A marketing decision support system (MDSS) helps a company to make better decisions. According to Kotler "A marketing decision support system is a coordinated collection of data, systems, tools, and techniques with supporting software and hardware by which an organization gathers and interprets relevant information from business and environment and turns it into a basis for marketing action."
A market decision support system consists of tools, models, and optimization routines. Let us have a look at each of them one by one.
A market decision support system consists of tools, models, and optimization routines. Let us have a look at each of them one by one.
The statistical tools used in decision support system are:
1. Multiple Regressions: It is a statistical tool to find out the correct strategy, it shows the change in dependent variable when the value of independent variable is changed. For example, if we add some promotional scheme with any milk tetra pack, how will it impact the sales?
2. Discriminant Analysis: A technique to differentiate an object or persons into different categories. For example, we are selling a dairy product in different locations of a city then finding out the location for maximum or minimum sale is discriminant analysis.
3. Factor Analysis: Factor analysis is a mathematical tool to determine the dimensions under interconnected variables. For example, dairy products can be divided into milk and milk products, and milk can further be subdivided into packaged milk, tetra pack, loose milk etc. This factorization is helpful in understanding a business better.
4. Cluster Analysis: A technique to divide the objects into identical groups. For example, in case of dairy, we can divide the city into different locations or clusters depending on the sale of a particular product. For example, with the help of Cluster analyses you can find out that the south zone in Delhi consumes more Ice-cream in brick form as compared to novelty or stick form.
5. Conjoint Analysis: A technique where the users ranked preferences for offers are used to find out the users utility for each attribute and the relative importance of each attribute. The attributes for milk can be defined as packaging quality,smell of milk, timely delivery etc.
6. Multidimensional Scaling: A variety of techniques for generating maps of the competitive products or brands on the basis of perception. For example,finding out the position of a dairy product with respect to other dairy brands.
Different models for marketing decision support system are:
1. Markov-process Model: It shows the possibility of movement of goods from
a current state to future state.
2. Queuing Model: This model shows how much expected time a system can take to process the queue, if the arrival time, service time, and number of service channels are given.
3. New-product Pretest Model: This model helps in estimating the relations between buyers awareness, trial, and repurchase on the basis of user's preferences, when the new product comes in the market for testing
4. Sales- response Models: This model is a set of models that helps in finding out the relations between the variables. For e.g. sales-promotion expenditure,advertising expenditure etc.As described by Kotler, optimization routines involve:
1. Differential Calculus: This technique helps in finding out the maximum or minimum value along well-behaved function.
2. Mathematical Programming: This technique helps finding the values that would optimize some objective function that is subject to a set of constraints.
3. Statistical Decision Theory: This technique allows determining the course of action that produces the maximum expected value.
4. Game Theory: This technique allows determining the course of action that will minimize the decision maker's maximum loss in the face of the uncertain behavior of one or more components.
5. Heuristics: This involves using a set of rules of thumb that shorten the time of work required to find a reasonably good solution in a complex system.
1. Multiple Regressions: It is a statistical tool to find out the correct strategy, it shows the change in dependent variable when the value of independent variable is changed. For example, if we add some promotional scheme with any milk tetra pack, how will it impact the sales?
2. Discriminant Analysis: A technique to differentiate an object or persons into different categories. For example, we are selling a dairy product in different locations of a city then finding out the location for maximum or minimum sale is discriminant analysis.
3. Factor Analysis: Factor analysis is a mathematical tool to determine the dimensions under interconnected variables. For example, dairy products can be divided into milk and milk products, and milk can further be subdivided into packaged milk, tetra pack, loose milk etc. This factorization is helpful in understanding a business better.
4. Cluster Analysis: A technique to divide the objects into identical groups. For example, in case of dairy, we can divide the city into different locations or clusters depending on the sale of a particular product. For example, with the help of Cluster analyses you can find out that the south zone in Delhi consumes more Ice-cream in brick form as compared to novelty or stick form.
5. Conjoint Analysis: A technique where the users ranked preferences for offers are used to find out the users utility for each attribute and the relative importance of each attribute. The attributes for milk can be defined as packaging quality,smell of milk, timely delivery etc.
6. Multidimensional Scaling: A variety of techniques for generating maps of the competitive products or brands on the basis of perception. For example,finding out the position of a dairy product with respect to other dairy brands.
Different models for marketing decision support system are:
1. Markov-process Model: It shows the possibility of movement of goods from
a current state to future state.
2. Queuing Model: This model shows how much expected time a system can take to process the queue, if the arrival time, service time, and number of service channels are given.
3. New-product Pretest Model: This model helps in estimating the relations between buyers awareness, trial, and repurchase on the basis of user's preferences, when the new product comes in the market for testing
4. Sales- response Models: This model is a set of models that helps in finding out the relations between the variables. For e.g. sales-promotion expenditure,advertising expenditure etc.As described by Kotler, optimization routines involve:
1. Differential Calculus: This technique helps in finding out the maximum or minimum value along well-behaved function.
2. Mathematical Programming: This technique helps finding the values that would optimize some objective function that is subject to a set of constraints.
3. Statistical Decision Theory: This technique allows determining the course of action that produces the maximum expected value.
4. Game Theory: This technique allows determining the course of action that will minimize the decision maker's maximum loss in the face of the uncertain behavior of one or more components.
5. Heuristics: This involves using a set of rules of thumb that shorten the time of work required to find a reasonably good solution in a complex system.
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