/grizbayr

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

Primary LanguageROtherNOASSERTION

grizbayr

CRAN status

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.