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An Intelligent omnichannel assortment model to manage webrooming: an optimization approach

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dc.contributor.author Sharma, Satyendra Kumar
dc.date.accessioned 2023-05-09T05:41:25Z
dc.date.available 2023-05-09T05:41:25Z
dc.date.issued 2022-05
dc.identifier.uri https://www.tandfonline.com/doi/full/10.1080/0965254X.2022.2067072
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/10707
dc.description.abstract Global retail industry players have witnessed a grave scenario due to the impact of the COVID-19 pandemic. The pandemic has changed the way shoppers think, manifested in the decelerating footfall and increasing threat for traditional brick and mortar stores. The strategy of switching over to omnichannel seemed to have provided the needed relief to traditional retailers and manufacturers in the consumer goods industry. However, a robust omnichannel product assortment model requires integrating channels and remodeling managers’ roles to provide consumer experience and satisfaction and maximize profitability across all touchpoints with minor disruptions. The paper formulates and simulates an omnichannel data-driven fulfillment analytical model to analyze customers’ product mix and manage assortment accordingly. Further, an optimization model that maximizes revenue and profitability is formulated as a suggestive framework with strategies for the current scenario. The paper is helpful for marketing researchers and retail planners for omnichannel assortment management en_US
dc.language.iso en en_US
dc.publisher Taylor & Francis en_US
dc.subject Management en_US
dc.subject Omnichannel en_US
dc.subject Retail en_US
dc.subject COVID-19 en_US
dc.subject E-Commerce en_US
dc.subject m- commerce en_US
dc.subject Optimization en_US
dc.title An Intelligent omnichannel assortment model to manage webrooming: an optimization approach en_US
dc.type Article en_US


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