Hi Hemant,
see inline below.
On Tue, Oct 10, 2017 at 4:19 PM, Hemant Sain <hemantsai...@gmail.com> wrote:
> Hello Jim,
> i have converted all my variable data type according to your attached
> example including date, and my dataset looks like this.
>
>
> ID purchase date
> 1234 10.2 2017-02-18
> 3453 18.9 2017-03-22
> 7689 8 2017-03-24
>
As I don't have your data set, I can't tell you why you are getting
the same values. First, I'll create a data set that looks something
like your example, except with 50 customers and 250 transactions, all
in 2017:
set.seed(12345)
x3<-data.frame(ID=sample(1234:1283,250,TRUE),
purchase=round(runif(250,5,100),2),
date=paste(rep(2017,250),sample(1:9,250,TRUE),
sample(1:28,250,TRUE),sep="-"))
Look at it carefully. Is there anything that you think is wrong?
>
>
> but when I'm passing the data into the function it is giving me same values
> for entire observations i. r=2, f=2, m=2
>
> and which part of your code is responsible to calculate recency and
> frequency score i mean how it will determine how many times a user made a
> purchase in last 30 days so that we can put that user into our own defined
> category.
>
Here is the function commented for easier understanding.
Recency is calculated as the most recent purchase for each customer
from the "finish" date, which defaults to the current date. If you are
examining historical data, you may want to set a different "finish"
date.
Frequency is simply the number of purchases recorded for each customer.
Monetary is the sum of the purchase amounts for each customer
The default breaks for each score are those calculated by the "cut"
function. If you want specific breaks, they _must_ cover the range of
the values or cut will generate NAs. I have added a printout of the
ranges of the raw recency, frequency and monetary scores so that you
can enter your own breaks.
qdrfm<-function(x,rbreaks=3,fbreaks=3,mbreaks=3,
date.format="%Y-%m-%d",weights=c(1,1,1),finish=NA) {
# if no finish date is specified, use current date
if(is.na(finish)) finish<-as.Date(date(), "%a %b %d %H:%M:%S %Y")
x$rscore<-as.numeric(finish-as.Date(x[,3],date.format))
cat("Range of purchase recency",range(x$rscore),"\n")
x$rscore<-as.numeric(cut(x$rscore,breaks=rbreaks,labels=FALSE))
cat("Range of purchase freqency",range(table(x[,1])),"\n")
cat("Range of purchase amount",range(by(x[,2],x[,1],sum)),"\n")
custIDs<-unique(x[,1])
ncust<-length(custIDs)
# initialize a data frame to hold the output
rfmout<-data.frame(custID=custIDs,rscore=rep(0,ncust),
fscore=rep(0,ncust),mscore=rep(0,ncust))
# categorize the minimum number of days
# since last purchase for each customer
rfmout$rscore<-cut(by(x$rscore,x[,1],min),breaks=rbreaks,labels=FALSE)
# categorize the number of purchases
# recorded for each customer
rfmout$fscore<-cut(table(x[,1]),breaks=fbreaks,labels=FALSE)
# categorize the amount purchased
# by each customer
rfmout$mscore<-cut(by(x[,2],x[,1],sum),breaks=mbreaks,labels=FALSE)
# calculate the RFM score from the
# optionally weighted average of the above
rfmout$cscore<-round((weights[1]*rfmout$rscore+
weights[2]*rfmout$fscore+
weights[3]*rfmout$mscore)/sum(weights),2)
return(rfmout[order(rfmout$cscore),])
}
# run the dataset with default breaks
qdrfm(x3)
# now specify breaks with respect to the printout of the raw scores
qdrfm(x3,rbreaks=c(0,150,300),fbreaks=c(0,5,11),mbreaks=c(0,300,600))
# now give the total amount purchased twice the weight
qdrfm(x3,rbreaks=c(0,150,300),fbreaks=c(0,5,11),
mbreaks=c(0,300,600),weights=c(1,1,2))
I hope that this will explain the function better.
Jim
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