Best Practices for Predictive Analytics in B2B Financial Services

Raul Domingos, Thierry Van de Merckt
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引用次数: 2

Abstract

Predictive analytics is a well known practice among corporations having business with private consumers (B2C) as a means to achieve competitive advantage. The first part of this article intends to show that corporations operating in a business to business (B2B) setting have similar conditions to use predictive analytics on their favor. Predictive analytics can be applied to solve a myriad of business problems. The solutions to solve some of these problems are well known while the resolution of other problems requires quite an amount of research and innovation. However, predictive analytics professionals tend to solve similar problems in very different ways, even those to which there are known best practices. The second part of this article uses predictive analytics applications identified in a B2B context to describe a set of best practices to solve well known problems (the “let's not re-invent the wheel” attitude) and innovative practices to solve challenging problems.
B2B金融服务中预测分析的最佳实践
预测分析在拥有私人消费者(B2C)业务的公司中是一种众所周知的实践,它是获得竞争优势的一种手段。本文的第一部分旨在表明,在企业对企业(B2B)环境中运营的公司也有类似的条件来使用对他们有利的预测分析。预测分析可以应用于解决无数的业务问题。其中一些问题的解决方案是众所周知的,而其他问题的解决需要相当多的研究和创新。然而,预测分析专业人员倾向于以非常不同的方式解决类似的问题,即使是那些已知的最佳实践。本文的第二部分使用在B2B上下文中确定的预测分析应用程序来描述一组解决众所周知问题的最佳实践(“让我们不要重新发明轮子”的态度)和解决具有挑战性问题的创新实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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