B2B客户转化预测：一种基于文档表示、图论与CatBoost的方法
B2B Customer Conversion Prediction: A Document Representation, Graph Theory, and CatBoost Driven Methodology
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中文总结 AI 辅助
针对B2B客户转化预测问题，该研究提出结合文档表示、图论与CatBoost的方法，实现91%的预测准确率，并探讨了个性化营销活动推荐方案。
中文摘要 AI 辅助
在一次性销售的B2B场景中，购买周期可能持续数月甚至数年。在这一漫长过程中，定位具有高购买潜力的客户并据此推荐个性化营销活动，对于高效营销至关重要。为实现该目标，我们研究了以下问题：B2B客户数据聚合、客户特征生成，以及预测B2B客户是否会表现出购买意向（即预测其转化为销售漏斗客户）。我们提出一种基于多键将单个联系人聚合至B2B客户层级的算法；针对公司名称等非标准化键，我们提出一种新颖架构，用于对包含拼写错误、拼写变体等不规则情况的领域进行聚类。随后，我们定义并生成一组特征，应用CatBoost模型进行客户转化预测。我们的框架达到91%的预测准确率。基于预测结果与模型分析，我们进一步探讨了用于促进转化的个性化营销活动推荐方案。
英文摘要
In the one-time selling B2B context, the buying cycle may last months or even years. During the long process, targeting customers that have a high potential to make purchases and recommending personalized campaigns accordingly are important for effective marketing. For this goal, we study the following problems, B2B customer data aggregation, customer feature generation, and prediction of whether a B2B customer would show interest in making a purchase (i.e., prediction of conversion into sales funnel). We propose an algorithm to aggregate individual contacts to the B2B customer level based on multiple keys. For non-standardized keys such as company names, we propose a novel architecture to cluster them in a domain encompassing irregularities such as spelling mistakes and spelling variants. We then define and generate a set of features and apply the CatBoost model for customer conversion prediction. Our framework achieves 91\% prediction accuracy. Based on the prediction results and analysis of the model, we then discuss personalized campaign recommendations to foster conversion.
发表机构
- Purdue University（普渡大学）
- Apple（苹果公司）
- Amazon（亚马逊公司）
- HP Inc.（惠普公司）
机构由 AI 辅助整理，请以论文原文为准。
