Business Case: “Netflix: The Customer Strikes Back” in Cutting Edge Marketing Analytics
#read in data
CLV_data2 <- CLV_data[c(2,4,5,8,11:15,18,19),3:45]
rnames <- c("Revenues","Cost","Expenses","Tech.Dev","Num.Sub", "Sub.Add", "Retention", "Cost.Rev", "Acq.Cost", "Share.Price", "Num.Share")
rownames(CLV_data2) <- rnames
colnames(CLV_data2) <- 1:43To analyze the lifetime value of a Netflix customer for each quarter, the “CLV with Initial Margin” was calculated assuming a 10% annual discount rate. Revenue and cost numbers are given in thousands.
To determine the margin, determined that: Cost of Revenues = Cost of subscription + Fulfillment Expenses and Margin (M) = Revenues - (Cost of Revenues + Tech&Development)
The retention rate was calculated in excel prior to loading in data (#Subscribers - Gross Additions)/(#Subscribers previous period)
d <- 0.1/4 #10% annual discount rate, quarterly
Revs <- 1000*CLV_data2[3,] #Revenue given in 1,000s
Cost.Rev <- 1000*CLV_data2[9,]
Tech.Dev <- 1000*CLV_data2[5,]
M <- Revs - (Cost.Rev + Tech.Dev)
r <- CLV_data2[7,]
nsub <- CLV_data2[5,]The Customer Lifetime Value Model that was used (page 136 of text):
CLV(initial margin)=$M[(1+d)/(1+d-r)];d=discount rate,r=retention rate,M=margin
CLV <- round(M*(r/(1+d-r)))/nsub
rownames(CLV) <- "CLV"
#To print CLV with appropriate Quarters labeled
periods <- CLV_data_dates[1,3:45]
colnames(periods) <- 1:43
rownames(periods) <- "Quarter"
CLV_period <- rbind(periods,CLV)
CLV_period## 1 2 3
## Quarter 06/01/01 09/01/01 12/01/01
## CLV 24968.3333333333 18369.9174603175 37670.8355263158
## 4 5 6
## Quarter 03/01/02 06/01/02 09/01/02
## CLV 7350.28291814947 11736.0769230769 11327.4560357675
## 7 8 9 10
## Quarter 12/01/02 03/01/03 06/01/03 09/01/03
## CLV 20040.3733955659 10905.4558969277 13728.83507109 16564.20221843
## 11 12 13 14
## Quarter 12/01/03 03/01/04 06/01/04 09/01/04
## CLV 12475.9362745098 12608.7896995708 27315.1877909984 9006.3
## 15 16 17
## Quarter 12/01/04 03/01/05 06/01/05
## CLV 22265.4072790295 14178.4789728322 23531.9499666444
## 18 19 20 21
## Quarter 09/01/05 12/01/05 03/01/06 06/01/06
## CLV 11416.1013472137 15992.144966443 15161.3988584475 19631.4052502051
## 22 23 24
## Quarter 09/01/06 12/01/06 03/01/07
## CLV 13113.7386255473 14341.2948651898 16506.2910366329
## 25 26 27 28
## Quarter 06/01/07 09/07/07 12/08/07 03/08/08
## CLV 21165.9900647298 14259.604578564 14570.3062376517 15397.6219859995
## 29 30 31
## Quarter 06/01/08 09/08/08 12/08/08
## CLV 20891.1266984943 16031.5829247878 12956.7776141384
## 32 33 34
## Quarter 03/08/09 06/01/09 09/08/09
## CLV 14233.6970954357 17354.2983603357 11900.4771334276
## 35 36 37
## Quarter 12/08/09 03/10/10 06/10/10
## CLV 10774.1312038015 10919.0123069687 14100.9782978723
## 38 39 40 41
## Quarter 09/10/10 12/10/10 03/10/11 06/10/11
## CLV 8133.28488830486 5905.98054611508 7000.24771890911 9200.8875257455
## 42 43
## Quarter 09/10/11 12/11/11
## CLV 7239.96876762243 5862.30560142258Or can also calculate using the finite loop as shown in class:
CLV_fin <- 0
CLV_fin_sub <- 0
for (i in 1:12){
CLV_q <- M*( r^i /((1+d)^i) )
CLV_fin <- CLV_fin + CLV_q
CLV_q_sub <- (M/nsub)*( r^i /((1+d)^i) )
CLV_fin_sub <- CLV_fin_sub + CLV_q_sub
}
rownames(CLV_fin_sub) <- "CLV Fin"
CLV_fin_sub## 1 2 3 4 5 6 7
## CLV Fin 24608.01 18236.4 33086.25 7346.095 11694.87 11269.92 18851.76
## 8 9 10 11 12 13 14
## CLV Fin 10811.02 13530.71 16174.06 12400.03 12454.94 25144.21 8715.396
## 15 16 17 18 19 20 21
## CLV Fin 21886.25 13844.53 21043.1 10985.61 15289.48 14262.16 16886.7
## 22 23 24 25 26 27 28
## CLV Fin 12335.95 13355.77 14991.63 17771.74 13279.69 13412.28 14173.84
## 29 30 31 32 33 34 35
## CLV Fin 17540.95 14560.49 12188.3 13255.56 15242.26 11082.68 10135.11
## 36 37 38 39 40 41 42
## CLV Fin 10168.66 12129.49 7775.926 5766.876 6692.672 8081.128 6742.442
## 43
## CLV Fin 5562.761CLV_period2 <- rbind(periods,round(CLV_fin_sub))
CLV_period2## 1 2 3 4 5 6 7
## Quarter 06/01/01 09/01/01 12/01/01 03/01/02 06/01/02 09/01/02 12/01/02
## CLV Fin 24608 18236 33086 7346 11695 11270 18852
## 8 9 10 11 12 13 14
## Quarter 03/01/03 06/01/03 09/01/03 12/01/03 03/01/04 06/01/04 09/01/04
## CLV Fin 10811 13531 16174 12400 12455 25144 8715
## 15 16 17 18 19 20 21
## Quarter 12/01/04 03/01/05 06/01/05 09/01/05 12/01/05 03/01/06 06/01/06
## CLV Fin 21886 13845 21043 10986 15289 14262 16887
## 22 23 24 25 26 27 28
## Quarter 09/01/06 12/01/06 03/01/07 06/01/07 09/07/07 12/08/07 03/08/08
## CLV Fin 12336 13356 14992 17772 13280 13412 14174
## 29 30 31 32 33 34 35
## Quarter 06/01/08 09/08/08 12/08/08 03/08/09 06/01/09 09/08/09 12/08/09
## CLV Fin 17541 14560 12188 13256 15242 11083 10135
## 36 37 38 39 40 41 42
## Quarter 03/10/10 06/10/10 09/10/10 12/10/10 03/10/11 06/10/11 09/10/11
## CLV Fin 10169 12129 7776 5767 6693 8081 6742
## 43
## Quarter 12/11/11
## CLV Fin 5563I expected that the decline of physical discs will be advantageous to Netflix’s business model, as they are able to convert their own physical disk customers to their own digital platform. However, with the rise of the digital platform, there will also be an increase in competition that will put pressure on Netflix to improve product offerings and pricing.
From the customer acquisition graph (created in Excel, see submitted file “Netflixgraphs.jpg”) we can see that Netflix has been able to increasingly add new customers over time. Revenues have also been increasing over time. Customer retention has also leveled off at a reasonable level between 0.7 - 0.8. Customer Lifetime Value, on the other hand, was negative in 2001, went positive in 2002, but went back negative in 2011. This drastic change in CLV is more a factor of changes in the profit margin than it is of any other factors.
Therefore, CLV is likely not a good indicator for Hunter Keay to use in order to determine the value of Netflix stock long-term. Because CLV is directly correlated with the present profit margin, it is a volatile metric and not a good indicator of long-term strategy.
Netflix needs to specifically define how its offerings and prices are unique compared to its competitors in order to continue to successfully compete in this industry. -For physical disks, Redbox is the clear competitor, offering physical videos on demand for a fraction of the price of Netflix. However, it can compete by providing better offerings and higher customer retention because of its subscription model, whereas Redbox’s revenue comes from a la carte. -For streaming video on demand, Amazon Prime is Netflix’s primary competition. Both have subscription revenue models, while Amazon Prime is $1.40 cheaper. Because this is a growing market segment, Netflix needs to either lower prices or find ways to make sure that its product is superior to Amazon’s.
While Netflix could consider selling DVDs, this would not fit neatly into their current subscription model. More research needs to be done to determine how it would affect CLV. If selling DVDs would positively impact Netflix’s profit margin, it could be a good idea. However, it is likely that the cost to implement this new offering would not make it profitable. On the otherhand, if selling DVDs has enough of a positive impact on customer retention and customer acquisition, it could be a good idea.