Consumer Segmentation Using Case Based Reasoning Approach To Printing Company

Rabiha, Sucianna Ghadati; Murmanto, Imanuel Revelino; Sasmoko,; Yossy, Emny Harna; Kusumastuti, Dwi Listriana

doi:10.1109/isriti.2018.8864372

Cited by 2 publications

(1 citation statement)

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“…There are two types of research: research conducted from the perspective of agents and research conducted from the perspective of a firm. Many researchers have investigated CBR problems in marketing to model a firm's behaviour (Changchien & Lin, 2005), whereas few with motivations similar to ours have investigated CBR problems to model agents' (consumers in previous articles) purchasing behaviour (Rabiha, Murmanto, Yossy, & Kusumastuti, 2018;Seo, Kim, & Jo, 2020). Herein, we showed that incorporating the PER utilized in BE into CBR increases the prediction accuracy.…”

Section: Relationship Between Cbr and Our Modelmentioning

confidence: 70%

Applying the peak‐end rule to decision‐making regarding similar products: A case‐based decision approach

Kinjo

Ebina

2021

Expert Systems

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This study proposes a marketing decision support system (DSS) for firms using casebased and peak-end approaches to model consumers. The proposed DSS is composed of model and estimation methods. We employ a similarity function used in case-based decision theory to examine the degree of similarity between the past and current products offered to a consumer. The contributions of this study are as follows: First, by extending the peak-end approach, the proposed model could be utilized to analyse not only the same product but also multiple similar products. The DSS could be applied to a broad range of decision problems. Second, by extending the case-based decision model, our DSS considerably reduces the number of computational operations needed. Third, the model demonstrated the best fit among the compared models and possessed high prediction accuracy when analysing the viewing data for Japanese television dramas. The DSS could increase the future purchase probability of a product. Our research bridges two significant concepts: case-based decision models in DSSs and peak-end rules in behavioural economics.case-based decision theory, marketing decision support system, peak-end rule, TV drama | INTRODUCTIONPeople in the real world determine their behaviour by adopting heuristics and biases. Behavioural economics (BE) attempts to clarify the mechanisms behind human behaviour. The use of BE in information systems has gradually become important in policy and corporate strategies (Arnott & Gao, 2019;Oduor & Oinas-Kukkonen, 2017). Arnott and Gao (2019) have stressed that the decision-making theories of BE have a long history in decision support system (DSS) research, although the use of BE has been relatively modest. Kahneman, Fredrickson, Schreiber and Redelmeier (1993) have proposed a key human decision-making concept in BE called the peak-end rule (PER) (Kahneman et al., 1993). Formally, the rule is defined as follows: the global retrospective rating is well predicted by the average of the levels of pain reported at the worst moment of the experience and at its end (Kahneman, 2011;Kahneman et al., 1993). PER states that noteworthy assessments of an event in the past and immediately before it ends considerably influence an individual's overall evaluation (Cojuharenco & Ryvkin, 2008). The PER becomes more significant when considering human behaviours in many fields, such as economics, marketing and psychology.In recent years, the PER has been applied for marketing DSSs in studies wherein the prices of the same product (referred to as "problems" in this article) during previous peaks and ends are examined. After instantaneous evaluations are performed, the PER is applied to consumer decision-making to determine the optimal price and marketing strategy for the firm both theoretically and empirically (Nasiry & Popescu, 2011).However, consumers (referred to as "agents" in this article) have the opportunity to choose not only the same product repeatedly but also similar products in the same category. For example, when deciding w...

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Section: Relationship Between Cbr and Our Modelmentioning

confidence: 70%