robyn_crossmmm: Cross-MMM Budget Optimization across Models

In laresbernardo/lares: Analytics & Machine Learning Sidekick

Cross-MMM Budget Optimization across Models

Description

Given a list of recreated Robyn models, this function optimizes budget allocation across MMM with respective constraints by maximizing incremental revenue/conversions. This method assumes each model is independent, that can be compared given its spends were cleanly and properly split, they modeled the same metric (revenue or conversion) and units (currency or type of conversion), and date granularity. For best results, ensure channels have similar granularity across markets to simplify interpretation and application of the outputs.

Given a list of recreated Robyn models, this function optimizes budget allocation across MMM with respective constraints by maximizing response across all channels. This method assumes each model is independent, that can be compared given its spends were cleanly and properly split, they modeled the same metric (revenue or conversion) and units (currency or type of conversion), and date granularity. Recommended to have same channels granularity across markets to simplify results readings and application.

Usage

robyn_xmodels(
  models,
  initial_budgets = NULL,
  start_dates = NULL,
  end_dates = NULL,
  budget_constr_low = 0.5,
  budget_constr_up = 1.5,
  channel_constr_low = budget_constr_low,
  channel_constr_up = budget_constr_up,
  cores = NULL,
  quiet = FALSE,
  ...
)

## S3 method for class 'robyn_crossmmm'
print(x, ...)

robyn_xchannels(
  models,
  initial_budgets = NULL,
  start_dates = NULL,
  end_dates = NULL,
  channel_constr_low = 0.5,
  channel_constr_up = 2,
  quiet = FALSE,
  ...
)

Arguments

models	Lists. Recreated Robyn models with robyn_recreate().
initial_budgets	Numeric vector. Default will use the total spends per model for the specified or total date range. Must be length 1 or same as models.
start_dates, end_dates	Character vector. Start and end dates for each specific model. You can specify a single date and will be used in all models. Default empty value will assume you want all available data and date range. Must be length 1 or same as models.
budget_constr_low, budget_constr_up	Numeric vector. Relative minimum and maximum budgets to consider based on initial_budgets. By default it'll consider 50 Must be length 1 or same as models.
channel_constr_low, channel_constr_up	Numeric vector. Relative lower and upper constraints per channel compared with mean spend during the time period defined. If mean was zero for date range, historical mean spend value will be used. Must have length 1 to replicate for all channels or same length (and order )as paid_media_spends.
cores	Integer. How many cores to use for parallel computations? Set to 1 to not use this option. Default will the minimum between 10 cores and all available cores - 1.
quiet	Boolean. Keep quiet? If not, informative messages will be printed.
...	Additional parameters to be passed to internal functions.
x	Object to print: robyn_crossmmm() output.

Details

This approach is faster and cleaner compared with previous proposal using robyn_xmodels().

Value

Invisible vector with results by letter.

List. Contains optimized allocation results and plots.

Examples

# You must have Robyn installed and some models stored as JSON files
## Not run: 
# Import and recreate the models
files <- c("BrandA.json", "BrandB.json", "BrandC.json", "BrandN.json")
models <- lapply(files, function(x) Robyn::robyn_recreate(x))
names(models) <- gsub("\\.json", "", files)

# Calculate cross-brand optimal allocation
res <- robyn_xmodels(
  models,
  cores = 10,
  start_dates = "2023-01-01",
  end_dates = "2023-12-01"
)
print(res)
res$summary

## End(Not run)
## Not run: 
# Calculate cross-brand optimal allocation
res <- robyn_xchannels(
  models,
  start_dates = "2023-01-01",
  end_dates = "2023-12-01"
)

## End(Not run)

laresbernardo/lares documentation built on Jan. 14, 2025, 2:22 a.m.