DECISION SUPPORT SYSTEM FOR MARKETING MIX MODELING
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating models. In some implementations, a system obtains data that comprises promotions and parameters for an opportunity. The system generates transformation spaces based on the promotions and the parameters, wherein each transformation space comprises states, each state is based on the parameters for a particular promotion. The system iterates over a number of iterations. For each transformation space, the system adjusts a state of the transformation space based on actions. The system generates a model by combining each adjusted state. The system generates an entropy for the model. The system compares the entropy to a threshold value, wherein the threshold value corresponds to one of the parameters. In response to determining that the entropy exceeds the threshold value, the system iterates. The system provides the generated model for output.
1 . A computer system-implemented method comprising:
obtaining, by one or more processors, data that comprises promotions and parameters of each promotion for an opportunity;
generating, by the one or more processors, one or more transformation spaces based on a number of the promotions and the parameters of each promotion, wherein each of the one or more transformation spaces comprises a plurality of states, each state for a transformation space is based on a combination of the parameters for a particular promotion;
for at most a predefined number of iterations:
for each of the one or more transformation spaces:
adjusting, by the one or more processors, a state of the transformation space based on a set of available actions;
generating, by the one or more processors, a model by combining each adjusted state from each of the adjusted transformation spaces;
generating, by the one or more processors, metrics for the model;
based on the generated metrics, determining, by the one or more processors, an entropy of the model;
comparing, by the one or more processors, the entropy of the model to a threshold value; and
in response to determining that the entropy exceeds the threshold value, proceeding, by the one or more processors, to the next iteration; and
providing, by the one or more processors, the generated model for output to use for the opportunity.
2 . The computer system-implemented method of claim 1 , wherein a number of the one or more transformation spaces correspond to the number of promotions.
3 . The computer system-implemented method of claim 1 , further comprising:
in response to determining that a zero entropy is observed when comparing the entropy to the threshold value, providing, by the one or more processors, the generated model for output to use for the opportunity.
4 . The computer system-implemented method of claim 1 , wherein the parameters of the promotion comprises (i) lag transformation, (ii) adstock transformation, and (iii) fractional root transformation.
5 . The computer system-implemented method of claim 4 , wherein generating the one or more transformation spaces based on the number of the promotions and the parameters of each promotion further comprises:
generating, by the one or more processors, a first transformation of the lag transformation;
generating, by the one or more processors, a second transformation of the adstock transformation;
generating, by the one or more processors, a third transformation of the fractional root transformation; and
wherein each state for each of the one or more transformation spaces is based on a combination of the first transformation of the lag transformation, the second transformation of the adstock transformation, and the third transformation of the fractional root transformation.
6 . The computer system-implemented method of claim 1 , wherein the generated metrics comprises at least one of volume contribution functions, beta coefficient functions, p-value functions, R-square functions, DW statistic functions, and MAPE functions.
7 . The computer system-implemented method of claim 6 , wherein generating the entropy of the model further comprises:
generating, by the one or more processors, a distance metric for each of the generated metrics;
generating, by the one or more processors, a total summation for each of the generated metrics;
generating, by the one or more processors, a second summation that comprises a ratio based on (i) the distance metric for each of the generated metrics and (ii) the total summation;
generating, by the one or more processors, the entropy of the model based on an entropy equation and the second summation;
generating, by the one or more processors, a reward based on the entropy of the model;
generating, by the one or more processors, Q-values corresponding to actions leading to adjustments in the transformation spaces; and
storing, by the one or more processors, the Q-values corresponding to a set of prior actions corresponding to a prior adjustment state for each of the transformation spaces in a database.
8 . The computer system-implemented method of claim 1 , further comprising:
identifying, by one or more processors, a random state for each of the one or more transformation spaces such that (i) the random state is chosen as a starting location for exploration through each transformation space during the predefined number of iterations and (ii) the random state is chosen such that exploration can be performed within the predefined number of iterations.
9 . The computer system-implemented method of claim 1 , wherein adjusting the state of the transformation space is based on the set of available actions and performed using an epsilon greedy policy.
10 . A system comprising:
one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
obtaining, by one or more processors, data that comprises promotions and parameters of each promotion for an opportunity;
generating, by the one or more processors, one or more transformation spaces based on a number of the promotions and the parameters of each promotion, wherein each of the one or more transformation spaces comprises a plurality of states, each state for a transformation space is based on a combination of the parameters for a particular promotion;
for at most a predefined number of iterations:
for each of the one or more transformation spaces:
adjusting, by the one or more processors, a state of the transformation space based on a set of available actions;
generating, by the one or more processors, a model by combining each adjusted state from each of the adjusted transformation spaces;
generating, by the one or more processors, metrics for the model;
based on the generated metrics, determining, by the one or more processors, an entropy of the model;
comparing, by the one or more processors, the entropy of the model to a threshold value; and
in response to determining that the entropy exceeds the threshold value, proceeding, by the one or more processors, to the next iteration; and
providing, by the one or more processors, the generated model for output to use for the opportunity.
11 . The system of claim 10 , wherein a number of the one or more transformation spaces correspond to the number of promotions.
12 . The system of claim 10 , further comprising:
in response to determining that a zero entropy is observed when comparing the entropy to the threshold value, providing, by the one or more processors, the generated model for output to use for the opportunity.
13 . The system of claim 10 , wherein the parameters of the promotion comprises (i) lag transformation, (ii) adstock transformation, and (iii) fractional root transformation.
14 . The system of claim 13 , wherein generating the one or more transformation spaces based on the number of the promotions and the parameters of each promotion further comprises:
generating, by the one or more processors, a first transformation of the lag transformation;
generating, by the one or more processors, a second transformation of the adstock transformation;
generating, by the one or more processors, a third transformation of the fractional root transformation; and
wherein each state for each of the one or more transformation spaces is based on a combination of the first transformation of the lag transformation, the second transformation of the adstock transformation, and the third transformation of the fractional root transformation.
15 . The system of claim 10 , wherein the generated metrics comprises at least one of volume contribution functions, beta coefficient functions, p-value functions, R-square functions, DW statistic functions, and MAPE functions.
16 . The system of claim 15 , wherein generating the entropy of the model further comprises:
generating, by the one or more processors, a distance metric for each of the generated metrics;
generating, by the one or more processors, a total summation for each of the generated metrics;
generating, by the one or more processors, a second summation that comprises a ratio based on (i) the distance metric for each of the generated metrics and (ii) the total summation;
generating, by the one or more processors, the entropy of the model based on an entropy equation and the second summation;
generating, by the one or more processors, a reward based on the entropy of the model;
generating, by the one or more processors, Q-values corresponding to actions leading to adjustments in the transformation spaces; and
storing, by the one or more processors, the Q-values corresponding to a set of prior actions corresponding to a prior adjustment state for each of the transformation spaces in a database.
17 . The system of claim 10 , further comprising:
identifying, by one or more processors, a random state for each of the one or more transformation spaces such that (i) the random state is chosen as a starting location for exploration through each transformation space during the predefined number of iterations and (ii) the random state is chosen such that exploration can be performed within the predefined number of iterations.
18 . The system of claim 10 , wherein adjusting the state of the transformation space is based on the set of available actions and performed using an epsilon greedy policy.
19 . A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:
obtaining, by one or more processors, data that comprises promotions and parameters of each promotion for an opportunity;
generating, by the one or more processors, one or more transformation spaces based on a number of the promotions and the parameters of each promotion, wherein each of the one or more transformation spaces comprises a plurality of states, each state for a transformation space is based on a combination of the parameters for a particular promotion;
for at most a predefined number of iterations:
for each of the one or more transformation spaces:
adjusting, by the one or more processors, a state of the transformation space based on a set of available actions;
generating, by the one or more processors, a model by combining each adjusted state from each of the adjusted transformation spaces;
generating, by the one or more processors, metrics for the model;
based on the generated metrics, determining, by the one or more processors, an entropy of the model;
comparing, by the one or more processors, the entropy of the model to a threshold value; and
in response to determining that the entropy exceeds the threshold value, proceeding, by the one or more processors, to the next iteration; and
providing, by the one or more processors, the generated model for output to use for the opportunity.
20 . The non-transitory computer-readable medium of claim 19 , wherein a number of the one or more transformation spaces correspond to the number of promotions.