Lean Analytics读后感锦集

《Lean Analytics》是一本由Alistair Croll / Benjamin Yoskov著作，O'Reilly Media出版的Hardcover图书，本书定价：USD 29.99，页数：440，特精心收集的读后感，希望对大家能有帮助。

《Lean Analytics》读后感(一)：怎样为产品做决策

适合读者：新手进阶（工作1个月以后读），还没工作的话就不必读了，会读不进去。

最开始觉得只是还算不错，后来读进去之后觉得很棒，主要是这个几个地方

- 涉及了非常多的metrics使用案例，很容易学习，尤其是对没有太多相关经验的团队而言.

- 对商业模式非常好的介绍，比如电商、UGC。一方面是我之前对这些不太懂，相当于系统性的梳理了一下，另一方面，每种模式的metrics关注点是有区别的，很实际。

- 最重要的，是案例中通过分析解决问题的思维方式，和依靠数据的决策方式。而不是随便想个idea，或者仅仅使用metrics。

如果时间不够，可以先看视频，貌似还算不错

《Lean Analytics》读后感(二)：创业初期如何靠数据分析来积累客户

Amazon评分还不错。评价摘录下面是

“This is one of these books that make you feel like you spent too little money for the value it provides. It's that good.

It is one of the most straightforward and comprehensive business books I've read. The information is clear, thorough and the case studies are not only interesting, but also very valuable since it allowed me to compare my business with other businesses. I now have a better feel of where we stands. The book stresses the importance of "actionable" metrics (vs vanity metrics) and helped me figure out the key metrics we should be tracking for our business.

Lean Analytics is an entertaining yet insightful read. Definitely worth every penny.”

《Lean Analytics》读后感(三)：粗读1-2022.10.16

1、与其说本书是数据分析，倒不如说是介绍商业的。

2、这和作者以目标为导向的数据分析思路有关。而目标的识别和商业模式、公司阶段密切相关。

正如作者在本书中说到的那样：

“The core idea behind Lean Analytics is this: by knowing the kind of business you are, and the stage you’re at, you can track and optimize the One Metric That Matters to your startup right now.”

3、所以，书的核心思想有以下几部分组成：各种商业模式（the kind of business)、公司所处的发展阶段（the stage you’re at）、OMTM方法、公司业务前进门槛。着重介绍了B2C（直到Chapter28），剩下的（29-31）主要介绍B2B以及内部创业。

4、首先介绍了公司业务的阶段，也就是说公司业务要回答的问题：FIGURE5-5.用本人的话讲就是：Empathy(用户存在的问题、市面上解决方案的不足、我的解决方案以及比较优势)、收益测算（收入模式、以及市场规模）、MPV开发、用户使用传播留存、可规模化持续发展的业务（最终目的）。最危险的是 In the long term, the riskiest part of a business is often directly tied to how it makes money.（不得不说公司业务是赚钱的最终归宿）。

5、接着介绍了商业模式，并展开介绍了集中比较常见的常见的商业模式。商业模式内容的组成： the acquisition channel, selling tactic, revenue source, product type, and delivery model.

6、追踪什么样的数据指标和你的商业模式、所处的业务阶段有关。请作出明智的抉择。

7、本书给出了一个原则，那就是OMTM：One Metric That Matters to your startup right now。另外，还提出了bechmark，也就是当前阶段你的数据指标好坏与否，需要以市场上的同类数据作为比较。 The Startup Genome project has collected key metrics from thousands of startups through its Startup Compass site.[77]。在上述基础上，你觉得是否要进入业务的下一个阶段？当当前阶段的指标数据达到一个值，那么就进入下一个阶段吧。这个值至少不能比平均值差。

8、其他内容：

（1） One of Lean Startup’s core concepts is build→measure→learn—the process by which you do everything, from establishing a vision to building product features to developing channels and marketing strategies

（2） If quantitative data answers “what” and “how much,” qualitative data answers “why.”Initially, you’re looking for qualitative data. You’re not measuring results numerically.

（3）Figure-1 Know-Know的四象限模型

（4） Segments, Cohorts, A/B Testing, and Multivariate Analysis模型

（5）数据分析能做什么，不能做什么。以及不能过度依赖数据分析的理念。

Optimization is all about finding the lowest or highest values of a particular function. A machine can find the optimal settings for something, but only within the constraints and problem space of which it’s aware. Data-driven optimization can perform this kind of iterative improvement. What it can’t do, however, is say, “You know what？ Four wheels would be way better!” .quantitative data is great for testing hypotheses, but it’s lousy for generating new ones unless combined with human introspection.

《Lean Analytics》读后感(四)：如果说lean startup讲的是方法论，那么这本就是方法

定位嘛，就是产品经理入门级读物了吧。不过也不用每章都读。

前面的一些方式方法的问题搞清楚，后面分开商业模型，每一类可以选取着读。

数据需要定性还是定量？Initially, you're looking for qualitative data. 在产品还没有庞大的稳定的用户群体时，定性分析是关键。

好数据应该是什么样子的呢？作者定出了一些vanity metrics, they are up-to-the-right and shed no light on anything. 好的数据首先是干净的，然后是可比的，以百分比的形式，衡量增长变化，其次要按照时间划分好，便于明晰某个时间点上，产品的的变化，所导致的用户行为上的变化。理想的情况下，随着产品设计的改变，数据上的统计方法也是一起设计好的，并且数据的结果应该有什么样的产品策略改变，也是清楚的。

但是，让团队内的每一个人，对于产品应该做的事情达成一致就已经很耗费时间了，更别说获取数据的干净程度，开发上能不能有无限的人力，都很难说。假设，干净的数据取来了，也并不意味着得到了因果关系。Usually causations are simple one-to-one relationships. Many factors conspire to cause something. ... You prove causality by finding a correlation, then running an experiment in which you control the other variables and measure the difference. 谁都知道因果关系好，可是现有的科学知识，都只是针对自然世界的因果关系推导。市场，用户，产品这三个都是变数没有特定规律，只能谈经验的东西。作者书里说，"unknown unknowns" are where the magic lives. They lead down plenty of wrong paths, and hopefully toward some kind of "eureka!" moment when the idea falls into place. 看来产品的魅力（也即，从业者们一切痛苦的根源）就在于这个"unknown unknowns"了。为了验证因果关系，测试的方法呢，有：segments, cohorts, A/ B testing, and multivariate analysis.

“You might think that people will play your multiplayer game, only to discover that they’re using you as a photo upload service. Unlikely？ That’s how Flickr got started.”

一切都要依靠数据么？“Humans do inspiration; machines do validation.” 用数据的方式可以帮我们找到局部最优，产品经理则需要找到全局最优。过度地依赖数据，很有可能会失去上帝视角。

lines in the sand？书中提到对于数据的统计结果，是要有预期的，并不能拿来数据看一看就做决策。怎么画这道线呢？

1）当40%的用户说，没有你他们会很失望，产品可以进入扩张期。

2）创业之前，至少找15个人，谈谈你的想法，再冲出门去找投资。

太多需求和问题，先做哪个后做哪个？作者用了一个squeeze toy来比喻，按照more stuff，more people，more often，more money，more efficient，从上往下挤。尤其在产品初期，将精力汇聚在一个目标上非常关键。

"UGC is all about turning visitors into creators." 想像一下明天tumblr flickr等等都不再有任何内容更新，大家仅仅根据已有的内容进行互动，就不难发现UGC的关键真的是内容创建，不是别的任何。怎么赚钱呢？“The UGC business might focus on user contribution above all else, but it still pays its bills with advertising most of the time.” “商业模式”是个有魔力的词汇，好像很多人都喜欢创造新的赚钱方法，只要UGC能通过广告赚到足够多的钱，何必为了创新而创新呢。书里面有一个business model flip book，供读者遍历每一种商业模式，看看怎么赚钱适合自己。

我要怎么用数据呢？对于创业公司，数据并不是在探索新机会，提示功能改进，而是检测自己的假设。“In a startup, your business model — and proof that your assumptions are reasonably accurate — is far more important than your business plan. ”看来TDD不仅仅只是程序管理方法，也适用于公司本身。“Deciding what business you’re in is usually quite easy. Deciding on the stage you’re at is complicated. This is where founders tend to lie to themselves. They believe they’re further along than they really are.”

可以参考的内容也不仅限于数字。用户访谈的方法书里面也有介绍。如果公司里面没有专门的用户研究部门，可以看看书里的用研技巧介绍。人们对话时会有认同别人的倾向，我们通过对方的穿着、谈吐，洞悉对方的身份，说话的时候会照顾这种“身份”，更何况直接表明来意的用户访谈。促使用户说出真相也有一些小tricks：1）问相反的问题，看她是不是会说出你想听的话。2）让他付出过多，看他什么时候放弃。

书里对产品工作的不同frameworks做了总结，着重介绍了lean framework，以及何时才能从一个phase移动到下一个phase：

所以说此书还是最适合当工具书。每个phase都有一些干货，实际操作中，同一时间一个产品只会有一个焦点（如果有多个的话，则与书里面提出的OMTM原则相悖，所以真的不用通读此书）。

具体论点不再赘述，只做摘抄：

Empathy:

如前

Stickiness Phase:

Don’t drive new traffic until you know you can turn that extra attention into engagement.

Expect to go through many iterations of your MVP before it’s time to shift your focus to customer acquisition.

Virality Phase:

viral coefficient

Revenue Phase:

Most people’s first instinct when things aren’t going incredibly well is to build more features.

the likelihood that any one feature is going to suddenly solve your customers’ problems is very small.

You’re moving from proving you have the right product to proving you have a real business.

more stuff to more people for more money more often more efficiently

In the Revenue stage, you need to figure out which “more” increases your revenues per engaged customer the most:

Scale Phase:

If the Revenue stage was about proving a business, the Scale stage is about proving a market.

Porter observed that firms with a large market share (Apple, Costco, Amazon) were often profitable, but so were those with a small market share (the coffee shop). The problem was companies that were neither small nor large. He termed this the “hole in the middle” problem — the challenge facing firms that are too big to adopt a niche strategy efficiently, but too small to compete on cost or scale.

Scaling is good if it brings in incremental revenue, but you have to watch for a decrease in engagement, a gradual saturation of the initial market, or a rising cost of customer acquisition. Changes in churn, segmented by channels, show whether you’re growing your most important asset — your customers — or hemorrhaging attention as you scale.

最后一个部分，才是全书的重点：how good is “good”？ 同样，也分了不同的商业模式来讨论。

当然用数据分析产品，也离不开产品上每个人的思维模式，数据是客观的，但是解读它的人仍然是主观的。前面细细碎碎一大堆，最后结尾回到了一些经验式的忠告和建议。

PS: "Our Lean Analytics stages suggest an order to the metrics you should focus on. The stages won’t apply perfectly to everyone. We’ll probably get yelled at for being so preｓｃｒｉｐｔive — in fact, we already have, as we’ve tested the material for the book online and in events. That’s OK; we have thick skins." 噗嗤

回头看来，这本的确非常preｓｃｒｉｐｔive，