A Bayesian Approach to Modeling Purchase Frequency
Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jen, Lichung [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2003 |
|---|
| Anmerkung: |
© Kluwer Academic Publishers 2003 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Marketing letters - Kluwer Academic Publishers, 1989, 14(2003), 1 vom: Feb., Seite 5-20 |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:2003 ; number:1 ; month:02 ; pages:5-20 |
| Links: |
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| DOI / URN: |
10.1023/A:1022833400454 |
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OLC2070810860 |
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| 520 | |a Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. | ||
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10.1023/A:1022833400454 doi (DE-627)OLC2070810860 (DE-He213)A:1022833400454-p DE-627 ger DE-627 rakwb eng 380 VZ 3,2 ssgn 85.00 bkl Jen, Lichung verfasserin aut A Bayesian Approach to Modeling Purchase Frequency 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. Chou, Chien-Heng aut Allenby, Greg M. aut Enthalten in Marketing letters Kluwer Academic Publishers, 1989 14(2003), 1 vom: Feb., Seite 5-20 (DE-627)170251217 (DE-600)1031012-5 (DE-576)023106794 0923-0645 nnns volume:14 year:2003 number:1 month:02 pages:5-20 https://doi.org/10.1023/A:1022833400454 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_24 GBV_ILN_31 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4082 GBV_ILN_4125 GBV_ILN_4311 GBV_ILN_4318 85.00 VZ AR 14 2003 1 02 5-20 |
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10.1023/A:1022833400454 doi (DE-627)OLC2070810860 (DE-He213)A:1022833400454-p DE-627 ger DE-627 rakwb eng 380 VZ 3,2 ssgn 85.00 bkl Jen, Lichung verfasserin aut A Bayesian Approach to Modeling Purchase Frequency 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. Chou, Chien-Heng aut Allenby, Greg M. aut Enthalten in Marketing letters Kluwer Academic Publishers, 1989 14(2003), 1 vom: Feb., Seite 5-20 (DE-627)170251217 (DE-600)1031012-5 (DE-576)023106794 0923-0645 nnns volume:14 year:2003 number:1 month:02 pages:5-20 https://doi.org/10.1023/A:1022833400454 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_24 GBV_ILN_31 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4082 GBV_ILN_4125 GBV_ILN_4311 GBV_ILN_4318 85.00 VZ AR 14 2003 1 02 5-20 |
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10.1023/A:1022833400454 doi (DE-627)OLC2070810860 (DE-He213)A:1022833400454-p DE-627 ger DE-627 rakwb eng 380 VZ 3,2 ssgn 85.00 bkl Jen, Lichung verfasserin aut A Bayesian Approach to Modeling Purchase Frequency 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Kluwer Academic Publishers 2003 Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. Chou, Chien-Heng aut Allenby, Greg M. aut Enthalten in Marketing letters Kluwer Academic Publishers, 1989 14(2003), 1 vom: Feb., Seite 5-20 (DE-627)170251217 (DE-600)1031012-5 (DE-576)023106794 0923-0645 nnns volume:14 year:2003 number:1 month:02 pages:5-20 https://doi.org/10.1023/A:1022833400454 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW GBV_ILN_24 GBV_ILN_31 GBV_ILN_2006 GBV_ILN_4012 GBV_ILN_4082 GBV_ILN_4125 GBV_ILN_4311 GBV_ILN_4318 85.00 VZ AR 14 2003 1 02 5-20 |
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Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. © Kluwer Academic Publishers 2003 |
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Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. © Kluwer Academic Publishers 2003 |
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Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. A critical element of their scoring systems is expected frequency of customer interaction. In this paper the authors develop a hierarchical Bayes model of purchase frequency that combines a Poisson likelihood with a gamma mixing distribution, where the mixing distribution is a function of covariates. The proposed model is evaluated with two direct marketing datasets, and is shown to provide improved estimates of purchase frequency, particularly for customers with short purchase histories or who have infrequent interaction with the firm. © Kluwer Academic Publishers 2003 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2070810860</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503173419.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2003 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1023/A:1022833400454</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2070810860</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)A:1022833400454-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">380</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jen, Lichung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Bayesian Approach to Modeling Purchase Frequency</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2003</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kluwer Academic Publishers 2003</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Direct marketers are often faced with the task of ranking, or scoring individual customers in terms of their expected value to the firm. 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