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客户影响度量值Measure for customer impact

有多种方法可以衡量客户影响。There are several ways to measure for customer impact. 本文将帮助你定义指标来验证假设,这些指标是由 客户理解而产生的。This article will help you define metrics to validate hypotheses that arise out of an effort to build with customer empathy.

战略指标Strategic metrics

策略方法会检查动机业务成果The Strategy methodology examines motivations and business outcomes. 这些做法提供一组指标来测试客户影响。These practices provide a set of metrics to test customer impact. 创新成功后,你通常会看到与你的战略目标保持一致的结果。When innovation is successful, you'll usually see results that are aligned with your strategic objectives.

在建立学习指标之前,请定义要使此创新影响的少量战略指标。Before establishing learning metrics, define a small number of strategic metrics that you want this innovation to affect. 通常,这些战略指标与以下一个或多个结果区域一致:Generally, those strategic metrics align with one or more of the following outcome areas:

记录议定的指标并经常跟踪其影响,但并不期望其中的任何一个指标为多个迭代而出现。Document the agreed-upon metrics and track their impact frequently, but don't expect results in any of these metrics to emerge for several iterations. 若要详细了解如何设置和协调涉及的各方的期望,请参阅 迭代承诺For more information about setting and aligning expectations across the parties involved, see Commitment to iteration.

除了动机和业务结果指标之外,本文的其余部分重点介绍旨在指导透明发现和以客户为中心的迭代的学习指标。Aside from motivation and business outcome metrics, the remainder of this article focuses on learning metrics designed to guide transparent discovery and customer-focused iterations. 有关这些方面的详细信息,请参阅 透明度承诺For more information about these aspects, see Commitment to transparency.

学习指标Learning metrics

如果与客户共享任何最低可行产品 (MVP) 的第一个版本,则在第一次开发迭代结束时,最好不要影响战略性指标。When the first version of any minimum viable product (MVP) is shared with customers, preferably at the end of the first development iteration, there will be no impact on strategic metrics. 几次迭代之后,团队可能仍在努力更改行为,使其足以影响到战略指标。Several iterations later, the team may still be struggling to change behaviors enough to materially affect strategic metrics. 在学习过程中,如构建度量-学习周期,我们建议团队采用学习指标。During learning processes, such as build-measure-learn cycles, we advise the team to adopt learning metrics. 这些指标跟踪和学习机会。These metrics tracking and learning opportunities.

客户流和学习指标Customer flow and learning metrics

如果 MVP 解决方案验证了以客户为中心的假设,解决方案将推动客户行为的一些变化。If an MVP solution validates a customer-focused hypothesis, the solution will drive some change in customer behaviors. 跨客户队列的这些行为变化应能改善业务成果。Those behavior changes across customer cohorts should improve business outcomes. 请记住,改变客户行为通常是一个多步骤的过程。Keep in mind that changing customer behavior is typically a multistep process. 由于每个步骤都有机会衡量影响,因此采用团队可以不断地进行学习并构建更好的解决方案。Because each step provides an opportunity to measure impact, the adoption team can keep learning along the way and build a better solution.

了解客户行为的变化首先要从 MVP 解决方案中映射希望看到的流。Learning about changes to customer behavior starts by mapping the flow that you hope to see from an MVP solution.


在大多数情况下,客户流程将具有易于定义的起点,并且不会有两个以上的端点。In most cases, a customer flow will have an easily defined starting point and no more than two endpoints. 在开始和终结点之间,会将各种学习指标用作反馈循环中的度量值:Between the start and endpoints are a variety of learning metrics to be used as measures in the feedback loop:

  1. 起始点 (初始触发器) : 起点是触发此解决方案的需求的方案。Starting point (initial trigger): The starting point is the scenario that triggers the need for this solution. 当使用客户理解生成解决方案时,初始触发器应吸引客户尝试 MVP 解决方案。When the solution is built with customer empathy, that initial trigger should inspire a customer to try the MVP solution.
  2. 满足客户需求: 当客户需要通过使用解决方案得到满足时,将验证该假设。Customer need met: The hypothesis is validated when a customer need has been met by using the solution.
  3. 解决方案步骤: 此术语是指将客户从初始触发器移动到成功的结果所需的步骤。Solution steps: This term refers to the steps that are required to move the customer from the initial trigger to a successful outcome. 每个步骤都根据客户决定执行下一步,生成一个学习指标。Each step produces a learning metric based on a customer decision to move on to the next step.
  4. 实现的单个采用: 下一次遇到触发器时,如果客户返回到解决方案以满足其需求,则已实现单项采用。Individual adoption achieved: The next time the trigger is encountered, if the customer returns to the solution to get their need met, individual adoption has been achieved.
  5. 业务结果指示器: 当客户以一种对定义的业务成果有贡献的方式工作时,将观察到业务结果指示器。Business outcome indicator: When a customer behaves in a way that contributes to the defined business outcome, a business outcome indicator is observed.
  6. 真正的创新:业务结果指示符个体采用均采用 所需的规模时,您已经实现了真正的创新。True innovation: When business outcome indicators and individual adoption both occur at the desired scale, you've realized true innovation.

客户流的每个步骤都将生成学习指标。Each step of the customer flow generates learning metrics. 每次迭代 (或发布) 后,将测试该假设的新版本。After each iteration (or release), a new version of the hypothesis is tested. 同时,对解决方案进行了调整,以反映假设中的调整。At the same time, tweaks to the solution are tested to reflect adjustments in the hypothesis. 如果客户在任何给定的步骤中遵循规定的路径,则会记录正的指标。When customers follow the prescribed path in any given step, a positive metric is recorded. 当客户偏离规定的路径时,将记录一个负的指标。When customers deviate from the prescribed path, a negative metric is recorded.

这些对齐和偏差计数器创建学习指标。These alignment and deviation counters create learning metrics. 每个都应记录和跟踪,因为云采用团队会发展业务成果和真正的创新。Each should be recorded and tracked as the cloud adoption team progresses toward business outcomes and true innovation. 了解客户的同时,我们将讨论如何应用这些指标来了解和构建更好的解决方案。In Learn with customers, we'll discuss ways to apply these metrics to learn and build better solutions.

分组并观察客户合作伙伴Group and observe customer partners

定义学习指标的第一项度量值是客户合作伙伴定义。The first measurement in defining learning metrics is the customer partner definition. 参与创新周期的任何客户都有资格作为客户合作伙伴。Any customer who participates in innovation cycles qualifies as a customer partner. 若要准确衡量行为,你应使用一个队列模型来定义客户合作伙伴。To accurately measure behavior, you should use a cohort model to define customer partners. 在此模型中,将对客户进行分组,以使您了解对 MVP 中更改的响应。In this model, customers are grouped to sharpen your understanding of their responses to changes in the MVP. 这些组通常类似于以下内容:These groups typically resemble the following:

  • 试验或关注组: 基于其参与特定实验的客户进行分组,以便在一段时间内测试更改。Experiment or focus group: Grouping customers based on their participation in a specific experiment designed to test changes over time.
  • 段: 按公司的大小对客户进行分组。Segment: Grouping customers by the size of the company.
  • 垂直: 按其代表的 行业垂直 对客户进行分组。Vertical: Grouping customers by the industry vertical they represent.
  • 单个人口统计信息: 根据个人人口统计信息(如年龄和物理位置)进行分组。Individual demographics: Grouping based on personal demographics like age and physical location.

这些类型的分组可帮助你在你的创新工作中选择与你合作的客户的各个交叉部分来验证学习指标。These types of groupings help you validate learning metrics across various cross-sections of those customers who choose to partner with you during your innovation efforts. 所有后续指标都应派生自可定义的客户分组。All subsequent metrics should be derived from definable customer grouping.

后续步骤Next steps

随着学习指标的积累,团队可以开始 与客户进行学习As learning metrics accumulate, the team can begin to learn with customers.

本文中的一些概念基于 Eric Ries 编写的 精益启动中的第一个主题。Some of the concepts in this article build on topics first described in The Lean Startup, written by Eric Ries.