Cross-MMM Budget Optimization

Given a list of recreated Robyn models, this function optimizes budget allocation across MMMs with respective constraints by maximizing incremental revenue/conversions. This method assumes each model is independent and can be compared given its spends were cleanly and properly split.

Usage

robyn_crossmmm(
  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, ...)

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 minimum and maximum budgets to consider per channel. By default it'll consider 50 Must be length 1 or same as models.

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.

Value

Invisible vector with results by letter.

Examples

# You must have Robyn installed and some models stored as JSON files
if (FALSE) { # \dontrun{
# 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_crossmmm(
  models,
  cores = 10,
  start_dates = "2023-01-01",
  end_dates = "2023-12-01"
)
print(res)
res$summary
} # }