Structural forecasts for marketing data
Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been dev...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Allenby, Greg M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Plastic contribution via DRX induced by kink and twin in a hot compressed Mg-Gd-Zn-Mn alloy with 14H LPSO - Luan, Shiyu ELSEVIER, 2023, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:33 ; year:2017 ; number:2 ; pages:433-441 ; extent:9 |
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| DOI / URN: |
10.1016/j.ijforecast.2016.09.003 |
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| 520 | |a Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. | ||
| 520 | |a Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. | ||
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10.1016/j.ijforecast.2016.09.003 doi GBVA2017006000018.pica (DE-627)ELV035759917 (ELSEVIER)S0169-2070(16)30102-9 DE-627 ger DE-627 rakwb eng 300 330 650 300 DE-600 330 DE-600 650 DE-600 600 670 530 VZ 51.00 bkl Allenby, Greg M. verfasserin aut Structural forecasts for marketing data 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. Constraints Elsevier Statistical pooling Elsevier Sparse data Elsevier Enthalten in Elsevier Science Luan, Shiyu ELSEVIER Plastic contribution via DRX induced by kink and twin in a hot compressed Mg-Gd-Zn-Mn alloy with 14H LPSO 2023 Amsterdam [u.a.] (DE-627)ELV009629270 volume:33 year:2017 number:2 pages:433-441 extent:9 https://doi.org/10.1016/j.ijforecast.2016.09.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 33 2017 2 433-441 9 045F 300 |
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10.1016/j.ijforecast.2016.09.003 doi GBVA2017006000018.pica (DE-627)ELV035759917 (ELSEVIER)S0169-2070(16)30102-9 DE-627 ger DE-627 rakwb eng 300 330 650 300 DE-600 330 DE-600 650 DE-600 600 670 530 VZ 51.00 bkl Allenby, Greg M. verfasserin aut Structural forecasts for marketing data 2017transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. Constraints Elsevier Statistical pooling Elsevier Sparse data Elsevier Enthalten in Elsevier Science Luan, Shiyu ELSEVIER Plastic contribution via DRX induced by kink and twin in a hot compressed Mg-Gd-Zn-Mn alloy with 14H LPSO 2023 Amsterdam [u.a.] (DE-627)ELV009629270 volume:33 year:2017 number:2 pages:433-441 extent:9 https://doi.org/10.1016/j.ijforecast.2016.09.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 51.00 Werkstoffkunde: Allgemeines VZ AR 33 2017 2 433-441 9 045F 300 |
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Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. |
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Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. |
| abstract_unstemmed |
Marketing applications often require disaggregate forecasts of demand that pertain to subsets of individuals who are targeted for action. Examples include targeted price promotions that are made available through on-site couponing and forecasts of market segments for which new products have been developed. One challenge in the production of disaggregate forecasts of demand, and of consumer responses to marketing actions, relates to the limited amount of data that is available at the individual level. This paper discusses approaches to the improvement of marketing forecasts through the use of both parsimonious structural models of demand and random-effect models that pool data statistically across individual consumers. |
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Structural forecasts for marketing data |
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https://doi.org/10.1016/j.ijforecast.2016.09.003 |
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| doi_str |
10.1016/j.ijforecast.2016.09.003 |
| up_date |
2024-07-06T18:23:56.908Z |
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