Systems and methods for measuring effectiveness of feature changes made within a store
Abstract:
Systems and methods for determining whether a particular feature or change implemented in at least one test store causes a significant change as compared to one or more control stores are discussed. More particularly, techniques for using a time-series clustering algorithm to identify comparable sister stores to a store in which a feature change is being considered are described. Once the sister stores are identified a testing module can perform an A/B testing so as to validate whether a particular feature change being implemented in the test store causes a significant change as compared to the control stores.
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