Learn the rules to Data Draw Up; a fun way to get your teams invested in data.
了解数据收集的规则; 一种让您的团队投入数据的有趣方式。
Let’s keep things short. Metrics are one of the most important things in Product Management. They help us to understand if the changes we are making to a product or feature are actually paying off and if we’re closer to accomplishing our goal, KPI, or OKR. But that’s not the only reason we need them, they also help everyone involved in the product answer key questions…and yes I do mean everyone. From Software Engineers to UX, it can help the whole team become more data-informed.
让我们简短点。 指标是产品管理中最重要的事情之一。 它们帮助我们了解我们对产品或功能所做的更改是否真正奏效,以及我们是否更接近实现目标, KPI或OKR 。 但这不是我们需要它们的唯一原因,它们还可以帮助产品中涉及的每个人回答关键问题……是的,我的意思是每个人。 从软件工程师到UX,它可以帮助整个团队了解更多数据。
What I’m going to show you now is an easy framework I put together using well-known tools like How Might We (HWM), Brainstorming, and Google HEART metrics. This is meant to be done in a workshop either in-person with post-it’s or online with Miro/Mural. Of course, it needs a catchy name which is (bare with me) Data Draw Up or DDU for short. It consists of 5 steps and should take a little less than 2 hours, depending on the size of the team which should be around 7.
现在,我将向您展示的是一个简单的框架,我使用众所周知的工具(例如, 我们的能力(HWM),头脑风暴法和Google HEART指标)将它们组合在一起。 这意味着可以在现场与Post-posts现场交流,也可以在Miro / Mural在线交流中进行。 当然,它需要一个易记的名称,即“ Data Draw Up”或“ DDU”的简称。 它由5个步骤组成,大约需要不到2个小时,具体取决于团队的规模(大约7个团队)。
为什么选择DDU? (Why DDU?)
But first, let’s see why this is so important and how it’s helped the teams I’ve worked with in the past.
但首先,让我们看看为什么这是如此重要,以及它如何帮助我过去与之合作的团队。
If your team is like most teams, then you’re probably the only person who keeps track of the product metrics. There might be a meeting that’s done every now and then where you share the metrics with the team but let’s be real, people usually don’t understand them or they might show interest but after the meeting is over they don’t see metrics again until the next one.
如果您的团队像大多数团队一样,那么您可能是唯一跟踪产品指标的人。 可能会不时进行一次会议,在此您与团队共享指标,但说实话,人们通常不理解它们,或者他们可能会表现出兴趣,但是会议结束后,他们不会再看到指标,直到下一个。
Let me say this once, it’s your job as a product manager to help everyone in your team, and the obvious stakeholders, to understand and be invested in the most important metrics.
让我这么说一次,作为产品经理的工作是帮助团队中的每个人以及明显的利益相关者理解和投资于最重要的指标。
The result of DDU is a team that creates its own metrics, shares different perspectives, and each individual member constantly checks if what they’re doing has an impact. This can be very powerful and will help you take your team to the next level, ever so close to that high performing team you’ve always dreamed of. Let’s get to it!
DDU的结果是,一个团队创建了自己的指标,拥有不同的观点,并且每个成员都不断检查自己的行为是否有影响。 这可能非常强大,并且可以帮助您将团队提升到一个新的水平,与您梦dream以求的高绩效团队如此接近。 让我们开始吧!
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DDU如何工作? (How DDU works?)
As I mentioned before, Data Draw Up can be done in a workshop using 5 simple steps:
正如我之前提到的,可以在车间中使用5个简单步骤完成数据收集:
步骤1:提出一些HMW问题 (Step 1: Make some HMW questions)
This is the first step in getting a team that cares about data. Every one, individually, writes in posts-it’s (physical or digital) what they would like to know about the product in the HMW format. For example, “How Might We know if the user is getting all the information he/she needs from our website”.
这是建立一支关心数据的团队的第一步。 每个人都以手写形式(物理或数字形式)写他们想要了解的HMW格式产品的信息。 例如,“ 我们如何知道用户是否正在从我们的网站获取他/她需要的所有信息”。
Make sure you explain how the HMW format works and keep everything time-boxed. This shouldn’t take more than 10 minutes. It’s ideal if each person has about 3 post-its. EACH HMW GOES IN AN INDIVIDUAL POST-IT. After, make sure everyone reads aloud their own post-it’s.
确保您说明了HMW格式的工作原理,并且将所有内容都保留在时间范围内。 此过程不应超过10分钟。 每个人都有大约3个便利贴是理想的。 每个HMW都在单独的便利贴中使用。 之后,请确保每个人都大声朗读自己的帖子。
步骤2:投票给您最喜欢的人 (Step 2: Vote your favorite)
Now that you have all the questions the team wants to answer, group them all based on which are similar. When you have them grouped, you can choose one to represent the whole group and rephrase it if needed. After this is done and clear, just vote!
现在,您已经有了团队想要回答的所有问题,请根据相似的问题将它们全部分组。 将它们分组后,可以选择一个代表整个组,并在需要时对其重新措辞。 完成并清除之后,只需投票!
Each person can vote 3 times. And no, you can’t vote on the same post-it more than once but you can vote on the one you created. Remove all the ones that didn’t receive a vote and prioritize them based on higher votes. You want to have between 4 and 6 HMW’s. So you can just go with the ones with the highest votes. Don’t vote again! Keep things simple and time-boxed for máx 5 minutes.
每个人可以投票3次。 不,您不能在同一张便利贴上进行多次投票,但可以对自己创建的帖子进行投票。 删除所有未获得投票的投票,并根据更高的投票对其进行优先排序。 您希望拥有4到6个HMW。 因此,您可以选择票数最高的人。 不要再投票! 让事情简单明了,限时5分钟。
步骤3:根据HEART指标进行分类 (Step 3: Categorize based on HEART metric)
This is where things start to get more interesting. Make sure you understand how to use the Google HEART metrics framework. There is tons of information online. For this step, all we need to do is categorize each HWM post-it into one of the five metric types which are: Happiness, Engagement, Adoption, Retention, and Task Success. Keed this time-boxed for 5 minutes.
这是事情开始变得更加有趣的地方。 确保您了解如何使用Google HEART指标框架。 在线上有大量信息。 对于这一步,我们需要做的是每个分类HWM后它进五种度量标准类型是哪一种:H appiness,E ngagement,A doption,R etention和T问成功。 保持这个时间限制5分钟。
步骤4:将HMW转换为指标 (Step 4: Convert the HMW’s into metrics)
Now that you have the post-it’s categorized you need to make each HMW into a goal following the Goals-Signals-Metrics process. You can go one by one, converting the HMW into a goal. Then specifying the signal or signals associated with each goal.
既然您已对帖子进行了分类,则需要按照“目标-信号-度量”流程,将每个HMW都变成一个目标。 您可以一步一步地将HMW转换为目标。 然后指定与每个目标相关的一个或多个信号。
Finally, the metric or metrics associated with each signal or signals. Yes, you can reuse the signals and metrics with different goals. So following the previous example, the HMW “How Might We know if the user is getting all the information he/she needs from our website” would be the goal “User needs additional information”.
最后,与每个信号或多个信号相关联的一个或多个度量。 是的,您可以重用具有不同目标的信号和指标。 因此,按照前面的示例,HMW“我们如何知道用户是否正在从我们的网站获取他/她需要的所有信息”将成为目标“用户需要其他信息”。
Basically, how would we know a user has all the information needed? One way is to know when information is missing. That’s how we get our goal. Now for the signal, a user contacting us to request more information or clicks on a “More Information” button are triggers or signals that tell us that a user needs more information.
基本上,我们如何知道用户拥有所需的所有信息? 一种方法是知道何时缺少信息。 这就是我们实现目标的方式。 现在,对于信号,与我们联系以请求更多信息或单击“更多信息”按钮的用户是触发或信号,它们告诉我们用户需要更多信息。
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Lastly, for the metrics, this could be % of clicks on “More Information” overall page visits. Which we can interpret as the closer it is to 100% the fewer users need additional information and the better you’re satisfying that need. This is probably the activity which takes the longest, try to keep it time-boxed at 15 minutes.
最后,对于指标而言,这可能是“更多信息”整体页面访问的点击次数的百分比。 我们可以将其解释为越接近100%,则用户需要更多信息的数量就越少,而您对这种需求的满足就越好。 这可能是耗时最长的活动,请尝试将其时间限制在15分钟以内。
步骤5:与您的团队分享 (Step 5: Share with your team)
Great! This is the last step. Here you basically have everything you need. Now you only have to make sure to document everything (keeping it short and sweet). And make sure you implement these metrics right away. You might already have the metrics ready but if you don’t, make sure to add them to the next sprint or user story.
大! 这是最后一步。 在这里,您基本上拥有了所需的一切。 现在,您只需要确保将所有内容都记录下来(保持简短而优美)即可。 并确保您立即实施这些指标。 您可能已经准备好指标,但如果没有,请确保将其添加到下一个Sprint或用户故事。
Also, create a dashboard in your favorite analytics tool so your whole team can see it. As advice, I would always keep the original HMW questions next to the goal/signal/metric so everyone can keep track of the questions they wanted to be answered.
另外,在您最喜欢的分析工具中创建一个仪表板,以便整个团队都能看到它。 作为建议,我将始终将原始HMW问题放在目标/信号/指标旁边,以便每个人都可以跟踪他们想回答的问题。
Your team which usually doesn’t care much for data just created the first team metrics. Which can go alongside your north star metrics or whichever primary metric you use. Try it out and let me know your results in the comments. Remember, you’re one step closer to a high performing team.
通常对数据不太在乎的团队只是创建了第一个团队指标。 哪个可以与您的北极星指标一起使用,也可以与您使用的任何主要指标一起使用。 尝试一下,并在评论中告诉我您的结果。 请记住,您离高绩效团队仅一步之遥。
翻译自: https://medium.com/productschool/data-draw-up-a-workshop-to-get-your-team-to-love-data-fefe2f911d72
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