gg {CLVTools} | R Documentation |

## Gamma/Gamma Spending model

### Description

Fits the Gamma-Gamma model on a given object of class `clv.data`

to predict customers' mean
spending per transaction.

### Usage

```
## S4 method for signature 'clv.data'
gg(
clv.data,
start.params.model = c(),
remove.first.transaction = TRUE,
optimx.args = list(),
verbose = TRUE,
...
)
```

### Arguments

`clv.data` |
The data object on which the model is fitted. |

`start.params.model` |
Named start parameters containing the optimization start parameters for the model without covariates. |

`remove.first.transaction` |
Whether customer's first transaction are removed. If |

`optimx.args` |
Additional arguments to control the optimization which are forwarded to |

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

`...` |
Ignored |

### Details

Model parameters for the G/G model are `p, q, and gamma`

.

`p`

: shape parameter of the Gamma distribution of the spending process.

`q`

: shape parameter of the Gamma distribution to account for customer heterogeneity.

`gamma`

: scale parameter of the Gamma distribution to account for customer heterogeneity.

If no start parameters are given, 1.0 is used for all model parameters. All parameters are required
to be > 0.

The Gamma-Gamma model cannot be estimated for data that contains negative prices. Customers with a mean spending of zero or a transaction count of zero are ignored during model fitting.

#### The G/G model

The G/G model allows to predict a value for future customer transactions. Usually, the G/G model is used in combination with a probabilistic model predicting customer transaction such as the Pareto/NBD or the BG/NBD model.

### Value

An object of class clv.gg is returned.

The function `summary`

can be used to obtain and print a summary of the results.
The generic accessor functions `coefficients`

, `vcov`

, `fitted`

,
`logLik`

, `AIC`

, `BIC`

, and `nobs`

are available.

### References

Colombo R, Jiang W (1999). “A stochastic RFM model.” Journal of Interactive Marketing, 13(3), 2-12.

Fader PS, Hardie BG, Lee K (2005). “RFM and CLV: Using Iso-Value Curves for Customer Base Analysis.” Journal of Marketing Research, 42(4), 415-430.

Fader PS, Hardie BG (2013). “The Gamma-Gamma Model of Monetary Value.” URL http://www.brucehardie.com/notes/025/gamma_gamma.pdf.

### See Also

`clvdata`

to create a clv data object.

`plot`

to plot diagnostics of the transaction data, incl. of spending.

`predict`

to predict expected mean spending for every customer.

`plot`

to plot the density of customer's mean transaction value compared to the model's prediction.

### Examples

```
data("apparelTrans")
clv.data.apparel <- clvdata(apparelTrans, date.format = "ymd",
time.unit = "w", estimation.split = 40)
# Fit the gg model
gg(clv.data.apparel)
# Give initial guesses for the model parameters
gg(clv.data.apparel,
start.params.model = c(p=0.5, q=15, gamma=2))
# pass additional parameters to the optimizer (optimx)
# Use Nelder-Mead as optimization method and print
# detailed information about the optimization process
apparel.gg <- gg(clv.data.apparel,
optimx.args = list(method="Nelder-Mead",
control=list(trace=6)))
# estimated coefs
coef(apparel.gg)
# summary of the fitted model
summary(apparel.gg)
# Plot model vs empirical distribution
plot(apparel.gg)
# predict mean spending and compare against
# actuals in the holdout period
predict(apparel.gg)
```

*CLVTools*version 0.10.0 Index]