| Type: | Package |
| Title: | Functions to Assess the Business Impact of Churn Prediction Models |
| Version: | 1.0.0 |
| Author: | Peer Christensen |
| Maintainer: | Peer Christensen <hr.pchristensen@gmail.com> |
| Description: | Calculate the financial impact of using a churn model in terms of cost, revenue, profit and return on investment. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/PeerChristensen/modelimpact |
| BugReports: | https://github.com/PeerChristensen/modelimpact/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Imports: | dplyr, magrittr, utils |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2021-05-05 14:33:56 UTC; peerchristensen |
| Repository: | CRAN |
| Date/Publication: | 2021-05-06 08:50:02 UTC |
Calculate cost and revenue
Description
Calculates cost and revenue after sorting observations.
Usage
cost_revenue(
x,
fixed_cost = 0,
var_cost = 0,
tp_val = 0,
prob_col = NA,
truth_col = NA
)
Arguments
x |
A data frame containing predicted probabilities of a target event and the actual outcome/class. |
fixed_cost |
Fixed cost (e.g. of a campaign) |
var_cost |
Variable cost (e.g. discount offered) |
tp_val |
The average value of a True Positive |
prob_col |
The unquoted name of the column with probabilities of the event of interest. |
truth_col |
The unquoted name of the column with the actual outcome/class. Possible values are 'Yes' and 'No'. |
Value
A data frame with the following columns:
row = row numbers
pct = percentiles
cost_sum = cumulated costs
cum_rev = cumulated revenue
Examples
cost_revenue(predictions,
fixed_cost = 1000,
var_cost = 100,
tp_val = 2000,
prob_col = Yes,
truth_col = Churn)
Predictions from a customer churn model.
Description
A dataset containing 2145 observations with four columns specifying predicted probabilities and predicted and actual class.
Usage
predictions
Format
A data frame with 2145 rows and 4 variables:
- predict
Predicted class
- No
Predicted probability of class 'No'
- Yes
Predicted probability of class 'Yes'
- Churn
Actual class
...
Calculate profit
Description
Calculates profit after sorting observations.
Usage
profit(
x,
fixed_cost = 0,
var_cost = 0,
tp_val = 0,
prob_col = NA,
truth_col = NA
)
Arguments
x |
A data frame containing predicted probabilities of a target event and the actual outcome/class. |
fixed_cost |
Fixed cost (e.g. of a campaign) |
var_cost |
Variable cost (e.g. discount offered) |
tp_val |
The average value of a True Positive |
prob_col |
The unquoted name of the column with probabilities of the event of interest. |
truth_col |
The unquoted name of the column with the actual outcome/class. Possible values are 'Yes' and 'No'. |
Value
A data frame with the following columns:
row = row numbers
pct = percentiles
profit = profit for number of rows selected
Examples
profit(predictions,
fixed_cost = 1000,
var_cost = 100,
tp_val = 2000,
prob_col = Yes,
truth_col = Churn)
Find optimal threshold for churn prediction (class)
Description
Finds the optimal threshold (from a business perspective) for classifying churners.
Usage
profit_thresholds(
x,
var_cost = 0,
prob_accept = 1,
tp_val = 0,
fp_val = 0,
tn_val = 0,
fn_val = 0,
prob_col = NA,
truth_col = NA
)
Arguments
x |
A data frame containing predicted probabilities of a target event and the actual outcome/class. |
var_cost |
Variable cost (e.g. of a campaign offer) |
prob_accept |
Probability of offer being accepted. Defaults to 1. |
tp_val |
The average value of a True Positive. 'var_cost' is automatically subtracted. |
fp_val |
The average cost of a False Positive. 'var_cost' is automatically subtracted. |
tn_val |
The average value of a True Negative. |
fn_val |
The average cost of a False Negative. |
prob_col |
The unquoted name of the column with probabilities of the event of interest. |
truth_col |
The unquoted name of the column with the actual outcome/class. Possible values are 'Yes' and 'No'. #' @return A data frame with the following columns: threshold = prediction thresholds |
Examples
profit_thresholds(predictions,
var_cost = 100,
prob_accept = .8,
tp_val = 2000,
fp_val = 0,
tn_val = 0,
fn_val = -2000,
prob_col = Yes,
truth_col = Churn)
Calculate Return on investment (ROI)
Description
Calculates ROI after sorting observations with ROI defined as (Current Value - Start Value) / Start Value
Usage
roi(x, fixed_cost = 0, var_cost = 0, tp_val = 0, prob_col = NA, truth_col = NA)
Arguments
x |
A data frame containing predicted probabilities of a target event and the actual outcome/class. |
fixed_cost |
Fixed cost (e.g. of a campaign) |
var_cost |
Variable cost (e.g. discount offered) |
tp_val |
The average value of a True Positive |
prob_col |
The unquoted name of the column with probabilities of the event of interest. |
truth_col |
The unquoted name of the column with the actual outcome/class. Possible values are 'Yes' and 'No'. |
Value
A data frame with the following columns:
row = row numbers
pct = percentiles
cum_rev = cumulated revenue
cost_sum = cumulated costs
roi = return on investment
Examples
roi(predictions,
fixed_cost = 1000,
var_cost = 100,
tp_val = 2000,
prob_col = Yes,
truth_col = Churn)