METHODS, SYSTEMS, ARTICLES OF MANUFACTURE AND APPARATUS TO DETERMINE ADVERTISEMENT CAMPAIGN EFFECTIVENESS USING COVARIATE MATCHING
Methods, apparatus, systems and articles of manufacture are disclosed to determine advertisement campaign effectiveness using covariate matching. An example method includes segregating, by executing an instruction with a processor, a data structure into treatment groups and covariate groups, the data structure including an index of sales associated with an advertisement campaign, reducing a bias associated with the covariate groups by applying, by executing an instruction with the processor, anentropy optimization to determine a first balancing factor for a first covariate group of the covariate groups based on a geometric mean of a first subset of treatment groups associated with the covariate groups and determining, by executing an instruction with a processor, a first balanced weight for a first sale of the index of sales based on (a) the first balancing factor and (b) a first sampling weight of the first sale, the first sale associated with the first covariate group and a first treatment group of the first subset of treatment groups, the first sale associated with a first response. The example method includes determining, by executing an instruction with a processor, a first aggregate response of the first treatment group based a sum of products of (1) a first set of balanced weights associated with the first treatment group and (2) a first set of responses associated with the first treatment group, the first set of balanced weights including the first balanced weight, the first set of responses including the first response and reducing computing resource waste by modifying, by executing an instruction with a processor, computing resource allocation to the advertisement campaign based on the first aggregate response.
1 . An apparatus to allocate advertising campaign resources, the apparatus comprising:
a group segregator to segregate a data structure into covariate groups, the data structure including an index of sales associated with an advertisement campaign;
a treatment segregator to segregate the data structure into treatment groups;
an entropy optimizer to reduce a bias associated with the covariate groups by applying an entropy optimization to determine a first balancing factor for a first covariate group of the covariate groups based on a geometric mean of a first subset of treatment groups associated with the covariate groups;
a weight balancer to determine a first balanced weight for a first sale of the index of sales based on (a) the first balancing factor and (b) a first sampling weight of the first sale, the first sale associated with the first covariate group and a first treatment group of the first subset of treatment groups, the first sale associated with a first response;
an aggregate response determiner to determine a first aggregate response of the first treatment group based a sum of products of (1) a first set of balanced weights associated with the first treatment group and (2) a first set of responses associated with the first treatment group, the first set of balanced weights including the first balanced weight, the first set of responses including the first response; and
a campaign interface to reduce computing resource waste by modifying computing resource allocation to the advertisement campaign based on the first aggregate response.
2 . The apparatus as defined in claim 1 , further including a network interface to retrieve the data structure from a results database, the results database associated with an advertisement provider associated with the advertisement campaign.
3 . The apparatus as defined in claim 1 , wherein:
the entropy optimizer is to determine a second balancing factor for a second covariate group of the covariate groups based on a geometric mean of a second subset of treatment groups associated with the covariate groups; and
the weight balancer is to determine a second balanced weight for a second sale of the index of sales based on (a) the second balancing factor and (b) a second sampling weight of the second sale, the second sale associated with the second covariate group and a first treatment group of the first subset of treatment groups, the second sale associated with a second response, the first set of balanced weights further including the second balanced weight, the first set of responses further including the second response.
4 . The apparatus as defined in claim 3 , wherein:
the weight balancer is to determine a third balanced weight for a third sale of the index of the sales based (1) the first balancing factor and (b) a third sampling weight of the third sale, the third sale associated with the first covariate group and a second treatment group of the first subset of treatment groups, the third sale associated with a third response; and
the aggregate response determiner is to determine a second aggregate response of the second treatment group based a sum of products of (1) a second set of balanced weights associated with the second treatment group and (2) a second set of responses associated with the second treatment group, the second set of balanced weights including the third balanced weight, the second set of responses including the first response.
5 . The apparatus as defined in claim 4 , wherein the second treatment group is a control group, the control group associated with consumers who were not exposed to the advertisement campaign.
6 . The apparatus as defined in claim 4 , further including a comparative advantage determiner to determine a first comparative advantage corresponding to the first treatment group by determining a difference between the first aggregate response and the second aggregate response.
7 . The apparatus as defined in claim 1 , wherein the entropy optimizer is to reduce the bias associated with the covariate groups by balancing all orders of interactions between the covariate groups.
8 .- 14 . (canceled)
15 . A non-transitory computer readable storage medium, comprising instructions, which when executed cause a processor to at least:
segregate a data structure into treatment groups and covariate groups, the data structure including an index of sales associated with an advertisement campaign;
reduce a bias associated with the covariate groups by applying an entropy optimization to determine a first balancing factor for a first covariate group of the covariate groups based on a geometric mean of a first subset of treatment groups associated with the covariate groups;
determine a first balanced weight for a first sale of the index of sales based on (a) the first balancing factor and (b) a first sampling weight of the first sale, the first sale associated with the first covariate group and a first treatment group of the first subset of treatment groups, the first sale associated with a first response;
determine a first aggregate response of the first treatment group based a sum of products of (1) a first set of balanced weights associated with the first treatment group and (2) a first set of responses associated with the first treatment group, the first set of balanced weights including the first balanced weight, the first set of responses including the first response; and
reduce computing resource waste by modifying, by executing an instruction with the processor, computing resource allocation to the advertisement campaign based on the first aggregate response.
16 . The storage medium as defined in claim 15 , wherein the instructions, when executed, cause the processor to retrieve the data structure from a results database, the results database associated with an advertisement provider associated with the advertisement campaign.
17 . The storage medium as defined in claim 15 , wherein the instructions, when executed, cause the processor to:
determine a second balancing factor for a second covariate group of the covariate groups based on a geometric mean of a second subset of treatment groups associated with the covariate groups; and
determine a second balanced weight for a second sale of the index of sales based on (a) the second balancing factor and (b) a second sampling weight of the second sale, the second sale associated with the second covariate group and a first treatment group of the first subset of treatment groups, the second sale associated with a second response, the first set of balanced weights further including the second balanced weight, the first set of response further including the second response.
18 . The storage medium as defined in claim 17 , wherein the instructions, when executed, cause the processor to:
determine a third balanced weight for a third sale of the index of the sales based (1) the first balancing factor and (b) a third sampling weight of the third sale, the third sale associated with the first covariate group and a second treatment group of the first subset of treatment groups, the third sale associated with a third response; and
determine a second aggregate response of the second treatment group based a sum of products of (1) a second set of balanced weights associated with the second treatment group and (2) a second set of responses associated with the second treatment group, the second set of balanced weights including the third balanced weight, the second set of responses including the first response.
19 . The storage medium as defined in claim 18 , wherein the second treatment group is a control group, the control group associated with consumers who were not exposed to the advertisement campaign.
20 . The storage medium as defined in claim 18 , wherein the instructions, when executed, cause the processor to determine a first comparative advantage corresponding to the first treatment group by determining a difference between the first aggregate response and the second aggregate response.
21 . An apparatus to allocate advertising campaign resources, the apparatus comprising:
means for group-segregating to segregate a data structure into covariate groups, the data structure including an index of sales associated with an advertisement campaign;
means for treatment-segregating to segregate the data structure into treatment groups;
means for bias reducing to reduce a bias associated with the covariate groups by applying an entropy optimization to determine a first balancing factor for a first covariate group of the covariate groups based on a geometric mean of a first subset of treatment groups associated with the covariate groups;
means for balanced weight determining to determine a first balanced weight for a first sale of the index of sales based on (a) the first balancing factor and (b) a first sampling weight of the first sale, the first sale associated with the first covariate group and a first treatment group of the first subset of treatment groups, the first sale associated with a first response;
means for aggregate response determining to determine a first aggregate response of the first treatment group based a sum of products of (1) a first set of balanced weights associated with the first treatment group and (2) a first set of responses associated with the first treatment group, the first set of balanced weights including the first balanced weight, the first set of responses including the first response; and
means for modifying to reduce computing resource waste by modifying computing resource allocation to the advertisement campaign based on the first aggregate response.
22 . The apparatus as defined in claim 21 , further including a means for retrieving to retrieve the data structure for a results database, the results database associated with an advertisement provider associated with the advertisement campaign.
23 . The apparatus as defined in claim 21 , wherein:
the bias reducing means is to determine a second balancing factor for a second covariate group of the covariate groups based on a geometric mean of a second subset of treatment groups associated with the covariate groups; and
the balanced weight determining means is to determine a second balanced weight for a second sale of the index of sales based on (a) the second balancing factor and (b) a second sampling weight of the second sale, the second sale associated with the second covariate group and a first treatment group of the first subset of treatment groups, the second sale associated with a second response, the first set of balanced weights further including the second balanced weight, the first set of response further including the second response.
24 . The apparatus as defined in claim 23 , wherein:
the balanced weight determining means is to determine a third balanced weight for a third sale of the index of the sales based (1) the first balancing factor and (b) a third sampling weight of the third sale, the third sale associated with the first covariate group and a second treatment group of the first subset of treatment groups, the third sale associated with a third response; and
the aggregate response determining means is to determine a second aggregate response of the second treatment group based a sum of products of (1) a second set of balanced weights associated with the second treatment group and (2) a second set of responses associated with the second treatment group, the second set of balanced weights including the third balanced weight, the second set of responses including the first response.
25 . The apparatus as defined in claim 24 , wherein the second treatment group is a control group, the control group associated with consumers who were not exposed to the advertisement campaign.
26 . The apparatus as defined in claim 24 , further including a means for comparative advantage determining to determine a first comparative advantage corresponding to the first treatment group by determining a difference between the first aggregate response and the second aggregate response.
27 . The apparatus as defined in claim 21 , wherein the bias reducing means is to reduce the bias associated with the covariate groups by balancing all orders of interactions between the covariate groups.