Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics
Data + Intuition: A Hybrid Approach to Developing Product North Star Metrics
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数据直觉：开发产品北极星指标的混合方法
DOI：
10.1145/3041021.3054199
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Xin Fu
中科院分区：
文献类型：
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作者：
Albert Chen;Xin Fu
在数据驱动型公司，“你创造你所衡量的东西”是一句耳熟能详的箴言。因此，企业必须谨慎选择能够创造出更好产品的“北极星”指标。参数分为两大类：直接计数参数，如总收入和月活跃用户，以及关于价值或用户体验其他方面的细微质量参数。计数指标，当仅仅作为北极星使用时，可能会影响影响用户体验的产品决策。因此，质量度量在产品开发中起着重要的作用。我们提出了一个结合机器学习和产品直觉开发质量指标的五步框架。机器学习确保度量准确地捕捉用户体验。产品直觉使度量具有可解释性和可操作性。通过对LinkedIn的背书产品的案例研究，我们说明了只针对计数指标进行优化的危险，并展示了我们的框架在开发质量指标方面的成功应用。我们展示了新的质量度量标准如何推动了重大的改进，以创建有价值的、用户优先的产品。
"You make what you measure" is a familiar mantra at data-driven companies. Accordingly, companies must be careful to choose North Star metrics that create a better product. Metrics fall into two general categories: direct count metrics such as total revenue and monthly active users, and nuanced quality metrics regarding value or other aspects of the user experience. Count metrics, when used exclusively as the North Star, might inform product decisions that harm user experience. Therefore, quality metrics play an important role in product development. We present a five-step framework for developing quality metrics using a combination of machine learning and product intuition. Machine learning ensures that the metric accurately captures user experience. Product intuition makes the metric interpretable and actionable. Through a case study of the Endorsements product at LinkedIn, we illustrate the danger of optimizing exclusively for count metrics, and showcase the successful application of our framework toward developing a quality metric. We show how the new quality metric has driven significant improvements toward creating a valuable, user-first product.