Learn the rules to Data Draw Up; a fun way to get your teams invested in data.
了解數據收集的規則; 一種讓您的團隊投入數據的有趣方式。
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.
讓我們簡短點。 指標是產品管理中最重要的事情之一。 它們幫助我們了解我們對產品或功能所做的更改是否真正奏效,以及我們是否更接近實現目標, KPI或OKR 。 但這不是我們需要它們的唯一原因,它們還可以幫助產品中涉及的每個人回答關鍵問題……是的,我的意思是每個人。 從軟件工程師到UX,它可以幫助整個團隊了解更多數據。
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.
現在,我將向您展示的是一個簡單的框架,我使用眾所周知的工具(例如, 我們的能力(HWM),頭腦風暴法和Google HEART指標)將它們組合在一起。 這意味著可以在現場與Post-posts現場交流,也可以在Miro / Mural在線交流中進行。 當然,它需要一個易記的名稱,即“ Data Draw Up”或“ DDU”的簡稱。 它由5個步驟組成,大約需要不到2個小時,具體取決于團隊的規模(大約7個團隊)。
為什么選擇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,讓我們看看為什么這是如此重要,以及它如何幫助我過去與之合作的團隊。
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.
如果您的團隊像大多數團隊一樣,那么您可能是唯一跟蹤產品指標的人。 可能會不時進行一次會議,在此您與團隊共享指標,但說實話,人們通常不理解它們,或者他們可能會表現出興趣,但是會議結束后,他們不會再看到指標,直到下一個。
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.
讓我這么說一次,作為產品經理的工作是幫助團隊中的每個人以及明顯的利益相關者理解和投資于最重要的指標。
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的結果是,一個團隊創建了自己的指標,擁有不同的觀點,并且每個成員都不斷檢查自己的行為是否有影響。 這可能非常強大,并且可以幫助您將團隊提升到一個新的水平,與您夢dream以求的高績效團隊如此接近。 讓我們開始吧!
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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,可以在車間中使用5個簡單步驟完成數據收集:
步驟1:提出一些HMW問題 (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”.
這是建立一支關心數據的團隊的第一步。 每個人都以手寫形式(物理或數字形式)寫他們想要了解的HMW格式產品的信息。 例如,“ 我們如何知道用戶是否正在從我們的網站獲取他/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.
確保您說明了HMW格式的工作原理,并且將所有內容都保留在時間范圍內。 此過程不應超過10分鐘。 每個人都有大約3個便利貼是理想的。 每個HMW都在單獨的便利貼中使用。 之后,請確保每個人都大聲朗讀自己的帖子。
步驟2:投票給您最喜歡的人 (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!
現在,您已經有了團隊想要回答的所有問題,請根據相似的問題將它們全部分組。 將它們分組后,可以選擇一個代表整個組,并在需要時對其重新措辭。 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.
每個人可以投票3次。 不,您不能在同一張便利貼上進行多次投票,但可以對自己創建的帖子進行投票。 刪除所有未獲得投票的投票,并根據更高的投票對其進行優先排序。 您希望擁有4到6個HMW。 因此,您可以選擇票數最高的人。 不要再投票! 讓事情簡單明了,限時5分鐘。
步驟3:根據HEART指標進行分類 (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.
這是事情開始變得更加有趣的地方。 確保您了解如何使用Google HEART指標框架。 在線上有大量信息。 對于這一步,我們需要做的是每個分類HWM后它進五種度量標準類型是哪一種:H appiness,E ngagement,A doption,R etention和T問成功。 保持這個時間限制5分鐘。
步驟4:將HMW轉換為指標 (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.
既然您已對帖子進行了分類,則需要按照“目標-信號-度量”流程,將每個HMW都變成一個目標。 您可以一步一步地將HMW轉換為目標。 然后指定與每個目標相關的一個或多個信號。
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,與每個信號或多個信號相關聯的一個或多個度量。 是的,您可以重用具有不同目標的信號和指標。 因此,按照前面的示例,HMW“我們如何知道用戶是否正在從我們的網站獲取他/她需要的所有信息”將成為目標“用戶需要其他信息”。
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,我們如何知道用戶擁有所需的所有信息? 一種方法是知道何時缺少信息。 這就是我們實現目標的方式。 現在,對于信號,與我們聯系以請求更多信息或單擊“更多信息”按鈕的用戶是觸發或信號,它們告訴我們用戶需要更多信息。
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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,對于指標而言,這可能是“更多信息”整體頁面訪問的點擊次數的百分比。 我們可以將其解釋為越接近100%,則用戶需要更多信息的數量就越少,而您對這種需求的滿足就越好。 這可能是耗時最長的活動,請嘗試將其時間限制在15分鐘以內。
步驟5:與您的團隊分享 (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! 這是最后一步。 在這里,您基本上擁有了所需的一切。 現在,您只需要確保將所有內容都記錄下來(保持簡短而優美)即可。 并確保您立即實施這些指標。 您可能已經準備好指標,但如果沒有,請確保將其添加到下一個Sprint或用戶故事。
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,在您最喜歡的分析工具中創建一個儀表板,以便整個團隊都能看到它。 作為建議,我將始終將原始HMW問題放在目標/信號/指標旁邊,以便每個人都可以跟蹤他們想回答的問題。
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.
通常對數據不太在乎的團隊只是創建了第一個團隊指標。 哪個可以與您的北極星指標一起使用,也可以與您使用的任何主要指標一起使用。 嘗試一下,并在評論中告訴我您的結果。 請記住,您離高績效團隊僅一步之遙。
翻譯自: https://medium.com/productschool/data-draw-up-a-workshop-to-get-your-team-to-love-data-fefe2f911d72
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