Methods, systems, and media for managing online advertising campaigns based on causal conversion metrics

Abstract

Methods, systems, and media for managing online advertising campaigns based on causal conversion metrics are provided. In some embodiments, the method comprises: receiving conversion information corresponding to test group including consumers that were presented with an advertisement using an advertising channel; receiving advertisement viewability information indicative of a probability that each of the consumers viewed the advertisement; determining that a subset of the consumers did not view the advertisement based on the probability; placing the consumers into a control group and a test group based on the probability corresponding to each of the consumers; calculating a causal conversion metric based on a comparison of the conversion information corresponding to consumers of the control group and conversion information corresponding to consumers of the test group; and determining whether to place an advertisement using the advertising channel based on the causal conversion metric.

What is claimed is: 
     
         1 . A method for placing advertisements, the method comprising:
 placing, using a hardware processor, a plurality of consumers into one of a control group and a test group based on viewability information indicative of a likelihood that each of the plurality of consumers viewed a content item based on a position of the content item with respect to a viewport presented by one of a plurality of first computing devices, wherein the viewability information was received by monitoring code loaded in association with the content item loaded as part of a page by a browser executed by one of the plurality of first computing devices corresponding to that consumer from the plurality of consumers;   calculating, using the hardware processor, a causal conversion metric for a content source based on a comparison of conversion information corresponding to consumers of the control group and the conversion information corresponding to consumers of the test group;   determining, using the hardware processor, in what proportion content items are to be placed using each of a plurality of content sources based at least in part on the causal conversion metrics; and   causing, using the hardware processor, the content source to be used to present content items to a portion of the plurality of consumers associated with a plurality of additional computing devices based on the proportion and the one or more parameters.   
     
     
         2 . The method of  claim 1 , wherein ones of the plurality of consumers having corresponding viewability information indicating that the one of the plurality of consumers was likely to not have viewed the content item are included in the control group and ones of the plurality of consumers having corresponding viewability information indicating that the one of the plurality of consumers was likely to have viewed the content item are included in the test group. 
     
     
         3 . The method of  claim 1 , wherein inclusion of a consumer into a group is based on whether the content item was either viewable or not viewable by the consumer. 
     
     
         4 . The method of  claim 1 , wherein the content item was placed using the content source as part of a campaign according to an initial allocation amongst a plurality of content sources including the content source, and wherein the method further comprises:
 receiving a budget for a particular portion of the campaign; and   allocating the budget among the plurality of content sources based on the causal conversion metric corresponding to the content source and the causal conversion metrics corresponding to the plurality of content sources other than the content source.   
     
     
         5 . The method of  claim 4 , further comprising transmitting instructions to at least one second computing device indicating in what proportion the content items for the campaign are to be placed using each of the plurality of content sources based at least in part on the causal conversion metrics and the budget, wherein the proportion is based at least on the allocation of the budget. 
     
     
         6 . The method of  claim 4 , wherein allocating the budget further comprises determining whether to place additional content items on the content source or one of the plurality of content sources based on a comparison of the causal conversion metric corresponding to the content source and the causal conversion metrics corresponding to the plurality of content sources. 
     
     
         7 . The method of  claim 1 , further comprising causing the monitoring code to be loaded in association with the content items presented as part of a campaign such that the monitoring code determines the viewability information indicative of a probability that the content item was viewable to a consumer based at least in part on the amount of time that the content item was within a viewport of the browser. 
     
     
         8 . The method of  claim 1 , wherein calculating the causal conversion metric further comprises:
 calculating a first conversion rate for the control group based on the conversion information corresponding to consumers included in the control group;   calculating a second conversion rate for the test group based on the conversion information corresponding to consumers included in the test group;   comparing the first conversion rate to the second conversion rate; and   calculating a causal conversion rate based on the comparison.   
     
     
         9 . The method of  claim 1 , wherein calculating the causal conversion metric further comprises:
 receiving cost information corresponding to the cost of presenting each of the plurality of users with the content item;   calculating a first return on investment for the control group based on the conversion information corresponding to consumers included in the control group and the cost information corresponding to consumers included in the control group;   calculating a second return on investment for the test group based on the conversion information corresponding to consumers included in the test group and the cost information corresponding to consumers included in the test group;   comparing the first return on investment to the second return on investment; and   calculating a causal return on investment based on the comparison.   
     
     
         10 . The method of  claim 1 , further comprising:
 categorizing the plurality of consumers into a subset of consumers based on a contextual category associated with the content item that was presented;   calculating a third causal conversion metric for the subset of consumers;   comparing the third causal conversion metric to the causal conversion metric; and   determining whether to place one or more content items on pages in the contextual category using the content source based on the comparison.   
     
     
         11 . A system for placing advertisements, the system comprising:
 a memory; and   a hardware processor that, when executing computer-executable instructions stored in the memory, is configured to:
 place a plurality of consumers into one of a control group and a test group based on viewability information indicative of a likelihood that each of the plurality of consumers viewed a content item based on a position of the content item with respect to a viewport presented by one of a plurality of first computing devices, wherein the viewability information was received by monitoring code loaded in association with the content item loaded as part of a page by a browser executed by one of the plurality of first computing devices corresponding to that consumer from the plurality of consumers; 
 calculate a causal conversion metric for a content source based on a comparison of conversion information corresponding to consumers of the control group and the conversion information corresponding to consumers of the test group; 
 determine in what proportion content items are to be placed using each of a plurality of content sources based at least in part on the causal conversion metrics; and 
 cause the content source to be used to present content items to a portion of the plurality of consumers associated with a plurality of additional computing devices based on the proportion and the one or more parameters. 
   
     
     
         12 . The system of  claim 11 , wherein ones of the plurality of consumers having corresponding viewability information indicating that the one of the plurality of consumers was likely to not have viewed the content item are included in the control group and ones of the plurality of consumers having corresponding viewability information indicating that the one of the plurality of consumers was likely to have viewed the content item are included in the test group. 
     
     
         13 . The system of  claim 11 , wherein inclusion of a consumer into a group is based on whether the content item was either viewable or not viewable by the consumer. 
     
     
         14 . The system of  claim 11 , wherein the content item was placed using the content source as part of a campaign according to an initial allocation amongst a plurality of content sources including the content source, and wherein the hardware processor is further configured to:
 receive a budget for a particular portion of the campaign; and   allocate the budget among the plurality of content sources based on the causal conversion metric corresponding to the content source and the causal conversion metrics corresponding to the plurality of content sources other than the content source.   
     
     
         15 . The system of  claim 14 , wherein the hardware processor is further configured to transmit instructions to at least one second computing device indicating in what proportion the content items for the campaign are to be placed using each of the plurality of content sources based at least in part on the causal conversion metrics and the budget, wherein the proportion is based at least on the allocation of the budget. 
     
     
         16 . The system of  claim 14 , wherein allocating the budget further comprises determining whether to place additional content items on the content source or one of the plurality of content sources based on a comparison of the causal conversion metric corresponding to the content source and the causal conversion metrics corresponding to the plurality of content sources. 
     
     
         17 . The system of  claim 11 , wherein the hardware processor is further configured to cause the monitoring code to be loaded in association with the content items presented as part of a campaign such that the monitoring code determines the viewability information indicative of a probability that the content item was viewable to a consumer based at least in part on the amount of time that the content item was within a viewport of the browser. 
     
     
         18 . The system of  claim 11 , wherein calculating the causal conversion metric further comprises:
 calculating a first conversion rate for the control group based on the conversion information corresponding to consumers included in the control group;   calculating a second conversion rate for the test group based on the conversion information corresponding to consumers included in the test group;   comparing the first conversion rate to the second conversion rate; and   calculating a causal conversion rate based on the comparison.   
     
     
         19 . The system of  claim 11 , wherein calculating the causal conversion metric further comprises:
 receiving cost information corresponding to the cost of presenting each of the plurality of users with the content item;   calculating a first return on investment for the control group based on the conversion information corresponding to consumers included in the control group and the cost information corresponding to consumers included in the control group;   calculating a second return on investment for the test group based on the conversion information corresponding to consumers included in the test group and the cost information corresponding to consumers included in the test group;   comparing the first return on investment to the second return on investment; and   calculating a causal return on investment based on the comparison.   
     
     
         20 . The system of  claim 11 , wherein the hardware processor is further configured to:
 categorize the plurality of consumers into a subset of consumers based on a contextual category associated with the content item that was presented;   calculate a third causal conversion metric for the subset of consumers;   compare the third causal conversion metric to the causal conversion metric; and   determine whether to place one or more content items on pages in the contextual category using the content source based on the comparison.   
     
     
         21 . A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for placing advertisements, the method comprising:
 placing a plurality of consumers into one of a control group and a test group based on viewability information indicative of a likelihood that each of the plurality of consumers viewed a content item based on a position of the content item with respect to a viewport presented by one of a plurality of first computing devices, wherein the viewability information was received by monitoring code loaded in association with the content item loaded as part of a page by a browser executed by one of the plurality of first computing devices corresponding to that consumer from the plurality of consumers;   calculating a causal conversion metric for a content source based on a comparison of conversion information corresponding to consumers of the control group and the conversion information corresponding to consumers of the test group;   determining in what proportion content items are to be placed using each of a plurality of content sources based at least in part on the causal conversion metrics; and   causing the content source to be used to present content items to a portion of the plurality of consumers associated with a plurality of additional computing devices based on the proportion and the one or more parameters.