predict.clv.fitted.transactions {CLVTools} | R Documentation |

## Predict CLV from a fitted transaction model

### Description

Probabilistic customer attrition models predict in general three expected characteristics for every customer:

"conditional expected transactions" (

`CET`

), which is the number of transactions to expect from a customer during the prediction period,"probability of a customer being alive" (

`PAlive`

) at the end of the estimation period and"discounted expected residual transactions" (

`DERT`

) for every customer, which is the total number of transactions for the residual lifetime of a customer discounted to the end of the estimation period. In the case of time-varying covariates, instead of`DERT`

, "discounted expected conditional transactions" (`DECT`

) is predicted.`DECT`

does only cover a finite time horizon in contrast to`DERT`

. For`continuous.discount.factor=0`

,`DECT`

corresponds to`CET`

.

In order to derive a monetary value such as CLV, customer spending has to be considered.
If the `clv.data`

object contains spending information, customer spending can be predicted using a Gamma/Gamma spending model for
parameter `predict.spending`

and the predicted CLV is be calculated (if the transaction model supports `DERT/DECT`

).
In this case, the prediction additionally contains the following two columns:

"predicted.mean.spending", the mean spending per transactions as predicted by the spending model.

"CLV", the customer lifetime value. CLV is the product of DERT/DECT and predicted spending.

### Usage

```
## S3 method for class 'clv.fitted.transactions'
predict(
object,
newdata = NULL,
prediction.end = NULL,
predict.spending = gg,
continuous.discount.factor = 0.1,
verbose = TRUE,
...
)
## S4 method for signature 'clv.fitted.transactions'
predict(
object,
newdata = NULL,
prediction.end = NULL,
predict.spending = gg,
continuous.discount.factor = 0.1,
verbose = TRUE,
...
)
```

### Arguments

`object` |
A fitted clv transaction model for which prediction is desired. |

`newdata` |
A clv data object for which predictions should be made with the fitted model. If none or NULL is given, predictions are made for the data on which the model was fit. |

`prediction.end` |
Until what point in time to predict. This can be the number of periods (numeric) or a form of date/time object. See details. |

`predict.spending` |
Whether and how to predict spending and based on it also CLV, if possible. See details. |

`continuous.discount.factor` |
continuous discount factor to use to calculate |

`verbose` |
Show details about the running of the function. |

`...` |
Ignored |

### Details

`predict.spending`

indicates whether to predict customers' spending and if so, the spending model to use.
Accepted inputs are either a logical (`TRUE/FALSE`

), a method to fit a spending model (i.e. `gg`

), or
an already fitted spending model. If provided `TRUE`

, a Gamma-Gamma model is fit with default options. If argument
`newdata`

is provided, the spending model is fit on `newdata`

. Predicting spending is only possible if
the transaction data contains spending information. See examples for illustrations of valid inputs.

The `newdata`

argument has to be a clv data object of the exact same class as the data object
on which the model was fit. In case the model was fit with covariates, `newdata`

needs to contain identically
named covariate data.

The use case for `newdata`

is mainly two-fold: First, to estimate model parameters only on a
sample of the data and then use the fitted model object to predict or plot for the full data set provided through `newdata`

.
Second, for models with dynamic covariates, to provide a clv data object with longer covariates than contained in the data
on which the model was estimated what allows to predict or plot further. When providing `newdata`

, some models
might require additional steps that can significantly increase runtime.

`prediction.end`

indicates until when to predict or plot and can be given as either
a point in time (of class `Date`

, `POSIXct`

, or `character`

) or the number of periods.
If `prediction.end`

is of class character, the date/time format set when creating the data object is used for parsing.
If `prediction.end`

is the number of periods, the end of the fitting period serves as the reference point
from which periods are counted. Only full periods may be specified.
If `prediction.end`

is omitted or NULL, it defaults to the end of the holdout period if present and to the
end of the estimation period otherwise.

The first prediction period is defined to start right after the end of the estimation period.
If for example weekly time units are used and the estimation period ends on Sunday 2019-01-01, then the first day
of the first prediction period is Monday 2019-01-02. Each prediction period includes a total of 7 days and
the first prediction period therefore will end on, and include, Sunday 2019-01-08. Subsequent prediction periods
again start on Mondays and end on Sundays.
If `prediction.end`

indicates a timepoint on which to end, this timepoint is included in the prediction period.

`continuous.discount.factor`

is the continuous rate used to discount the expected residual
transactions (`DERT/DECT`

). An annual rate of (100 x d)% equals a continuous rate delta = ln(1+d).
To account for time units which are not annual, the continuous rate has to be further adjusted
to delta=ln(1+d)/k, where k are the number of time units in a year.

### Value

An object of class `data.table`

with columns:

`Id` |
The respective customer identifier |

`period.first` |
First timepoint of prediction period |

`period.last` |
Last timepoint of prediction period |

`period.length` |
Number of time units covered by the period indicated by |

`PAlive` |
Probability to be alive at the end of the estimation period |

`CET` |
The Conditional Expected Transactions |

`DERT or DECT` |
Discounted Expected Residual Transactions or Discounted Expected Conditional Transactions for dynamic covariates models |

`actual.x` |
Actual number of transactions until prediction.end. Only if there is a holdout period and the prediction ends in it, otherwise it is not reported. |

`actual.total.spending` |
Actual total spending until prediction.end. Only if there is a holdout period and the prediction ends in it, otherwise it is not reported. |

`predicted.mean.spending` |
The mean spending per transactions as predicted by the spending model. |

`predicted.CLV` |
Customer Lifetime Value based on |

### See Also

models to predict transactions: pnbd, bgnbd, ggomnbd.

models to predict spending: gg.

`predict`

for spending models

### Examples

```
data("apparelTrans")
# Fit pnbd standard model on data, WITH holdout
apparel.holdout <- clvdata(apparelTrans, time.unit="w",
estimation.split=37, date.format="ymd")
apparel.pnbd <- pnbd(apparel.holdout)
# Predict until the end of the holdout period
predict(apparel.pnbd)
# Predict until 10 periods (weeks in this case) after
# the end of the 37 weeks fitting period
predict(apparel.pnbd, prediction.end = 10) # ends on 2010-11-28
# Predict until 31th Dec 2016 with the timepoint as a character
predict(apparel.pnbd, prediction.end = "2016-12-31")
# Predict until 31th Dec 2016 with the timepoint as a Date
predict(apparel.pnbd, prediction.end = lubridate::ymd("2016-12-31"))
# Predict future transactions but not spending and CLV
predict(apparel.pnbd, predict.spending = FALSE)
# Predict spending by fitting a Gamma-Gamma model
predict(apparel.pnbd, predict.spending = gg)
# Fit a spending model separately and use it to predict spending
apparel.gg <- gg(apparel.holdout, remove.first.transaction = FALSE)
predict(apparel.pnbd, predict.spending = apparel.gg)
# Fit pnbd standard model WITHOUT holdout
pnc <- pnbd(clvdata(apparelTrans, time.unit="w", date.format="ymd"))
# This fails, because without holdout, a prediction.end is required
## Not run:
predict(pnc)
## End(Not run)
# But it works if providing a prediction.end
predict(pnc, prediction.end = 10) # ends on 2016-12-17
```

*CLVTools*version 0.10.0 Index]