How do you choose a buyer question worth answering?
The September Index contains 140 target questions; their first words describe a fixed research bank, not live search demand (Broadcastwell, release 2026-09).
140 target questions appear in the September release: a fixed research bank, not a popularity ranking of what buyers currently type or say. The wording was chosen for a measurement design. Counting its first words tells us about that bank’s construction, rather than the prevalence of those words in the market. Broadcastwell, release 2026-09, category question arrays.
Start a content page with the decision it can help someone make. The buyer may need to establish whether software supports a workflow, compare two products, understand a restriction or identify the evidence needed to evaluate an offer. Those needs are more useful than replacing a keyword with a question mark.
Do historical voice-search studies identify the best question word?
Not in the evidence used here. Backlinko’s opened study reports results from 10,000 Google Home searches, including an average 29-word answer. That number is not the average length of a query. It does not say that a new product page should contain exactly that many words. Dean, 2018, answer-length section.
BrightLocal’s April 2018 online panel of 1,012 US consumers reports that 58% said they had used voice search for local-business information in the preceding year. That is historical, self-reported local-consumer behaviour. It is not an estimate of current B2B software buying or a frequency table for question words. The published percentage is rounded; we do not manufacture an exact numerator or interval from it. [Murphy, 2018, study method and first question].
Neither observation justifies assigning a universal priority to “how,” “what” or “which” for a software vendor today. No verified frequency ordering of current voice-query opening words is asserted on this page. Preserve the original task and measured object when applying a historical statistic.
What can our own question inventory show?
The following counts use the literal first whitespace-separated token, lowercased, in each released target question. Other includes product names and remaining openings. Questions do not receive extra weight because more engines or repeats answered them.
| First-token group | Questions / fixed base | Share | Descriptive Wilson 95% interval |
|---|---|---|---|
| what | 89 / 140 | 63.6% | 55.3% to 71.1% |
| best | 14 / 140 | 10.0% | 6.1% to 16.1% |
| which | 9 / 140 | 6.4% | 3.4% to 11.8% |
| how | 4 / 140 | 2.9% | 1.1% to 7.1% |
| other | 24 / 140 | 17.1% | 11.8% to 24.2% |
Question-level inventory, counts and calculation, and offline reproduction. The intervals are descriptive calculations conditional on this fixed bank, not population confidence claims about buyer searches. The complete texts let a reader inspect what “other” includes.
The dominance of an opening word in this table does not establish that using it improves a page. It reflects the chosen questions, including alternatives and comparison tasks. There is no random sampling of all possible formulations and no intervention varying only the opening word.
Identify the task before choosing the heading
Ask what the reader must decide after reading. A broad question about the “best” product may hide several incompatible use cases. A narrow question about a required workflow can make both the answer and its limitations clearer.
For an invented product called Cedar Ledger, consider these fictional planning examples, which report no actual capabilities:
| Buyer decision | A question to investigate | Evidence needed before answering |
|---|---|---|
| Whether the software fits an existing process | Can Cedar Ledger export invoice line items in the required format? | Current documentation, format details and configuration limits. |
| Whether a comparison is meaningful | How do the two products handle an offline approval step? | Comparable conditions and supported facts for both products. |
| Whether an advertised price applies | Which plan includes the required approval feature? | Current terms, exclusions and the scope of the published price. |
| Whether a migration is feasible | What data can be transferred, and what requires manual review? | A documented migration route and clearly stated exceptions. |
A product team may discover that it cannot support a desired answer. That is useful. It is better to explain the boundary or improve the documentation than to invent a favourable response to a question buyers genuinely need resolved.
Give each page a question it can settle
The answer library organizes pages around individual questions. That structure helps a reader arrive at a focused explanation and follow related evidence. It does not mean every variation of a phrase deserves a separate page.
Before creating a new destination, compare the intended answer with existing material. If the same explanation already answers both formulations, improve the existing page and link the alternate wording to it where appropriate. If the evidence, audience or decision differs materially, explain that difference in the new page’s scope.
Keep the heading plain. A title framed around a question should be followed by an answer, not an unrelated sales introduction. If the answer is conditional, put the condition near the beginning so that an excerpt does not imply a universal capability.
Choose evidence the buyer can inspect
Use the product’s current primary documentation for a capability, a dated official source for a rule and a recoverable study for an empirical claim. A third-party summary can supply context, but it should not silently become proof of a product feature.
For comparisons, define the same outcome on both sides. A vendor’s own assertion about ease of use is different from a measured completion time. If comparable evidence is unavailable, state what can be checked and leave the unsupported comparison unresolved.
Provide a useful next step after the answer. That may be a document, specification, worked example or related question. It should follow from the reader’s task rather than forcing every page toward the same commercial request.
Validate usefulness without inventing demand
Before publication, ask whether a reader can identify the supported answer, its exceptions and the evidence source. Read it at phone width, where a long table can obscure a crucial qualification. Check that the link labels identify their destinations.
After publication, distinguish discovery, visits, answer citations and successful buyer tasks. A page receiving few visits may still answer a narrow operational question; a popular page may still fail to support a buying decision. Define which outcome matters before treating a count as success.
Question-led content is a way to organize an answerable task. It is not a measured guarantee that a particular opening word, page length or conversational tone will cause an engine to quote the page.
Brian Dean writes that “the average voice search answer is 29 words”. The measured object in that sentence is the answer, so it cannot establish a typical query length. [Dean, Voice search SEO study, “Voice Search Results Tend to Be Short and Concise”; verified 30 October 2026].
What our own data shows
The literal first token is “what” in 89/140 released target questions, 63.6% (Wilson 95% interval 55.3% to 71.1%). This describes the fixed question bank, not buyer search demand. The new inventory counts the first tokens of the released target-question texts. It is separate from the registered citation studies and reports no search-volume estimate. The September release, DOI 10.5281/zenodo.22907695, and the public calculation make that narrow count reproducible.
We have no experiment showing that changing a question’s first word increases citations, naming rate or sales. The library’s single-question format is an editorial choice whose usefulness should be evaluated against the reader’s task.
Sources
- Broadcastwell. (2026). Absence Index, release 2026-09 [Data set]. Zenodo. Release JSON, answer JSONL and README. https://doi.org/10.5281/zenodo.22907695 Verified 30 October 2026.
- Dean, B. (2018, February 28). We analyzed 10,000 Google Home results. Here's what we learned about voice search SEO. Backlinko. https://backlinko.com/voice-search-seo-study Verified 30 October 2026.
- Murphy, R. (2018, April 26). Voice search for local business study. BrightLocal. Online panel of 1,012 US consumers, April 2018. https://www.brightlocal.com/research/voice-search-for-local-business-study/ Verified 30 October 2026.
Cite this page
Broadcastwell research team. (2026, October 30). How do you choose a buyer question worth answering?. Broadcastwell. https://docs.broadcastwell.com/research/playbooks/question-led-content/ Published 30 October 2026; updated 30 October 2026.
@online{broadcastwell2026research34,
author = {{Broadcastwell research team}},
title = {How do you choose a buyer question worth answering?},
year = {2026},
date = {2026-10-30},
url = {https://docs.broadcastwell.com/research/playbooks/question-led-content/},
urldate = {2026-10-30},
note = {Published 2026-10-30; updated 2026-10-30}
}
Prepared by the Broadcastwell research team. Lead: Ananya Rao, AI engineer.
Sources checked 30 October 2026. Independent check: 30 October 2026, Broadcastwell verification desk. This page has not been peer reviewed.
Last updated: 30 October 2026. Next review: 30 January 2027.