WO2000058878A2 - Evolving advertisements via an evolutionary algorithm - Google Patents
Evolving advertisements via an evolutionary algorithm Download PDFInfo
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- WO2000058878A2 WO2000058878A2 PCT/EP2000/002596 EP0002596W WO0058878A2 WO 2000058878 A2 WO2000058878 A2 WO 2000058878A2 EP 0002596 W EP0002596 W EP 0002596W WO 0058878 A2 WO0058878 A2 WO 0058878A2
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- ads
- effectiveness
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
Definitions
- This invention relates to the field of advertising, and in particular to the use of evolutionary algorithms in the generation and evaluation of alternative advertisements.
- the proposed ad is released publicly to a small test market, for continued feedback.
- surveys are often conducted to determine the ad's effectiveness on a random sample of possible viewers. An ad's effectiveness is typically assessed with regard to retention, appeal, and any actions taken in response to the ad.
- the surveys are also conducted periodically, to determine the effectiveness of a continuing ad campaign, and may address the detrimental factors associated with a continually repeated ad, such as viewer boredom or annoyance
- the characteristics of an ad such as content, color, action, placement, duration, and so on, have a significant effect on the effectiveness of the ad.
- the cause and effect relationship is not easily described or quantified. Correlations are assumed to exist between particular ad characteristics and ad effectiveness, and each advertisement developer uses those characteristics that he or she believes are correlated to potential success. The validity of the assumed Correlations, however, cannot be determined directly, nor can the effects of cross-Correlations.
- An evolutionary algo ⁇ thm that effects a directed trial and error search of alternative advertisement characteristics.
- An initial population of sample advertisements is provided, and the characteristics of each advertisement is encoded as a set of genes associated with each member of the population.
- the effectiveness of each member is assessed, using for example the number of times an Internet user clicks on each advertisement.
- the members of the population generate plurality of offsp ⁇ ng ads that inherit charactenstics from their parents.
- the members of the population that exhibit more effectiveness than others are preferentially selected for offsp ⁇ ng generation. By continued preferential selection of parents having more effectiveness than others, the likelihood of generating offsp ⁇ ng that have a higher degree of effectiveness increases.
- the propagation of particular charactenstics or combinations of characteristics in this evolutionary process provides an indication and ve ⁇ fication of those ad charactenstics that produce effective results.
- Fig. 1 illustrates an example web page containing a va ⁇ ety of advertisements.
- Fig. 2 illustrates an example evolution of an advertisement in accordance with this invention.
- Fig. 3 illustrates an example evolution of advertisements having charactenstics that are encoded as chromosomes of an evolutionary algonthm in accordance with this invention.
- Fig. 4 illustrates an example flow diagram for evaluating and evolving advertisements in accordance with this invention.
- Fig. 1 illustrates an example web page 100 containing a va ⁇ ety of advertisements 110-170.
- Each advertisement 110-170 has charactenstics that distinguish it from every other advertisements 110-170.
- Each of these advertisements is vying for a viewer's attention, and the designer of each advertisement has selected charactenstics that he or she believes will catch the viewer's attention better than other charactenstics.
- the designer of ad 110 chose a heavy bold type, while the designer of ad 120 chose a lighter scnpt type.
- the designer of ad 110 also chose different words than the designer of ad 120. Heretofore, it would have been difficult to ascertain whether the text content "Look Here! in ad 110 is preferable to the text "Buy Now'" in ad 120. If it is determined, in some manner, that ad 120 attracted more viewers than ad 110, it would be difficult to ascertain whether it was the scnpt type charactenstic, the text content characte ⁇ stic, or a combination of the two charactenstics that led to ad 120 being more effective.
- Ad 170 has a revolving banner 170A and an associated figure 170B that may or may not affect its effectiveness in terms of attracting a viewer's attention.
- Ads 140 and 150 contain no text content, while ad 130 has a predominant figure 130A and diminutive text 130B
- the Internet environment provides a unique opportunity to evaluate the effectiveness of advertisements.
- the number of times an advertisement is selected by the viewer is used as a measure of effectiveness of the advertisement, and, based on this measure, the effectiveness of particular charactenstic or combinations of charactenstics are assessed.
- this measure of effectiveness is "noisy", in that it may not truly be a measure of the ad's appeal in all cases.
- a viewer may, for example, be looking specifically for airline tickets.
- the ad 170 may be the only ad on the web page 100 that addresses airline tickets, and its selection by the viewer will be unrelated to its attention getting abilities.
- Substantially similar advertisements with different charactenstics can be provided to the aforementioned millions of potential viewers weekly, daily, or even hourly.
- the alternative advertisements can be automatically generated using, for example, a rules or knowledge based system, or a simple algonthm.
- vanations of an advertisement are provided to the potential viewers, and the number of times each va ⁇ ation is selected is used as the measure of effectiveness for each vanation. Subsequent vanations are generated based upon the measure of effectiveness of pnor vanations.
- a class of algo ⁇ thms termed evolutionary algonthms, have been found to be particularly effective in the determination of the most effective combinations of charactenstics to maximize their effectiveness, without requinng a specific determination of each charactenstic's individual or combmatonal effectiveness.
- an evolutionary algonthm is used to direct the generation and evaluation of alternative advertisement charactenstics.
- Evolutionary algonthms operate via an iterative offsp ⁇ ng production process
- Evolutionary algo ⁇ thms include genetic algo ⁇ thms, mutation algo ⁇ thms, and the like.
- certain attnbutes, or genes are assumed to be related to an ability to perform a given task, different combinations of genes resulting in different levels of effectiveness for performing that task.
- the evolutionary algonthm is particularly effective for problems wherein the relation between the combination of attnbutes and the effectiveness for performing the task does not have a closed form solution.
- the offsp ⁇ ng production process is used to determine a particular combination of genes that is most effective for performing a given task, using a directed t ⁇ al and error search.
- a combination of genes, or attnbutes is termed a chromosome.
- a reproduction-recombination cycle is used to propagate generations of offspnng.
- Members of a population having different chromosomes mate and generate offsp ⁇ ng
- These offsp ⁇ ng have attnbutes passed down from the parent members, typically as some random combination of genes from each parent.
- the individuals that are more effective than others in performing the given task are provided a higher opportunity to mate and generate offspring.
- the individuals having preferred chromosomes are given a higher opportunity to generate offspring, in the hope that the offspring will inherit whichever genes allowed the parents to perform the given task effectively.
- the next generation of parents are selected based on a preference for those exhibiting effectiveness for performing the given task. In this manner, the number of offspring having attributes that are effective for performing the given task will tend to increase with each generation. Paradigms of other methods of generating offspring, such as asexual reproduction, mutation, and the like, are also used to produce generations of offspring having an increasing likelihood of improved abilities to perform the given task.
- the population consists of member advertisements having chromosomes that reflect different ad characteristics. Some combinations of ad characteristics are more effective for attracting a viewer's attention than other combinations.
- the effectiveness of the offspring ads for attracting viewer attention is likely to increase.
- Fig. 2 illustrates an example evolution of an advertisement 201 in accordance with this invention.
- An offspring of ad 201 is illustrated as ad 211.
- the offspring ad 211 has characteristics of the parent ad 201, such as its shape, message content, and font style. It differs from the parent ad 211 in the size of the text, and thus could be termed a mutation of the parent ad 211.
- Ad 212 illustrates an offspring ad that is the combination of characteristics from two parent ads 201 and 291; in this example, the parent ad 291 is assumed to have an italics characteristic, such that the offspring ad 212 inherits its shape, message content, font style, and text size characteristics from its parent 201, and its italics characteristic from its parent 291. As in natural evolution, some of the characteristics of an offspring may differ from both parents, or may be a blending of the characteristics of each parent. For example, ad 292 may have an oval shape, and the offspring 222 of ad 292 (oval) and ad 211 (rectangle) is illustrated in Fig. 2 as having a square shape.
- Ad 241 illustrates the inheritance of its message content from a parent ad 293, and its square appearance from ad 231, as it was passed down from ad 222 via ad 232.
- Ad 251 illustrates the inheritance of a graphic message content from ad 294 and a shape from ad 242; although ad 251 is a descendant of the original ad 201, it exhibits few, if any, of the characteristics of ad 201.
- the likelihood of each charactenstic being passed on from generation to generation is dependent upon the success rate of p ⁇ or ads that have this charactenstic, as discussed below
- the CHC algonthm is a genetic algonthm that employs a "survival of the fittest" selection, wherein only the better performing individuals, whether parent or offsp ⁇ ng, are used to generate subsequent offspnng.
- the CHC algonthm avoids incestuous matmgs, matings between individuals having very similar attnbutes.
- each evolutionary algonthm exhibits pros and cons with respect to the schema used to effect an iterative solution, and the particular choice of evolutionary algonthm for use in this invention is optional.
- a chromosome is defined to contain the characte ⁇ stic aspects of advertisements.
- the charactenstics may be any feature or attnbute that an advertising designer deems relevant to the attention getting abilities of an ad, and may include, for example, colors, shape, texture, content, animation, and so on.
- the charactenstics may also be indirect representations of sets of charactenstics, or the use of particular design rules and guidelines.
- FIG. 3A illustrates an example set of charactenstics that consists of the message content 391, the text font 392, the size of the text 393, and whether the text is italicized 394.
- Illustrated in Fig. 3B are 8 member advertisements 301-308.
- Each of the member ads 301-308 are charactenzed using these charactenstics 391-394.
- Fig. 3F contains a key, or mapping, of individual charactenstics to the code used to encode the characte ⁇ stic.
- ad 301 contains the message "Buy Now!; the message table of Fig. 3F shows a code 381 of "00" associated with the message 381' "Buy Now!.
- the message field 391 of the chromosome 301C corresponding to the ad 301 contains the encoding "00" in Fig. 3A.
- Ads 302 and 304 contain the message "Look Here!, and are encoded with "01” in the message field 391, because Fig. 3F shows the code 381 of "01” associated with the message 381' "Look Here!.
- the text of ad 301 is presented in a script font.
- Fig. 3F illustrates the code 382 of "010” corresponds to a font characteristic 381' of "script”.
- the font field 392 of the chromosome 301C corresponding to the ad 301 contains the code "010".
- the font size 393 of the ad 301 is encoded as "10", corresponding to a 14 point pitch characteristic, as defined in the mapping 393-393' of Fig. 3F.
- Each characteristic of the eight example ads 301-308 are similarly encoded.
- a measure of effectiveness 301E-308E is determined for each.
- the measure of effectiveness is based upon the number of times each ad was selected within a given time period, and is normalized to the total number of selections during that period. In the example of Fig. 3A, the measure of effectiveness is given as the total number of times each ad is selected per thousand total selections.
- Ads 304 and 302 are illustrated as having the highest (201) 304E and lowest (9) 302E measures of effectiveness, respectively.
- the chromosomes 301C-308C of each ad 301-308 are pairwise coupled 350 to produce offspring chromosomes 311C-318C.
- each offspring inherits all of the genes that are common to both parents, and a random selection of the genes that differ in each parent.
- each bit value of each field of the chromosome constitutes a gene.
- each of the offspring 317C, 318C have identical 1 st , 5 th , and 8 th genes.
- a random number of differing genes are switched; in this example, the 4 th 361 and 7 th 362 genes are switched in each offspring 317C, 318C. That is, chromosome 317C is identical to 307C except in the 4 th and 7 th genes, and chromosome 318C is also identical to 308C except in the 4 th and 7 th genes.
- Each of the underlined gene values in Figs. 3B and 3D indicate a randomly switched gene value.
- the offspring chromosome values 311C-318C correspond to new offspring ads 311-318. That is, for example, chromosome 317C (code 01-000-00-0) corresponds to a "Look Here! Message (code 01), an Ariel Font (code 000), an 8 point Size (code 00), and no Italics (code 0), as illustrated by ad 317 in Fig. 3B. Chromosome 318C (code 00-110-11-0) corresponds to a "Buy Now! Message (00), a Lucita Font (110), a 12 point Size (11), and no Italics (0), illustrated by ad 318 in Fig. 3B.
- each of the other offspring chromosomes are similarly decoded to their corresponding offspring ad.
- the offspring ads 311-318 are presented to potential viewers, and evaluated for effectiveness in the same manner as the member ads 301-308.
- the normalized scores 311E- 318E corresponding to each offspring are presented in the dashed boxes of Fig. 3B.
- Fig. 3C illustrates the selection of the eight best performing ads for subsequent pairwise mating 351.
- the first ad in Fig. 3C is ad 315, which had an effectiveness rating of 208; the next ad in Fig. 3C is ad 304, which had an effectiveness rating of 201; and so on.
- the lowest scoring member in Fig. 3C has an effectiveness rating of 73. All prior members and offspring with an effectiveness rating less than 73 are not selected for propagation of offspring.
- each generation is produced from a better performing gene pool, thereby increasing the likelihood of producing a high performing offspring.
- the ads are ordered in Fig. 3C so as to provide a high degree of diversity between the mating parent pairs.
- ads 315 and 304 are selected for mating because their chromosomes are substantially different, having only three gene values in common (2 nd , 4 th , and 6 th ).
- Illustrated in Fig. 3D the offspring chromosome 321C-328C are produced from the members of Fig. 3C, in the same manner as discussed with regard to Fig. 3B.
- the new offspring ads 321-328 corresponding to the offspring chromosomes 321C-328C are presented to potential viewers, and evaluated for effectiveness in the same manner as the prior ads 301-308, 311-318.
- the normalized scores 321E-328E corresponding to each offspring are presented in the dashed boxes of Fig. 3D.
- Fig. 3E illustrates the selection of the eight best performing ads for subsequent pairwise mating.
- the average effectiveness of this third generation of parent members is substantially higher than each of the two previous generations.
- the occurrence of ads having a small Size characteristic is rare in this third generation.
- the gene 368 that distinguishes between the smaller (8-10pt) and larger (12-14pt) size text has substantially converged to a value of 1, corresponding to the larger (12-14pt) Size characteristic.
- Five of the eight high performing chromosomes have a Size characteristic (10) of 14pt; two have a Size characteristic (11) of 12pt; and one has a Size characteristic (01) of lOpt.
- the encoding of the Size characteristic uses a Grey-code encoding, wherein adjacent values differ by only one bit. Such an encoding helps to insure that the offspring are near in value to the parent for those characteristics that have an ordered sense, such as size and intensity characteristics.
- FIG. 4 illustrates an example flow diagram for the evolutionary generation of advertisements to optimize their attention-getting effectiveness.
- the characteristics that will be evaluated and varied are identified, as well as the manner in which the characteristics will be encoded as genes of a chromosome.
- the initial population of ads is defined; and, at 430, the characteristics of these ads are encoded into chromosomes for future offspring generation.
- the desired characteristics could be encoded first, then the ads having these characteristics drawn or created automatically.
- the effectiveness of using each ad is determined.
- a common effectiveness measure for the effectiveness of an ad is based on the number of times the ad is selected for further information.
- Alternative measures would be common to one of ordinary skill in the art in light of this disclosure. For example, the time that a user remains on a page that contains the ad may be used as an indication that the ad has attracted the user's attention, and used in lieu of or in addition to the ad selection measure.
- the number of times a viewer purchased an item via a link through the ad, or the dollar amount of such a purchase could be used independent of, or in conjunction with, the number of times the ad is selected.
- a purchase is given a larger effectiveness value than a mere selection, and the ad's value is based on a sum of the effectiveness values.
- the value of the measure of effectiveness may include a weighting factor that depends upon a demographic classification of the viewer that selects the ad.
- the weighting factor itself may be controlled by a gene or set of genes, as well as other factors that control how demographics influence how ads are generated.
- the loop 450-472 performs the evolutionary process.
- offspring ad characteristics are generated from the characteristics of the current members of the population.
- the CHC algorithm is used.
- Parent ads having maximum diversity of characteristics are selected to generate a pair of offspring ads, as discussed with regard to Fig. 3C.
- Offspring ads having the offspring ad characteristics are generated at 455. This ad generation may be an automated process, a manual process, or a combination of both. For example, a change of font style may be effected automatically, but a change of text size or message content may require more than a direct substitution, due to other constraining factors or artistic considerations.
- Each of the offsp ⁇ ng ads is evaluated, at 460, using the same measure of effectiveness that was used for the o ⁇ ginal members at 440. For example, the offspnng ads may be displayed on va ⁇ ous web pages for a week, and the number of selections counted for each ad.
- space for one advertisement is allocated on a selected web page
- a different offspnng ad is placed in the allocated space.
- Each offspnng ad's effectiveness is measured by the number of times the ad is selected after a predetermined number of viewer accesses to the web page containing the ad. The predetermined number of accesses is determined based on the degree of evaluation accuracy desired. Taking additional samples reduces the noise associated with the measure and increases the reliability of the measure for companson purposes. Conversely, taking additional samples requires additional evaluation time per generation of ads.
- common engmeenng tradeoff analysis techniques and statistical sampling techniques are applied to determine the appropnate evaluation sample size and/or evaluation duration. It is important to note that the evaluation techniques employed must be such that the results of an evaluation of one generation of ads is comparable to the evaluation results of each of the p ⁇ or generations of ads.
- the selection of the next generation of parent ads is effected at 470. Any offspnng ad that has a better measure of effectiveness than one of any of the parent ads replaces that parent ad, and becomes a parent ad in the next generation, as the program loops back to 450 to generate new offspnng ads. If, at 472, the process has converged, or the process is terminated by, for example, a time-out signal, or a user interrupt, the evolutionary process ceases. Optionally, at 476, the entire process may be repeated, to search along a new evolutionary path In a preferred embodiment, mutations are introduced to all remaining member ads except the best performing member, and the entire process is repeated, via 440.
- the best performing ad of the remaining member ads is selected as the best solution found, at 490.
- the selected best performing ad, or set of better performing ads is used for subsequent "production" advertising, without the burden of data collection and effectiveness evaluations. Recognizing the transient nature of viewer preferences, and the adverse effects of repetitive exposure, the evolutionary ad generation process of Fig 4 is pe ⁇ odically repeated, to assure that the identified better performing ads are still performing better, and to potentially improve each ad's effectiveness by introducing changes to alleviate the effects of boredom. j ⁇
- Fig. 5 illustrates an example block diagram of a system for providing evolving advertisements/An advertisement 501 is characterized 510 to form a chromosome 501C.
- the advertisement 501 is also evaluated 550 to provide a measure of effectiveness 551 associated with the advertisement 501, and correspondingly, the advertisement chromosome 501C.
- a number of advertisements 501 are similarly processed.
- the evolutionary algorithm device 540 collects the measure of effectiveness 551 associated with each chromosome 501 C, and produces a next generation chromosome 51 IC, based on the measure of effectiveness of each of the advertisements, as discussed above.
- the advertisement creator 580 which may be human, machine, or combination of both, creates a next generation ad 511 based on the characteristics of the next generation chromosome 51 IC.
- the next generation ad 511 replaces the original ad 501, which is the characterize 510 and evaluated 550, as above.
- the evaluator 550 includes a presenter 552 that presents the ad 501 to one or more viewers, and a means for determining a user reaction to the ad 501. In a preferred embodiment, the number of times a user selects the ad is counted 554. Optionally, other counters 556 and measuring devices may be coupled to the evaluator 550 to enhance the quality or significance of the measure of effectiveness, as discussed above. The counts from the counters 554, 556 are processed by the measure of effectiveness generator 558 to produce the measure of effectiveness 551.
- a 'computer program' is to be understood as any software product stored on a computer-readable medium, downloadable via a network such as the Internet, or marketable in any other manner.
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
BR0005552-2A BR0005552A (en) | 1999-03-26 | 2000-03-22 | Process to develop a preferred ad population, computer program to develop a preferred ad population, and an evolutionary ad system |
JP2000608301A JP2003529116A (en) | 1999-03-26 | 2000-03-22 | Ad evolution with evolution algorithms |
KR1020007013341A KR20010052410A (en) | 1999-03-26 | 2000-03-22 | Evolving advertisements via an evolutionary algorithm |
EP00920561A EP1183630A2 (en) | 1999-03-26 | 2000-03-22 | Evolving advertisements via an evolutionary algorithm |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US27765099A | 1999-03-26 | 1999-03-26 | |
US09/277,650 | 1999-03-26 |
Publications (2)
Publication Number | Publication Date |
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WO2000058878A2 true WO2000058878A2 (en) | 2000-10-05 |
WO2000058878A8 WO2000058878A8 (en) | 2001-11-15 |
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PCT/EP2000/002596 WO2000058878A2 (en) | 1999-03-26 | 2000-03-22 | Evolving advertisements via an evolutionary algorithm |
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Country | Link |
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EP (1) | EP1183630A2 (en) |
JP (1) | JP2003529116A (en) |
KR (1) | KR20010052410A (en) |
CN (1) | CN1378672A (en) |
BR (1) | BR0005552A (en) |
WO (1) | WO2000058878A2 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2377773A (en) * | 2001-03-21 | 2003-01-22 | Thomas Francis Kennett | Using an algorithm to optimise advertisements and web pages |
EP1969504A1 (en) * | 2005-12-29 | 2008-09-17 | 3M Innovative Properties Company | Systems and methods for designing experiments |
US9519916B2 (en) | 2009-01-07 | 2016-12-13 | 3M Innovative Properties Company | System and method for concurrently conducting cause-and-effect experiments on content effectiveness and adjusting content distribution to optimize business objectives |
US11386318B2 (en) * | 2016-01-05 | 2022-07-12 | Evolv Technology Solutions, Inc. | Machine learning based webinterface production and deployment system |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
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CN100373942C (en) * | 2004-06-25 | 2008-03-05 | 中国科学院软件研究所 | Thin terminal oriented multi-video stream display method and system |
KR100892847B1 (en) * | 2007-05-29 | 2009-04-10 | 엔에이치엔(주) | Method and system supporting public opinion according to advertisement performance |
AU2008272900B9 (en) | 2007-07-03 | 2012-07-26 | 3M Innovative Properties Company | System and method for generating time-slot samples to which content may be assigned for measuring effects of the assigned content |
KR20100039875A (en) | 2007-07-03 | 2010-04-16 | 쓰리엠 이노베이티브 프로퍼티즈 컴파니 | System and method for assigning pieces of content to time-slots samples for measuring effects of the assigned content |
JP2010250827A (en) * | 2009-04-16 | 2010-11-04 | Accenture Global Services Gmbh | Touchpoint customization system |
JP5887816B2 (en) * | 2011-10-11 | 2016-03-16 | 凸版印刷株式会社 | ADVERTISEMENT EFFECT MEASUREMENT DEVICE, ADVERTISEMENT EFFECT MEASUREMENT METHOD, AND COMPUTER PROGRAM |
CN109583952B (en) * | 2018-11-28 | 2022-03-22 | 深圳前海微众银行股份有限公司 | Advertisement case processing method, device, equipment and computer readable storage medium |
-
2000
- 2000-03-22 BR BR0005552-2A patent/BR0005552A/en not_active IP Right Cessation
- 2000-03-22 WO PCT/EP2000/002596 patent/WO2000058878A2/en not_active Application Discontinuation
- 2000-03-22 JP JP2000608301A patent/JP2003529116A/en active Pending
- 2000-03-22 EP EP00920561A patent/EP1183630A2/en not_active Withdrawn
- 2000-03-22 KR KR1020007013341A patent/KR20010052410A/en not_active Application Discontinuation
- 2000-03-22 CN CN00800911A patent/CN1378672A/en active Pending
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Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2377773A (en) * | 2001-03-21 | 2003-01-22 | Thomas Francis Kennett | Using an algorithm to optimise advertisements and web pages |
EP1969504A1 (en) * | 2005-12-29 | 2008-09-17 | 3M Innovative Properties Company | Systems and methods for designing experiments |
EP1969504A4 (en) * | 2005-12-29 | 2010-11-10 | 3M Innovative Properties Co | Systems and methods for designing experiments |
US9519916B2 (en) | 2009-01-07 | 2016-12-13 | 3M Innovative Properties Company | System and method for concurrently conducting cause-and-effect experiments on content effectiveness and adjusting content distribution to optimize business objectives |
US11386318B2 (en) * | 2016-01-05 | 2022-07-12 | Evolv Technology Solutions, Inc. | Machine learning based webinterface production and deployment system |
US20220351016A1 (en) * | 2016-01-05 | 2022-11-03 | Evolv Technology Solutions, Inc. | Presentation module for webinterface production and deployment system |
US11803730B2 (en) | 2016-01-05 | 2023-10-31 | Evolv Technology Solutions, Inc. | Webinterface presentation using artificial neural networks |
Also Published As
Publication number | Publication date |
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WO2000058878A8 (en) | 2001-11-15 |
BR0005552A (en) | 2001-01-30 |
KR20010052410A (en) | 2001-06-25 |
EP1183630A2 (en) | 2002-03-06 |
JP2003529116A (en) | 2003-09-30 |
CN1378672A (en) | 2002-11-06 |
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