Overview
This plugin uses machine learning algorithms to dynamically allocate traffic to variants that are performing well, while allocating less traffic to variants that are under performing. It still allows them opportunities to improve while doing this, however.
When started, the Multi-Armed Bandit will process rows going through the Rule, assign Variants and count Variant Attempts and Successes.
Until a statistically significant "winner" is calculated the Multi-Armed Bandit will continue with an even split to all active variants
Once a Statistically Significant winner is calculated the Multi-Armed Bandit will start to send to the winner.
As the Multi-Armed Bandit continues to work it will (at a frequency you select) pick a non-winner to test. This allows you to continually test the incumbent winners against previous losing options, and any new variants, ensuring the winner you have at any given time is tested to ensure it's still the best one.
If the Rule is restarted it will recalculate the results based on any previously collected variants and select a winner if it can.
For example a website has a homepage banner and has to make a decision about which banner to display to a visitor to encourage a booking:
10% of the time, you split your traffic equally between the four versions of a website banner
For the other 90% of the time, you send traffic to the best performing banner.
Plugin Configuration
Within the plugin:
The following settings can be configured:
Setting | Description |
|---|---|
MAB Mode | Select Variant Allows the user to enter the Variants they would like to Test in the Multi-Armed Bandit. Log an Attempt Allows the user to create a Rule to count the attempts using the selected Variants. Log Success Allows the user to create a Rule to count the success for each Variant. The measurement of success may take place in a different rule to the Select Variant and Log and Attempt modes. using the selected Variants.  |
Keyspace | Tables exist in Keyspace (similar to a database). If the rule is running in DDE then this will automatically be populated with the client name and Target within DDE and the value will be ignored. This can be set via a Parameter if required. |
Table | The name of the table which will be created in Cassandra. If left blank this will be the same as the key field name. This can be set via a Parameter if required. |
Experimentation Frequency | The experimentation frequencies have the following definition:
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Field |  Field that will contain the Variants generated by the Multi-Armed Bandit. It will also be the field that should be used to count the Attempts and Successes in those modes. |
Statistical Significance | The user can choose a Statistical Significance level for the Multi-Armed Bandit. The Multi-Armed bandit will continue to send an even split to ALL variants until Statistical Significance has been reached and at this point it will decide on the “winning” variants. Statistical Significance is based on a Chi-Squared Test and its P-Value. The P-Value is is between 0 and 0.1 For ease of customer use we will accept 0-100 From a users perspective:
This can be set via a Parameter if required. |
Variant ( Field/ Parameter) |  You can add a Variant from a field or from a Parameter. Here we are using the MAB String field to provide the Variant for Variant 1. We could select the Parameter “Test Variant 1” to provide the Variant for Variant 2. The Variant (Static) column is where the Variants are set when Fields or Parameters are NOT being used. In this scenario where Parameters and Fields are being used this column is only used when the Field of Parameter is not provided. In essence it becomes a default if the value is not provided in the Rule. |
Start and End Dates |  You can add either Variant Start or End dates or both. The Variant will only be included in the Multi-Armed Bandit calculations if the time when the Rule is run is within the “Active” time period for this Rule. If the Rule is invoked outside of this time period the Variant will not be included as a winner or a loser. It's possible that a successful Variant within a restricted time period that has reached Statistical Significance may fall out of selection and the remaining Variants have not met the required level of Statistical Significance and therefore an general even split to all Variants recommences. |
Multi-Armed Bandit Statistics
Multi-Armed Bandit has been designed to work automatically, calculating the “Winning” option automatically and passing in new variants to test the winning option against.
Users may want more information on the decisions the Multi-Armed Bandit is making. The fields below are automatically created by the Multi-Armed Bandit and can be output to S3 for review if required.
{"MAB_StatisticalSignificance":"1.0","MAB_IsSignificant":"false","MAB_DidExperiment":"false",,"MAB_OriginalWinner":"","MAB_Options":"{"options":[{"successes":1,"name":"Variant 3","attempts":1},{"successes":1,"name":"Variant 2","attempts":1},{"successes":1,"name":"Variant 1","attempts":1}]}"}
MAB_StatisticalSignificance = The Statistical Significance it calculated
MAB_IsSignificant = Was the Statistical Significance lower than the value selected in the rule?
MAB_DidExperiment = If the decision was Statistically Significance, did it experiment?
MAB_OriginalWinner = Once you hit Statistical Significance this is always set to the current winner, if did experiment, this will be different than the field output
MAB_Options = The options it considered when selecting a winner and the success and attempts of each
Example Scenario
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