Bayesian Inference for A|B and Bandit Marketing Tests.

grizbayr

A Bayesian Inference Package for A|B and Bandit Marketing Tests

Description:

Uses simple Bayesian conjugate prior update rules to calculate the following metrics for various marketing objectives:

1. Win Probability of each option
2. Value Remaining in the Test
3. Percent Lift Over the Baseline

This allows a user to implement Bayesian Inference methods when analyzing the results of a split test or Bandit experiment.

Examples

See the intro vignette for examples to get started.

Marketing objectives supported:

• Conversion Rate
• Response Rate
• Click Through Rate (CTR)
• Revenue Per Session
• Multi Revenue Per Session
• Cost Per Activation (CPA)
• Total Contribution Margin (CM)
• CM Per Click
• Cost Per Click (CPC)
• Session Duration (seconds)
• Page Views Per Session

Contributing

New Posterior Distributions

To add a new posterior distribution you must complete the following:

1. Create a new function called sample_...(input_df, priors, n_samples). Use the internal helper functions update_gamma, update_beta, etc. included in this package or you can create a new one.

2. This function (and the name) must be added to the switch statement in sample_from_posterior()

3. A new row must be added to the internal data object distribution_column_mapping.

   • Select this object from the package
   • Add a new row with a 1 for every column that is required for this distribution (this is for data validation and clear alerting for the end user)
   • Save the updated tibble object using use_data(new_tibble, internal = TRUE, overwrite = TRUE) and it will be saved as sysdata.rda in the package for internal use.
   • Update the intro.Rmd markdown table to include which columns are required for your function.

4. Create a PR for review.

New Features Ideas (TODO)

• High Density Credible Intervals with each option
• Conjugate Prior Update Rules vignette deriving each marketing objective update_rules

Package Name

The name is a play on Bayes with an added r (bayesr). The added griz (or Grizzly Bear) creates a unique name that is searchable due to too many similarly named packages.