![\"\u2192](\"https:\/\/no-cache.hubspot.com\/cta\/default\/53\/6ba52ce7-bb69-4b63-965b-4ea21ba905da.png\")
<\/a><\/p>\nAnd trust me: The results are well worth the effort you and your team will put into analyzing your data.<\/p>\n
Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n <\/strong><\/p>\n Before we jump in, here\u2019s the fictional business I\u2019ll be sharing throughout the post for added context:<\/p>\n I sell high-quality goat soap to customers seeking natural ingredients for their skincare needs. To better understand my customers, why they like (or dislike) my products, and what they need, I sent them a survey.<\/p>\n Let\u2019s say of the 100 customers I asked to participate in my survey, 94 of them participated \u2014 thanks to my well-written survey questions<\/a>. Now, I have some raw data to sort through.<\/p>\n Here\u2019s what I\u2019m doing to analyze the survey results.<\/p>\n Before analyzing data, it\u2019s helpful to refresh vocabulary from stats class and define the four measurement levels.<\/p>\n These levels determine how survey questions should be measured<\/a> and what statistical analysis should be performed. The four measurement levels are nominal scales, ordinal scales, interval scales, and ratio scales.<\/p>\n If you\u2019re like me and your statistics course feels like another lifetime ago, let\u2019s take a minute to define what each of these terms means.<\/p>\n Nominal scales classify data without any quantitative value, similar to labels. An example of a nominal scale is, \u201cSelect your goat soap scent from the list below.\u201d<\/p>\n The choices have no relationship to each other. Due to the lack of numerical significance, you can only keep track of how many respondents chose each option and which option was selected the most.<\/p>\n Ordinal scales are used to depict the order of values. For this scale, there’s a quantitative value because one rank is higher than another. An example of an ordinal scale is, \u201cRank the reasons for using goat soap.\u201d<\/p>\n You can analyze both mode and median from this type of scale, and ordinal scales can be analyzed through cross-tabulation analysis<\/a>.<\/p>\n Interval scales depict both the order and difference between values. These scales have quantitative value because data intervals remain equivalent along the scale, but there’s no true zero point.<\/p>\n An example of an interval scale is in an IQ test. You can analyze mode, median, and mean from this type of scale and analyze the data through ANOVA<\/a>, t-tests<\/a>, and correlation analyses<\/a>. ANOVA tests the significance of survey results, while t-tests and correlation analyses determine if datasets are related.<\/p>\n Ratio scales depict the order and difference between values, but unlike interval scales, they do have a true zero point. With ratio scales, there’s quantitative value because the absence of an attribute can still provide information.<\/p>\n For example, a ratio scale could be, \u201cSelect the average amount of money you spend shopping for soap.\u201d<\/p>\n You can analyze mode, median, and mean with this type of scale and ratio scales can be analyzed through t-tests, ANOVA, and correlation analyses as well.<\/p>\n Once you understand how survey questions are analyzed, I suggest you take note of the overarching survey question(s) you\u2018re trying to solve. Perhaps it\u2019s \u201cHow do respondents rate our brand?\u201d<\/p>\n Then, look at survey questions that answer this research question, such as \u201cHow likely are you to recommend our brand to others?\u201d Segmenting your survey questions will isolate data relevant to your goals, and using customer service software<\/a> can help you connect with your customers.<\/p>\n If I learned anything from my college professors, it was this: Your experiments are only as good as your design. The same goes for your surveys. So, you should take some time to select the best, most appropriate survey questions.<\/p>\n Or, work smarter, not harder. Use a survey maker.<\/a> A survey maker can help you create a mix of close-ended and open-ended questions to get the best, most accurate responses.<\/p>\n Here are a few example questions I might ask my soap buyers to hopefully elicit their opinion, instead of just \u201cYes\u201d or \u201cNo.\u201d<\/p>\n Pro tip: <\/strong>You can present your questions in a form. Plus, using a form builder<\/a> makes it easy to conduct survey data analysis. You can use it to look at the results of individual questions, which, in my opinion, makes it easy to glance at and quickly analyze.<\/p>\n Quantitative data is valuable because it uses statistics to draw conclusions. While qualitative data can bring more interesting insights about a topic, this information is subjective, making it harder to analyze.<\/p>\n To me, it\u2019s helpful to recognize that most qualitative data comes from open-ended questions. These questions allow participants to share their opinions and feelings without being pigeon-holed into pre-populated responses.<\/p>\n Quantitative data, however, comes from close-ended questions that can be converted into numeric values.<\/p>\n Once data is quantified, it’s much easier to compare results and identify trends in customer behavior<\/a>. It\u2019s best to start with quantitative data when conducting survey data analysis.<\/p>\n That\u2019s because quantitative data can help you better understand your qualitative data.<\/p>\n Let\u2019s look at my goat soap sales. If only 50% of my soap customers say they’re very satisfied with my product, I should focus on why the rest are not as happy. This can help me identify roadblocks in the customer journey<\/a> and correct any pain points causing churn.<\/p>\n If you analyze all of your responses in one group, it isn\u2019t entirely effective for gaining accurate information. Respondents who aren\u2019t ideal customers can overrun your data and skew survey results.<\/p>\n For example, let\u2019s say my target soap buyers fall within the age range of 18 to 34-year-olds. If I were to include data outside of my intended audience, the results of my satisfaction analysis would be incorrect.<\/p>\n Here are two pie charts to compare the differences. The first is my target demographic. Notice the neutral result.<\/p>\n However, when I include data from every customer, my results change.<\/p>\n Instead of lumping my results together for survey data analysis, I should segment responses using cross-tabulation. With cross-tabulation, you can analyze how your target audience responded to your questions.<\/p>\n Cross-tabulation records 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.<\/p>\n For example, you may be curious about customer advocacy<\/a> among your customers based in Boston, MA. Cross-tabulation helps you to see how many respondents said they were from Boston and said they would recommend your brand.<\/p>\n Pulling multiple variables into one chart allows us to narrow down survey results to a specific group of responses. That way, you know your data only considers your target audience.<\/p>\n Below is an example of a cross-tabulation chart. It records respondents\u2019 favorite baseball teams and the city in which they reside.<\/p>\n Source<\/em><\/a><\/p>\n As I 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.<\/p>\n For instance, let\u2019s say your data finds:<\/p>\n However, the problem is that your target audience is 18 to 29 years old. In this case, this data isn\u2019t statistically significant as the people who took your survey don\u2019t represent your ideal consumer.<\/p>\n Random sampling \u2014 selecting an arbitrary group of individuals from a larger population \u2014 can help create a more diverse sample of survey responses. Additionally, the more people you survey from your target audience, the more accurate the results will be.<\/p>\n Running an analysis on software like SPSS<\/a> (shown above) will tell you if a data point is statistically significant using a p-value<\/a>.<\/p>\n If you look just below the table, the key indicates:<\/p>\n This indicates which values are statistically relevant in your analysis.<\/p>\n If a data point’s statistical significance or p-value is equal to or lower than 0.05, it has moderate statistical significance since the error probability is less than 5%. If the p-value is lower than 0.01, it is highly statistically significant because the probability of error is less than 1%.<\/p>\n Pro tip: <\/strong>I like to use analytical reporting software<\/a> to help with my survey data analysis.<\/p>\n Another important aspect of survey analysis is knowing whether your conclusions are accurate.<\/p>\n For example, let\u2019s 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.<\/p>\n While this data may suggest a link between these variables, we know there\u2019s probably no relationship. Just because the two are correlated doesn\u2018t mean one causes the other.<\/p>\n In cases like these, a third independent variable typically influences the two dependent variables. In this specific example, it’s temperature.<\/p>\n As the temperature increases, more people buy ice cream. Additionally, more people leave their homes and go out, leading to more crime opportunities. While I know this is an extreme example, you never want to draw an inaccurate or insufficient conclusion.<\/p>\n Pro tip: <\/strong>Analyze all the data before<\/em> assuming what influences a customer to think, feel, or act a certain way.<\/p>\n While current data is good for keeping you updated, it should be compared to data you’ve collected in the past.<\/p>\n If you know that 33% of respondents said they would recommend your brand, is that better or worse than last year? How about last quarter?<\/p>\n If this is your first year analyzing data, make these results the benchmark for your following analyses. Compare future results to this record and track changes over:<\/p>\n You can even track data for specific subgroups to see if their experiences improve with your initiatives.<\/p>\n Now that you\u2018ve gathered and analyzed your data, the next step is to share it with coworkers, customers, and other stakeholders. Don\u2019t just share a list of numbers \u2014 presentation is key!<\/p>\n A clean presentation aids in helping others understand the insights you\u2019re trying to explain.<\/p>\n In the next section, I\u2019ll explain how to present your survey results and share essential customer data with the rest of your organization.<\/p>\n <\/a> <\/p>\n Believe it or not, writing the intro to a survey report isn’t always the best first step. If I\u2019m writing anything, whether it\u2019s a report, a blog post, or a case study, I rarely write the introduction first. Instead, I focus on the outcome.<\/p>\n By starting with the outcome, I get a better feel for the specific takeaways I want my readers to understand.<\/strong><\/p>\n So, 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.<\/p>\n The outcome of the survey report should:<\/p>\n Pro tip: <\/strong>When writing this section, ensure the survey analysis data you\u2018ve collected fully supports the outcome. For instance, I try to avoid ideas that can’t be substantiated by the other information in the report.<\/p>\n Next, summarize your research findings.<\/p>\n In this part of the survey report, you\u2018ll include notable results and any information that correlates with other studies your company may have done in the past.<\/p>\n The summary is the part of your survey that readers will focus on the most. It\u2019s a condensed version of your survey findings, jam-packed with golden nuggets. The research summary should be no more than a page long, as you\u2019ll get into the meat of your report in other sections.<\/p>\n Pro tip: <\/strong>To make it even easier to read, I suggest including headers above paragraphs to guide your readers through the content.<\/p>\n Now that you have your outcome and summary, it\u2018s time to develop the outline. Because the survey report is typically around eight to ten pages long, you\u2019ll want to use a concise outline that includes all the relevant information the stakeholders will want to know.<\/p>\n Here’s a sample outline I like that you can customize:<\/p>\n After completing the outline, you\u2018ll know how much space you\u2019ll need for each section. Depending on your preference, survey reports can be published in portrait or landscape layouts.<\/p>\n Let\u2019s take a brief look at when you should use one layout over the other.<\/p>\n When to Use Portrait Layout for a Survey Report:<\/strong><\/p>\n When to Use Landscape Layout for a Survey Report:<\/strong><\/p>\n Pro tip:<\/strong> In terms of the overarching design and layout of your survey, I highly recommend starting from a survey template<\/a>. HubSpot also has free customer satisfaction templates<\/a> that are a great starting point as well.<\/p>\n The methodology section of your report should explain the who, what, and how of your research. It explains:<\/p>\n You might use charts or graphs to help communicate this data. It’s okay to be detailed here \u2014 the readers will want to know that the outcomes of the survey are valid and based on relevant research methods.<\/p>\n Make sure you also include:<\/p>\n In my opinion, the methodology section of your report is one of the most important sections. This section can help you or your readers conduct an exact survey analysis at a later time with new information.<\/p>\n No matter how much you prepare before conducting your survey, you\u2018ll find some information in the results that could\u2019ve been more conclusive had you considered another variable. But I\u2019ve got good news for you: Research can be continued in the future.<\/p>\n The limitations section sets the stage for future researchers to pick up where you left off or correct any mistakes you made in the current survey.<\/p>\n If you’re fortunate enough that all of the survey analysis data you present fits neatly into a chart within the report, you may not require an appendix at the end.<\/p>\n But if the graphs and charts you include on the pages are truncated versions of large data sets that provide context, you should include them in their raw forms at the end of your report.<\/p>\n Pro tip: <\/strong>If you use an appendix, reference it throughout the reports so your readers can review it for a deeper understanding of your content.<\/p>\n <\/a> <\/p>\n Now that you have your results, you must present them well \u2014 as in, accurately and intelligibly.<\/p>\n Graphs and charts are visually appealing ways to share data. Colors and patterns are easy on the eyes, and data is often easier to understand when shared visually.<\/p>\n However, it’s important to choose a graph that highlights your results in a relevant way.<\/p>\n The image above is an example of a stacked bar graph my team created using data on the brand Allbirds.<\/p>\n If you\u2018re having trouble reading it, you\u2019re in good company. I received feedback that it was confusing to understand. That\u2018s because the data wasn\u2019t organized in a way that would make sense to a stakeholder who’s unfamiliar with our project.<\/p>\n So, I decided to revamp our graph’s image and came up with the design below:<\/p>\n This bar graph is much simpler to read because it has a clear key and individual bars for each variable. The design fits the data that we’re trying to display. Readers can easily understand the information we obtained from our survey.<\/p>\n Depending on the survey you’ve conducted, there are many types of graphs and charts<\/a> you can use. A few options you can choose from are:<\/p>\n Pro tip: <\/strong>Pick one that accurately displays your data and is clear to your stakeholders.<\/p>\n Tables are a great way to share numerical data. You can use software like SPSS or Excel<\/a> to display data easily, like in the survey data analysis example below.<\/p>\n This table was created from a cross-tabulation analysis.<\/p>\n I removed the unnecessary information \u2014 statistical significance, mean, median, etc. \u2014 and focused on the data we wanted to share: the percentage of each gender that preferred each promotional incentive.<\/p>\n This gave us a format that demonstrated the percentages we were looking to share with our stakeholders.<\/p>\n Former Vice President of Merchandising at Chewy, Andreas von der Heydt<\/a>, shared a profound message on LinkedIn about the power of storytelling with data.<\/p>\n In the image below, Legos are arranged in five different ways:<\/p>\n Source<\/em><\/a><\/p>\n One of the primary goals of good data analysis is to weave information together so that it builds upon each other \u2014 just like building a house.<\/p>\n Some data will serve as the foundation of your story, and all the points in your presentation will tie back to this foundation.<\/p>\n From there, you\u2018ll structure your key findings like the walls of a house. These key findings support the conclusion of your research, which acts as the roof. That\u2019s the primary point you want to make when presenting your data analysis.<\/p>\n I understand that communicating data can be tricky, especially when stakeholders have varying degrees of analytical skills. But no matter how sophisticated (or not) your team is, a story will always resonate.<\/p>\n Pro tip: <\/strong>Take the time to identify the point the data leads to and structure a story around that conclusion.<\/p>\n Sometimes, combining visuals with text creates a thorough description of your findings. A presentation could be a good fit for showcasing your data in these cases.<\/p>\n This gives you a chance to present the earlier stages of your survey, including:<\/p>\n This slide from my presentation combines a graph with a table and some text. The same data is shared in three different ways to appeal to people with different learning styles:<\/p>\n If you need to share data that’s easy to read and quick to consume, infographics might be your best bet. I like infographics because they can quickly share lots of information in a visual representation.<\/p>\n Remember the Lego post? That was an infographic.<\/p>\n Source<\/em><\/a><\/p>\n This HubSpot Research infographic explains survey results through icons, numbers, and descriptive text. Infographics are incredibly effective for this purpose, breaking down complex ideas into simple messages that are more appealing to read than blocks of text.<\/p>\n Sometimes, those blocks of text are essential for persuading stakeholders. If you present data to senior executives or business clients, you might want to prepare a full report on your findings.<\/p>\n You wouldn\u2019t refer to this document during a presentation, but you might hand this to your audience to read through on their own time.<\/p>\n This is the table of contents page from my report on our survey project. It\u2018s important to keep track of all the work you\u2019ve done and maintain records of how you conducted your survey. That way, you won’t make similar errors or have to duplicate any research.<\/p>\n <\/a> <\/p>\n I find creating new survey reports from scratch to be difficult, so I like to use survey report templates for analyzing and presenting my data.<\/p>\n Below, I\u2019ll share some free downloadable templates to make things easier. When choosing a template, look for one that includes placeholders for graphs and images and multiple page layouts, so you have some variety.<\/p>\n Not only is this report eye-catching, but it also includes key insights at the top of the infographic. This placement automatically provides readers with added value, without causing them to search for it.<\/p>\n This template can be customized to fit your brand easily. As an infographic-style layout, you’ll love this template for data-heavy reports.<\/p>\n Source<\/em><\/a><\/p>\n I like this Vengage report template. First, this is a great template for multiple-choice survey questions. It offers plenty of space to describe the varied answers your audience might give.<\/p>\n One of the rare gems of this template, in my opinion, is its ability to balance large, clear images that don’t crowd out the important written information on the page.<\/p>\n Use this template for hybrid text-visual designs.<\/p>\n Source<\/em><\/a><\/p>\n The Green Minimalist Content Analysis Report template by Canva is a vertical-style presentation that can also be used for survey reports. It comes with plenty of space to provide context as well as charts and graphs that you can tailor to your research and data.<\/p>\n Don\u2019t love the green? No problem. Canva\u2019s editor makes it easy to customize any template to fit your brand.<\/p>\n Source<\/em><\/a><\/p>\n <\/a> <\/p>\n Now that you know all of the ins and outs of survey analysis, it\u2019s time for me to share some of my expert tips to make conducting a survey and running an analysis of the results easier for you.<\/p>\n Before you begin asking your customers survey questions, define your objectives. I know it can be tempting to pepper your customers with all manner of questions, but doing so will lead to results that might not make any sense.<\/p>\n Instead, I suggest you take some time to plan your survey.<\/p>\n Ask yourself why you want to conduct a survey and what you hope to accomplish with it. This will help you define your objectives and stay within their bounds.<\/p>\n Once you\u2019ve collected your survey data and before you run analysis on it, you need to check your data.<\/p>\n As much as I would like to think all customers take the time to answer survey questions appropriately, I also know that some will just click responses to get through it.<\/p>\n Silly responses will skew your results and, ultimately, give you a lousy survey analysis.<\/p>\n Take some time to check your data and clean it. Remove any obvious junk or corrupt results before you begin.<\/p>\n You can run all the mathematical equations on your data by hand. However, this will take some time, and if you\u2019re not strong in math, it can introduce the potential of errors.<\/p>\n Instead, consider using analytical software<\/a> (like HubSpot\u2019s dashboard and reporting software<\/a> or customer feedback software<\/a>) to help with your survey analysis. Analytical software can save you time and reduce errors.<\/p>\n Plus, thanks to the emergence of AI, some software can forecast your data so you can make better predictions and understand potential outcomes.<\/p>\n\n
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1. Understand the four measurement levels.<\/strong><\/h3>\n
Nominal Scale<\/strong><\/h4>\n
Ordinal Scale<\/strong><\/h4>\n
Interval Scale<\/strong><\/h4>\n
Ratio Scale<\/strong><\/h4>\n
2. Select your survey question(s).<\/strong><\/h3>\n
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![\"open-ended](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-1-20250529-4892520.webp\" "\\"\\"")
<\/p>\n3. Analyze quantitative data first.<\/strong><\/h3>\n
![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-2-20250529-3210315.webp\" "\\"\\"")
<\/p>\n4. Use cross-tabulation to better understand your target audience.<\/strong><\/h3>\n
![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-3-20250529-3667358.webp\" "\\"\\"")
<\/p>\n![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-4-20250529-6781986.webp\" "\\"\\"")
<\/p>\nSplit Up Data by Demographics<\/strong><\/h4>\n
![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-5-20250529-540993.webp\" "\\"\\"")
<\/p>\n5. Understand the statistical significance of the data.<\/strong><\/h3>\n
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![\"find](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-6-20250529-365498.webp\" "\\"\\"")
<\/p>\n\n
6. Consider causation versus correlation.<\/strong><\/h3>\n
7. Compare new data with past data.<\/strong><\/h3>\n
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How to Write a Survey Report<\/strong><\/h2>\n
![\"how](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-7-20250529-7617407.webp\" "\\"\\"")
<\/p>\n1. Decide the outcome of the survey.<\/strong><\/h3>\n
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2. Write your research summary.<\/strong><\/h3>\n
3. Create an outline for the report.<\/strong><\/h3>\n
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4. Choose a layout.<\/strong><\/h3>\n
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5. Include the methodology of your research.<\/strong><\/h3>\n
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6. Mention any limitations in your research.<\/strong><\/h3>\n
7. Add appendices if needed.<\/strong><\/h3>\n
How to Present Survey Results<\/strong><\/h2>\n
1. Use a graph or chart.<\/strong><\/h3>\n
![\"example](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-8-20250529-6117451.webp\" "\\"\\"")
<\/p>\n![\"example](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-9-20250529-10004.webp\" "\\"\\"")
<\/p>\n\n
2. Create a data table.<\/strong><\/h3>\n
![\"example](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-10-20250529-1572216.webp\" "\\"\\"")
<\/p>\n3. Tell a story with survey data analysis.<\/strong><\/h3>\n
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![\"visual](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-11-20250529-6982514.webp\" "\\"\\"")
<\/p>\n4. Make a visual presentation.<\/strong><\/h3>\n
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![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-12-20250529-5985755.webp\" "\\"\\"")
<\/p>\n\n
5. Put together an infographic.<\/strong><\/h3>\n
![\"hubspot](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-13-20250529-481514.webp\" "\\"\\"")
<\/p>\n6. Use survey results in a report.<\/strong><\/h3>\n
![\"how](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-14-20250529-8668488.webp\" "\\"\\"")
<\/p>\nSurvey Report Template Examples<\/strong><\/h2>\n
1. <\/strong>Customer Satisfaction Report – Piktochart<\/a><\/strong><\/h3>\n
![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-15-20250529-8900870.webp\" "\\"\\"")
<\/p>\n2. <\/strong>Education Logo Survey Results Template – Vengage<\/a><\/strong><\/h3>\n
![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-16-20250529-9899816.webp\" "\\"\\"")
<\/p>\n3. <\/strong>Green Minimalist Content Analysis Report – Canva<\/a><\/strong><\/h3>\n
![\"survey](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-17-20250529-1168622.webp\" "\\"\\"")
<\/p>\nTips for Survey Data Analysis<\/strong><\/h2>\n
1. Define your objectives.<\/strong><\/h3>\n
2. Check and clean your data.<\/strong><\/h3>\n
3. Use the best, most appropriate tools.<\/strong><\/h3>\n
![\"interface](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/06\/survey-results-18-20250529-2467751.webp\" "\\"\\"")
<\/p>\n
![\"\"](\"https:\/\/track.hubspot.com\/__ptq.gif?a=53&k=14&r=https%3A%2F%2Fblog.hubspot.com%2Fservice%2Fsurvey-results&bu=https%253A%252F%252Fblog.hubspot.com%252Fservice&bvt=rss\")
<\/p>\n","protected":false},"excerpt":{"rendered":"