使用 Search Console 气泡图优化网站表现
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
2021 年 4 月 6 日(星期三)
分析网站在 Google 搜索中的表现数据始终是一项挑战,存在可视化和理解难度更大的大量长尾查询时,情况更是如此。在这篇博文中,我们会为您提供一些提示,帮助您发掘优化网站在 Google 搜索中表现的机会。
如果您还没有阅读我们关于将 Search Console 关联至数据洞察以及使用数据洞察实现 Google 搜索流量监控的近期博文,不妨先看一看这些内容,详细了解在数据洞察中使用 Search Console 可实现哪些功能。
今天,我们将讨论气泡图,它可以帮助您了解进行哪些查询时网站表现良好,进行哪些查询时网站的表现有待改进。首先,我们将说明该图表中的主要元素,说明各类具体设置及其对数据的影响。然后,我们将提供一些指引,说明您在分析数据时需要注意的事项。
首先是好的方面,您无需从头开始构建图表,只要使用此模板关联到您的数据,并调整您想要的各种设置即可。
废话少说,直接看图…
解读图表
存在多个指标和维度时,气泡图是一种较直观的可视化图表,因为它可以让您更高效地发现数据中的关系和模式。在本例中,您可以同时查看不同维度(查询、设备)的各种流量属性(点击率、平均排名)和数量(总点击次数)。
我们将通过一些图表元素来说明图中展示的内容,以及未展示的内容。
数据源
在该图表中,我们使用的是通过 Search Console 数据源获取的网站展示次数表格,其中包括按网站和查询汇总的 Google 搜索表现数据。
过滤条件和数据控制
为了便于您有效地控制您的数据,我们在图表中提供了 5 个自定义选项:
- 数据控制:选择要分析的 Search Console 资源。
- 日期范围:选择您要在报告中查看的日期范围;默认情况下,您会看到过去 28 天的数据。
- 查询:包含或排除要重点关注的查询。您可以使用正则表达式,方法与在 Search Console 中使用它们时类似。
- 国家/地区:包含或排除国家/地区。
- 设备:包含或排除设备类别。
坐标轴
图表中的坐标轴是平均排名(y 轴)和网站点击率(x 轴),但为使图表展示出更深入的分析数据,我们进行了 3 项重要转换:
- 逆转 y 轴方向:由于 y 轴显示平均排名,逆转该轴是指 1 位于顶部。
对于大多数商务图表而言,最佳排名是右上角,因此使用 y 轴显示平均排名时,逆转其方向会显得更直观。
- 对数刻度:对数刻度是指“以紧凑的方式显示较大范围的数值数据,沿刻度移动一个距离单位表示数字乘以 10”。对这两条轴使用对数刻度,您可以更好地了解图表中几种极端查询(点击率非常低、平均排名非常低或两者都非常低)的情况。
- 参考行:参考行非常有助于突出显示高于或低于特定阈值的值。查看平均值、中位数或特定百分位数可以让您注意到偏离特定模式的情况。
气泡
图表中的每个气泡都表示单个查询,为了使图表更有用,我们使用了两个样式属性:
- 大小:用气泡大小代表点击次数,有助于您一目了然地看出哪些查询带来了大部分流量:气泡越大,查询产生的流量越多。
- 颜色:用气泡颜色代表不同设备类别,有助于您了解网站在移动设备上与桌面设备上的 Google 搜索表现的差异。您可以使用颜色区分各维度,但随着值数量的增加,识别模式的难度会随之增大。
分析数据
这种可视化方式的目标是帮助发现查询优化机会。该图表显示了查询相关表现,其中 y 轴表示平均排名,x 轴表示点击率,气泡大小表示总点击次数,气泡颜色表示设备类别
红色参考线显示了每条轴的平均值,将图表拆分为象限,以显示四种类型的查询表现。您的象限划分情况可能与这篇博文中显示的结果有所不同;具体取决于您的网站查询的分布方式。
一般来说,该图表会显示四个类别,您可以对其进行分析,以确定优化查询表现时要在哪些方面投入时间。
- 高排名,高点击率:您无需针对这些查询采取任何行动;现在的效果很理想。
- 低排名,高点击率:此类查询似乎与用户相关;即使这些查询的排名低于您网站的平均查询排名,也会获得较高的点击率。如果这些查询的排名提高,可能会带来巨大的贡献,请花时间优化这些查询!
- 低排名,低点击率:查看点击率较低的查询(本类别以及下一条的类别)时,请特别注意查看气泡大小,了解哪些查询点击率较低但仍然带来了大量流量。虽然此象限中的查询看似不值得您投入时间进行优化,但它们可以分为两大类:
- 相关查询:如果这些查询对您来说非常重要,那么这是个很好的开始,因为这表示这些查询已经显示在 Google 搜索中。
请优先优化这些查询而非完全不会出现在搜索结果中的查询,因为这类查询更容易优化。
- 不相关查询:如果查询与您的网站无关,建议您调整内容,使其侧重能带来相关流量的查询。
- 高排名,低点击率:这些查询可能会因各种原因而点击率较低。您应该查看最大的气泡,以发现是否存在以下情况:
- 您的竞争对手可能使用了结构化数据标记,且展示的是富媒体搜索结果,可能会吸引用户点击竞争对手的结果而非您的结果。不妨为您的网站启用搜索结果功能。
- 您可能优化了与您的网站有关但用户并不感兴趣的查询,或偶然获得了此类查询的高排名。
- 用户可能已经找到所需的信息,如贵公司的营业时间、地址或电话号码。
当您找到值得投入时间和精力优化的查询后,请务必参考搜索引擎优化 (SEO) 新手指南针对这些查询进行优化。以下是一些建议:
- 确保
title
元素、说明 meta
标记以及 alt
属性具有描述性、具体且准确。
- 使用标题元素强调重要文字,为您的内容创建层次结构,便于用户和搜索引擎浏览文档。
- 添加结构化数据标记向搜索引擎说明您的内容,以便在搜索结果中以有用的(且吸引用户的)方式显示您的内容。
- 思考用户找到您的某些内容时可能用到的搜索字词。您可以借助 Google Ads 提供的关键字规划师来发现新关键字变体,并了解每个关键字的大概搜索量。您还可以使用 Google 趋势,从与您的网站相关的热门主题和查询中寻找优化灵感。
反馈
与往常一样,如果您遇到任何问题,请通过 Google 搜索中心社区或数据洞察社区告知我们。此外,如果您使用 Twitter,请务必关注我们;我们会在未来推送更多相关博文。
发布者:Daniel Waisberg,搜索技术推广工程师
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
[null,null,[],[[["\u003cp\u003eThis blog post provides tips and techniques using a bubble chart in Data Studio to analyze website search performance and identify optimization opportunities.\u003c/p\u003e\n"],["\u003cp\u003eThe bubble chart visualizes query performance by showing average position, click-through rate, total clicks (bubble size), and device category (bubble color).\u003c/p\u003e\n"],["\u003cp\u003eThe chart helps identify four types of queries: top-performing, high-CTR but low-ranking, low-CTR and low-ranking, and high-ranking but low-CTR, offering optimization strategies for each.\u003c/p\u003e\n"],["\u003cp\u003eOptimization tips include improving title tags, meta descriptions, headings, structured data, and keyword research using tools like Keyword Planner and Google Trends.\u003c/p\u003e\n"],["\u003cp\u003eUsers can leverage a provided Data Studio template to connect to their Search Console data and customize the analysis according to their needs.\u003c/p\u003e\n"]]],["This blog post explains how to use a bubble chart to analyze Google Search performance data. The chart visualizes query performance, with average position on the y-axis, CTR on the x-axis, click volume as bubble size, and device category as bubble color. Users can customize the data with filters like date range, query, country, and device. The chart identifies four query performance groups to target for optimization: high or low position and CTR. Optimization tips include improving titles, descriptions, structured data, and keyword research.\n"],null,["# Optimizing website performance with a Search Console bubble chart\n\nWednesday, April 06, 2022\n| It's been a while since we published this blog post. Some of the information may be outdated (for example, some images may be missing, and some links may not work anymore). Check out the new documentation on [improving SEO with a Search Console bubble chart](/search/docs/monitor-debug/bubble-chart-analysis).\n\n\nAnalyzing Search performance data is always a challenge, but even more so when you have plenty of long-tail queries,\nwhich are harder to visualize and understand. In this post, we'll provide tips to help you uncover opportunities to\noptimize your site's Google Search performance.\n\n\nIf you haven't read our recent posts on [connecting Search Console to Data Studio](/search/blog/2022/03/connecting-data-studio)\nand [monitoring Search traffic with Data Studio](/search/blog/2022/03/monitoring-dashboard),\nconsider checking them out to understand more about what you can do with Search Console in Data Studio.\n\n\nToday we'll discuss a [bubble chart](https://support.google.com/datastudio/answer/7207785)\nthat can help you understand which queries are performing well for your site, and which could be improved. We'll first\nexplain the main elements in the chart, describing specific settings and how they influence the data. Then we'll provide\nsome pointers on what to look for when analyzing the data.\n\n\nStarting with the good news: you don't need to build the chart from scratch, you can use\n[this template](https://datastudio.google.com/reporting/1e5b5f6a-38d7-4547-a54b-69594681a09b/page/xFbeC/preview), connect to your data,\nand tweak any settings you want.\n\n\n*Without further ado...*\n\nUnderstanding the chart\n-----------------------\n\n\nA **bubble chart** is a great visualization when you have multiple metrics and dimensions because it enables you to see\nrelationships and patterns in your data more effectively. In the example shown here, you can see traffic attributes\n(click-through rate (CTR), [average position](https://support.google.com/webmasters/answer/7042828#position))\nand volume (total [clicks](https://support.google.com/webmasters/answer/7042828#click)) for different dimensions (query, device) at the same time.\n\n\nWe'll go through some of the chart elements to clarify what it shows, and what it doesn't.\n\n### Data source\n\n\nFor this chart, we're using the **Site Impression** table available through the [Search Console data source](https://support.google.com/datastudio/answer/7314895), which includes [Search\nperformance data](https://support.google.com/webmasters/answer/7576553) aggregated by site and queries.\n\n### Filters and data controls\n\n\nIn order to make it easy for you to control your data effectively, we included five customization options in the chart:\n\n1. **[Data control](https://support.google.com/datastudio/answer/7415591)**: Choose the Search Console property you'd like to analyze.\n2. **Date range**: Choose the date range you'd like to see in the report; by default you'll see the last 28 days.\n3. **Query** : Include or exclude queries to focus on. You can [use regular\n expressions](/search/blog/2021/06/regex-negative-match) similar to the way you use them in Search Console.\n4. **Country**: Include or exclude countries.\n5. **Device**: Include or exclude device categories.\n\n### Axes\n\n\nThe axes in the chart are **Average position** (y-axis) and **Site CTR** (x-axis), but we made three significant transformations\nto make the chart more insightful:\n\n- **Reverse y-axis direction**: Since the y-axis shows average position, inverting it means that 1 is at the top. For most business charts, the best position is in the top right corner, so it is more intuitive to invert the y-axis when using it to display average position.\n- **Log scale** : A [logarithmic scale](https://en.wikipedia.org/wiki/Logarithmic_scale) is \"a way of displaying numerical data over a very wide range of values in a compact way (...) moving a unit of distance along the scale means the number has been multiplied by 10\". Using log scale for both axes enables you to have a better understanding of queries that are in the extremities of the chart (very low CTR, average position, or both).\n- **[Reference lines](https://support.google.com/datastudio/answer/9921462)**: The reference line is very helpful to highlight values that are above or below a certain threshold. Looking at the average, median, or a certain percentile can call attention to deviations from the pattern.\n\n### Bubbles\n\n\nEach bubble in the chart represents a single query, and in order to make the chart more useful, we used two\n[style properties](https://support.google.com/datastudio/answer/7207785#style-properties):\n\n- **Size**: Using the number of clicks as the bubble size helps you see in a glance which queries are driving the bulk of the traffic --- the larger the bubble the more traffic the query generates.\n- **Color**: Using the device category as the bubble color helps you understand the differences between mobile and desktop Search performance. You can use any dimension as the color, but as the number of values increases, the harder it is to recognize patterns.\n\nAnalyzing the data\n------------------\n\n\nThe goal of this visualization is to help surface query optimization opportunities. The chart shows query performance, where\nthe y-axis represents **average position** , the x-axis represents **CTR** , the bubble size represents total number of\n**clicks** , and the bubble color represents **device category**.\n\n\nThe red reference lines show the average for each of the axes, which split the chart into quadrants, showing four types of\nquery performance. Your quadrants are likely to look different than the one shared in this post; they'll depend on how your\nsite queries are distributed.\n\n\nIn general, the chart will show four groups you can analyze to help you decide where to invest your time when optimizing your query performance.\n\n1. **Top position, high CTR**: There's not much you need to do for those; you're doing a great job already.\n2. **Low position, high CTR** : Those queries seem relevant to users; they get a high CTR even when ranking lower than the average query on your website. They could represent a significant contribution if their position goes up --- *invest in optimizing them!*\n3. **Low position, low CTR** : When looking at queries with low CTR (this and the next bullet), it's especially interesting to look at the bubble sizes to understand which queries have a low CTR but are still driving significant traffic. While the queries in this quadrant might seem unworthy of your effort, they can be divided into two main groups:\n - *Related queries*: If the query in question is important to you, it's a good start to have it appearing in Search already. Prioritize these queries over queries that are not appearing in Search results at all, as they'll be easier to optimize.\n - *Unrelated queries*: If the query is unrelated to your site, maybe it's a good opportunity to fine-tune your content to focus on queries that will bring relevant traffic.\n4. **Top position, low CTR** : Those queries might have a low CTR for various reasons. You should check the largest bubbles to find signs of the following:\n - Your competitors may have structured data markup and are showing up with rich results, which might attract users to click their results instead of yours. Consider [enabling Search result\n features for your site](/search/docs/appearance/search-result-features).\n - You may have optimized, or be \"accidentally\" ranking, for a query that users are not interested in relation to your site.\n - Users may have already found the information they needed, for example your company's opening hours, address, or phone number.\n\nOptimizing your website performance\n-----------------------------------\n\n\nOnce you find queries that are worth the time and effort, make sure to optimize for them with the help of the\n[SEO starter guide](/search/docs/fundamentals/seo-starter-guide). Here are some tips:\n\n- Ensure that your [`title`](/search/docs/appearance/title-link#page-titles) elements, [description `meta` tags](/search/docs/appearance/snippet#meta-descriptions), and `alt` attributes are descriptive, specific, and accurate.\n- Use heading elements to emphasize important text and help create a hierarchical structure for your content, making it easier for users and search engines to navigate through your document.\n- Add [structured data markup](/search/docs/appearance/structured-data/intro-structured-data) to describe your content to search engines and be eligible to display your content in useful (and eye-catching) ways in search results.\n- Think about the words that a user might search for to find a piece of your content. You can use the [Keyword Planner](https://ads.google.com/home/tools/keyword-planner/) provided by Google Ads to help you discover new keyword variations and see the approximate search volume for each keyword. You can also use [Google Trends](https://trends.google.com/trends/) to find ideas from rising topics and queries related to your website.\n\nFeedback\n--------\n\n\nAs always, let us know if you have any questions through the [Google\nSearch Central Community](https://support.google.com/webmasters/threads?thread_filter=(category:search_console)) or the [Data Studio Community](https://support.google.com/datastudio/threads?thread_filter=(category:connect_to_data)).\nAlso, if you're on Twitter, make sure to [follow us](https://twitter.com/googlesearchc), as we'll announce future posts over there.\n\nPosted by [Daniel Waisberg](https://www.danielwaisberg.com), Search Advocate"]]