{"created":"2021-03-01T07:02:16.684882+00:00","id":42393,"links":{},"metadata":{"_buckets":{"deposit":"9af9166f-d5d0-4515-bc22-2413a70e2fdd"},"_deposit":{"id":"42393","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42393"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042393","sets":["62:7433:7434","9:7435:7436"]},"item_8_alternative_title_1":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"消費者行動理論に基づいた個人レベルのRF分析"}]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2006-03","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-408","bibliographic_titles":[{"bibliographic_title":"Discussion paper series. CIRJE-F"}]}]},"item_8_description_13":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In customer relationship management (CRM), ad hoc rules are often employed to judge whether customers are active in a \"non-contractual\" setting. For example, a customer is considered to have dropped out if he or she has not made purchase for over three months. However, for customers with a long interpurchase time, this three-month time frame would not apply. Hence, when assessing customer attrition, it is important to account for customer heterogeneity. Although this issue was recognized by Schmittlein et al. (1987), who proposed the Pareto/NBD \"counting your customers\" framework almost 20 years ago, today's marketing demands a more individual level analysis. This research presents a proposed model that captures customer heterogeneity through estimation of individual-specific parameters, while maintaining theoretically sound assumptions of individual behavior in a Pareto/NBD model (a Poisson purchase process and a memoryless dropout process). The model not only relaxes the assumption of independence of the two behavioral processes, it also provides useful outputs for CRM, such as a customer-specific lifetime and retention rate, which could not have been obtained otherwise. Its predictive performance is compared against the benchmark Pareto/NBD model. The model extension, as applied to scanner panel data, demonstrates that recency-frequency (RF) data, in conjunction with customer behavior and demographics, can provide important insights into direct marketing issues, such as whether long-life customers spend more and are more profitable.","subitem_description_type":"Abstract"},{"subitem_description":"CRMで重要な概念である顧客の生涯価値を計算するには、顧客の離脱率または維持率を把握することが必要である。しかし離脱する顧客は単に購買を止めるだけで、年会費などの支払い義務がないような“契約に基づかない状況”では、わざわざ離脱を申告することは稀だ。通常このような場合、企業は独自の経験則に基づいて、例えば顧客が３ヶ月購買しなければ離脱したと判断したりする。実務家の間でよく使われるRFM分析では、（RECENCY＝３ヶ月）のようなアドホックで一律なルールが基本になっているが、ここには２つの大きな問題がある。第１に、このルールが主観的なことである。なぜ２ヶ月や４ヶ月でなく、３ヶ月なのだろうか？２つ目の問題は、マーケティングの基本的概念である顧客の異質性を無視していることである。同じ３ヶ月のRECENCYでも、購買間隔が長い顧客は離脱の心配が無いが、購買間隔が短い顧客は離脱している可能性が高いであろう。つまり離脱率の推測に顧客の異質性に配慮する必要があるだろう。この問題は、Schmittlein et al.(1987)らがPareto/NBDモデルを使った“counting your customers”フレームワークによって20年ほど前に研究したが、今日のマーケティングでは個々の顧客に焦点をあてた、よりミクロレベルの分析が求められている。本論文では、Pareto/NBDモデルにおけるロバストな消費者行動の仮定（ボアソン購買プロセスとメモリレス離脱プロセス）は残しつつ、個人ごとにパラメータを推定することによって顧客の異質性をモデル化することを提案する。手法としては階層ベイズモデルをMCMC法によって推定する。このモデルでは、Pareto/NBDモデルと違って２つの行動プロセルの独立性を仮定する必要がなく、かつ顧客ごとの生存期間や維持率など、従来得られなかったCRMに有用な指標が求められる。この研究では顧客の購買予測をベンチマークであるPareto/NBDモデルと比較する。スキャンパネルデータを使ったモデルの拡張では、RFデータに顧客の購買行動データやデモグラフィック情報を加えることによって、ロイヤル顧客はより多くの金額を使うのか、またはより多くの利益を生むのか、などのCRMに重要な示唆が得られることを示した。","subitem_description_type":"Abstract"}]},"item_8_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"本文フィルはリンク先を参照のこと","subitem_description_type":"Other"}]},"item_8_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"97599","nameIdentifierScheme":"WEKO"}],"names":[{"name":"阿部, 誠"}]}]},"item_8_publisher_20":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"日本経済国際共同センター"}]},"item_8_relation_25":{"attribute_name":"関係URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.cirje.e.u-tokyo.ac.jp/research/dp/2006/2006cf408ab.html","subitem_relation_type_select":"URI"}}]},"item_8_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11450569","subitem_source_identifier_type":"NCID"}]},"item_8_subject_15":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"330","subitem_subject_scheme":"NDC"}]},"item_8_text_21":{"attribute_name":"出版者別名","attribute_value_mlt":[{"subitem_text_value":"Center for International Research on the Japanese Economy"}]},"item_8_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"University of Tokyo"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Abe, Makoto"}],"nameIdentifiers":[{"nameIdentifier":"97598","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"CRM","subitem_subject_scheme":"Other"},{"subitem_subject":"direct marketing","subitem_subject_scheme":"Other"},{"subitem_subject":"customer lifetime","subitem_subject_scheme":"Other"},{"subitem_subject":"Poisson process","subitem_subject_scheme":"Other"},{"subitem_subject":"Bayesian method","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"technical report","resourceuri":"http://purl.org/coar/resource_type/c_18gh"}]},"item_title":"Counting Your Customers One by One : An Individual Level RF Analysis Based on Consumer Behavior Theory","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Counting Your Customers One by One : An Individual Level RF Analysis Based on Consumer Behavior Theory"}]},"item_type_id":"8","owner":"1","path":["7436","7434"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-06-03"},"publish_date":"2013-06-03","publish_status":"0","recid":"42393","relation_version_is_last":true,"title":["Counting Your Customers One by One : An Individual Level RF Analysis Based on Consumer Behavior Theory"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:14:34.524087+00:00"}