MarKeting decision support systems (MKDSS) is an information system that helps with decision-making in the formation of a marketing plan. The reason for using an MKDSS is because it helps to support the software vendors’ planning strategy for marketing products; it can help to identify advantageous levels of pricing, advertising spending, and advertising copy for the firm’s products (Arinze, 1990). This helps determines the firms marketing mix for product software.
This model, from Arinzes’ paper, is an example of market response model for a product manufacturer of expert system shells which uses only direct marketing for sales (so no retailers are included) (1990). The boxes represent sources of information with overlapping boxes representing many. The arrows represent data flows and are labeled with the specific type. This model helps to predict the actions of customers which good to know to increase you marketing efficiency. Models like this are used to construct an MKDSS by providing an illustration of the analysis that it contains; included are two sub-models for advertising copy and spending, and price which use calculus for computation. (Arinze, 1990) Both methodological and technological options are available in an MKDSS such as statistical science models, managerial models, and decision-making support for managers. (Arinze, 1990) It includes information from customer analysis and industry analysis as well as general market conditions. This decision support system combines external data obtained from market analysis with internal data to form a comprehensive marketing plan of action for advertising and price setting.
To get a better idea of how an MKDSS is constructed, a low-level meta-model of the process was constructed for the data flows. This lists the steps of the process in boxes and the arrows refer to data flows. The previous figure shows the actual application of the first step of modeling the market response structure for a particular business. There are other ways that this step could have been done, just like many different sub-models could be used for different calculations. The arrow going from the create recommendations to the gather information is used when there was not enough information available before to come to sufficient recommendations.