第63回発表資料です。

1. 1

5. ü
ü

6. LTV
LTV
LTV

8. 1.
2. 1
3.
4.

9. A
B
C
0.9
0.7
0.4
2.0
2

10. P |

12. 1. λ
2. µ
3. λ
4. µ 3
5. Λ µ
5

13. 0 Tt
X
×
τ

14. 0 Tt
X
×
τ

15. 0 Tt
X
×
τ

16. 0 Tt
X
×
τ

17. 0 Tt
X
×
τ

18. 0 Tt
X
×
τ

19. 0 Tt
X
×
τ
λ, µ

20. 0 Tt
X
×
τ
r, s, α, β

21. 0 Tt
X
×
τ
T

24. ID
1 19970101 2 29.33
1 19970118 2 29.73
1 19970802 1 14.96
1 19971212 2 26.48
2 19970101 3 63.34
2 19970113 1 11.77
3 19970101 1 6.79
4 19970101 1 13.97

25. > library(dplyr)
> library(BTYD)
> cdnowElog<-system.file("data/cdnowElog.csv",package="BTYD")
> elog<-dc.ReadLines(cdnowElog,cust.idx=2,date.idx=3,sales.idx=5)
> elog$date<-as.Date(elog$date,"%Y%m%d")
> head(elog)
cust date sales
1 1 1997-01-01 29.33
2 1 1997-01-18 29.73
3 1 1997-08-02 14.96
4 1 1997-12-12 26.48
5 2 1997-01-01 63.34
6 2 1997-01-13 11.77

26. > nrow(elog)
[1] 6919
> elog<-dc.MergeTransactionsOnSameDate(elog);
Started merging same-date transactions...
... Finished merging same-date transactions.
> nrow(elog)
[1] 6696

27. > elog.cal<-elog[which(elog$date<= as.Date("1997-09-30"), ]
> split.data<-dc.SplitUpElogForRepeatTrans(elog.cal);
> freq.cbt<-dc.CreateFreqCBT(split.data$repeat.trans.elog);
> tot.cbt<-dc.CreateFreqCBT(elog)
> cal.cbt<-dc.MergeCustomers(tot.cbt, freq.cbt)
> birth.periods<-split.data$cust.data$birth.per
> last.dates<-split.data$cust.data$last.date
> cal.cbs.dates<-data.frame(birth.periods, last.dates,end.of.cal.period)
> cal.cbs<-dc.BuildCBSFromCBTAndDates(cal.cbt,cal.cbs.dates,per="week")
> head(cal.cbs)
x t.x T.cal
1 2 30.428571 38.85714
2 1 1.714286 38.85714
3 0 0.000000 38.85714
4 0 0.000000 38.85714
5 0 0.000000 38.85714
6 7 29.428571 38.85714
X, t, T

28. > params<-pnbd.EstimateParameters(cal.cbs);
> params
[1] 0.5533971 10.5801985 0.6060625 11.6562237
> LL<-pnbd.cbs.LL(params, cal.cbs);
> p.matrix<-c(params, LL);
> for(i in 1:2){
+ params<-pnbd.EstimateParameters(cal.cbs, params);
+ LL<-pnbd.cbs.LL(params, cal.cbs);
+ p.matrix.row<-c(params, LL);p.matrix<-rbind(p.matrix, p.matrix.row);
+ }
> colnames(p.matrix)<-c("r","alpha","s","beta","LL");
> rownames(p.matrix)<-1:3;p.matrix;
r alpha s beta LL
1 0.5533971 10.58020 0.6060625 11.65622 -9594.976
2 0.5534354 10.57952 0.6060276 11.65666 -9594.976
3 0.5533710 10.57952 0.6059074 11.65812 -9594.976

29. > pnbd.PlotTransactionRateHeterogeneity(params)
> pnbd.PlotDropoutRateHeterogeneity(params)

30. > pnbd.Expectation(params,t=52);
[1] 1.473434
> cal.cbs["1516",]
x t.x T.cal
26.00000 30.85714 31.00000
> x<-cal.cbs["1516","x"]
> t.x<-cal.cbs["1516","t.x"]
> T.cal<-cal.cbs["1516","T.cal"]
> pnbd.ConditionalExpectedTransactions(params,T.star=52,x, t.x, T.cal)
[1] 25.45647
> pnbd.PAlive(params, x, t.x, T.cal)
[1] 0.997874
← 52
←
← 52
←

31. > pnbd.PlotFrequencyInCalibration(params, cal.cbs, 7)

32. > comp<-bgnbd.PlotFreqVsConditionalExpectedFrequency(params,
T.star,cal.cbs, x.star, censor)

33. • Counting Your Customers: Who Are They and What Will They Do Next?
David C. Schmittlein, Donald G. Morrison and Richard Colombo, 1987)
• Buy ’Til You Die - A Walkthrough Daniel McCarthy, Edward Wadsworth,
2014

34. Thank you!