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8 bgbb.DERT are as long as the longest vector. It is advisable to keep vectors to the same length and to use single values for parameters that are to be the same for all calculations. If one of these parameters has a length greater than one, the output will be a vector of probabilities. Value The number of transactions a customer is expected to make in the n.star transaction opportunities following the calibration period, conditional on their behavior during the calibration period. References Fader, Peter S., Bruce G.S. Hardie, and Jen Shang. “Customer-Base Analysis in a Discrete-Time Noncontractual Setting.” Marketing Science 29(6), pp. 1086-1108. 2010. INFORMS. http: //www.brucehardie.com/papers/020/ Examples params
gbb.DERT 9 Arguments params x t.x n.cal d rf.matrix BG/BB parameters - a vector with alpha, beta, gamma, and delta, in that order. Alpha and beta are unobserved parameters for the beta-Bernoulli transaction process. Gamma and delta are unobserved parameters for the beta-geometric dropout process. number of repeat transactions a customer made in the calibration period, or a vector of calibration period transaction frequencies. recency - the last transaction opportunity in which a customer made a transaction, or a vector of recencies. number of transaction opportunities in the calibration period, or a vector of calibration period transaction opportunities. discount rate. recency-frequency matrix. It must contain columns for frequency ("x"), recency ("t.x"), and the number of transaction opportunities in the calibration period ("n.cal"). Note that recency must be the time between the start of the calibration period and the customer’s last transaction, not the time between the customer’s last transaction and the end of the calibration period. Details Value DERT(d | alpha, beta, gamma, delta, x, t.x, n). This is the present value of the expected future transaction stream for a customer with x transactions and a recency of t.x in n.cal transaction opportunities, discounted by a rate d. x, t.x, and n.cal may be vectors. The standard rules for vector operations apply - if they are not of the same length, shorter vectors will be recycled (start over at the first element) until they are as long as the longest vector. It is advisable to keep vectors to the same length and to use single values for parameters that are to be the same for all calculations. If one of these parameters has a length greater than one, the output will be also be a vector. The present value of the expected future transaction stream for a particular customer. References Fader, Peter S., Bruce G.S. Hardie, and Jen Shang. “Customer-Base Analysis in a Discrete-Time Noncontractual Setting.” Marketing Science 29(6), pp. 1086-1108. 2010. INFORMS. http: //www.brucehardie.com/papers/020/ See equation 14. Examples params
Page 1 and 2: Type Package Title Implementing Buy
Page 3 and 4: BTYD-package 3 dc.GetLastPurchasePe
Page 5 and 6: BTYD-package 5 Package: BTYD Type:
Page 7: gbb.ConditionalExpectedTransactions
Page 11 and 12: gbb.Expectation 11 Details The best
Page 13 and 14: gbb.HeatmapHoldoutExpectedTrans 13
Page 15 and 16: gbb.PAlive 15 Value For bgbb.cbs.LL
Page 17 and 18: gbb.PlotDropoutRateHeterogeneity 17
Page 19 and 20: gbb.PlotFrequencyInHoldout 19 # plo
Page 21 and 22: gbb.PlotFreqVsConditionalExpectedFr
Page 23 and 24: gbb.PlotTrackingCum 23 Value Holdou
Page 25 and 26: gbb.PlotTrackingInc 25 # get the an
Page 27 and 28: gbb.pmf 27 Arguments params BG/BB p
Page 29 and 30: gbb.pmf.General 29 Arguments params
Page 31 and 32: gbb.PosteriorMeanLmProductMoment 31
Page 33 and 34: gnbd.compress.cbs 33 rf.matrix rece
Page 35 and 36: gnbd.ConditionalExpectedTransaction
Page 37 and 38: gnbd.EstimateParameters 37 Details
Page 39 and 40: gnbd.ExpectedCumulativeTransactions
Page 41 and 42: gnbd.PAlive 41 Details x, t.x, T.ca
Page 43 and 44: gnbd.PlotDropoutRateHeterogeneity 4
Page 45 and 46: gnbd.PlotFreqVsConditionalExpectedF
Page 47 and 48: gnbd.PlotRecVsConditionalExpectedFr
Page 49 and 50: gnbd.PlotTrackingInc 49 Examples da
Page 51 and 52: gnbd.pmf 51 Arguments params lim BG
Page 53 and 54: gnbd.pmf.General 53 Arguments param
Page 55 and 56: dc.BuildCBSFromCBTAndDates 55 Detai
Page 57 and 58: dc.BuildCBTFromElog 57 dc.BuildCBTF
Page 59 and 60:
dc.CreateSpendCBT 59 Value Reach cu
Page 61 and 62:
dc.ElogToCbsCbt 61 Arguments Value
Page 63 and 64:
dc.GetFirstPurchasePeriodsFromCBT 6
Page 65 and 66:
dc.MakeRFmatrixCal 65 Arguments fre
Page 67 and 68:
dc.MergeCustomers 67 Value Matrix w
Page 69 and 70:
dc.MergeTransactionsOnSameDate 69 #
Page 71 and 72:
dc.PlotLogLikelihoodContour 71 Exam
Page 73 and 74:
dc.PlotLogLikelihoodContours 73 Det
Page 75 and 76:
dc.RemoveTimeBetween 75 Value Event
Page 77 and 78:
discreteSimElog 77 discreteSimElog
Page 79 and 80:
pnbd.ConditionalExpectedTransaction
Page 81 and 82:
pnbd.DERT 81 pnbd.DERT Pareto/NBD D
Page 83 and 84:
pnbd.EstimateParameters 83 Details
Page 85 and 86:
pnbd.ExpectedCumulativeTransactions
Page 87 and 88:
pnbd.LL 87 Details x, t.x, T.cal ma
Page 89 and 90:
pnbd.Plot.DERT 89 Examples params
Page 91 and 92:
pnbd.PlotFrequencyInCalibration 91
Page 93 and 94:
pnbd.PlotFreqVsConditionalExpectedF
Page 95 and 96:
pnbd.PlotTrackingCum 95 Value Matri
Page 97 and 98:
pnbd.PlotTrackingInc 97 Usage pnbd.
Page 99 and 100:
pnbd.pmf 99 pnbd.pmf Pareto/NBD Pro
Page 101 and 102:
spend.EstimateParameters 101 Value
Page 103 and 104:
spend.LL 103 m.x x the customer’s
Page 105 and 106:
spend.marginal.likelihood 105 sum(s
Page 107 and 108:
spend.plot.average.transaction.valu
Page 109:
INDEX 109 dc.FilterCustByBirth, 62
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