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:
- Win Probability of each option
- Value Remaining in the Test
- 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:
- 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.
- This function (and the name) must be added to the switch statement
in
sample_from_posterior()
- 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.
- 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.