Product teams need to continuously monitor their product usage data to understand the user pain points and iteratively improve the product experience. As a product manager, you need to have tools that improve visibility, make it easier for the team to comprehend, and derive actionable insights from the product data. A few months ago, I wrote an article that outlined the process of building tools to enable that for your team. I’d highly recommend reading that article as well, but here is the summary:

产品团队需要不断监视其产品使用情况数据，以了解用户的痛点并迭代地改善产品体验。 作为产品经理，您需要拥有可提高可见性，使团队更容易理解并从产品数据中获得可行见解的工具。 几个月前，我写了一篇文章 ，概述了为您的团队构建工具的过程。 我也强烈建议您阅读该文章，但这是摘要：

1. Identify your product assumptions. Create a list of data points required to prove or disprove those assumptions.确定您的产品假设。 创建证明或反证这些假设所需的数据点列表。
2. Ensure that the above data points are being recorded.确保正在记录以上数据点。
3. Build APIs to query data and model it in the required format.构建API以查询数据并以所需格式对其进行建模。
4. Share product usage data with the team and help them derive insights out of it.与团队共享产品使用情况数据，并帮助他们从中获取见解。
5. Connect those APIs with visualization tools and then create widgets and dashboards as per need.将这些API与可视化工具连接起来，然后根据需要创建小部件和仪表板。

Now that you have a working dashboard, you’d expect everyone on the team to open it up frequently and use relevant data for their work. But does that really happen? Does the team start consuming data for their everyday decision making? Nope, not really.

现在您有了工作仪表板，您可以期望团队中的每个人都经常打开它，并使用相关数据进行工作。 但这真的发生了吗？ 团队是否开始使用数据进行日常决策？ 不，不是这样。

Here are some of the challenges I faced when I was trying to drive adoption for data dashboards inside my team:

当我试图推动团队内部数据仪表板的采用时，以下是我面临的一些挑战：

1. People heavily rely on their intuition for making decisions and do not refer to data even though it is available. This is normal human behavior!

   人们在很大程度上依靠直觉来做出决策，即使有可用数据也不会引用。 这是正常的人类行为！

2. During meetings, data was often needed to propel the conversation forward but people would proceed with behind-the-envelope calculations for the sake of everyone’s time and avoiding friction to access data sitting inside some view of the dashboard.在会议期间，通常需要数据来推动对话前进，但是人们会为了每个人的时间而进行幕后计算，并避免摩擦访问位于仪表板某些视图内的数据。

3. Over a period of time, each function of the product had its own dashboard. For e.g. engineering, dev-ops, sales, and marketing had their own dashboards running. This created data silos and no one had the bird’s eye view of whether the product was heading in the right direction. Yes, dashboard fatigue is real!

   在一段时间内，产品的每个功能都有其自己的仪表板。 例如，工程，开发人员，销售和市场营销都有自己的仪表板正在运行。 这造成了数据孤岛 ，没有人对产品是否朝着正确的方向鸟瞰。 是的， 仪表板疲劳 是真实的！

4. And, we humans tend to ask questions and validate hypotheses with our colleagues and trust them rather than looking for facts and information ourselves.

   而且，我们人类倾向于向同事提出问题并验证假设并信任他们，而不是自己寻找事实和信息。

欢迎Alexa语音助手 (Welcome Alexa Voice Assistant)

In order to sustain the data-driven culture and cater to all the natural human tendencies, I realized that voice interfaces were the way to go forward. I chose Amazon Alexa to create my own skill and plug them with the APIs which were already created to query the data for the dashboards.

为了维持数据驱动的文化并适应人类的所有自然趋势，我意识到语音接口是前进的道路。 我选择Amazon Alexa来创建自己的技能，然后将其插入已经创建的API中，以查询仪表板的数据。

我创建了一个MVP (I created an MVP)

Within a week, I had an MVP ready which could answer questions like:

一周之内，我准备好了MVP，可以回答以下问题：

• How many users signed up in the last 7 days?最近7天有多少用户注册？
• How many users bought a subscription in the last one month?最近一个月有多少用户购买了订阅？
• How many users have downloaded and signed up from the mobile app?有多少用户已从移动应用程序下载并注册？

I took feedback from different team members and created a list of questions that everyone wanted to be answered on a day-to-day basis. I slowly started connecting APIs from different data sources with this one skill.

我从不同的团队成员那里获得了反馈，并创建了一个每个人每天都希望得到回答的问题清单。 我慢慢用这种技能开始连接来自不同数据源的API。

我确保MVP可以提供所有产品功能 (I ensured that the MVP served all the product functions)

After a couple of weeks of further development, the skill was ready to answer questions like:

经过几周的进一步发展，该技能可以回答以下问题：

• What is the uptime of the product in the last 1 month? — DevOps最近1个月产品的正常运行时间是多少？ — DevOps
• What is the mean response time of our APIs? — Engineering我们的API的平均响应时间是多少？ —工程
• How many unique user sessions did we have on our website in the last month? — Marketing上个月我们在我们的网站上有多少次独立用户会话？ —行销
• What is the dollar value of our current sales pipeline? — Sales我们目前的销售渠道的美元价值是多少？ —销售
• How many users have completed the user funnel? — Product有多少用户完成了用户渠道？ —产品

All of this took roughly 3–4 weeks, given the fact that all of the APIs were available to query the required data. Once the skill was ready, we bought Alexa devices for each of our meeting rooms and common areas.

考虑到所有API均可查询所需数据，所有这些花费了大约3-4周。 一旦技能准备就绪，我们便为每个会议室和公共区域购买了Alexa设备。

使用语音界面的好处 (Benefits of using the voice interface)

With Alexa device placed in different locations inside the office, I ensured that:

将Alexa设备放置在办公室内的不同位置时，我确保：

1. No meeting or conversation was ever paused due to the lack of a data point.由于缺少数据点，因此从未暂停过会议或对话。
2. Reduce the friction of opening a dashboard and getting data as soon as a question popped up in someone’s head. Essentially, cater to the innate human tendency of asking questions.一旦有人想到某个问题，就可以减少打开仪表板和获取数据的麻烦。 本质上，迎合了人类提出问题的先天倾向。
3. Get rid of all the different data dashboards and create a unified interface to access all the data irrespective of the product function. (Though, we were not able to completely get rid of the dashboards as there were always complicated queries and analysis that had to be performed which couldn’t be done through a voice interface.)摆脱所有不同的数据仪表板，创建一个统一的界面以访问所有数据，而与产品功能无关。 (尽管如此，我们无法完全摆脱仪表盘，因为总是需要执行复杂的查询和分析，而这是无法通过语音界面完成的。)

我不断提高技能 (I continuously improved the skill)

Through logs, I closely monitored what were the top questions that the team members were asking and the skill did not have an answer to or wasn’t responding to as per need. I kept adding those questions to the skill to ensure that team members kept coming back to the voice assistant for all their data needs.

通过日志，我密切监视了团队成员提出的最重要的问题，并且该技能没有根据需要做出答复或没有做出响应。 我一直将这些问题添加到技能中，以确保团队成员不断满足语音助手的所有数据需求。

Over a period of months, the team not only had access to data but were using it for their day-to-day needs of understanding how our users were using the product. With this arsenal, the team was able to improve the user conversion funnel from 10% to more than 50% over the period of 6 months!

在几个月的时间里，该团队不仅可以访问数据，而且还可以将其用于满足了解我们的用户如何使用该产品的日常需求。 有了这个武器库，团队可以在6个月内将用户转化渠道从10％改善到50％以上！

Have any thoughts, comments, or feedback? Feel free to share it below :)

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