Decision models or descriptive models?
This paper contrasts a classic example of a logit decision model with a widely used descriptive model, the Dirichlet. Decision modeling, reviewed by Leeflang and Wittink in this issue of IJRM, aims to help make marketing-mix decisions. However, we have serious doubts about this sort of modeling: its...
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| Veröffentlicht in: | International journal of research in marketing 2000-09, Vol.17 (2), p.147-158 |
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| container_title | International journal of research in marketing |
| container_volume | 17 |
| creator | Ehrenberg, Andrew S.C. Barnard, Neil R. Sharp, Byron |
| description | This paper contrasts a classic example of a logit decision model with a widely used descriptive model, the Dirichlet.
Decision modeling, reviewed by Leeflang and Wittink in this issue of
IJRM, aims to help make marketing-mix decisions. However, we have serious doubts about this sort of modeling: its inputs, its outputs, its assumed causality, and its frequent lack of empirically grounded predictability. It also seems to seldom really take account of already well-established marketing knowledge.
In contrast, descriptive modeling more simply aims to depict actual or potential marketing knowledge, and to apply it. Such modeling often deals with marketing-mix factors separately instead of attempting to do so in one overall model. |
| doi_str_mv | 10.1016/S0167-8116(00)00018-5 |
| format | Article |
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Decision modeling, reviewed by Leeflang and Wittink in this issue of
IJRM, aims to help make marketing-mix decisions. However, we have serious doubts about this sort of modeling: its inputs, its outputs, its assumed causality, and its frequent lack of empirically grounded predictability. It also seems to seldom really take account of already well-established marketing knowledge.
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IJRM, aims to help make marketing-mix decisions. However, we have serious doubts about this sort of modeling: its inputs, its outputs, its assumed causality, and its frequent lack of empirically grounded predictability. It also seems to seldom really take account of already well-established marketing knowledge.
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Decision modeling, reviewed by Leeflang and Wittink in this issue of
IJRM, aims to help make marketing-mix decisions. However, we have serious doubts about this sort of modeling: its inputs, its outputs, its assumed causality, and its frequent lack of empirically grounded predictability. It also seems to seldom really take account of already well-established marketing knowledge.
In contrast, descriptive modeling more simply aims to depict actual or potential marketing knowledge, and to apply it. Such modeling often deals with marketing-mix factors separately instead of attempting to do so in one overall model.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0167-8116(00)00018-5</doi><tpages>12</tpages></addata></record> |
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| identifier | ISSN: 0167-8116 |
| ispartof | International journal of research in marketing, 2000-09, Vol.17 (2), p.147-158 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Brand performance measures Decision making Decision support Dirichlet Empirical generalisations Logits Marketing Marketing insights Marketing mix Sales Statistical analysis |
| title | Decision models or descriptive models? |
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