Conversational survey or traditional survey: which fits your research?
A survey measures what you thought to ask in advance. An AI interview follows up on what the participant has just said. Both have their place. Here is what each one gives you, where each falls short, and when to choose which.
Last checked in October 2026. Sources and corrections
The difference in short
A traditional survey is a fixed list of questions you decide on in advance. Everyone gets the same questions, mostly rating scales and multiple choice with the odd open field. That makes the answers easy to compare: you know exactly what you asked, and whom.
An AI interview, also called a conversational survey, turns that around. The participant gives one score, for example how likely they are to recommend you. An AI interviewer then asks follow-up questions based on what they have just said: why that score, what exactly happened, what would have made it better. The questions arise in the conversation, within the research goal you set.
Which one is better depends on what you want to know. If you want to measure ten things you already know about, a questionnaire is strong. If you want to understand why people give the score they do, including what you never thought to ask, a conversation is stronger.
Side by side
| Aspect | AI interview (ScoreStory) | Traditional survey |
|---|---|---|
| Who decides the questions | You set the goal and the themes. The follow-up questions come from what the participant says. | You write every question and its order in advance, perhaps with branching. |
| What the participant does | Gives one score, then spends a few minutes typing or speaking. | Works through a series of questions, mostly scales and multiple choice. |
| Depth of open answers | Yes. The interviewer asks every participant for reasons and examples. | Partly. Depends on what someone chooses to type into an open field. |
| What you didn't think to ask | Yes. Comes to the surface, because the participant leads the story. | Partly. Only if there is an open question and someone makes the effort. |
| Measuring many topics comparably | Partly. One score, comparable over time and across groups. The explanation differs from person to person. | Yes. Everyone answers the same questions, so you can set many items side by side. |
| Statistical analysis | Partly. One score, plus participant attributes such as department or location, and the themes from the conversations. | Yes. Many structured variables to cross-tabulate and model. |
| Processing open answers | Yes. Themes, sentiment and a short conclusion, automatically, with the quotes behind them. | Partly. Charts per question are ready at once. Open answers you read or code yourself, unless your tool has text analysis. |
| Role of AI | Essential: the interviewer and the analysis run on a language model. | Not needed. Many tools offer AI as optional help. |
When to choose which
A questionnaire is stronger when
- you want to measure many topics in a fixed, comparable way, such as an annual employee survey with a validated model and benchmarks;
- you want to analyse the answers statistically, with correlations and segments across dozens of variables;
- you are collecting data rather than opinions: sign-ups, orders, a quiz or a form;
- you may not, or would rather not, use AI for this data;
- you already know exactly which question to ask and only need the answer.
A conversation is stronger when
- you have a score and want to know what lies behind it. A 7 doesn't tell you what would have made it a 9;
- you want to hear what you hadn't thought of. A fixed list only contains the topics you chose beforehand;
- you are after nuance. Someone who gives a 9 may have three concrete improvements in mind, and someone who gives a 6 may be happy apart from one bad experience;
- you want open answers that actually get processed, rather than an export of hundreds of comments nobody gets round to;
- you want to keep it short for the participant: one question and a conversation of a few minutes.
What to watch for with an AI interview
A conversation with an AI is not a magic fix. These are the points we take seriously ourselves.
A language model can get things wrong
That includes the analysis. If a conclusion surprises you, look at the quotes behind it. In ScoreStory they sit with every theme, right down to the full conversation, and below three conversations ScoreStory doesn't write a conclusion at all.
Conversations vary
Two conversations are harder to compare like for like than two answers to the same question. The score is your fixed anchor, and the themes show what keeps coming up and how many people mention it.
Participants should know they are talking to an AI
ScoreStory tells them up front, on the invitation page and at the score question.
Where the conversation text goes
ScoreStory hosts the application and all stored data in the Netherlands. The language model that runs and analyses the conversation is Anthropic's, a US company, so the conversation text leaves the EEA for that step. Our privacy page sets out exactly what goes there and how we intend to change it.
You can use both
You don't have to choose. Keep your regular survey and bring in a conversation where the questionnaire stops: after a support contact, when a score drops, or to understand why one department scores differently from the rest. If all you measure today is one recurring score, such as your NPS, a conversation can take over that measurement entirely: the same score, with the explanation alongside it.
How it works in ScoreStory
You describe in plain language what you want to find out. ScoreStory suggests a metric (NPS, eNPS, CSAT or Customer Effort Score) and sets up the themes. You invite people with a personal or public link, a QR code, a pop-up on your website, an email mailing, an import or the API. The participant gives a score and then talks to Vox, the AI interviewer, in one of eight languages. Your results show, per theme, what is going well and what needs work, with the sentiment and the quotes behind it.
See everything ScoreStory does, or read why we chose a conversation.
Frequently asked questions
Is an AI interview the same as a chatbot survey?
Not quite. A chatbot survey often asks the same fixed questions, just in a chat window. An AI interview adapts the next question to the answer: it follows up on what the participant has just said, within the research goal you set.
How long does an AI interview take?
A few minutes. In ScoreStory, participants see the estimated duration before they start, and they can wrap up the conversation whenever they like.
Can I still compare my score with an earlier survey?
The score uses the usual scale, for example 0 to 10 for NPS and eNPS, and you can write the wording of the score question yourself. That way the question stays the same as in your previous measurement. Bear in mind that a different invitation or a different moment can also affect a score.
Do participants know they are talking to an AI?
Yes. ScoreStory says so on the invitation page and at the score question, before the interviewer says anything. If a participant asks during the conversation, the interviewer simply says it is an AI.
Sources
What we say about ScoreStory itself is on these pages of ours: Product, Pricing, Privacy.
Spotted something that is out of date or wrong? Email welcome@scorestory.eu and we will check it and correct the page.
Product names belong to their respective owners. ScoreStory is not affiliated with, or endorsed by, any of the products compared here.
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