用文档与图论方法提升B2B客户转化预测准确率
B2B Customer Conversion Prediction: A Document Representation, Graph Theory, and CatBoost Driven Methodology
- 通过多键聚合接触信息，构建企业级客户表示
- 针对公司名不规范问题设计聚类架构，实现91%预测准确率
- 适合做精准营销推荐的B2B企业用户画像系统
在一次性销售的B2B场景中，购买周期可能长达数月甚至数年。在此过程中，识别高转化潜力客户并推荐个性化营销活动对高效营销至关重要。为此，本文研究了B2B客户数据聚合、客户特征生成以及客户是否会产生购买兴趣（即销售漏斗转化）的预测问题。提出一种基于多个关键字段的算法，将个体联系人聚合至企业客户层级。针对公司名称等非标准化字段，设计了一种新型架构，在包含拼写错误和变体的领域内实现聚类。随后定义并生成一组特征，采用CatBoost模型进行客户转化预测。该框架达到91%的预测准确率。基于预测结果与模型分析，进一步讨论个性化营销活动推荐以促进转化。
原文摘要 · Abstract (English)
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.
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