EMPIRICAL INVESTIGATION ON IMPULSIVE PURCHASE BEHAVIOR: THE INTERPLAY BETWEEN PRODUCT CATEGORIES AND MARKETING ACTIVITIES

2018 ◽  
Vol 2018 ◽  
pp. 1400-1402
Author(s):  
Sungjoon Nam ◽  
◽  
Minki Kim ◽  
Sang-Hoon Kim
Author(s):  
Sanjukta Ghosh ◽  
Doan Van Thang ◽  
Suresh Chandra Satapathy ◽  
Sachi Nandan Mohanty

Environment protection and basic health improvement of all social communities is now considered as one of the key parameters for the development. It has become a responsibility for both industry and academia to optimize the usage of finite natural resources and preserve them. Efficient promotion and strategic marketing of Eco Friendly products can contribute to this development. It is important to consider any market as a heterogeneous mix, which requires well-organized and intelligent split or segmentation. A survey was conducted in Kolkata, metropolitan city in India, through a structured questionnaire to measure Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to 18 product categories identified by Central Pollution Control Board for Eco Mark Scheme, 2002. Two hundred and twenty three data inputs from the respondents were analysed for this study. Here in this study a fuzzy rule based clustering technique was performed to segregate customers into two sections considering three parameters like Perceived Environmental Knowledge, Perceived Environmental Attitude and Green Purchase Behavior associated to Eco friendly product, which acts as an input variable. The rule base has linguistic variables like Significantly High, Little High, Medium, Little Low and Significantly Low and output as “Eco friendly” or “Non-ecofriendly” consumers. A set of 5×5×5= 125 rules were developed for output determination. They were designed manually and the method is applied for detection of a set of good rules. Thirteen such good rules were identified through Fuzzy Reasoning Tool, which can lead to better Decision Making and facilitate the marketers to develop strategy and take up effective marketing decisions.


2017 ◽  
Vol 40 (7) ◽  
pp. 768-782 ◽  
Author(s):  
M. Deniz Dalman ◽  
Kartikeya Puranam

Purpose Prior research in ingredient branding (IB) has identified several important decision variables consumers use when evaluating IB alliances. This exploratory research aims to investigate the relationship between these variables and consumers’ buying likelihood of the IB alliance and the relative importance of these variables for low- vs high-involvement product categories. Design/methodology/approach A study with the participation of 458 mTurkers was conducted and the data were analyzed using random forests. Findings Findings reveal relative importance of different variables for an IB alliance and that these differ for low- vs high-involvement categories. Research limitations/implications Being exploratory in nature, this research has several limitations, such as using only one high- and one low-involvement categories. Practical implications Results of this research will help brand managers as they make decisions entering an IB alliance as well as with investing their budget on different aspects of their brand, and tailoring their marketing activities for low- vs high-involvement product categories. Originality/value To the best of authors’ knowledge, this paper is the first to discuss the relative importance of different decision variables in an IB context empirically.


2010 ◽  
Vol 29 (2) ◽  
pp. 291-314 ◽  
Author(s):  
S. Sriram ◽  
Pradeep K. Chintagunta ◽  
Manoj K. Agarwal

2018 ◽  
Vol 36 (6) ◽  
pp. 1125-1144 ◽  
Author(s):  
Stefan Mau ◽  
Irena Pletikosa ◽  
Joël Wagner

Purpose The purpose of this paper is to demonstrate the value of enriched customer data for analytical customer relationship management (CRM) in the insurance sector. In this study, online quotes from an insurer’s website are evaluated in terms of serving as a trigger event to predict churn, retention, and cross-selling. Design/methodology/approach For this purpose, the records of online quotes from a Swiss insurer are linked to records of existing customers from 2012 to 2015. Based on the data from automobile and home insurance policyholders, random forest prediction models for classification are fitted. Findings Enhancing traditional customer data with such additional information substantially boosts the accuracy for predicting future purchases. The models identify customers who have a high probability of adjusting their insurance coverage. Research limitations/implications The findings of the study imply that enriching traditional customer data with online quotes yields a valuable approach to predicting purchase behavior. Moreover, the quote data provide supplementary features that contribute to improving prediction performance. Practical implications This study highlights the importance of selecting the relevant data sources to target the right customers at the right time and to thus benefit from analytical CRM practices. Originality/value This paper is one of the first to investigate the potential value of data-rich environments for insurers and their customers. It provides insights on how to identify relevant customers for ensuing marketing activities efficiently and thus avoiding irrelevant offers. Hence, the study creates value for insurers as well as customers.


2015 ◽  
Vol 5 (1) ◽  
pp. 1-11
Author(s):  
Shekar Prabhakar ◽  
Madhavi Lokhande

Subject area Marketing. Study level/applicability MBA students. Case overview Titan Industries Limited is the world's fifth-largest wristwatch manufacturer and India's leading producer of watches under the Titan, Fastrack, Sonata, Nebula, Raga, Regalia, Octane and Xylys brand names. When a joint venture with Timex came to an end, Titan found themselves without a range of watches for the youth, a growing segment with significant disposable incomes. To serve that segment, they launched a range of “cool” casual watches under the Fastrack from Titan sub-brand in 1998. Sunglasses were also launched but under the Accessories division of the company. In 2003, a decision was taken to combine the watches and sunglasses and spin it off under a new group called “Fastrack and New Brands”. Post this spin-off, Fastrack was launched as a standalone brand with the vision of becoming the most iconic and exciting fashion brand for youth. The overarching strategy was to bring affordable fashion to the youth and bridging the gap between the unorganized market and international brands. The product strategy was to extend the brand rapidly into other accessories such as belts, wallets, bags and wristbands. The brand personality was to be irreverent and comfortable with impropriety. Their communications reflected the brand attitude with edgy advertising. The distribution model adopted was to have their own branded stores. The brand grew from a mere INR30 crores in 2003 to INR770 crores in 2013. As the brand grew largely from moving into adjacent product categories, Fastrack managers were always looking for the next product category to enter and dominate. In 2013-2014, the product category seriously being looked at was two-wheeler helmets – a category dominated largely by the unorganized sector with low quality. The challenge was to take a product category that existed mainly due to safety regulations and turn it into a personal, fashion accessory. Was it a large enough market to penetrate and dominate? Would they be able to change consumer perception of helmets being a necessary evil to being a fashion accessory proudly displayed? Can they change consumer purchase behavior to go shopping for helmets instead ofjust buying the cheapest, comfortable helmet? Would the brand extension into helmets strengthen or dilute brand equity? These were the questions that faced Ronnie Talati, the Chief Marketing Officer. Expected learning outcomes Understand how to go about creating a brand strategy when re-launching it as a standalone brand without the support of the corporate umbrella brand; analyze different product markets to enter and how to arrive at a go/no-go decision; comprehend the challenges of extending the brand into different and sometimes unrelated product categories. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email: [email protected] to request teaching notes.


Author(s):  
Yi Sun ◽  
Teruaki Hayashi ◽  
Yukio Ohsawa

AbstractDeciding when and which products to recommend to whom is always an essential issue for retailers. In this study, we propose a mixed framework with two components to capture customer buying behavior and its changes over time and visualize these results to better help retailers choose and target products strategically for marketing. In this framework, a topic model is first used to extract customer’s purchase behavior instead of association rules or K-means as mainly used in market field. To automatically choose the optimal number of topics, we implement an approach proposed by Koltcov et al. on point-of-sale (POS) data in the supermarket. Meanwhile, to grasp the change of topics over time, we divided monthly POS data in half and applied the topic model with Renyi entropy separately. The results suggest that splitting data might be a better way to understand customer behavior. Second, we consider how to develop an effective way to visualize the results of the topic model, which is essential, because in a supermarket context, simply knowing which product categories are included under which topics is not enough to support how a supermarket promotes their products. To address this, we design a three-layer visualization approach to better interpret the topic model results and to help retailers design target promotion strategies. The design of visualization was overlooked by studies related to the use of topic models on supermarket data. Finally, to demonstrate the usefulness of our proposed framework, we conduct a simple scenario-based analysis between our framework and other models, such as Latent Dirichlet Allocation (LDA) and the Dynamic Topic Model (DTM). The results show that for most periods, our proposed framework outperforms LDA and DTM.


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