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We've selected the best free & advanced survey analysis tools for a variety of use cases.
Whether you're conducting a one-off research project or want continuous analysis of your CSAT surveys—we've got you covered.
Our Shortlist
Sentisum packages advanced text and sentiment analytics technology into a clean, user-friendly interface that anyone can pick up and start using.
The platform's innovative machine learning-based AI algorithms ensure you know the root cause of free-text survey feedback. The granular tagging engine means you'll be able dive and deep dive to explore the finer nuances of customer friction and brand sentiment.
Sentisum's design philosophy centers on "simplicity", which means you'll spend less time confused and more time getting your questions answered (you can quite literally ask any question and it will tell you the answer).
Rating: 4.8/5
Best for: Customer support & experience teams with high volumes of survey responses.
Highlights: Detailed, root cause insights • Enables powerful insight explorations • Simple to use for every user • Summarizes results with AI • Analytics capabilities not isolated to surveys • Helpful, dedicated customer success • Enterpise-level data security • Combines datasets to give complete picture • Works across NPS, CSAT, and reviews • Also tags support tickets with incredible detail
SurveyMonkey's survey creation software is used by 17 million people worldwide. It's quick and easy to launch a survey on any topic you need. But, what about analytics?
SurveyMonkey Genius uses natural language processing to categorize survey free-text as positive, neutral, or negative. That allows you to filter your survey responses into clear buckets.
However, if you're looking for topic (rather than sentiment) categorization and insights, you're out of luck. However, Sentisum does integrate with SurveyMonkey in 1-click if you need more advanced analytics.
Rating: 4.4/5
Best for: The average joe doing one-off surveys.
Highlights: Sentiment-based survey categorization • Simple to use for every user • Well-known provider with trusted product capabilities
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If you're willing to put in the leg work, both Google and Microsoft have text and sentiment analytics integrations for their spreadsheet software.
The benefits are that the integrations are typically free or very low cost, making them a good option for one-off surveys for small scale research.
The downside? For teams with a more regular survey cadence, like CSAT surveys in customer service, this option is slow and it's hard to ensure your analysis is uniform from week to week. The results will be painful and messy if this is a daily or weekly task.
Example integrations:
1. Sentiment analysis for Google Sheets
2. Sentiment analysis for Excel
Both are free up to 1,000 transactions and, frankly, for solid analytics on any more transactions than this you'll want to invest in a better solution.
Rating: 4.9/5
Best for: Those who have time, don't mind getting their hands dirty, and have data analytics skills.
Highlights: Integrations help with advanced analytics • Well-known provider with trusted product capabilities • Typically free
Tableau's leading business intelligence & analytics software is a widely-used tool for data manipulation.
Working with survey data in Tableau kinda sucks. It takes a lot of data preparation (see guide here) but if you've run a large scale market research project and need to make sense of the results—it may be worth the time investment.
Unlike the other tools in this list, Tableau doesn't offer text or sentiment analysis within it. So if it's free-text you'd like to analyze, Tableau isn't for you.
Rating: 4.2/5
Best for: 10 question surveys—in other words, surveys that are not NPS or CSAT which characteristically have just one question and answer.
Highlights: Integrations help with advanced analytics • Well-known provider with trusted product capabilities • Typically free
CustomerGauge's B2B Net Promoter Software is used by global corporations like Coca-Cola to send and analyze NPS surveys.
The analytics suite excels itself. It's built to turn customer feedback into opportunities for revenue growth, a much needed kick up the backside for the traditional CX team.
The insights help you quickly identify which customer accounts are at-risk of churn, which are ready to be upsold, and which individuals should be prompted to leave you a public review.
Rating: 4.9/5
Best for: Companies running NPS surveys who have large sales teams and want to identify accounts at-risk of churn.
Highlights: Integrations help with advanced analytics • Well-known provider with trusted product capabilities • Typically free
Freebies
There are several providers offering free software for survey data analysis.
None will be as advanced or impactful as paid offerings, but for smaller surveys and ad-hoc analyses they may well be enough:
Choosing a tool is the most difficult step to take. We suggest gathering a small team and first determining what you want the final results of your analysis to look like. What are your goals? What insights would be useful and for whom? Will these be a one-off project or do you need regular analyses? Do the results have to be real-time and accessible companywide at all times? What budget is reasonable for your use case?
Answering questions like these up front will help you avoid chasing the rabbit down the hole and wasting a ton of your time. Once you've decided a few of the basics, you can start booking product tours with the sales teams at each company.
A key differentiator for us when choosing technology is the relationship and rapport we can build with the solution provider. We want to know we can trust them to hold our hand and teach us the best way of doing things. You want an expert playing on your team when undertaking something as important as a survey analysis and the analysis reporting.
There are a number of different techniques out there for survey data analysis. We use these three advanced methods within our AI platform at SentiSum:
Sentiment analysis is the process of detecting positive or negative sentiment in text.
Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment.
A keyword or aspect analysis identifies specific 'things' in the text. For example, if a customer mentions the word 'discount' it will label or categorize the feedback as being about discounts.
A keyword analysis is very dependent on the language used by the customer, making it prone to error and inaccuracies.
Topic analysis, or classification, is a form of AI-powered analytics that reads and analyses like a human does, but considerably faster.
A topic analysis doesn't simply see a keyword, and label the piece of feedback. It takes into account the context of that word and the meaning of the piece of text it sits within. Correct categorisation is not dependent on any specific words used, making the results much more accurate.
For example, a topic analysis tool can identify that a customer is complaining about 'discount code not working' even when they say something like 'the offer didn't apply at checkout'.
Whichever you choose depends on the outcome you desire. Getting deep into the root cause of customer feedback can useful for it to be more actionable—for that you'll want aspect-based topic and sentiment analysis. To understand at a high level why customers are happy/unhappy to guide future research? For that a simple sentiment analysis might be enough.
The AI tool for analyzing survey data is Sentisum, which packages advanced text and sentiment analytics technology into a clean, user-friendly interface anyone can pick up and start using.
The platform's innovative machine learning-based AI algorithms ensure you know the root cause of free-text survey feedback. The granular tagging engine means you'll be able dive and deep dive to explore the finer nuances of customer friction and brand sentiment.
For research analysis, which likely has multiple questions, we'd probably recommend a tool like Tableau to manipulate the dataset.
Although the text and sentiment analysis integrations for Google Sheets may also be extremely helpful for research analysis projects.
The best tool for analyzing survey data is easily Sentisum. If you're a customer support or experience leader, then Sentisum's product is exactly what you need to analyze surveys on a regular basis.
Check out these golden nuggets of advice from Quora to guide your survey collection and analysis project.
P.S. This was a huge answer, we've cut a small segment out of it but we recommend reading the whole thing!
• Avoid averages! Averages give average insights. Segments, cohorts, and eliminating certain responses from your evaluation (like the Net Promoter Score® does) are the best ways to get more clarity from your data.
• Avoid aggregate data. Just like with averages, when data is aggregated it loses its ability to give insights. Stick to more meaningful analysis methods like the ones mentioned above.
• Context is key. When comparing numbers, use ratios to understand context. Is 50% a good number? Only in context to some other number.
• If you are trying to measure effects, it’s also important to understand the difference between descriptive and inferential statistics. Descriptive statistics are just that—they allow you to summarize your data, and typically refer to measures such as sample size, mean age of participants, percentage of males and females, range of scores on a study measure, and so on. Inferential statistics, on the other hand, help you to establish statistical significance by taking into consideration things like confidence intervals and parameter estimations. (Source)
A: The answer depends on how the questions were phrased and how the survey was conducted. the key question is: are the answers from a random selection of the population. if yes (or you can define the population it is representative of) you are good to go. the methods used depend on whether the answers numerical or coded (categorical), multiple response, the level of “missingness" etc.
You can spend many years learning how to do it properly. The short answer is that you look at the responses to find anything that is not random and explain that from your theoretical perspective.
A: The one most important thing -- which so many people don't do -- is figure out what you want to get out of the survey. I like to do this in the form of "goal" questions, as in "after this survey, we will be able to answer the following questions which we can't answer today."
Then, figure out what you need to do to answer those questions -- including what (type of) people should respond and how many of them to get to whatever level of confidence you want, but obviously what you need to ask in the survey in order to answer those questions.
Then, be ruthless about eliminating anything in the survey which won't help you answer your "goal" questions.
There are lots of other things to consider -- how to write good questions, incentives, survey format, etc. -- but if you don't have the goals figured out, none of those other things matter, and the goals are necessary in order to figure out all of those other details.
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Here are 8 Customer Experience Survey Best Practices:
• Word your question clearly.
• Know when and when not to use open-ended questions.
• Avoid questions that yield difficult to interpret, subjective responses.
• Consider your respondents’ experience while determining question order.
• Respond to negative feedback as soon as possible.
• Thank the customers that provide delightful feedback.
• Develop a Board of Customers program, or something similar.
• Make sure that personal questions are marked as optional.
Turn every customer feedback into clear, easy-to-use insights.
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