Systems and methods for price testing and optimization in brick and mortar retailers
Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by generalized linear models. A set of constraints are then received and used, along with the elasticities.
1. A method for updating an elasticity curve executed on a computer system including non-transitory storage medium, the method comprising:
collecting transaction logs for products in a plurality of physical retail spaces;
validating the transaction logs by comparing a set of pricing instructions provided to the plurality of physical retail spaces against the transaction logs;
discarding any transaction log that is found in validation to have a mismatch between the instructions and the transaction logs;
adjusting the transaction logs based on day, retailer and weather;
computing elasticity for the products using the transaction logs;
receiving constraints, wherein the constraints include a minimum margin value, a statistically insignificant price adjustment, and a maximum percentage price adjustment;
computing an estimated optimal price for profitability for each product responsive to the elasticity and above the minimum margin constraints;
computing test values above and below the estimated optimal price for each product less than the maximum percentage and above the statistically insignificant price adjustment, and which has not been previously tested for;
evaluating the estimated optimal price and test values in three groups of retailers, wherein each of the plurality of physical retail spaces is randomly assigned to one of the three groups; and
refining the elasticity responsive to the evaluation by modeling for the test value that is determined to be an actual optimal price.
2. The method of claim 1 , wherein the collecting the transaction logs includes aggregating the transaction logs by day and by each physical retail space.
3. The method of claim 1 , wherein the adjusting includes adjusting by day, each physical retail space and by an external factor.
4. The method of claim 1 , wherein the adjusting includes normalizing the transaction logs.
5. The method of claim 1 , wherein the adjusting includes filtering the transaction logs for data deemed inaccurate during the validating.
6. The method of claim 1 , wherein the computing the elasticity includes generalized linear models.
7. The method of claim 1 , wherein the evaluating includes D-optimal designs via exchange algorithm and Box-Behnken design.
8. The method of claim 1 , further comprising updating the estimated optimal price using the refined elasticity and testing the updated estimated optimal price, test values and control price in four groups of retailers, wherein each of the plurality of physical retail spaces is randomly assigned to one of the four groups.
9. A system for updating an elasticity curve comprising:
a database containing a collection of transaction logs for products in a plurality of physical retail spaces;
a server including non-transitory memory for executing the steps of:
validating the transaction logs by comparing a set of pricing instructions provided to the plurality of physical retail spaces against the transaction logs;
discarding any transaction log that is found in validation to have a mismatch between the instructions and the transaction logs;
adjusting the transaction logs based on day, retailer and weather;
computing elasticity for the products using the transaction logs;
receiving constraints, wherein the constraints include a minimum margin value, a statistically insignificant price adjustment and a maximum percentage price adjustment;
computing an estimated optimal price for profitability for each product responsive to the elasticity and the above the minimum margin constraints;
computing test values above and below the estimated optimal price for each product less than the maximum percentage and above the statistically insignificant price adjustment, and which has not been previously tested for;
a test designer, embodied in a computer system, for evaluating the estimated optimal price and test values in three groups of retailers, wherein each of the plurality of physical retail spaces is randomly assigned to one of the three groups; and
the server further executing the step of refining the elasticity responsive to the evaluation by modeling for the test value that is determined to be an actual optimal price.
10. The system of claim 9 , wherein the collecting the transaction logs includes aggregating the transaction logs by day and by each physical retail space.
11. The system of claim 9 , wherein the adjusting includes adjusting by day, each physical retail space and by an external factor.
12. The system of claim 9 , wherein the adjusting includes normalizing the transaction logs.
13. The system of claim 9 , wherein the adjusting includes filtering the transaction logs for data deemed inaccurate during the validating.
14. The system of claim 9 , wherein the computing the elasticity includes generalized linear models.
15. The system of claim 9 , wherein the evaluating includes D-optimal designs via exchange algorithm and Box-Behnken design.
16. The system of claim 9 , wherein the optimizer refines the estimated optimal price using the refined elasticity, and the test designer tests the updated estimated optimal price, test values and control price in four groups of retailers, wherein each of the plurality of physical retail spaces is randomly assigned to one of the four groups.