It is evident that companies invest considerable resources in creating and implementing marketing initiatives mainly to attract customers and thereby increase their market presence. Determining if these marketing efforts are effective in convincing customers to take desired actions is challenging since companies need to ensure that their marketing campaigns are feasible in order to target the right customers who are most likely to respond to the marketing treatment. Prescriptive analytics provide better means to measure the impact of a marketing campaign on customer behavior when compared to conventional predictive analytics. Uplift modeling presents an opportunity to maximize the incremental impact of marketing treatment by determining the most responsive customer segment who will take positive action only because of receiving the treatment. This paper aims to suggest an uplift modeling approach in a marketing campaign in B2B context for better evaluation of performance which has not gained enough attention as B2C in literature. By applying three uplift modeling techniques to a real-world B2B cross-sell campaign, it is demonstrated that the campaign effectiveness can be increased significantly by determining the customers who are truly responsive to the related campaign.

显然，公司投入大量资源来制定和实施营销计划，主要是为了吸引客户，从而提高其市场占有率。确定这些营销工作是否能有效说服客户采取所需的行动具有挑战性，因为公司需要确保其营销活动可行，以便瞄准最有可能对营销待遇做出反应的正确客户。与传统的预测分析相比，规范性分析提供了更好的方法来衡量营销活动对客户行为的影响。提升模型提供了一个机会，通过确定反应最快的客户群体（只有因为接受治疗才会采取积极行动）来最大化营销治疗的增量影响。本文旨在提出一种 B2B 环境下营销活动的提升建模方法，以便更好地评估绩效，而该方法在文献中并未像 B2C 那样获得足够的关注。通过将三种提升建模技术应用于现实世界的 B2B 交叉销售活动，结果表明，通过确定真正对相关活动做出响应的客户，可以显着提高活动有效性。