Learning about Customers: Demand Implications of Logistics-Related Decision-Making in B2B
In business-to-business (B2B) exchanges, customers are more likely to buy from suppliers who know them well and consistently provide good service. Yet, when planners optimize the operations with a focus on cost-reduction, they risk overlooking the importance of building long-term relationships with their customers. These relationships critically depend on learning the customer’s preferences, priorities, and service expectations. While building strong customer relationships in a B2B context has traditionally been the salespeople’s responsibility, AI developments now open the possibility for AI-based learning about customers. AI-based learning about customers in a B2B setting is complex though because each customer has their own needs and preferences, leading to highly customized offerings. These customized offerings often include agreements on critical logistics-related decisions such as lead times, delivery, and maintenance planning. In this setting, close contact between the people from sales and operations – i.e., a strong marketing-operations interface – benefits the customer relationship. Yet as information on customers is embedded both in IT systems (e.g., CRM systems) and people (e.g., salespersons), this is a domain where B2B firms can benefit greatly from AI. This PhD project thus studies on how AI can help planners tailor their operations to better serve customer needs.
Tolunay Alankaya – Sarah Gelper – Nevin Mutlu – Fred Langerak
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