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产品经理博客:数据分析的作用

来源:互联网 作者:佚名 时间:2015-10-09 08:31
The Role of Analytics 数据分析的角色 Posted by marty cagan on February 23, 2014 marty cagan 2014年2月23日 One of the most significant changes in how we do product today is our use of analytics. Any capable product leader today is expected t
The Role of Analytics
数据分析的角色

Posted by marty cagan on February 23, 2014
marty cagan 2014年2月23日




One of the most significant changes in how we do product today is our use of analytics.   Any capable product leader today is expected to be comfortable with data, and understand how to leverage analytics to learn and improve quickly.
今天我们做产品的方法有一个最大的改变就是对于数据分析的运用。任何一个合格的产品经理都要适应数据,并且理解如何利用数据分析进行快速分析和产品提升。



Note: For the purposes of this article, consider the terms: “analytics,” “data,” “KPI" (Key Performance Indicator), and “metrics" to be synonymous.
注:为了方便文章表意,请视以下词语为同义词:“数据分析”“数据”“KPI”(关键绩效指数)“度量”。



I attribute this change to several factors.  First, as the market for our products has expanded dramatically due to access globally and also via connected devices, the sheer volume of data has dramatically increased which gives us interesting and statistically significantly results much faster.   Second, the tools for accessing and learning from this data have improved significantly.  Mostly, however, I see an increased awareness of the role that data can play in helping you learn and adapt quickly.
我将这一变化归因于几个方面。第一,由于全球统一入口和硬件联网导致产品的市场急剧地扩大,数据的绝对值也急剧地增长。这给了我们有趣且统计上显著的结果,并且速度快得多。第二,获得和学习这些数据的工具有了显著地提升。然而,最主要的是,我注意到数据对于学习和快速适应方面的帮助正引起越来越多的注意。



In this article I wanted to highlight what I see as the five main uses of analytics in strong product teams. 
在这篇文章中,我想要突出说明的是,我在强大的产品团队中观察的数据分析的五种主要应用。




1. Understand User and Customer Behavior
1.理解使用者和用户行为


When most people think of analytics they think of web analytics.  That is but one type.  But the idea is to understand how our users and customers (remember there can be many users at a single customer at least in the B2B context) are actually using our products.  We may do this to identify features that are not being used, or to confirm that features are being used as we expect, or simply to gain a better understanding of the difference between what people say and what they actually do.

多数人想到数据分析时,想到的是网页数据分析。这只是一种类型。但是这里的思想是理解我们的使用者和用户(记住,至少在B2B语境中,一个用户可以代表多个使用者)在真实环境中是怎样使用我们的产品的。我们有可能做数据分析是为了识别哪些功能事实上没有被用到,或者来确认哪些功能如预期的那样在被使用,或者只是简单地为了更好地理解用户的言语和实际行动的差异。



This type of analytic has been collected and used for this purpose by good product teams for literally 30 years.  A solid decade before the Internet, desktops and servers have been able to “call home” and upload behavior analytics which were then used by the product team to make improvements.  This to me is one of the very few “non-negotiables” in product.  My view is that if you’re going to put a feature in, you need to put in at least the basic usage analytics for that feature, otherwise how will you know if it’s actually working as it needs to? (see www.svpg.com/flying-blind).
这种数据已经被优秀的产品团队收集和使用了整整30年了。在互联网,台式机和服务器能够自动通报然后传回用户行为数据以供产品人员来改进产品之前就有10年了。这对于我来说是产品中少数几个“没得商量”之一。我的观点是,如果你要加入一个功能,那么你至少要加入一个基本使用数据分析,否则你怎么知道这个功能真的在按照需要工作?(参见 www.svpg.com/flying-blind)


2. Measure Product Progress

2. 测量产品进度


I have long been a strong advocate of using data to drive product teams.  Rather than provide the team an old-style roadmap listing someone’s best guess as to what features may or may not work, I strongly prefer to provide the product team with a prioritized set of KPI’s, and then the team makes the calls as to what are the best ways to achieve those goals.  It’s part of a larger trend in product to focus on outcome not output (see www.svpg.com/the-product-scorecard).
我长久以来一直是个数据驱动产品的强力支持者。相比于给团队一个罗列着某人最佳猜测的老派规划图,以此来确定功能是否有效,我强力支持给团队一个按照优先级排序的KPI序列,然后团队决定达到这些目标的最佳方式。这是产品设计中的一个潮流的一部分,专注于结果而非成果(参见www.svpg.com/the-product-scorecard)。



3. Prove If Product Ideas Work

3. 证明产品是否成功


Today, especially for consumer companies, we can isolate the contribution of new features, or new versions of workflows, or new designs, by running A/B tests and comparing the results.

今天,尤其是在消费者型的公司,我们可以通过A/B测试把一个新功能、新版流程或者新设计的贡献独立出来,并且比较他们的结果。



This lets us prove which of our ideas actually work.  We don’t have to do this with everything, but with things that have high risk or high deployment costs, or require changes in user behavior, this can be a tremendously powerful tool.  Even where the volume of traffic is such that collecting statistically significant results is difficult or time consuming, we can still collect actual data from our live-data prototypes to make much better informed decisions (see http://www.svpg.com/product-discovery-with-live-data-prototypes/).

这让我们可以验证我们的想法中哪些成功了哪些没有。我们不需要将这种方法付诸一切,只用在那些高风险的、改变用户高部署成本的或者需要改变用户行为习惯的项目,这将成为极为强大的工具。即便是在流量巨大,收集显著性数据困难或者过于耗时的情况下,我们仍就可以从实时数据模型中收集数据,以此来做出有根据得多的决定(参见http://www.svpg.com/product-discovery-with-live-data-prototypes/)




4. Inform Product Decisions
4. 数据支撑产品决定


In my experience, the worst thing about product in the past was that is was all about opinions.  And usually, the higher up in the organization, the more that opinion counted.  Today, in the spirit of “data beats opinions” we have the option of simply running a test and collecting some data and then using that data to inform our opinions.  The data is not everything, and we are not slaves to the data, but I find countless examples today in the best product teams of decisions informed by test results.  I hear constantly from teams now how often they are surprised by the data and how minds are changed.

在我的经验里,产品中最糟糕的事情莫过于所有东西都是根据意见决定的。并且通常组织中的层级越高,意见也越有决定性。现在,在“数据击败意见”的精神下,我们可以简单地选择做一个测试,收集一些数据,然后用这些数据来支持我们的意见。数据不是一切,并且我们也不是数据的奴隶,但是现今,我发现了无数的最优秀的产品团队通过测试结果做出决定的例子。我不断地从他们那里听到他们多常惊讶于数据然后改变想法的。


5. Inspire Product Work
5. 激励产品工作


While I am personally hooked on each of the above roles of analytics, I have to say that my personal favorite is this last point.  The data we aggregate (from all sources) can be a gold mine.  You will need to dig, and there’s no guarantees.  Often it boils down to asking the right questions.  But by exploring the data, we can find some very powerful product opportunities.  Some of the best product work I see going on right now was actually inspired by the data.  Yes, we often get great ideas by observing our customers.  And yes we often get great ideas by applying new technology.  But a form of leveraging technology is product work inspired by the data itself.

虽然我个人痴迷于以上的数据分析的角色中的每一种,但是我不得不说我个人的最爱,那就是这最后一点。我(从所有的资源)收集的数据就是一座金矿。你需要挖掘,并且不保证有收货。经常最后归结为问出正确的问题。但是通过探测这些数据,我们可以发现一些非常强大的产品机会。一些我看到的当下最好的产品工作就是受数据所激发的。是的,我们常常通过观察我们的用户来得到卓越的想法。是的,我们常常通过应用新的技术来的到卓越的想法。但是利用科技的一种形式就是用数据激发产品的想法。


Hopefully you can see the power of the analytics for product teams.  However, as powerful as the role of data is, the most important thing to keep in mind about the role of analytics is that the data will just shine a light on what is actually happening, but it won’t explain why.  We need our qualitative techniques to explain the quantitative results.

很有希望你们可以看到数据分析对产品团队产生的力量。然而,尽管数据分析如此强大,我们该记在心中的数据分析的最重要的一点是,数据只能揭示在发生什么,但是不能解释为什么。我们需要定性技术来解释定量结果。


I’m hoping you will ask yourself if your team is using data for all five of these purposes.  If not, consider how you can expand the role that data and analytics play for your team.

我希望你能自问,你的团队是否将数据分析用于以上五个目的了。如果没有,考虑一下如何能够扩大数据和数据分析在你的队伍中发挥的作用。


In the next article, I’ll discuss the main flavors of analytics.


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