ad call. The operation of the auction module 404 is described in more detail below with reference to
The bid collector 406 is software and routines for gathering bids and sorting them for submission to the auction module 404 in response to an auction. The bid collector 406 has an input coupled to signal line 222 to receive bids from independent bidders or performance advertisers. The bid collector 406 is also has an input coupled to signal line 236 to receive bids directly from the direct demand source 210. In one embodiment, the bid collector 406 receives information about the campaign from the direct demand source 210 to generate events. The bid collector 406 also has an input coupled to the output of the direct bid engine 408. The direct bid engine 408 generates bids based upon optimization information from the optimization engine 208. The bid collector 406 receives the bids for impressions from these sources. The bids also include information about class type to which the bids are related. In response to a request from the auction module 404 for bids meeting particular parameters, the bid collector 406 searches its storage and sends bids that match the request from the auction module 404 for consideration in the auction.
The direct bid engine 408 generates bids based upon optimization information from the optimization engine 208. In one embodiment, the direct bid engine 408 receives priority values that are used to generate bids from the optimization engine 208. The direct bid engine 408 is advantageously adaptive in response to the information from the optimization engine 208. The directive bid engine 408 is used to generate bids for campaigns from the direct demand source 210. The direct bid engine 408 advantageously generates bids with different prices based upon the information from the optimization engine 208. Depending on factors such as priority, forecasts, availability provided by the optimization engine 208, the direct bid engine 408 generates bids and sends them to the bid collector 406 for consideration in the auction. The direct bid engine 408 is coupled to signal line 234 to receive the information from the optimization engine 208. The direct bid engine 408 has an output coupled to an input of the bid collector 406.
Statistics Collector 204
Referring now to
The data collector 502 is temporary storage for information received from the delivery engine 202 on signal line 224. The data collector 502 has an input to receive information from the delivery engine 202. Since the information is received from both the ad processor 402 and the auction module 404, this information needs to be temporarily stored until it can be processed by the data modification module 504.
The data modification module 504 is responsible for processing and transferring information about impressions and the auction from the data collector 502. The data modification module 504 receives the information from the data collector 502 and stores it immediately in the real-time module 508. The data modification module 504 continues to process the information and after some processing stores it in the near-term module 510. The output of the real-time module 508 and the near-term module 510 are provided on signal line 226 to the forecasting module 206 and the optimization engine 208. These real-time and near-term modules 508, 510 are also responsive to requests for additional information from these modules 206, 208. After additional processing, the information is stored in the off-line module 512. For example, information that is immediately available about ad calls, corresponding served ads and information about completed auctions is stored in the real-time module 508. After additional processing by the data modification module 504, the specific ad call, corresponding served ad, and corresponding auction performed to identify the ad are grouped together and stored in the near-term module 510. After yet further processing by the data modification module 504, additional information about whether the impression was actually delivered and presented or other information is added to the record and the information is moved to off-line module 512. The data modification module 504 is also coupled to signal line 228 to receive corrections or modifications to the data from the forecasting module 206. Based on input information on signal line 228, the data modification module 504 modifies the information in the off-line module 512. In one embodiment, the off-line module 512 is accessible by APIs and other user interfaces and tools. The data modification module 504 also communicates with the database 506 to store information related to each of the modules 508, 510 and 512.
Optimization Engine 206
Referring now to
The multiplier calculation module 602 is coupled to receive classification information from the classification module 606, priority information from the priority module 608, forecasting and availability information from the budget smoothing module 610 and historical information from the data retrieval module 604. The multiplier calculation module 602 uses this information to generate a set of parameters that are sent to and used by the direct bid engine 408 of the delivery engine 202 to generate bids for direct campaigns. In one embodiment, the set of parameters includes a priority value and cutoff, an alpha multiplier value, a reserved value and a sell through value. The multiplier (α) is a number greater than one. This mitigates cherry picking by increasing the second highest value. The multiplier (α) is also influenced by sell-through rates. The higher the sell-through, the larger the multiplier (α). This controls under delivery risk. Under delivery is also mitigated by sell-through rate. If a publisher's inventory is sold through at a certain rate, indirect demand is bypassed. In one embodiment, the multiplier calculation module 602 is adapted a dynamic based upon information received from the data retrieval module 604, forecast availability information received from the budget smoothing module 610 and classification information received from the classification module 606. The output of the multiplier calculation module 602 is provided on signal line 234 to the delivery engine 202. The operation of the multiplier calculation module is described in more detail below with reference to
The data retrieval module 604 has an input coupled to signal line 226 to retrieve data from the statistics collector 204. The output of the data retrieval module 604 is provided as input to the multiplier calculation module 602. The data retrieval module 604 retrieves any historical information about impressions, auctions, bids, ad calls or served ads needed by the multiplier calculation module 602 from the real-time module 508 or the near-term module 510 of the statistics collector module 204.
The classification module 606 has an input coupled to signal line 232 to receive information about campaigns from the direct demand source 210. The classification module 606 retrieves any necessary campaign information from the direct demand source 210. In particular, the classification module 606 provides the classification type of the impression and campaign to the multiplier calculation module 602.
The priority module 608 also has an input coupled to signal line 232 to retrieve priority information about the campaign from the direct demand source 210. In one embodiment, the priority module 608 provides the priority value and a cutoff value to the multiplier calculation module 602. In another embodiment, the priority value and cutoff were calculated by the multiplier calculation module 602 and the priority module 608 provides the information that allows the multiplier calculation module 602 to make that determination. The output of the priority module 608 is coupled to an input of the multiplier calculation module 602 to provide this priority information.
The budget smoothing module 610 has an input coupled to signal line 230 to retrieve forecast and availability information from the forecasting module 206. The budget smoothing module 610 provides forecast and availability information that is used by the multiplier calculation module 602 to modify the parameters provided to the delivery engine 202. In one embodiment, the budget smoothing module 610 is provided with a set of curves that allow for the control of placement of impressions over time. For example, there may be a set of three curves, one conservative, one normal and one aggressive. Each of the curves will cause the impressions from the direct campaign to be placed more conservatively or aggressively. These curves are provided by the budget smoothing module 610 to the multiplier calculation module 602 so that it may modify the parameters so that the ads are placed in accordance with the input curve. In another embodiment, the budget smoothing module 610 provides an administrator with control on the placement of impressions. In effect, the budget smoothing module 610 includes settings that can be modified at different times to affect the placement rate of impressions. This acts as a control dial which the administrator can turn up and increase impression placement if the rate of placement is below that for the campaign or alternatively can be turned down to decrease the ad placement rate if the current placement rate for the campaign is above that of the campaigns goals.
Methods
Referring now to
Referring now to
Auctions
The present invention advantageously uses an auction mechanism for selling items, in particular impressions. The auction mechanism includes the following constraints. First, the auction needs to be strategy proof such that the performance advertisers are incentivized to bid honestly and accurately. Secondly, the auction needs to be free of adverse selection (“cherry picking”) such that the performance advertisers do not secure all the “good” impressions leaving only the less valuable impressions for the traditional advertisers or direct demand.
The present invention provides an auction mechanism for addressing these concerns. The auction mechanism generates bids based on losing bids. More specifically, the auction mechanism generates bids that are a function of losing bids and a function homogeneous of degree one (multiplicatively scalable). Still more specifically, the auction mechanism generates bids based on a second highest bid. In the context of impressions, the auction mechanism generates bids for publishers that are bidding on behalf of traditional advertisers. Since the bids generated for publishers are based upon losing bids or the second highest bid, they preserve the incentive of performance bidders to bid honestly and accurately (preserve the strategy proof characteristic). For example, the auction mechanism generates bids that are a multiplier (α) times a second highest value (V2) or α·V2. For this discussion we assume that value (V) is the product of a common factor (C) and a match factor (M) so that V=C·M, where C=a common factor or information known to everyone and M=a match factor that is a private value or specific information known by the bidder. In this case, the mechanism determines that that the publisher wins the auction if
For this discussion, we assume that there is a power law distribution (80/20 rule) of the match factor. This implies that, compared to the method of setting aside a fraction of impressions, this mechanism benefits all participants. For the seller, it raises the average price paid by performance advertisers by a factor of alpha. For the performance bidders, it raises the profits earned by allowing those advertisers to acquire their most valuable impressions. And for the traditional advertisers, it eliminates adverse selection, because the publisher's bid rises when the performance advertisers' bids rise. Additionally, this auction is immune to manipulation by low, intentionally losing bids submitted by performance advertisers, making the mechanism strategy-proof in a more extended sense than is usual for that term.
The present invention includes at least three embodiments for implementing the above auction mechanism which are described below with reference to
Referring now to
A second embodiment of the method 1000 for auctioning impressions is described below with reference to
Referring now to
A third embodiment of the method 1100 for auctioning impressions is described below with reference to
Referring now also to
Referring now to
Referring now to
Referring now to
The foregoing description of the embodiments of the present invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the present invention be limited not by this detailed description, but rather by the claims of this application. As will be understood by those familiar with the art, the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Likewise, the particular naming and division of the modules, routines, features, attributes, methodologies and other aspects are not mandatory or significant, and the mechanisms that implement the present invention or its features may have different names, divisions and/or formats. Furthermore, as will be apparent to one of ordinary skill in the relevant art, the modules, routines, features, attributes, methodologies and other aspects of the present invention can be implemented as software, hardware, firmware or any combination of the three. Also, wherever a component, an example of which is a module, of the present invention is implemented as software, the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way known now or in the future to those of ordinary skill in the art of computer programming. Additionally, the present invention is in no way limited to implementation in any specific programming language, or for any specific operating system or environment. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the present invention, which is set forth in the following claims.
Number | Name | Date | Kind |
---|---|---|---|
7624083 | Micaelian et al. | Nov 2009 | B2 |
8086518 | Maor | Dec 2011 | B1 |
8326693 | Chan | Dec 2012 | B1 |
20020082971 | Le et al. | Jun 2002 | A1 |
20030055729 | Bezos | Mar 2003 | A1 |
20050197950 | Moya | Sep 2005 | A1 |
20080052219 | Sandholm et al. | Feb 2008 | A1 |
20100100414 | Lin et al. | Apr 2010 | A1 |
20100114716 | Heilig | May 2010 | A1 |
20100161408 | Karson et al. | Jun 2010 | A1 |
20100198695 | Muthukrishnan et al. | Aug 2010 | A1 |
20100268609 | Nolet | Oct 2010 | A1 |
20120030034 | Knapp | Feb 2012 | A1 |
20150066740 | DiCarlo | Mar 2015 | A1 |
20170024778 | Knapp | Jan 2017 | A1 |
Entry |
---|
Beck, Marissa, Milgrom, Paul, “Adverse-Selection-Free Auctions for Internet Display Adverting”, Aug. 18, 2011, 29 pages. |
Ghosh et al., Bidding for Representative Allocations for Display Advertising, WINE '09, Proceedings of the 5th International Workshop on Internet and Network Economics, 2009, 17 pgs. |
International Search Report and Written Opinion for PCT/US12/42074, dated Sep. 6, 2012, 13 pgs. |
Number | Date | Country | |
---|---|---|---|
20120323701 A1 | Dec 2012 | US |