Abstract:
In the rapidly evolving digital realm, data has emerged as an invaluable resource, enabling businesses to optimize their performance with customer-centric strategies. This research focuses on the application of Next Best Offer (NBO) recommendation models within the B2B context, exemplified by a case study of an Italian Bio food company.
The journey begins with an initial exploration of NBO models within the broader B2C framework. It traces the development of NBO solutions within business processes, subsequently delving into the construction of NBO recommendation models. Various models are scrutinized, with the aim of assisting companies in crafting pertinent and personalized offers for customers, leveraging customer data to enhance marketing and sales performance. The models under examination encompass association rules, customer-based and user-based collaborative filtering, as well as content-based recommendation.
The investigation extends beyond the theoretical realm, venturing into the unique challenges and benefits associated with each model, not only in the general B2C landscape but also within the nuanced landscape of B2B interactions. The study aims to shed light on the potential hurdles faced by B2B enterprises in the implementation of such strategies. Through practical application utilizing R, this research uncovers that each model bears its own set of advantages and limitations. Factors such as result relevance, sparsity, scalability, and complexity play pivotal roles in determining the effectiveness of these models.
