sample_total_cm: Sample Total CM (Given Impression Count)

In grizbayr: Bayesian Inference for A|B and Bandit Marketing Tests

Sample Total CM (Given Impression Count)

Description

Adds 4 new nested columns to the input_df: 'beta_params_ctr', 'beta_params_conv','gamma_params_rev', 'gamma_params_cost' and 'samples'.

Usage

sample_total_cm(input_df, priors, n_samples = 50000)

Arguments

input_df	Dataframe containing option_name (str), sum_conversions (dbl), sum_revenue (dbl), and sum_clicks (dbl).
priors	Optional list of priors alpha0, beta0 for Beta, k0, theta0 for Gamma Inverse Revenue, and k01, theta01 for Gamma Cost (uses alternate priors so they can be different from Revenue). Default Beta(1,1) and Gamma(1, 250) will be use otherwise.
n_samples	Optional integer value. Defaults to 50,000 samples.

Details

'beta_params' and 'gamma_params' in each row should be a tibble of length 2 (\alpha and \beta params and k and \theta params). 'samples' in each row should be a tibble of length 'n_samples'.

One assumption in this model is that sum_impressions is not stochastic. This assumes that Clicks are stochastically generated from a set number of Impressions. It does not require that the number of impressions are equal on either side. Generally this assumption holds true in marketing tests where traffic is split 50/50 and very little variance is observed in the number of impressions on either side.

See update_rules vignette for a mathematical representation.

TotalCM = Impr * ExpectedCTR * (RevPerOrder * OrdersPerClick - ExpectedCPC)

Value

input_df with 5 new nested columns 'beta_params_conv', 'beta_params_ctr', 'gamma_params_rev','gamma_params_cost', and 'samples'

grizbayr documentation built on Oct. 9, 2023, 5:10 p.m.