Exam 9: Marketing Analytics With Multiple Goals

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.

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

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.

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

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.

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.

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

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

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:

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

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.

Critics of the RFM approach claim that this methodology is:

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

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

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.

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

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.

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.

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