From Discounts to Delivery: Decoding Customer Care Interactions in Warehousing

Leogrande, Angelo (2024): From Discounts to Delivery: Decoding Customer Care Interactions in Warehousing.

Abstract

The present research has delved deeper into the complex relationship of customer care calls with purchasing behavior in a WM system and has developed actionable insights to optimize operations. In this regard, the following critical factors have been considered: product attributes-cost, weight, and discount-on one hand, and delivery performance in terms of timeliness and reliability on the other, with a view to understand their impacts on customer satisfaction and interactions. Key takeaways are that high volumes of customer care calls reflect operational failure; there is a delay or expectation mismatch, and hence one needs strong process optimization. Also, heavy products, since perceived to be reliable, have fewer customer enquiries; lighter, cheap products cause more frequent queries since impulsive buying and lack of information occur. It further identifies timeliness of delivery as a main determinant of customer satisfaction while delays in delivery result in heightened discontent and rising demands for support. The study underlines the strategic relevance of advanced analytics, machine learning, and real-time monitoring to finally resolve the recurring inefficiencies. This may also be a good basis on which recommendations could be made concerning the use of predictive analytics for demand forecasting, effective logistical frameworks, and methods of customer service that would be in line with product-specific needs. Discounts become a two-edged factor: enhancing satisfaction but threatening brand value when used too frequently. In the end, strategies with discounts should be put into balance, proactive customer engagement should be there, with crystal clear communications with them, and the products to be more correctly described. The given study also identified how a warehouse clears the expectation from customers by applying data-driven strategies for better efficiency, customer satisfaction, and long-term loyalty. The above findings provide a comprehensive road map on how to integrate technology and customer-centric strategies in modern warehouse management.

• Item Type: MPRA Paper
• Language: English
• Keywords: e-Commerce, Warehouse, Logistics, Machine Learning, Tobit.
• Subjects: L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L90 - General; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L91 - Transportation: General; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L92 - Railroads and Other Surface Transportation; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L93 - Air Transportation; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L94 - Electric Utilities; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L98 - Government Policy

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