Deck 9: Marketing Analytics With Multiple Goals

Question 1

The linear programming model for any two dimension cases is relevant to companies in which these two dimensions are significant factors in their direct marketing campaigns.

Answer 1

True

Question 2

All different RFM variations of linear programming models have an objective function that seeks to minimize the expected cost to reach potential customers.

Answer 2

False

Question 3

All different RFM variations of linear programming models have an objective function that seeks to minimize the expected cost to reach potential customers while ensuring profit levels.

Answer 3

False

Question 4

The decision variables for frequency and monetary value are defined as 1 if customers in a given frequency and given monetary value group should be reached. Otherwise, they are 0.

Question 5

RFM models with dual dimensions use the AVERAGEIFS function to calculate the probability that a customer in a given group will make a purchase.

Question 6

The decision maker may use the IFERROR function when trying to avoid a division by zero error.

Question 7

The decision maker may enforce the continuous value for decision variables in order to calculate the percentage of customers to be reached in a given RFM group.

Question 8

A goal programming model for RFM analysis is used when the analyst wants to assign priorities to each of the dimensions.

Question 9

The objective function of the goal programming model for RFM analysis seeks to maximize the expected revenue.

Question 10

Goal programming models can be formulated for RFM analysis with three dimensions only.

Question 11

Goal programming models can be formulated for RFM analysis with only a single dimension.

Question 12

The implementation of RFM-based linear programming models allows organizations to group their customers into iso-profit lines.

Question 13

Customers who belong to the same iso-profit line group can generate the same expected revenues.

Question 14

RFM-based mathematical programming models can help the decision maker to identify RFM segments that are not worthy of pursuing because they are not profitable.

Question 15

RFM-based mathematical programming models can help the decision maker to identify RFM segments that are not worthy of pursuing due to marketing budget constraints.

Question 16

Which of the following is an RFM-based goal programming model?

A) An RFM model that combines any two dimensions, including recency-frequency value, recency-monetary value, or frequency-monetary value
B) An RFM model that combines all three dimensions in one linear programming model
C) An RFM model that incorporates all three dimensions but assigns different weights to each
D) All of the above

Question 17

An RFM mathematical model that combines all three dimensions of an RFM model is always:

A) A goal programming model.
B) A linear programming model.
C) A non-linear programming model.
D) None of the above can be used to formulate an RFM model.

Question 18

An RFM model that combines any two dimensions, including recency-frequency value, recency-monetary value, or frequency-monetary value, is always:

A) A goal programming model.
B) A linear programming model.
C) A non-linear programming model.
D) None of the above can be used to formulate such a model.

Question 19

An RFM model that combines any two dimensions, including recency-frequency value, recency-monetary value, or frequency-monetary value, and assigns priorities to each dimension is always:

A) A goal programming model.
B) A linear programming model.
C) A non-linear programming model.
D) None of the above can be used to formulate such a model

Question 20

All different RFM variations of linear programming models have the following objective function and constraints:

A) To maximize the expected revenues from potential customers while not exceeding budget constraints
B) To minimize the expected cost to reach potential customers while not exceeding budget constraints
C) Minimize the expected cost to reach potential customers while not ensuring profit levels
D) Any of the above can be a construct for the recency and frequency case.

Question 21

Which of the following serves as a definition for decision variables for recency and frequency?

A) 1 if customers in a given recency and frequency should not be reached; 0 otherwise
B) 1 if customers in a given recency and frequency should be reached; 0 otherwise
C) 1 if customers in a given recency or frequency should not be reached; 0 otherwise
D) 1 if customers in a given recency or frequency should be reached; 0 otherwise

Question 22

Why may the decision maker encapsulate an IFERROR function in the AVERAGEIFS function when building the Microsoft Excel template for RFM models with dual dimensions?

A) To avoid any potential input error
B) To avoid a division by zero error
C) To avoid errors when calculating the expected revenue
D) To avoid errors when calculating the utilized budget

Question 23

The decision maker may change the binary constraints of the decision variables in the Solver Parameters box to be continuous decision variables. This change will:

A) Limit the decision variables to a number between 0 and 1.
B) Indicate that the decision variables represent the percentage of customers to be reached in a given group.
C) Most likely increase the amount of budget used.
D) All of the above will occur

Question 24

When implementing linear programming models with three dimensions of the RFM framework:

A) The objective function remains the same as previous models: to maximize expected revenue.
B) The objective function changes: to minimize the budget of the marketing campaign.
C) Both a and b are true.
D) Neither a nor b is true.

Question 25

The linear programming model with single dimensions of the RFM framework and five groups in this dimension has the following number of decision variables.

A) 5
B) 25
C) 50
D) 75
E) 125

Question 26

The linear programming model with two dimensions of the RFM framework and five groups in each of these dimensions has the following number of decision variables.

A) 5
B) 25
C) 50
D) 75
E) 125

Question 27

The linear programming model with three dimensions of the RFM framework and five groups in each of these dimensions has the following number of decision variables.

A) 3
B) 15
C) 25
D) 75
E) 125

Question 28

Which of the following is an objective function of the goal programming model for RFM analysis?

A) To maximize the expected revenue
B) To minimize the budget
C) To minimize the deviation variables based on priority goals
D) All of the above

Question 29

Critics of the RFM approach claim that this methodology is:

A) Less likely to be used successfully in predictive and prescriptive analytics.
B) Fails to indicate anything about the propensity of a prospect to respond to marketing stimuli.
C) It simply shows who purchased from the company in the past.
D) All of the above

Question 30

The availability of Big Data allows the decision maker to use the RFM approach successfully by:

A) Incorporating other metrics such as response rate.
B) Creating iso-profit lines.
C) Relating RFM with the concept of customer lifetime value.
D) All of the above