如何分析调查结果，例如Data Pro

Obtaining customer feedback is difficult. You need strong调查问题that effectively derive customer insights. Not to mention a distribution system that shares the survey with the right customers at the right time.但是，调查数据不仅对自己进行分析并分析。您需要一个致力于筛选调查结果的团队，并突出营销，销售和客户服务团队的关键趋势和行为。在这篇文章中，我们将不仅讨论如何分析调查结果，还要讨论如何向组织的其他成员展示您的发现。

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如何分析调查结果

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调查报告模板示例

如何分析调查结果

1.了解四个测量水平。

Before analyzing data, you should understand the four levels of measurement. These levels determine how survey questions should be measured and what statistical analysis should be performed. The four measurement levels are nominal scales, ordinal scales, interval scales, and ratio scales.

Nominal Scale

名义量表对数据进行分类，而没有任何定量值，类似于标签。名义量表的一个示例是：“从下面的列表中选择您的汽车品牌”。选择没有关系。由于缺乏数值意义，您只能跟踪有多少受访者选择了每个选项，并且选择了哪种选项最多。

顺序尺度

顺序尺度用于描述值的顺序。对于此量表，有一个定量值，因为一个等级比另一个等级高。序数量表的一个例子是：“对使用笔记本电脑的原因进行排名。”您可以分析此类量表的模式和中位数，可以通过交叉检查分析。

Interval Scale

间隔量表描述了值之间的顺序和差异。这些量表具有定量值，因为数据间隔沿量表保持等效，但是没有真正的零点。间隔量表的一个示例是在智商测试中。您可以分析这种类型的量表模式，中位数和平均值，并通过方差分析，，，，t检验， 和correlation analyses。方差分析tests the significance of survey results, while t-tests and correlation analyses determine if datasets are related.

比例量表

比率量表描述了值之间的顺序和差异，但与间隔尺度不同，它们确实具有真实的零点。使用比例量表​​，存在定量值，因为缺乏属性仍然可以提供信息。例如，比例量表可能是：“选择您在线购物的平均金额”。您可以通过t检验，方差分析和相关分析分析这种类型的量表和比例量表的模式，中值和平均值。

2.选择您的调查问题。

Once you understand how survey questions are analyzed, you should take note of the overarching survey question(s) that you're trying to solve. Perhaps, it's "How do respondents rate our brand?"

Then, look at survey questions that answer this research question, such as "How likely are you to recommend our brand to others?" Segmenting your survey questions will isolate data that are relevant to your goals.

此外，提出封闭式和开放式问题很重要。

Close-Ended Questions

一个封闭式调查问题提供了有限的答案。受访者无法解释他们的答案，他们只能从预定的选项中进行选择。这些问题可能是是或否，多项选择，复选框，下拉或刻度问题。提出各种问题对于获取最佳数据很重要。

开放式问题

一个开放式调查问题将要求被告解释他们的意见。例如，在NPS调查中，您会询问客户推荐您的品牌的可能性。之后，您可能会考虑要求客户解释他们的选择。这可能是“为什么或为什么您为什么不向您的朋友/家人推荐我们的产品？”

3.首先分析定量数据。

定量数据是有价值的，因为它使用统计数据得出结论。尽管定性数据可以为主题带来更多有趣的见解，但此信息是主观的，因此很难进行分析。但是，定量数据来自封闭式问题，可以将其转换为数字值。一旦量化数据，比较结果并确定趋势要容易得多客户行为。

最好在执行调查分析时从定量数据开始。这是因为定量数据可以帮助您更好地了解定性数据。例如，如果60％的客户表示对您的产品不满意，则可以将注意力集中在有关用户体验的负面评论上。这可以帮助您识别障碍customer journey并纠正引起流失的任何疼痛点。

4.使用交叉策划更好地了解目标受众。

If you analyze all of your responses in one group, it isn't entirely effective for gaining accurate information. Respondents who aren't your ideal customers can overrun your data and skew survey results. Instead, if segment responses using cross-tabulation, you can analyze how your target audience responded to your questions.

将数据拆分为人口统计

Cross-tabulationrecords the relationships between variables. It compares two sets of data within one chart. This reveals specific insights based on your participants' responses to different questions. For example, you may be curious about客户倡导among your customers based in Boston, MA. You can use cross-tabulation to see how many respondents said they were from Boston and said they would recommend your brand.

通过将多个变量提取到一个图表中，我们可以将调查结果缩小到特定的响应组。这样，您知道您的数据仅考虑您的目标受众。

Below is an example of a cross-tabulation chart. It records respondents' favorite baseball teams and what city they reside in.

图像源

5. Understand the statistical significance of the data.

As we mentioned in the last section, not all data is as reliable as you may hope. Everything is relative, and it's important to be sure that your respondents accurately represent your target audience.

例如，您的数据指出，有33％的受访者会向他人推荐您的品牌。他们中有75％的年龄超过40岁，但您的目标受众年龄在18至29岁之间。在这种情况下，这些数据在统计上并不重要，因为接受调查的人并不代表您的理想消费者。

Random sampling — selecting an arbitrary group of individuals from a larger population — can help create a more diverse sample of survey responses. Additionally, the more people you survey, the more accurate the results will be.

When you run an analysis on software likeSPSS— as shown above — it will tell you if a data point is statistically significant using ap值。If you look just below the table, it says "*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed)." This indicates which values are statistically relevant in your analysis.

If the statistical significance or p-value for a data point is equal to or lower than 0.05, it has moderate statistical significance since the probability for error is less than 5%. If the p-value is lower than 0.01, that means it has high statistical significance because the probability for error is less than 1%.

6. Consider causation versus correlation.

另一个重要spect of survey analysis is knowing whether the conclusions you're drawing are accurate. For instance, let's say we observed a correlation between ice cream sales and car thefts in Boston. Over a month, as ice cream sales increased so did reports of stolen cars. While this data may suggest a link between these variables, we know that there's probably no relationship.

仅仅因为两者的相关性并不意味着一个引起另一个。在这种情况下，通常有第三个变量（自变量）会影响两个因变量。在这种情况下，是温度。随着温度的升高，越来越多的人购买冰淇淋。此外，越来越多的人离开家去外出，这会带来更多的犯罪机会。

While this is an extreme example, you never want to draw a conclusion that's inaccurate or insufficient. Analyze all the data before assuming what influences a customer to think, feel, or act a certain way.

7. Compare new data with past data.

While current data is good for keeping you updated, it should be compared to data you've collected in the past. If you know 33% of respondents said they would recommend your brand, is that better or worse than last year? How about last quarter?

If this is your first year analyzing data, make these results the benchmark for your next analysis. Compare future results to this record and track changes over quarters, months, years, or whatever interval you prefer. You can even track data for specific subgroups to see if their experiences improve with your initiatives.

Now that you've gathered and analyzed all of your data, the next step is to share it with coworkers, customers, and other stakeholders. However, presentation is key in helping others understand the insights you're trying to explain.

下一节将说明如何呈现您的调查结果并与组织的其他成员共享重要的客户数据。

如何提出调查结果

1.使用图形或图表。

Graphs and charts are visually appealing ways to share data. Not only are the colors and patterns easy on the eyes, but data is often easier to understand when shared through a visual medium. However, it's important to choose a graph that highlights your results in a relevant way.

这是我的团队使用Brand Allbird上的数据创建的堆叠条形图的一个示例。如果您在阅读时遇到麻烦，那没关系！我们收到的反馈意见令人困惑。那是因为数据并非以对我们项目不熟悉的利益相关者有意义的方式组织。

So, we decided to revamp our graph's image and came up with the design below.

This bar graph is much simpler to read because it has individual bars for each variable and a clear key. And, the design fits the data that we're trying to display. A reader can easily understand the information we obtained from our survey.

根据您进行的调查，可以使用许多类型的图形和图表。您可以选择的一些选项包括饼图，维恩图，线图，散点图，直方图，象形图等。请务必选择一个准确显示您的数据并向利益相关者清楚的数据。

2. Create a data table.

表是共享数值数据的好方法。您可以使用SPSS或Exbob电竞官方下载cel之类的软件来轻松显示数据，例如以下示例。

该表是根据交叉表分析创建的。我们删除了不必要的信息 - 统计意义，平均值，中位数等 - 并专注于我们要分享的数据：每个偏爱每种促销激励措施的性别的百分比。这给了我们一种格式，以证明我们希望与利益相关者分享的百分比。

3. Tell a story with data analysis.

Chewy商品销售副总裁，Andreas von der Heydt，在LinkedIn上分享了有关与数据讲故事的力量的深刻信息。在下图中，乐高积木以五种不同的方式排列：一个随机分类，颜色编码，以不同的高度排列，以视觉吸引力的方式定位并构造成房屋。

图像源

良好数据分析的主要目标之一是将信息编织在一起，以便彼此建立，就像建造房屋一样。一些数据将作为您故事的基础，您演示文稿中的所有要点都将与该基础联系起来。从那里，您将构建关键发现，例如房屋的墙壁。这些关键发现支持您的研究的结论，该结论是屋顶。这是您在展示数据分析时要提出的要点。

Communicating data can be tricky when your stakeholders have varying degrees of analytical savviness. But no matter how sophisticated (or not) your team is, a story will resonate no matter what. So take the time to identify the point the data leads to and structure a story around that conclusion.

4. Make a visual presentation.

有时，将视觉效果与文本结合在一起会对您的发现进行详尽的描述。在这些情况下，演示可能非常适合展示您的数据。这使您有机会介绍调查的早期阶段，包括研究问题，假设，调查问题和分析方法。

我的演示文稿的幻灯片将图与表格和一些文本结合在一起。相同的数据以三种不同的方式共享，以吸引具有不同学习风格的人：喜欢视觉效果，喜欢数字的人以及喜欢单词的人。

5.整理一个信息图。

如果您需要共享易于阅读和快速消费的数据，则信息图可能是您最好的选择。

图像源

此HubSpot研究信息图通过图标，数字和描述性文本解释了调查结果。信息图表对此目的非常有效，将复杂的想法分解成比文本块更具吸引力的简单消息。

Using Survey Results in a Report

有时，这些文本块对于说服利益相关者至关重要。如果您要向高级管理人员或业务客户介绍数据，则可能需要准备有关发现的完整报告。您不会在演示过程中参考此文档，但是您可以将其交给听众以自己的时间阅读。

这是我关于调查项目的报告中的目录页面。重要的是要跟踪您所做的所有工作，并保留您进行调查的记录。这样，您将不会犯类似的错误或必须复制任何研究。

1.确定调查的结果。

Believe it or not, writing the intro to a survey report isn't always the best first step. To keep your report focused on a specific outcome that you want the reader to take away, start by explaining the outcome in detail. This section of the survey report will be included in the middle, but it's a great way to get your bearings when writing — especially with a longer report.

调查报告的结果应揭示如果阅读报告的利益相关者在您的分析中可能会发生什么。结果的一些想法可能是客户反馈，调整服务价格以增加收入或进入新市场的新产品，从而显示了组织扩展的潜力。

When writing this section, be sure that the data you've collected supports the outcome fully. It's best to avoid ideas that can't be substantiated by the other information in the report.

2.写您的研究摘要。

接下来，总结您的研究结果。在调查报告的这一部分中，您将包括明显的结果以及与您公司过去可能进行的其他研究相关的任何信息。bob全站app该摘要将是您的调查报告的一部分，人们将重点关注，因为它是您在报告中其他地方讨论的所有细节的凝结版本。

The research summary should be no more than a page long. To make it even easier to read, include headers above paragraphs to guide the reader through the content.

3.为报告创建大纲。

现在您有了结果和摘要，现在该开发大纲了。由于调查报告通常约为八到十页，因此您需要使用简洁的概述，其中包括利益相关者希望知道的所有相关信息。这是您可以自定义的示例大纲：

• 简介 - 为报告设定舞台。
• 摘要 - 总结研究的发现和关键要素。
• 方法论 - 解释如何进行研究。
• 结果 - 描述研究所揭示的内容。
• Outcomes - analyzing the results in the context of your organization's goals.
• Limitations - revealing any research oversights or areas for further research.
• Conclusion - a summary of the research analysis and why the audience should care about it.

4.选择一个布局。

大纲完成后，您将了解每个部分需要多少空间。调查报告可以根据您的喜好在肖像或景观布局中发表。

何时使用portrait layout for a survey report

Portrait landscape is best used when the report is text-heavy. Charts and graphs with more rows than columns also work well in portrait-style layouts. Avoid dedicating entire pages to images or infographics in this layout as it takes away space from important text-based content.

When to use landscape layout for a survey report

Use a landscape layout for your survey report when your report will be design-heavy. High-quality imagery, graphs, and charts work well in landscape because there's more space for text-based content to the side of the visuals. Too many text-heavy pages can make a landscape report feel much longer than it actually is.

5. Include the methodology of your research.

你的报告应该expl的方法论部分ain exactly how your survey was conducted, who was invited to participate, and the types of tests used to analyze the data. You might use charts or graphs to help communicate this data.

It's OK to be detailed here — the readers will want to know that the outcomes of the survey are valid and based on relevant research methods. Include the number of participants, how they were selected, and any demographic data you collected about them. Also, include your process for selecting the survey questions and why you chose certain tests for the analyses.

6.提及您的研究中的任何局限性。

无论您在进行调查之前准备多少准备工作，如果您考虑了另一个变量，您都会在结果中找到一些可能是更明确的信息。关于研究的重要部分是将来可以继续。限制部分为未来的研究人员设定了阶段，以便在当前调查中纠正您停止的地方或纠正任何错误。

7. Add appendices if needed.

如果您很幸运，您提供的所有数据都符合报告中的图表，则可能在报告结束时不需要附录。但是，如果您在页面上包含的图形和图表是提供上下文的大型数据集的截断版本，则应在报告的末尾以原始形式包含它们。在整个报告中，请参考附录，以便读者可以对其进行审查，以深入了解您的内容。

调查报告模板示例

Canva具有可用于调查报告的免费报告模板。选择模板时，请查找包含图形和图像的占位符以及多个页面布局的模板，以便您有一些变化。以下是我们最喜欢的调查报告模板示例。

1. Geometrical Corporate General Report

不仅该报告引人注目，还包括一张目录，以帮助组织报告以及可自定义的图表和图表。可以自定义此模板以轻松将调查报告中的每个元素包括在内。作为肖像画风格的布局，您会喜欢这个帆布模板，以进行文本分析和报告。

2. Minimal Formal Annual Report

此CANVA报告模板可以使数据自言自语。最小的肖像布局在内容周围提供了很多负空间，因此可以呼吸。大胆的数字和百分比可以保留或省略，具体取决于您对每页的需求。该模板的罕见宝石之一是它能够平衡大型，清晰的图像，这些图像不会挤出页面上重要的书面信息。使用此模板进行混合文本视觉设计。

3. Digital Advertising Approach Presentation

Bold and punchy, the Digital Advertising Approach Template by Canva is a landscape-style presentation that can also be used for survey reports. It comes with several types of charts and graphs that you can tailor to your research and data. With more than 28 individual page templates included in this design, you'll probably run out of content in your report before you run out of ways to design it.

4. Empowerment Keynote Presentation

由于其干净的线条，对比的图形元素以及宽敞的视觉效果，因此该演示模板制造了一个很好的研究报告模板。该模板中的标头实际上跳出页面以吸引读者的注意力。使用此模板示例，没有太多方法可以提供定量数据，但是它适用于定性调查报告，例如焦点小组或产品设计研究，其中将讨论原始图像。

分析调查数据像专业人士

收集数据是一回事，但是如果没有战略分析过程，您可能会很难得出结论以证明追求业务目标的合理性。遵循本文中的提示，制作精心制作的数据分析，这些数据将支持最有抱负的项目。

Editor's note: This post was originally published in July 2019 and has been updated for comprehensiveness.