Cost per Click

Description

A ratio showing how much actual clicks on the ad cost.

Usage

CPC(data)

Arguments

Value

A ratio of revenue to clicks - a cost of one click.

Note

Dataset has to be named 'data' and include columns 'Clicks' and 'Revenue'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
CPC <- function(data){
  x <- data$Clicks
  y <- data$Revenue
  round(
    y/x,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CPC <- CPC(data)
  

Cost per thousand viewable impressions

Description

Calculating CPMv based on Viewable Impressions and Revenue data. The dataset has to be named 'data' and include columns 'Viewable' and 'Revenue'.

Usage

CPMv(data)

Arguments

Value

Cost per thousand viewable impressions of an ad, shown in the currency the original file had for 'Revenue'.

Note

Dataset has to be named 'data' and include columns 'Viewable' and 'Revenue'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
CPMv <- function(data){
  x <- data$Viewable
  y <- data$Revenue
  round(
    y/x*1000,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CPMv <- CPMv(data)
  

Clickthrough rate

Description

A ratio showing how often people who see your ad end up clicking on it.

Usage

CTR(data)

Arguments

Value

A ratio of clicks to impressions.

Note

Dataset has to be named 'data' and include columns 'Impressions' and 'Clicks'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
CTR <- function(data){
  x <- data$Impressions
  y <- data$Clicks
  round(
    y/x,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$CTR <- CTR(data)
  

All metrics

Description

A combination of functions view_percent, eCPM, CPMv, CTR, and fill_rate in one.

Usage

adstats(data)

Arguments

Value

Note

Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Requests', 'Impressions', 'Revenue', 'Viewable', and 'Clicks'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
adstats <- function(data){
  data$ViewablePercent <- view_percent(data)
  data$eCPM <- eCPM(data)
  data$CPMv <- CPMv(data)
  data$CTR <- CTR(data)
  data$FillRate <- fill_rate(data)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data <- adstats(data)
  

Effective cost per thousand impressions

Description

Calculating eCPM based on Impressions and Revenue data. The dataset has to be named 'data' and include columns 'Impressions' and 'Revenue'.

Usage

eCPM(data)

Arguments

Value

Cost per thousand impressions of an ad, shown in the currency the original file had for 'Revenue'.

Note

Dataset has to be named 'data' and include columns 'Impressions' and 'Revenue'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
eCPM <- function(data){
  x <- data$Impressions
  y <- data$Revenue
  round(
    y/x*1000,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$eCPM <- eCPM(data)
  

Fill Rate/Show Rate

Description

A percentage of returned ads that were displayed in the app to the user out of all requested ones.

Usage

fill_rate(data)

Arguments

Value

A ratio of impressions to requests as a percentage.

Note

Dataset has to be named 'data' and include columns 'Requests' and 'Impressions'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
fill_rate <- function(data){
  x <- data$Requests
  y <- data$Impressions
  round(
    y/x*100,
    digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$FillRate <- fill_rate(data)
  

Plot with trend and forecast

Description

Plotting metrics of the dataset with trend and forecast, splitting them by groups of different ad blocks.

Usage

plot_trend_and_forecast(data, x_col, y_col, group_col)

Arguments

Value

A plot of the chosen data with trend and forecast.

Note

This function also requires packages 'ggplot2', and 'forecast'. Dataset downloaded from excel file, has to be assigned to 'data' and include columns 'Date', 'Block', and at least one with the data needed to plot ('Requests', 'Impressions', 'Revenue', 'Viewable', 'Clicks').

Author(s)

Ivan Nemtsev

References

Hyndman, R. J., & Athanasopoulos, G. (2018). "Forecasting: Principles and Practice" - https://otexts.com/fpp3/ Documentation for 'forecast' - https://pkg.robjhyndman.com/forecast/ Documentation for 'ggplot2' - https://ggplot2.tidyverse.org/ Wickham, H. (2016). "ggplot2: Elegant Graphics for Data Analysis" - https://ggplot2-book.org/

Examples

## The function is currently defined as
plot_trend_and_forecast <- function(data, x_col, y_col, group_col) {
  data$Date <- as.Date(data$Date)
  data_groups <- split(data, data[[group_col]])
  for (group in names(data_groups)) {
    group_data <- data_groups[[group]]
    time_series <- ts(group_data[[y_col]], frequency = 1, start = min(group_data[[x_col]]))
    arima_model <- auto.arima(time_series)
    forecast <- forecast(arima_model, h = 6)
    forecast_dates <- seq(max(group_data[[x_col]]) + 1,
    to = max(group_data[[x_col]]) + length(forecast$mean),
    by = "day")
    forecast_data <- data.frame(Date = forecast_dates, Forecast = forecast$mean)
    print(ggplot() +
            geom_line(data = group_data, aes(x = !!rlang::sym(x_col), y = !!rlang::sym(y_col))) +
            geom_line(data = forecast_data, aes(x = Date, y = Forecast), color = "blue") +
            geom_ribbon(data = forecast_data, aes(x = Date, ymin = forecast$lower[, 2],
            ymax = forecast$upper[, 2]), fill = "blue", alpha = 0.2) +
            labs(x = x_col, y = y_col, title = group) +
            theme_minimal())
  }
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
plot_trend_and_forecast(data, "Date", "Impressions", "Block")
#Any value instead of Impressions can be here
  

Percentage of views

Description

A ratio of viewable impressions to all impressions, shown as a percent.

Usage

view_percent(data)

Arguments

Value

A percentage of viewable impressions among the total number of impressions.

Note

Dataset has to be named 'data' and include columns 'Impressions' and 'Viewable'.

Author(s)

Ivan Nemtsev

Examples

## The function is currently defined as
view_percent <- function(data){
  x <- data$Impressions
  y <- data$Viewable
  round(
    y/x*100,
  digits=2)
}

## Example of use:
data <- data.frame(
  Date = c("2022-07-01", "2022-07-02", "2022-07-03", "2022-07-29", "2022-07-30", "2022-07-31"),
  Block = c("1_234", "1_234", "1_234", "1_235", "1_235", "1_235"),
  Requests = c(372234, 268816, 291224, 1928854, 1928290, 786539),
  Impressions = c(18537, 12432, 13764, 2839269, 2682648, 1114773),
  Revenue = c(13.5, 9.13, 8.85, 1669.0, 1654.0, 739.0),
  Clicks = c(1167, 720, 856, 214451, 196657, 93178),
  Viewable = c(13320, 8214, 9768, 2446884, 2243865, 1063158)
)
data$ViewablePercent <- view_percent(data)