Customer Analytics Data Formatting


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Documentation for package ‘CADF’ version 0.1

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annualhalfingmodel Annual Halfing Model
annualhalfing_LL Likelihood maximization for annual halfing customer retention model
bass.answeringmachines Answering machine data
bigT_expand_via_apply bigT_expand_via_apply
billionaire Billionaires
cadf cadf.
cadf.data.sample CADF-formatted sample data
CADF_to_annualhalfing_data Convert CADF dataset into annualhalfing model dataset
CADF_to_btyd_pareto_nbd CADF to btyd pareto nbd model
CADF_to_logistic_regression CADF to logistic regression
CADF_to_migration_model CADF_to_migration_model converts CADF data to migration model data
CADF_to_nth_purchase CADF_to_nth_purchase
CADF_to_nth_purchase_allrows CADF_to_nth_purchase_allrows inputs CADF data and the desired purchase number that you want to count the nth result of.
ca_SRM ca_SRM
ca_SRM_time_varying Time varying Simple retention model Estimates retention rate using logistic regression and the simple regression model Mostly used for contractual models where there are clear opportunities for cancellation. Could be used in non-contractional situations although the cancellation opportunities should be defined. Not recommended for use with services that consumers use rotating-door style. Use the migration model there.
ca_to_ps_matrix CADF to purchase string Extracts purchase strings from the CADF and formats as a R matrix.
create.purchase.string Function called during Customer$new() (the Customer R6 class) to create purchase string for the customer.
create.recency.string create_recency_string
Customer R6 Class representing a customer. Otherwise known as the CADF.
discretechoice Discrete choice
exceldata Excel data
fp Health Data
frequency_from_ps Purchase string to frequency count
frequency_from_rle RLE object to frequency count
f_CustomerModelingMatrix For each customer, return a modeling matrix that is utilized for logistic regression
f_CustomerSurvivalModelingMatrix For each customer, return a survival modeling matrix that is utilized for survival analysis
f_intMonths Compute the months between two purchase dates
gammagamma Gamma gamma spend model data
generate_date_template generate_date_template
id_to_CADF Convert to CADF for a single customer id
ld_sample_customer_matrix LD functions are utilized for learning and diagnostic use.
ltv.transactions LTV transactions data
modeling.annualhalfing.likelihood Likelihood function for annual halfing model
modeling.LL.gamma_spend LL function for the gamma gamma spend model
pdf_gamma PDF probability function for gamma distribution
pdf_gamma2 Probability density function for gamma distribution
print.glossary The glossary for the CADF data format
psmatrix_to_psstring psmatrix_to_psstring
psmatrix_to_recency_attimeof_matrix accepts a psmatrix converts 1/0 purchase strings to recency at timeof
ps_to_T_custom Calculates T from a purchase string. Custom.
ps_to_T_strict_quitter Calculates T from a purchase string
ps_to_T_strict_stayer Calculates T from a purchase string under the "strict stayer" assumption.
qc_transactional_data The customer analytics data format (CADF) relays heavily on correct input data. Transactional data must: 1.) be a data frame with two columns 2.) Column one is the customer id 3.) Column 2 is the transaction date. Column 2 must be formatted as a date object in R.
segltv Segmentation and LTV data
simple_migration Simple Migration
split.transaction.file_to_CADF Create a CADF dataset from a dataframe
srm_data #' Simple retention model data
srm_summaries SRM model data
stocks Stockmarket put/call data
transactions Transactions data
transactions.merged #' Transaction data
transitions Calculate transition periods between two timeperiods