adstocks: Adstocking Transformation (Geometric and Weibull)

In Robyn: Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

Adstocking Transformation (Geometric and Weibull)

Description

adstock_geometric() for Geometric Adstocking is the classic one-parametric adstock function.

adstock_weibull() for Weibull Adstocking is a two-parametric adstock function that allows changing decay rate over time, as opposed to the fixed decay rate over time as in Geometric adstock. It has two options, the cumulative density function "CDF" or the probability density function "PDF".

Usage

adstock_geometric(x, theta)

adstock_weibull(x, shape, scale, windlen = length(x), type = "cdf")

transform_adstock(
  x,
  adstock,
  theta = NULL,
  shape = NULL,
  scale = NULL,
  windlen = length(x)
)

plot_adstock(plot = TRUE)

Arguments

x	A numeric vector.
theta	Numeric. Theta is the only parameter on Geometric Adstocking and means fixed decay rate. Assuming TV spend on day 1 is 100€ and theta = 0.7, then day 2 has 100 x 0.7 = 70€ worth of effect carried-over from day 1, day 3 has 70 x 0.7 = 49€ from day 2 etc. Rule-of-thumb for common media genre: TV c(0.3, 0.8), OOH/Print/ Radio c(0.1, 0.4), digital c(0, 0.3).
shape, scale	Numeric. Check "Details" section for more details.
windlen	Integer. Length of modelling window. By default, same length as x.
type	Character. Accepts "CDF" or "PDF". CDF, or cumulative density function of the Weibull function allows changing decay rate over time in both C and S shape, while the peak value will always stay at the first period, meaning no lagged effect. PDF, or the probability density function, enables peak value occurring after the first period when shape >=1, allowing lagged effect.
adstock	Character. One of: "geometric", "weibull_cdf", "weibull_pdf".
plot	Boolean. Do you wish to return the plot?

Details

Weibull's CDF (Cumulative Distribution Function)

has two parameters, shape & scale, and has flexible decay rate, compared to Geometric adstock with fixed decay rate. The shape parameter controls the shape of the decay curve. Recommended bound is c(0.0001, 2). The larger the shape, the more S-shape. The smaller, the more L-shape. Scale controls the inflexion point of the decay curve. We recommend very conservative bounce of c(0, 0.1), because scale increases the adstock half-life greatly.

Weibull's PDF (Probability Density Function)

also shape & scale as parameter and also has flexible decay rate as Weibull CDF. The difference is that Weibull PDF offers lagged effect. When shape > 2, the curve peaks after x = 0 and has NULL slope at x = 0, enabling lagged effect and sharper increase and decrease of adstock, while the scale parameter indicates the limit of the relative position of the peak at x axis; when 1 < shape < 2, the curve peaks after x = 0 and has infinite positive slope at x = 0, enabling lagged effect and slower increase and decrease of adstock, while scale has the same effect as above; when shape = 1, the curve peaks at x = 0 and reduces to exponential decay, while scale controls the inflexion point; when 0 < shape < 1, the curve peaks at x = 0 and has increasing decay, while scale controls the inflexion point. When all possible shapes are relevant, we recommend c(0.0001, 10) as bounds for shape; when only strong lagged effect is of interest, we recommend c(2.0001, 10) as bound for shape. In all cases, we recommend conservative bound of c(0, 0.1) for scale. Due to the great flexibility of Weibull PDF, meaning more freedom in hyperparameter spaces for Nevergrad to explore, it also requires larger iterations to converge.

Run plot_adstock() to see the difference visually.

Value

Numeric values. Transformed values.

See Also

Other Transformations: mic_men(), saturation_hill()

Examples

adstock_geometric(rep(100, 5), theta = 0.5)
adstock_weibull(rep(100, 5), shape = 0.5, scale = 0.5, type = "CDF")
adstock_weibull(rep(100, 5), shape = 0.5, scale = 0.5, type = "PDF")

# Wrapped function for either adstock
transform_adstock(rep(100, 10), "weibull_pdf", shape = 1, scale = 0.5)

Robyn documentation built on June 27, 2024, 9:06 a.m.