The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Type: Package
Title: Projecting Customer Retention Based on Fader and Hardie Probability Models
Version: 0.2.0
Author: Srihari Jaganathan
Maintainer: Srihari Jaganathan <sriharitn@gmail.com>
Description: Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class Discrete Weibull Models.This package is based on Fader and Hardie (2007) <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.
Depends: R (≥ 3.0.1)
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: stats,nloptr
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-04-08 04:40:25 UTC; haria
Repository: CRAN
Date/Publication: 2019-04-08 05:02:53 UTC

Beta Geometric (BG) Model for Projecting Customer Retention.

Description

BG is a beta geometric model implemented based on Fader and Hardie probability based projection methedology. The survivor function for BG is

Beta(a,b+t)/Beta(a,b)

Usage

BG(surv_value, h, lower = c(0.001, 0.001))

Arguments

surv_value

a numeric vector of historical customer retention percentage should start at 100 and non-starting values should be between 0 and less than 100

h

forecasting horizon

lower

lower limit used in R optim rotuine. Default is c(1e-3,1e-3).

Value

fitted:

Fitted values based on historical data

projected:

Projected h values based on historical data

max.likelihood:

Maximum Likelihood of Beta Geometric

params - a, b:

Returns a and b paramters from maximum likelihood estimation for beta distribution

References

Fader P, Hardie B. How to project customer retention. Journal of Interactive Marketing. 2007;21(1):76-90.

Examples

surv_value <- c(100,86.9,74.3,65.3,59.3)
h <- 6
BG(surv_value,h)

Beta discrete Weibull (BdW) Model for Projecting Customer Retention.

Description

BdW is a beta discrete weibull model implemented based on Fader and Hardie probability based projection methedology. The survivor function for BdW is

Beta(a,b+t^c)/Beta(a,b)

Usage

BdW(surv_value, h, lower = c(0.001, 0.001, 0.001), upper = c(10000,
  10000, 10000))

Arguments

surv_value

a numeric vector of historical customer retention percentage should start at 100 and non-starting values should be between 0 and less than 100

h

forecasting horizon

lower

lower limit used in R optim rotuine. Default is c(1e-3,1e-3).

upper

upper limit used in R optim rotuine. Default is c(10000,10000,10000).

Value

fitted:

Fitted values based on historical data

projected:

Projected h values based on historical data

max.likelihood:

Maximum Likelihood of Beta discrete Weibull

params - a, b and c:

Returns a and b paramters from maximum likelihood estimation for beta distribution and c

References

Fader P, Hardie B. How to project customer retention. Journal of Interactive Marketing. 2007;21(1):76-90.

Fader P, Hardie B, Liu Y, Davin J, Steenburgh T. "How to Project Customer Retention" Revisited: The Role of Duration Dependence. Journal of Interactive Marketing. 2018;43:1-16.

Examples

surv_value <- c(100,86.9,74.3,65.3,59.3)
h <- 6
BdW(surv_value,h)

Latent Class Weibull (LCW) Model for Projecting Customer Retention

Description

LCW is a latent class weibull model implementation based on Fader and Hardie probability based projection methedology. The survivor function for LCW is

wS(t|t1,c1)+(1-w)S(t|t2,c2), 0<w<1

Usage

LCW(surv_value, h, lower = c(0.001, 0.001, 0.001, 0.001, 0.001),
  upper = c(0.99999, 10000, 0.999999, 10000, 0.99999))

Arguments

surv_value

a numeric vector of historical customer retention percentage should start at 100 and non-starting values should be between 0 and less than 100

h

forecasting horizon

lower

lower limit used in R optim rotuine. Default is c(0.001,0.001,0.001,0.001,0.001).

upper

upper limit used in R optim rotuine. Default is c(0.99999,10000,0.999999,10000,0.99999).

Value

fitted:

Fitted Values based on historical data

projected:

Projected h values based on historical data

max.likelihood:

Maximum Likelihood of LCW

params - t1, t2, c1, c2, w:

Returns t1,c1,t2,c2,w paramters from maximum likelihood estimation

References

Fader P, Hardie B. How to project customer retention. Journal of Interactive Marketing. 2007;21(1):76-90.

Fader P, Hardie B, Liu Y, Davin J, Steenburgh T. "How to Project Customer Retention" Revisited: The Role of Duration Dependence. Journal of Interactive Marketing. 2018;43:1-16.

Examples

surv_value <- c(100,86.9,74.3,65.3,59.3,55.1,51.7,49.1,46.8,44.5,42.7,40.9,39.4)
h <- 6
LCW(surv_value,h)

Observed % Customers Surviving at Least 0-12 Years

Description

A dataset containing customer retention.

Usage

data(customer_retention)

Format

A data frame 13 observations and 3 variables.

Details

year

Time in years

regular

% of regular customers surviving

high_end

% of high_end customers surviving

References

Fader P, Hardie B. How to project customer retention. Journal of Interactive Marketing. 2007;21(1):76-90.

Excel based trendlines for projecting customer retention.

Description

exltrend generates Microsoft(r) Excel(r) based linear, logarthmic, exponential, polynomial of order 2, power trends.

Usage

exltrend(surv_value, h)

Arguments

surv_value

a numeric vector of historical customer retention percentage should start at 100 and non-starting values should be between 0 and less than 100

h

forecasting horizon

Value

fitted:

A data frame of fitted Values based on historical data for linear (lin.p), exponential (exp.p), logarthmic (log.p), polynomial (poly.p) of order 2 and power (pow.p) trends.

projected:

A data frame of projected h values based on historical data for linear (lin.p), exponential (exp.p), logarthmic (log.p), polynomial (poly.p) of order 2 and power (pow.p) trends.

Examples

surv_value <- c(100,86.9,74.3,65.3,59.3)
h <- 6
exltrend(surv_value,h)

Drug persistency (retention) rates by different therapeutic class.

Description

A dataset containing drug persistency of patients in different therapeutic classes.

Usage

data(persistency_data)

Format

A data frame 334 observatios and 3 variables:

therapy

Type of therapy. Unique values include: "Hypertension" "Occular Hypertension" "Statin" "Insulin" "Epilepsy" "RA" "Osteoporosis" "Alzheimer""ADHD" "Atrial Fibrillation". See references below. Data was extracted using https://automeris.io/WebPlotDigitizer/ and discretized using akima package.

time_period

Time Period

value

% Patients retained

References

Hypertension: Solomon M, Goldman D, Joyce G, Escarce J. Cost Sharing and the Initiation of Drug Therapy for the Chronically Ill.Archives of Internal Medicine. 2009;169(8):740-748.

Occular Hypertension: Campbell J, Schwartz G, LaBounty B, Kowalski J, Patel. Patient adherence and persistence with topical ocular hypotensive therapy in real-world practice: a comparison of bimatoprost 0.01% and travoprost Z 0.004% ophthalmic solutions. Clinical Ophthalmology. 2014;8:927-935.

Statin: Kiss Z, Nagy L, Reiber I, Paragh G, Molnar M, Rokszin G et al. Persistence with statin therapy in Hungary. Archives of Medical Science. 2013;9(3):409-417.

Insulin: Roussel R, Charbonnel B, Behar M, Gourmelen J, Emery C, Detournay B. Persistence with Insulin Therapy in Patients with Type 2 Diabetes in France: An Insurance Claims Study. Diabetes Therapy. 2016;7(3):537-549.

Epilepsy: Lai E, Hsieh C, Su C, Yang Y, Huang C, Lin S et al. Comparative persistence of antiepileptic drugs in patients with epilepsy: A STROBE-compliant retrospective cohort study. Medicine. 2016;95(35):e4481.

RA: Neovius M, Arkema E, Olsson H, Eriksson J, Kristensen L, Simard J et al. Drug survival on TNF inhibitors in patients with rheumatoid arthritis comparison of adalimumab, etanercept and infliximab. Annals of the Rheumatic Diseases. 2013;74(2):354-360.

Osteoporosis: Kishimoto H, Maehara M. Compliance and persistence with daily, weekly, and monthly bisphosphonates for osteoporosis in Japan: analysis of data from the CISA. Archives of Osteoporosis. 2015;10(27):1-6.

Alzheimer: Suh D, Thomas S, Valiyeva E, Arcona S, Vo L. Drug persistency of two cholinesterase inhibitors: rivastigmine versus donepezil in elderly patients with Alzheimer's disease. Drugs & Aging. 2005;22(8):695-707.

ADHD: Beau-Lejdstrom R, Douglas I, Evans S, Smeeth L. Latest trends in ADHD drug prescribing patterns in children in the UK: prevalence, incidence and persistence. BMJ Open. 2016;6(6):1-8.

Atrial Fibrillation: Gomes T, Mamdani M, Holbrook A, Paterson J, Juurlink D. Persistence With Therapy Among Patients Treated With Warfarin for Atrial Fibrillation. Archives of Internal Medicine. 2012;172(21):1687-1689.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.