How technical writers use chatbot analytics to improve docs
Use recurring questions, content gaps, and feedback to decide which documentation to investigate and improve next.
Technical writers often hear about a documentation problem after a reader has already failed to complete a task. The reader opens a support ticket, asks in a community channel, or gives up.
Chatbot analytics provides another signal: what readers ask, which questions recur, and where the assistant cannot find enough information to answer.
The goal is not to turn those signals into a documentation score. Use them to decide which pages, topics, or reader tasks deserve a closer look.
This guide shows a small weekly workflow for doing that with Biel.ai Analytics.
Start with questions the chatbot could not answer
In Biel.ai, Content Gaps groups questions the chatbot could not answer by semantic similarity. The analysis runs daily.
These gaps are useful because they show where readers asked for help but the indexed documentation did not provide enough information for the assistant to respond.
Open Analytics, choose the period you want to review, and start with Content Gaps and recurring questions. The Analytics documentation also covers chatbot sessions, messages, distinct users, satisfaction, activity, and conversation origin URLs.
A content gap is a reason to investigate, not an instruction to write a new page.
| What you find | What to investigate |
|---|---|
| The same question appears repeatedly and no relevant page exists | Whether the docs need a new task-focused page |
| A relevant page exists but is incomplete | Whether it needs a prerequisite, example, error case, or clearer procedure |
| The content exists but uses different terminology | Whether titles, headings, metadata, or wording match the language readers use |
| The reader is asking for unsupported product behavior | Whether the request belongs with the product team instead |
Before creating work, find the documentation a reader should have reached and read it yourself. The analytics signal tells you where to look. It does not tell you why the answer failed.
Use feedback to decide what deserves a closer look
Low satisfaction tells you that some readers were unhappy with the answers they received. By itself, it does not tell you which page is wrong or what to rewrite.
Look at feedback alongside the question and the relevant documentation.
If readers repeatedly ask something that an existing page does not explain clearly, review that page. If the answer exists but readers describe the task differently, the better fix may be a clearer title, heading, example, or explanation rather than another page.
Analytics can point you toward a problem. The documentation review determines the edit.
For a broader pre-launch evaluation method, see how to evaluate a documentation chatbot.
Run a small weekly documentation review
A weekly review should produce a few decisions, not a large report.
Choose a consistent period and work through the same four steps:
| Step | What to do | Outcome |
|---|---|---|
| Review recurring questions | Look at common questions and Content Gaps | A short list of reader needs worth investigating |
| Classify each item | Decide whether it suggests missing content, incomplete content, unclear wording, or product feedback | A reason to investigate the item |
| Check the documentation | Read the page or workflow the reader should have found | A documentation change, product handoff, or decision to do nothing |
| Recheck later | Review the same type of question after the relevant documentation change | Evidence that the change helped or needs more work |
Keep the list small enough that someone can inspect each item properly.
If chatbot traffic is low, treat repeated questions as leads rather than a ranked backlog. Higher traffic gives you more signals, but launches, seasonality, and changes in your audience can still affect what appears in the dashboard.
Separate documentation gaps from product feedback
Not every unanswered question belongs in the documentation backlog.
A reader may be asking for a feature the product does not support. They may have found a real product limitation. Or the question may be outside the intended scope of the documentation.
If the product supports the task but the docs do not explain it, that is a documentation problem.
If the product does not support the task, send the request to the appropriate product team and make the limitation clear in the docs where readers are likely to encounter it.
This distinction helps prevent analytics from generating unnecessary or misleading documentation.
Look for trends, but investigate the cause
Analytics also shows activity and satisfaction over the period you select.
A noticeable change can be worth investigating, especially around a product or documentation release. But do not assume the documentation change caused it.
Traffic mix, release timing, a new integration, or a change in the questions readers ask can all affect the numbers.
When a trend looks interesting, record:
- the period you reviewed
- the question or cluster involved
- the relevant documentation
- the change you are considering
- what you plan to check again later
That gives the next reviewer enough context to understand why the change was made.
Know what chatbot analytics cannot tell you
Chatbot analytics reflects the people who used the assistant. It does not represent every documentation reader, every failed task, or every question someone might have had.
It also cannot prove that a documentation change caused a later change in satisfaction, activity, or question volume. Product releases, traffic mix, seasonality, and changes in reader behavior can affect those numbers too.
Use chatbot analytics as one source of evidence alongside support patterns, product releases, direct reader feedback, and your own review of the documentation.
Content Gaps and common-question analysis run daily and may take up to 24 hours to appear. They are available on Professional, Business, and Enterprise plans and process conversations collected after the relevant upgrade. See the Analytics documentation for current plan and data-availability details.
Frequently asked questions
What is a chatbot content gap?
In Biel.ai, a Content Gap is a question the chatbot could not answer. Similar questions are grouped together to make recurring gaps easier to review. Treat a gap as something to investigate, not automatic evidence that you need a new page.
Does low chatbot satisfaction mean the documentation is wrong?
No. It means some readers rated their chatbot answers poorly during the selected period. Review the question and the relevant documentation before deciding whether the problem is content, terminology, retrieval, product behavior, or something else.
How often should technical writers review chatbot analytics?
A short weekly review is a practical starting point when you have enough traffic to see recurring questions. Focus on a few items that someone can actually investigate, then revisit relevant questions after the documentation changes.
Can chatbot analytics replace documentation user research?
No. It only captures questions from people who used the assistant. Use it alongside support data, product context, direct reader feedback, and user research when you need a broader view.
Turn chatbot questions into documentation work
Start with recurring questions and Content Gaps in Biel.ai Analytics. Find the documentation the reader should have reached, decide whether the problem belongs in the docs or elsewhere, and make only the changes you can justify from the evidence. Then recheck the same type of question after the next relevant documentation release.
If you need to set up the assistant before collecting these signals, follow the Biel.ai Quickstart.