Customer Analytics
—Individual Research Project—
市场分析代写 Finally, present the correct results in a table for management that compares the undiscounted cash flow, the CLV and the probability
1.Background Information 市场分析代写
You are working for a telecommunications provider. The company wants to improve their customer lifetime value (CLV) calculations for newly acquired customers. The key question that the firms marketing managers have is how they can account for the fact that it is very difficult to know how long a particular customer will be loyal to the customer. Yet customer relationship duration is one of the key information to be considered in CLV calculations.
When they describe this problem to you, you suggest that you might be able to help. In particular, you believe that you can use a survival model in order to predict customer survival probabilities and then use those probabilities to improve the CLV calculations. You agree with the marketing managers that you will check the data available and perform the necessary analyses for them.
2.Data, Sample and Variables
You are provided a dataset that contains information about 2,846 randomly sampled customer relationships. The dataset is called “telecom_churn” and is a csv file. The dataset has been cleaned already. It contains the following variables:
Churn: Information whether or not a customer has churned.
AccountWeeks: The duration of the customer relationship as it is reflected in the time the customer had an active account at the firm. The variable is captured in weeks.
DataPlan: Whether or not a customer has a data plan
DataUsage: Gigabytes of average monthly data usage
DayMins: Average daytime minutes (calling time) per month 市场分析代写
DayCalls: Average number of daytime calls
MonthlyCharge: Average monthly bill
OverageFee: Largest overage fee in the last year
RoamMins: Average number of roaming minutes
servicecall: Whether or not a customer has called the service line more than once.
3.Your Tasks 市场分析代写
Please use R to perform the following tasks. You can earn a total of 100 points.
Please submit one file (word or PDF) with the answers to the individual questions in the submission box provided on Moodle. Please make sure that each answer is correctly numbered and that the answer to each question starts on a new page.
- (10 points) Estimate a base survival model for an average customer. Call this mod0. Please provide the model summary and visualize the survival curve (screenshots suffice).
- (20 points) Please estimate a model that includes servicecall as an explanatory variable. Call it mod1. Please provide the model summary and visualize the survival curve (screenshot suffices). Would you prefer mod0 or mod1 for predicting customer survival probabilities. Why?
- (10 points) You decide to move on with mod1. Critically evaluate the predicted curve. Do you see any reason for concern? 市场分析代写
- (15 points) You want to use the model to make predictions of survival probabilities to improve the targeting of existing customers (e.g., which customers should be offered promotions etc.). Do you see a chance to improve model performance given the data at hand? Please explain your answer.
- (25 points) You decide to use mod1 to calculate the expected CLV for customers who have made 1 or less service calls and those who have made more than 1 service call. The annual interest rate is 4% (note that you have to translate this into weekly discount rates). For an assumed customer lifetime of 500 weeks, please calculate the CLV and the probability corrected CLV for both customer types.
Here is a little helper on how to achieve that: First, you have to derive monthly average cash flows for each customer prototype separately (from the variable MonthlyCharge; use servicecall as a grouping variable).
Second you have to calculate the average weekly cash flows from this data (average monthly cash flow * 12/52); you can use the weekly average cash flow as a cash flow for each of the 500 weeks.
Third, you have to derive the predicted survival probabilities for each customer. We have not done the coding for this in the tutorial but it can be achieved in a few steps. You just have to make sure that you use your own variable names in the code below.
Suggested code for this step
First install the rms package, which is required to derive predictions for different points in # time.
install.packages("rms")
library ("rms") 市场分析代写
Then you have to rerun mod1 using the psm function that is equivalent to the survreg
function used in the tutorial
mod1_psm <- psm(Surv(AccountWeeks, Churn) ~ servicecall, data = Telco1, dist="weibull")
mod1_psm # This model is the same as the previous model mod1
We produce a sequence which will define the points in time at which we want predicted survival probabilities from our model.
weeks <- seq(1,500, by = +1)
We define the levels of the variable for which we want probabilities
n.dat <- expand.grid(servicecall = levels(servicecall))
We ask the model for predictions for 500 weeks ahead.
b1<-survest(mod1_psm, newdata = data.frame(n.dat), time=weeks)
We rearrange the data such that we can easily use it for the cash flow predictions.
b2<-cbind(n.dat, b1)
b3<-melt(b2, id.vars=c("servicecall"), variable.name="time", value.name="surv prob")
b3
Fourth, you now have all necessary information to calculate the CLV and probability corrected CLV for both customer prototypes. You can do this either in R or in Excel. 市场分析代写
Finally, present the correct results in a table for management that compares the undiscounted cash flow, the CLV and the probability corrected CLV for both customer prototypes. Should the firm focus on either type of the two customers in their future customer acquisition efforts?
- (20 points) Please present a simple visualization that demonstrates your key insight from the probability corrected CLV to managers. (It is easiest to use PowerPoint to provide an appropriate chart.)
Good luck!
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