Automated specification, estimation, discovery of causal drivers and market response elasticities or lift factors
View Patent ↗In some embodiments, a software facility performs a method of automated specification of models, estimation of elasticities, and discovery of drivers using the framework(s) discussed elsewhere herein is provided. The facility first obtains the client, business, and/or brand goals in terms of profit optimization, volume or revenue goals, acquisition of new customers, retention of customers, share of wallet and upsell. In conjunction with these goals, the facility obtains cross-section meta-data related to the planning time horizon, markets, geographies, channels of trade and customer segments. In combination, the goals and meta-data define the structure of the data stack and the number of demand generation equations that are needed.
1. A system, having a memory, for constructing a demand generation equation to predict for an offering a level of demand that will be generated by allocating particular levels of resources to different marketing activities, comprising:
a component configured to receive a plurality of observations from a user;
a component configured to, for each of at least two of the plurality of observations:
for each of a plurality of candidate driver variables, retrieve a value for the candidate driver variable as it relates to the observation, and
for each of at least one outcome variable, retrieve a value for the outcome variable as it relates to the observation;
a component configured to estimate, using the variable values retrieved for the observations, an equation that relates the candidate driver variables to the outcome variable to produce coefficients for each of the candidate driver variables; and
a component configured to conduct statistical diagnostics on the estimated equation to validate the estimated equation,
wherein the component configured to conduct statistical diagnostics comprises a component configured to determine whether the estimated equation falls within the range of elasticity norms from an elasticity library, and
wherein at least one of the components comprises computer-executable instructions stored in the memory for execution by the system.
2. The system of claim 1 wherein each of the candidate driver variables falls into a candidate driver variable class, and wherein the component configured to estimate is configured to estimate under the constraint that at least one candidate driver variable in each candidate driver variable class must have a non-zero coefficient.
3. The system of claim 1 , wherein the component configured to conduct statistical diagnostics comprises a component configured to verify compliance with at least two pre-determined business or statistical confidence rules.
4. The system of claim 3 , further comprising:
a component configured to apply the estimated equation to candidate driver variable values and outcome variable values not among those retrieved to validate the estimated equation.
5. A computer-readable storage medium storing instructions that, if executed by a computing system having a processor, cause the computer system to perform operations for constructing a demand generation equation to predict for an offering a level of demand that will be generated by allocating particular levels of resources to different marketing activities, the operations comprising:
receiving a plurality of observations from a user;
for at least two of the plurality of observations:
for each of a plurality of candidate driver variables, retrieving a value for the candidate driver variable as it relates to the observation, and
for each of at least one outcome variable, retrieving a value for the outcome variable as it relates to the observation;
using the variable values retrieved for the observations, estimating an equation that relates the candidate driver variables to the outcome variable to produce coefficients for each of the candidate driver variables; and
conducting statistical diagnostics on the estimated equation to validate the estimated equation,
wherein conducting statistical diagnostics on the estimated equation to validate the estimated equation comprises determining whether the estimated equation falls within the range of elasticity norms from an elasticity library.
6. The computer-readable storage medium of claim 5 wherein the estimating is performed by stacking multiple equations under the Seemingly Unrelated Regression technique or by solving simultaneous equations using 2SLS, 3SLS, or maximum likelihood methods.
7. The computer-readable storage medium of claim 5 wherein each of the candidate driver variables falls into a candidate driver variable class, and wherein estimating is performed under the constraint that at least one candidate driver variable in each candidate driver variable class must have a non-zero coefficient.
8. The computer-readable storage medium of claim 7 , wherein the step of conducting statistical diagnostics on the estimated equation to validate the estimated equation comprises verifying compliance with at least two pre-determined business or statistical confidence rules.
9. The computer-readable storage medium of claim 5 , the operations further comprising:
applying the estimated equation to candidate driver variable values and outcome variable values not among those retrieved to validate the estimated equation.
10. One or more computer memories collectively storing a demand generation equation data structure to predict for an offering a level of demand that will be generated by allocating particular levels of resources to different marketing activities, the demand generation equation data structure comprising:
a plurality of observations received from a user;
for at least two of the plurality of observations:
a plurality of candidate driver variables, and a value for the candidate driver variable as it relates to the observation, and
at least one outcome variable, and a value for the outcome variable as it relates to the observation;
a demand generation equation that relates the candidate driver variables to the outcome variable to produce coefficients for each of the candidate driver variables, wherein the demand generation equation is estimated using the variable values retrieved for the observations;
an elasticity library;
coefficients for each of the candidate driver variables; and at least two statistical diagnostic results generated from conducting statistical diagnostics on the demand generation equation to validate the demand generation equation,
wherein conducting statistical diagnostics on the demand generation equation comprises determining whether the demand generation equation falls within the range of elasticity norms from the elasticity library.
11. The one or more computer memories of claim 10 , wherein each of the candidate driver variables falls into a candidate driver variable class, and wherein the demand generation equation is further estimated under the constraint that at least one candidate driver variable in each candidate driver variable class must have a nonzero coefficient.
12. The one or more computer memories of claim 10 , wherein the at least two statistical diagnostic results are further generated by verifying compliance with at least two pre-determined business or statistical confidence rules.
13. The one or more computer memories of claim 10 , the demand equation data structure further comprising:
coefficients for candidate driver variables other than the plurality of candidate driver variables.
14. The one or more computer memories of claim 13 , wherein the coefficients for candidate driver variables other than the plurality of candidate driver variables are generated by applying the estimated equation to the candidate driver variables other than the plurality of candidate driver variables.
15. A method, performed by a computing system having a processor, for prescribing a total budget and allocation of marketing resources for a subject offering, the method comprising:
during an initialization phase, collecting data relating to historical marketing efforts for a first offering other than the subject offering;
based on the collected data, calculating an average elasticity measure indicating the impact, on a business outcome, of allocating resources to a total marketing budget for the first offering;
deriving, with the processor, an adjustment factor for the average elasticity measure that specifies how much the average elasticity measure is to be adjusted to reflect characteristics of the historical marketing efforts;
for each of a plurality of spending categories, deriving an elasticity measure indicating the extent to which the marketing spending category impacted the business outcome; and
determining, with the processor, a total marketing budget for the subject offering based on the calculated average elasticity measure and the derived adjustment factor.
16. The method of claim 15 wherein the collected data comprises characteristics of the first offering, the total marketing budget for the first offering, an allocation of the total marketing budget for the first offering across spending categories, and a business outcome.
17. The method of claim 15 wherein the collecting, calculating, deriving, and identifying are each performed for a second offering, wherein determining the ideal total marketing budget is further based on the average elasticity measure calculated for the second offering, wherein the second offering is not the first offering, and wherein the second offering is not the subject offering.
18. The method of claim 15 wherein the plurality of spending categories include television, movie theatre, radio, and newspaper.
19. The method of claim 18 wherein the plurality of spending categories include outdoor, home shopping TV, product placement, airport, public transportation, sponsorship of sports events, and sponsorship of non-sports events.
20. The method of claim 19 wherein the plurality of spending categories include television, movie theatre, radio, newspapers, video game, streaming video, and interactive TV.
21. The method of claim 20 wherein the plurality of spending categories include magazine, print article, loose insert, internet advertising, internet search, brand/company website, and email.
22. The method of claim 21 wherein the plurality of spending categories include doctor's office, toll free lines, celebrity endorsement, in-store advertising, and promotion/special offer.
23. A computer-readable storage medium storing instructions that, if executed by a computer system having a processor, cause the computer system to perform operations, comprising:
receiving a plurality of observations from a user;
for at least two of the plurality of observations, creating a data stack comprising data observations and explanatory variables by:
retrieving a value for a candidate driver variable as it relates to the observation, and
retrieving a value for an outcome variable as it relates to the observation;
estimating a demand generation equation that relates the candidate driver variable to the outcome variable to produce coefficients for the candidate driver variable using the variable values retrieved for the observations; and
conducting statistical diagnostics on the demand generation equation to validate the demand generation equation,
wherein conducting statistical diagnostics on the demand generation equation to validate the demand generation equation comprises determining whether the demand generation equation falls within the range of elasticity norms from an elasticity library.
24. The computer-readable storage medium of claim 23 wherein each of the candidate driver variables falls into a candidate driver variable class, and wherein estimating is performed under the constraint that at least one candidate driver variable in each candidate driver variable class must have a non-zero coefficient.
25. The computer-readable storage medium of claim 24 , the operations further comprising:
applying the estimated equation to candidate driver variable values and outcome variable values not among those retrieved to validate the demand generation equation.