网站的 A/B 测试是什么意思?

发布于 2024-09-04 10:15:39 字数 27 浏览 4 评论 0原文

请解释(举例)网站 A/B 测试的含义。

Please explain (with examples) what is meant by A/B testing of a website.

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勿忘心安 2024-09-11 10:15:39

在网站上下文中,这意味着网站/网络应用程序的某些用户获得稍微修改的版本,并且您验证与基线“控制”版本相比,每个修改如何影响可用性/用户的行为。

与往常一样,A/B 测试是一种统计技术,您需要足够大的样本来正确评估变更的影响。

这是由 Amazon 和 Google 知名网络公司完成的。

关于网络 A/B 测试的一篇非常好的文章(从营销角度)是:http://www.clickz.com/ 3349901

为了完整起见,以下是维基百科上完整的 A/B 测试定义:

A/B 测试或桶测试是
营销测试方法
基线对照样本与
多种单变量检验
样品以提高响应
费率。经典的直邮策略,
这个方法最近被采用了
在互动空间内进行测试
横幅广告、电子邮件等策略
登陆页面。

明显的改进
通过测试诸如复制之类的元素
文本、布局、图像和颜色。
然而,并非所有元素都会产生
相同的改进,并通过查看
不同测试的结果,
可以识别那些元素
始终倾向于产生
最大的改进。

采用这种 A/B 测试方法的雇主
将分发一个的多个样本
测试,包括对照,看看
哪个单变量最多
有效增加反应
率或其他期望的结果。这
测试为了有效,必须
达到足够规模的受众
有合理的机会
检测有意义的差异
控制策略与其他策略之间的关系

In the context of a web site it means that some users of the site/webapp get slightly modified version(s) and you verify how each modification affects the usability/user's behavior compared to baseline "control" version.

As always with A/B testing, which is a statistical technique, you need a large enough sample to properly assess impact of change.

This was done by both Amazon and Google among well known web companies.

A VERY good article (with marketing angle) on web A/B testing is : http://www.clickz.com/3349901

In the interest of completeness, here's the full A/B testing definition from Wikipedia:

A/B testing or bucket testing is a
method of marketing testing by which a
baseline control sample is compared to
a variety of single-variable test
samples in order to improve response
rates. A classic direct mail tactic,
this method has been recently adopted
within the interactive space to test
tactics such as banner ads, emails and
landing pages.

Significant improvements can be seen
through testing elements like copy
text, layouts, images and colors.
However, not all elements produce the
same improvements, and by looking at
the results from different tests, it
is possible to identify those elements
that consistently tend to produce the
greatest improvements.

Employers of this A/B testing method
will distribute multiple samples of a
test, including the control, to see
which single variable is most
effective in increasing a response
rate or other desired outcome. The
test, in order to be effective, must
reach an audience of a sufficient size
that there is a reasonable chance of
detecting a meaningful difference
between the control and other tactics

浪漫之都 2024-09-11 10:15:39

免责声明:我拥有一家名为 Visual Website Optimizer http://visualwebsiteoptimizer.com/

A/B 的 A/B 测试初创公司测试是一种向访问者展示网站的两个或多个不同版本的方法。例如,假设您有一个网站,然后您的老板/合作伙伴建议将注册表单从左向右移动可能会增加注册总数,因为它可以减少摩擦。现在,您可以对上帝有信心并立即实施建议,或者您可以出于好奇并制作一个表单位于左侧的页面版本。然后,您将 50% 的流量重定向到右表单,将 50% 的流量重定向到左表单。然后你观察哪个版本实际上获得了更多的注册。

这个问题变得有点复杂,因为您必须通过统计证明一个版本比另一个版本更好。为此,您可以使用在线计算器,例如我的网站 http://visualwebsiteoptimizer 上提供的计算器。 com/ab-split-significance-calculator/

如果您正在寻找现实世界的案例研究,我的网站上也有很多案例http://visualwebsiteoptimizer.com/case-studies.php

如果您还有任何其他问题,请随时询问。我喜欢这个话题:)

Disclaimer: I own an A/B testing startup called Visual Website Optimizer http://visualwebsiteoptimizer.com/

A/B testing is a method whereby you present two or more different versions of a website to visitors. For example, suppose you have a website and then your boss/partner suggests that moving sign up form from left to right may increase total number of signups because it reduces friction. Now either you can have faith in god and implement the suggestion right away or you can be curious and make a version of page where form is on the left. Then you redirect 50% of traffic to right-form and 50% to left-form. Then you observe which version actually gets more sign ups.

The issue becomes a bit complicated because you have to statistically prove that one version is better than the other. For that you can use online calculators such as the one available on my site http://visualwebsiteoptimizer.com/ab-split-significance-calculator/

If you are looking for real-world case studies, again there are lots of them on my site http://visualwebsiteoptimizer.com/case-studies.php

If you have any additional questions, please feel free to ask. I LOVE this topic :)

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