Version: Latest (3.20.2)

mmm_attribution

R

D_variables maps each raw input variable to a final channel and measure. It must contain only the structural columns variable, channel, and measure.

D_measures is optional. When provided, the recommended user-facing structure is one row per measure with measure and aggregation_weight:

measure	aggregation_weight
`direct_searches`	`0.45`
`clicks`	`0.45`
`impressions`	`0.10`

aggregation_weight controls how strongly each measure contributes when variable-level attributions are aggregated into final channel-level attribution. The internal signal scaling is estimated automatically from the data.

library(ChannelAttributionPro)

token <- "yourtoken"

Data <- read.csv("https://app.channelattribution.io/data/data_mmm_3.csv")

D_variables <- data.frame(
  variable = c(
    "direct_searches",
    "facebook_impressions",
    "facebook_clicks",
    "google_impressions",
    "google_clicks",
    "tv"
  ),
  channel = c(
    "direct",
    "facebook_ads",
    "facebook_ads",
    "google_ads",
    "google_ads",
    "tv"
  ),
  measure = c(
    "direct_searches",
    "impressions",
    "clicks",
    "impressions",
    "clicks",
    "impressions"
  ),
  stringsAsFactors = FALSE
)

D_measures <- data.frame(
  measure = c("direct_searches", "clicks", "impressions"),
  aggregation_weight = c(0.45, 0.45, 0.10),
  stringsAsFactors = FALSE
)

target <- "conversions"

res <- mmm_attribution(
  Data = Data,
  D_variables = D_variables,
  D_measures = D_measures,
  target = target,
  model = "linear", # "linear", "reward", or "copula"
  max_p = 12,
  seed = 1234567,
  verbose = 1,
  password = token
)

print(head(res))

Using a different internal model:

res_reward <- mmm_attribution(
  Data = Data,
  D_variables = D_variables,
  D_measures = D_measures,
  target = target,
  model = "reward",
  max_p = 12,
  seed = 1234567,
  password = token
)

res_copula <- mmm_attribution(
  Data = Data,
  D_variables = D_variables,
  D_measures = D_measures,
  target = target,
  model = "copula",
  max_p = 12,
  nsim = 1000,
  seed = 1234567,
  password = token
)

R​

R