《“北极星指标”》 有 1 条评论

1. Choosing the Right Metric – 选择正确的衡量标准
   https://www.juiceanalytics.com/writing/choosing-right-metric

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   Step 1: 明确衡量标准的目标(Set the context)
   是为了【发现问题】还是为了【衡量绩效】，不同的目标对于衡量标准的选取是有影响的。

   Step 2: 平衡良好度量标准的四个维度(Balance the four dimensions of a good metric)
   * 常见含义——指标的名称和含义应该要常见且大家都好理解，不易产生歧义
   * 可操作——指标的变化对应着可操作的行为，即指标应该能够指导行动
   * 数据可靠且易于获取——最有价值、最显而易见的指标如果难以追踪是非常令人沮丧的
   * 计算方式透明且简单——不复杂

   Step 3: 避免出现度量错误(Avoid the metrics bugaboos)
   * 趋势和分布：不要总是试图将指标压缩成一个数字。通常，跨时间或以分布形式显示指标更能揭示差异。
   * 少数极端情况：总有一些少数极端情况，某个指标的含义可能与你所想的不同。这些情况值得了解，但不应让完美成为优秀的敌人。
   * 设定目标：你能让别人对这一指标负责，而不会让他们提出一大堆不合理的理由吗？这是对指标价值的一个很好的测试。
   * 自我感动：注意不要仅仅因为知道指标会让自己看起来更有面子而选择指标。

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   Misaligned goals, distorted behaviors, and a misguided sense of success… no, I’m not referring to college graduates. I’m talking about the problems caused by using the wrong metrics in your organization. You’ve probably seen examples like tracking average customer profitability and losing perspective on the variance in profitability or evaluating customer service reps on calls handled without regard for the quality of the experience. I’d like to offer up a quick-bake recipe for choosing the right metric.
   目标错位、行为扭曲、成功感被误导……不，我指的不是大学毕业生。我指的是**在企业中使用错误的衡量标准所造成的问题**。你可能见过这样的例子，比如跟踪客户的平均盈利能力，却忽略了盈利能力的差异；或者只根据客服代表处理的电话数量来评估客服代表，却忽略了客户体验的质量。我想提供一个选择正确指标的速成秘诀。

   Step 1: Set the context
   步骤 1：设置背景

   Metrics generally serve one of two purposes. Start by understanding what you are trying to achieve.
   指标通常有两种用途。首先了解您要实现的目标。

   1. Identifying problems. Defining the right metrics in this case requires you to do a little detective work: What is the data residue of a problem? What evidence can be found and how exactly does it show up?
   1.发现问题。在这种情况下，要确定正确的衡量标准，需要做一些侦查工作：问题的数据残留是什么？可以找到哪些证据，以及这些证据是如何显示出来的？

   2. Measuring performance. The right success metrics need to focus on measures that can be controlled and where improvement in the number is unabiguously a good thing.
   2.衡量绩效。正确的成功衡量标准需要侧重于可以控制的衡量标准，而且数字的提高无疑是件好事。

   Step 2: Balance the four dimensions of a good metric
   步骤 2：平衡良好度量标准的四个方面

   Lots of metrics fail in at least one of these dimensions. A few examples:
   很多度量标准至少在其中一个方面存在问题。举几个例子：

   * Common interpretation: We had a client who made a distinction between “leads” and “prospects” in their marketing organization. Prospects had theoretically expressed more interest in the service through their actions. Unfortunately the line between leads and prospects was always hard to decipher and the definitions were hard to communicate. On a related note, we got a kick out of Tom Davenport’s (author of “Competing on Analytics”) assertion that a company competing on analytics needs to “invent proprietary metrics for use in key business processes.” There is nothing inherently wrong with “invented proprietary metrics” but it sounds like something that is designed to confuse anyone outside of the inner sanctum.
   易于理解的常识：我们有一个客户，他们的营销组织对”线索”和”潜在客户”进行了区分。理论上，潜在客户通过他们的行动表达了对服务的更多兴趣。遗憾的是，”潜在客户”和”潜在客户”之间的界限总是难以厘清，定义也难以沟通。与此相关的是，汤姆-达文波特（Tom Davenport，《在分析中竞争》一书的作者）断言，在分析中竞争的公司需要”发明用于关键业务流程的专有指标”，这让我们很是受用。发明专有度量标准”本质上并没有错，但听起来像是为了迷惑内部圣殿之外的任何人。

   * Actionable: Metrics are frequently too broad for the impact that a particular group can have. Customer satisfaction is a popular dashboard staple, but it is hard for most managers to see how they can have a significant impact on the number.
   可操作：指标往往过于宽泛，无法对特定群体产生影响。客户满意度是仪表板上最常用的指标，但大多数管理人员很难看到他们如何能对这一数字产生重大影响。

   * Accessible, credible data: Sometimes the most valuable and obvious metrics are frustratingly hard to track. In the web analytics world, unique visitors is important to know, but user deletion of cookies has thrown a wrench into the works.
   可获取的可靠数据：有时，最有价值、最显而易见的指标却难以追踪，令人沮丧。在网络分析领域，唯一访问者的数据非常重要，但用户删除 cookie 的行为却给这一工作带来了麻烦。

   * Transparent, simple calculation: Top NFL agent Leigh Steinberg says of the famous quarterback ratings metric:”Other than one attorney in our office, I am unaware of a single human being who has the capacity to figure a quarterback rating.” I don’t know what kind of art majors he hires, but all they need to do is use the simplified formula: (83.33 * Comp %) + (4.16667 * Yds per att) + (333.333 * TD pct) – (416.667 * INT pct) + 25/12.
   透明、计算简单：对于著名的四分卫评分标准，NFL 顶级经纪人利-斯坦伯格（Leigh Steinberg）说：”除了我们办公室的一位律师，我不知道还有谁有能力计算出四分卫的评分。我不知道他雇的都是些什么艺术专业的学生，但他们只需要使用简化公式就可以了：(83.33 * Comp %) + (4.16667 * Yds per att) + (333.333 * TD pct) – (416.667 * INT pct) + 25/12。

   (Want a little validation of this framework? Avinash, respected web analytics guru, just published a post with “Four Attributes of Great Metrics” and he landed on a strikingly similar set of four: 1) instantly useful (i.e. actionable); 2) relevant (i.e. common interpretation); 3) timely (i.e. accessible); 4) uncomplex (i.e. transparent and simple).)
   (想要验证一下这个框架吗？受人尊敬的网络分析大师 Avinash 刚刚发表了一篇题为 “优秀指标的四个属性 “的文章，他提出了惊人相似的四个属性：1）即时有用（即可操作）；2）相关（即常见解释）；3）及时（即可访问）；4）不复杂（即透明和简单）。

   Step 3: Avoid the metrics bugaboos
   步骤 3：避免出现度量错误

   Finally, here are a few traps that I’ve seen in deciding on appropriate metrics:
   最后，在决定适当的衡量标准时，我发现了一些陷阱：
   * Trending and distributions: Don’t always try to compress a metric into a single number. Often it is more revealing to show the metric across time or as a distribution to uncover variance.
   趋势和分布：不要总是试图将指标压缩成一个数字。通常，跨时间或以分布形式显示指标更能揭示差异。

   * Edge cases: There will always edge cases where a metric may not mean what you think it means. These situations are worth understanding, but you shouldn’t allow the perfect to be the enemy of the good.
   边缘情况：总有一些边缘情况，某个指标的含义可能与你所想的不同。这些情况值得了解，但不应让完美成为优秀的敌人。

   * Setting goals: Could you hold someone accountable for this metric without them throwing out a half-dozen reasons why it doesn’t make sense? It’s a decent test of the value of the metric.
   设定目标：你能让别人对这一指标负责，而不会让他们提出一大堆不合理的理由吗？这是对指标价值的一个很好的测试。

   * Self-serving: Be careful that you don’t select metrics simply because you know they’ll make you look good.
   自以为是：注意不要仅仅因为知道指标会让自己看起来更有面子而选择指标。
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