def relationBetweenPurchaseValueFrequency():
#gamma gamma model has an assumption that there is no relationship between purchase value and the frequency.
#let find out the correlation between them.
summaryDataFromTransactionDataForCLV = readsummaryDataFromTransactionDataForCLV(
)
print(summaryDataFromTransactionDataForCLV[["monetary_value",
"frequency"]].corr())
def conditionalExpectedAverageProfit():
gammaGammaFitterModel = readGammaGammaFitterModel()
summaryDataFromTransactionDataForCLV = readsummaryDataFromTransactionDataForCLV(
)
summaryDataFromTransactionDataForCLV["pred_txn_value"] = round(
gammaGammaFitterModel.conditional_expected_average_profit(
summaryDataFromTransactionDataForCLV["frequency"],
summaryDataFromTransactionDataForCLV["monetary_value"]), 2)
print(summaryDataFromTransactionDataForCLV.head(10))
def trainGammaGammaModel():
summaryDataFromTransactionDataForCLV = readsummaryDataFromTransactionDataForCLV(
)
#getting those customers who have done at least one transaction with the company
shortlistedCustomers = summaryDataFromTransactionDataForCLV[
summaryDataFromTransactionDataForCLV["frequency"] > 0]
gammaGammaFitterModel = GammaGammaFitter(penalizer_coef=0.0)
gammaGammaFitterModel.fit(shortlistedCustomers["frequency"],
shortlistedCustomers["monetary_value"])
saveGammaGammaFitterModel(gammaGammaFitterModel)
def trainBetaGeoFitterModel():
summaryDataFromTransactionDataForCLV = readsummaryDataFromTransactionDataForCLV(
)
#training model
betaGeoFitterModel = BetaGeoFitter(penalizer_coef=0.0)
betaGeoFitterModel.fit(summaryDataFromTransactionDataForCLV["frequency"],
summaryDataFromTransactionDataForCLV["recency"],
summaryDataFromTransactionDataForCLV["T"])
#saving the model in pickle file
saveBetaGeoFitterModel(betaGeoFitterModel)
print(betaGeoFitterModel.summary)
def customerLifeTimeValue():
gammaGammaFitterModel = readGammaGammaFitterModel()
betaGeoFitterModel = readBetaGeoFitterModel()
summaryDataFromTransactionDataForCLV = readsummaryDataFromTransactionDataForCLV(
)
#calculate customerlife time value
summaryDataFromTransactionDataForCLV["CLV"] = round(
gammaGammaFitterModel.customer_lifetime_value(
betaGeoFitterModel,
summaryDataFromTransactionDataForCLV["frequency"],
summaryDataFromTransactionDataForCLV["recency"],
summaryDataFromTransactionDataForCLV["T"],
summaryDataFromTransactionDataForCLV["monetary_value"],
time=12,
discount_rate=0.01), 2)
print(
summaryDataFromTransactionDataForCLV.sort_values(
by="CLV", ascending=False).head(10).reset_index())