Dynamic Response Rate Estimation for A/B Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing A/B testing methods, such as multi-armed bandit algorithms, fail to adapt quickly to changes in response rates over time, leading to inefficiencies and incorrect identification of the best offer, especially when response rates change suddenly or regularly.
Innovation Solution
The method calculates a current response rate by obtaining successive estimates over increasingly larger time periods, defining a range of uncertainty with upper and lower bounds, and selecting the estimate that falls outside or within these bounds, allowing for timely adaptation to changes in response rates, and implements 'even exploration' to distribute serves more evenly across time, ensuring ongoing exploration and accurate identification of the best offer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-armed bandit algorithms are used for A/B testing, then the system can identify the best offer, but the system fails to adapt quickly to changes in response rates over time
Solution Approach 1:
The patent implements dynamic exploration serve proportions that adjust over time based on the current iteration number. The exploration proportion starts higher and gradually decreases, allowing the system to adapt its behavior dynamically. This resolves the contradiction by making the system responsive to changes while maintaining efficient identification of the best offer through time-varying exploration rates.
Solution Approach 2:
The patent introduces periodic exploration phases where a fixed proportion of serves are allocated to exploration at regular intervals. This periodic action allows the system to continuously detect changes in response rates while maintaining overall exploitation efficiency, thereby adapting to changes without excessive time loss.
2Adaptability or versatility
If exploration serves are increased to detect changes in response rates, then the system can adapt to changes, but the number of serves of inferior offers increases
Solution Approach 1:
The exploration serve proportion is made dynamic, decreasing as the iteration number increases. This allows high exploration early when changes are most likely to occur, while reducing exploration later when the best offer is already identified, thus detecting changes efficiently without excessive serves of inferior offers.
Solution Approach 2:
The patent changes the exploration parameter (serve proportion) as a function of iteration number. By adjusting this parameter dynamically, the system optimizes the balance between detecting response rate changes and minimizing serves to inferior offers, resolving the contradiction between adaptability and productivity.
3Device complexity
If standard A/B testing with fixed serve fractions is used, then the testing process is simple, but the system serves many more serves of inferior offers than necessary
Solution Approach 1:
The patent introduces dynamic exploration proportions that automatically adjust based on iteration number, replacing simple fixed serve fractions. This adds minimal complexity while dramatically reducing the number of serves needed to identify the best offer, as the system adapts its exploration rate to the current state of knowledge.
Solution Approach 2:
The system uses feedback from observed response rates to dynamically adjust exploration serve proportions. This feedback mechanism allows the system to learn from past performance and optimize future serves, reducing wasted serves on inferior offers while maintaining a straightforward testing process.
Data Source
AI summary
Methods and systems for calculating a current response rate to an offer are disclosed. Examples of offers include but are not limited to design options for a web page and scripts used by operators in a call center. An offer may be served multiple times to one or more users and the current response rate may be determined for a point in time. The method may comprise obtaining estimates of response rate in successive iterations of response rate estimation. Each estimate may be determined over a period of time ending at the point in time, and each estimate may be determined over a larger period of time than the previous estimate. A range of uncertainty may be defined by upper and lower bounds for the estimates of response rate, with at least the first estimate being within the range of uncertainty. Each estimate may be compared to one or both of the upper and lower bounds and the result of the comparison may be used in the selection of one of the estimates as the calculated current response rate.