The survey was the easy part — now there's a response file and a stakeholder waiting for "what did we learn?". Upload the export and get computed distributions, segment comparisons, and a written findings report you can send.
Steady growth across the quarter, with the strongest week late in the period
Key insight
Organic clicks grew +14.3% over the last 30 days while average position improved to 8.4. Rising impressions with stable CTR means the growth is coming from new query visibility — not just better rankings on existing terms.
Recent weekly performance
Week
Clicks
Impressions
WoW change
Week of 05-21
2,813
59.6k
+9.1%
Week of 05-28
3,071
66.4k
+9.2%
Week of 06-04
3,114
69.2k
+1.4%
Week of 06-11
3,132
66.3k
+0.6%
Week of 06-18
3,122
67.2k
-0.3%
Week of 06-25
2,865
62k
-8.2%
A real report generated by the engine from a sample Search Console-style dataset — every number computed, provenance attached.
From response export to insight
The statistics, computed honestly
Response distributions, averages, NPS math, crosstabs by segment — executed as real code over every response, not an AI skimming a sample.
Segment stories surfaced
Ask how satisfaction differs by plan, tenure, or demographic and get the comparison charted and explained — the finding that usually justifies the whole survey.
A findings report, not a data dump
The deliverable is a document: key numbers, the charts that matter, and written findings — ready for the team readout or the client.
How it works today
1
Export responses from your survey tool — Google Forms, Typeform, SurveyMonkey, Qualtrics — as CSV or Excel.
2
Ask your analysis questions: overall distributions, differences between segments, drivers of satisfaction.
3
Generate the findings report and share it with stakeholders.
On the roadmap: Deeper open-ended response coding (theme extraction across free-text answers at scale) is on the roadmap as part of the survey vertical.
Want to see an example survey report first?
The example report — the button at the top of this page opens it — is generated live from a real sample dataset: response overview, per-question distributions, segment comparisons, and written findings, with every number linked to the Python that computed it. Your own upload produces the same structure from your data and your questions. For the method behind it, our step-by-step guide walks through analyzing survey data from raw export to readout.
Survey analysis is the work of turning a file of raw responses into defensible answers: what people said, how groups differ, and what it means for a decision. Done well it moves in a fixed order — clean the data, summarize each closed-ended question, compare segments, code the open text, then write it up — with the method matched to each question type rather than one blunt average applied to everything.
This page is the method reference: how each survey question type is actually analyzed, the mistakes that quietly distort findings, and where computation and AI genuinely help. For a click-by-click tutorial on a real export, see the walkthrough linked below.
The survey analysis process, in the order that works
Survey data analysis has a natural order, and skipping ahead is where most findings go wrong. You cannot trust a segment gap until the data is clean, and you cannot interpret an open-ended theme until you know how common it is. Each step feeds the next.
For a hands-on, click-by-click version of this workflow on a real export — with the exact questions to ask and the charts to build — see our tutorial on how to analyze survey data. The rest of this page goes deeper on the part most guides skip: how each question type is actually analyzed, and how to avoid over-reading the results.
The reliable sequence for analyzing survey data
Clean and screen. Remove test rows, duplicates, speeders (respondents who finished implausibly fast), and straight-liners (the same answer down a whole grid). Decide how you will treat partial completes and 'prefer not to say' before you compute anything.
Summarize each closed-ended question on its own. Frequencies and percentages first — the full distribution, not just an average. This is your base layer, and it is what every later comparison is measured against.
Cross-tabulate and compare segments. Break each key question by the groups that matter — plan tier, tenure, region, acquisition channel — and look for gaps that are large enough to be real.
Code the open-ended answers. Build a small set of themes, tag responses to them, and count, so free text becomes something you can report alongside the numbers.
Write the findings. Lead with the decision-relevant result, support it with one chart, and always show the base size (n).
How to analyze each survey question type
The single biggest cause of misleading survey findings is analyzing every question the same way. A Likert grid, an NPS item, a multi-select, and a ranking each need a different summary statistic — and each has a pitfall a generic 'take the average' walks straight into. The table shows the honest default for each; the detail follows.
Summary method and common mistake, by question type
Question type
How to summarize it
Common mistake
Likert / rating scale (ordinal)
Full distribution plus top-2-box and bottom-2-box — the share choosing the two most favorable / least favorable options; mode as the honest central tendency
Averaging the numbered labels as if 1–5 were equal distances, then reporting '4.2' as a precise quantity
Net Promoter Score (0–10)
% Promoters (9–10) minus % Detractors (0–6); passives (7–8) are excluded from the subtraction but stay in the base
Treating NPS as an average score, or forgetting passives dilute the base
Multiple choice, single-select
Frequency and % of respondents; percentages sum to about 100%
Comparing raw counts across questions that have different base sizes
Multiple choice, multi-select ('select all')
% of respondents choosing each option; percentages sum to more than 100% because the base is people, not selections
Dividing by total selections instead of respondents, which understates every option
Ranking
% ranking each item in the top N is usually more honest than a single 'average rank'
Averaging ranks, which hides polarized items — loved by some, last for others
Open-ended text
Code to a theme frame, then count; sentiment and word frequency as a first pass
Presenting a word cloud as if it were the analysis
Likert scales: report the distribution, not the label average
A Likert scale is ordinal — the options are ranked (strongly disagree to strongly agree), but the gap from 'disagree' to 'neutral' is not provably the same size as 'agree' to 'strongly agree.' Averaging the 1–5 labels treats those gaps as equal and produces a mean like 3.7 that implies a precision the data does not have.
The honest defaults are the full distribution (what share landed on each point) plus top-2-box: the percentage choosing the two most favorable options. Top-2-box is what most trackers report because it is stable, easy to compare period over period, and does not pretend the scale is a ruler. Use a mean only when you have deliberately accepted the equal-interval assumption and your audience understands it.
NPS: the exact formula
Net Promoter Score has one formula: the percentage of Promoters (respondents who scored 9 or 10) minus the percentage of Detractors (0 through 6). Passives (7 and 8) are excluded from that subtraction but still count in the base you divide by.
Worked example: of 100 respondents, 50 score 9–10, 30 score 7–8, and 20 score 0–6. NPS = 50% − 20% = +30. The passives quietly hold the score down by sitting in the denominator without adding to the numerator. NPS ranges from −100 to +100, and it is a net percentage, not an average — reporting it as 'an average of 8.1' is a different (and less standard) number.
Multi-select and ranking: mind the base
On a 'select all that apply' question, the base is respondents, not selections. If 200 people answer and each picks two or three options, the percentages for each option are computed out of 200 and will sum to well over 100%. Divide by the total number of selections instead and you understate every option — a common, silent error.
Ranking questions have their own trap. An 'average rank' collapses a distribution into one number and hides polarization: an item that half the sample ranks first and half ranks last can average out to the middle and look mediocre. Reporting the percentage of respondents who put each item in their top N usually tells the truer story.
Cross-tabs and segment comparisons: reading a difference honestly
A cross-tabulation breaks one question down by another — satisfaction by plan tier, purchase intent by region — so you can see which groups drive an overall number. It is where most survey insight actually lives, and where most over-claiming happens.
The first discipline is base sizes. A '62% versus 48%' gap looks like a headline on a slide, but if those cells hold 15 and 12 respondents it is noise, not a finding. A frequently used practitioner floor is roughly 30 responses in a subgroup before you report a rate for it separately; treat anything thinner as directional. The second discipline is resisting the sampling wobble: a difference is 'statistically significant' when it is larger than what random sampling alone would plausibly produce, and smaller samples need larger gaps to clear that bar. You do not have to compute a p-value for every cell, but you should not narrate a gap that a coin-flip's worth of sampling luck could have produced.
Weighting is the last piece and the most misunderstood. If your sample skews — too many power users, too few new customers — weighting rebalances the responses to match the population you care about. It is powerful, but it works by inflating the influence of the underrepresented people you did reach, so it can correct a mild skew and cannot rescue a group you barely sampled. For turning free-text answers into coded themes — coding frames, second-coder checks, saturation — see qualitative data analysis.
The mistakes that quietly wreck survey findings
Most survey errors are not in the arithmetic; they are in the interpretation, and they survive into the readout because they look reasonable. These are the ones that mislead a real decision.
Averaging Likert labels. The numbers 1–5 are ranks, not measured quantities, so a mean of 3.7 implies a precision the data cannot support. Report the distribution and top-2-box instead.
Ignoring base sizes. A percentage off 14 respondents looks identical to one off 1,400 on a chart. Always show n, and do not break out subgroups you barely sampled.
Survivorship and nonresponse bias. The people who finished your survey are not a random slice of everyone you invited — the unhappy and the busy drop out first — so a satisfaction score can be quietly inflated. Check completion rates and think about who did not answer.
Reading causation into a cross-tab. Two questions moving together (satisfaction and tenure) is a correlation, not proof that one causes the other.
Leading the interpretation. Deciding the story before you code the open text, then counting only the quotes that fit it.
Treating a word cloud as analysis. Frequent words are a starting point, not a finding — 'price' appearing often does not tell you whether people think it is too high, fair, or the reason they stay.
Where AI helps with survey analysis — and where it doesn't
AI is genuinely useful for the mechanical parts of survey analysis — running the same breakdowns fast, computing the statistics without spreadsheet slips, and drafting a first summary — but it cannot decide what a theme means or whether a difference matters. Keep that line clear and it earns its place.
Where it helps: computing distributions, cross-tabs, and NPS over the full response file rather than a skimmed sample; a first pass on open text through sentiment and word frequency; and drafting the narrative from computed results so you edit rather than write from scratch. In the AnalyzeData workspace you upload the response export (CSV, Excel, JSON, or TSV) and ask in plain English — 'satisfaction by plan tier versus last quarter,' 'NPS overall and by region.' The engine writes and runs real pandas code over every response and shows the code behind each number, so a computed figure has a receipt when a stakeholder challenges it. For the comments, the free sentiment analysis and word frequency tools give a fast read of what is in the text before you code it by hand — both run entirely in your browser, so the raw responses never leave your machine.
Where it does not help: interpretive coding at rigor, judging what an open-ended theme actually means, and any causal claim. AnalyzeData handles the closed-ended statistics and a first pass on text; it is not a substitute for reading your open-ended answers and coding them yourself, and it will tell you that a segment is unhappy, not why.
From analysis to a stakeholder report
Analysis that stays in a spreadsheet does not change a decision. The last step is packaging it: the handful of numbers that matter, the charts that carry them, and a written interpretation — in a form someone can read in a few minutes.
A findings report earns its keep with a tight structure: a response overview with base sizes, the key distributions, the segment differences that justified running the survey in the first place, the coded open-ended themes with two or three representative quotes each, and a clear recommendation. Order it by decision relevance, not by the order the questions appeared in the survey — the most important finding rarely sits at question one.
You can go from the response file to that document in one pass. The survey report template shows the default structure with sample data, and the AI report generator turns your analysis into a shareable link and a print-perfect PDF.
Everything you need to know about using AnalyzeData.
Start with distributions per question, then comparisons across segments, then relationships between answers. AnalyzeData runs those steps as computed analyses on your export — ask in plain English and each result arrives charted, with the code attached.
Basic text analysis works today (frequencies, patterns within the file). Rigorous large-scale thematic coding of free-text answers is on the roadmap — we'd rather promise that honestly than pretend keyword counts are qualitative analysis.
A document presenting what the survey found: response overview, key distributions, segment differences, and interpreted findings. The survey report template shows the structure live with sample data.
Use the "See an example survey report" button at the top of this page. It opens a live sample report, not a screenshot: generated from real example data, with response overview, distributions, segment comparisons, and written findings. Click any number in it to see the computation behind it. Your own export produces the same structure.
Any tool that exports CSV or Excel — Google Forms, Typeform, SurveyMonkey, Qualtrics, and the rest. Upload the export directly; no reformatting needed.
There is no universal number — it depends on how finely you will slice the data. For an overall read, a few hundred responses give reasonably stable percentages. For reporting a subgroup on its own, a common practitioner floor is around 30 responses in that cell, and anything thinner should be treated as directional rather than reported as a rate. The finer your cross-tabs, the more total responses you need.
Usually not. A Likert scale is ordinal — the options are ranked, but the gaps between them are not guaranteed to be equal — so the mean implies a precision the data does not support. Report the full distribution plus top-2-box (the share choosing the two most favorable options). Use a mean only when you have deliberately accepted the equal-interval assumption and your audience understands it.
Net Promoter Score is the percentage of Promoters (people who scored 9 or 10) minus the percentage of Detractors (those who scored 0 through 6). Passives (7 and 8) are excluded from that subtraction but still count in the base you divide by. The result ranges from −100 to +100, and it is a net percentage, not an average score.
Analysis is the computation and interpretation — cleaning, distributions, cross-tabs, coding the open text, and deciding what is real. Reporting is packaging the result into a deliverable a stakeholder actually reads. This page covers the analysis method; for turning it into a document, see the survey report template.
Keep them visible rather than silently dropping them. 'Don't know' is a real finding — it measures uncertainty — so report it as its own category instead of folding it into a denominator. Decide up front whether your percentages are of everyone who saw the question or only those who answered it, and state which; the two figures can differ a lot when skips are common.
Analyze your survey responses now
Upload the export and have findings — with receipts — before the readout.