The Complete Guide to 'How Did You Hear About Us?' Surveys for B2B (2026)

"How did you hear about us?" can be one of the most useful questions in B2B marketing. Teams sometimes skip it, constrain it to a dropdown, or collect responses that sit in a spreadsheet without analysis. Done well, HDYHAU surveys can reveal demand-creating channels that click-based analytics may miss.

The problem is not that companies don't ask the question. Many do. The problem is how they ask it — and what they do with the answers. A dropdown menu with ten options and no text field produces data that confirms what you already believe. An open-text field with the right framing produces data that changes how you allocate your budget.

This guide covers common HDYHAU failure modes, how to design a useful question, when to ask it, what answer options to include, how to analyse responses at scale with AI, and which patterns to examine in your own data.

Why HDYHAU Actually Matters (When Done Right)

Let's start with why most implementations fail. The standard approach is a dropdown on the demo request form: Google, LinkedIn, Referral, Event, Other. The prospect clicks whatever is fastest — usually the most recent touchpoint they remember — and moves on. The data goes into a CRM field that nobody queries. Marketing uses it once a quarter to justify existing spend. Nothing changes.

Dropdowns are worthless because they force a single answer to a multi-touch question. A buyer who spent six months listening to a podcast, saw three LinkedIn posts, got a recommendation from a colleague, and then Googled your brand name will select "Google." Your dashboard will credit Google. Your podcast investment will look like it produces nothing. Your colleague-referral channel — the one that actually created the demand — will not appear anywhere in your data.

The magic happens when you switch to open text. When you ask "What first put us on your radar?" instead of "How did you hear about us?" and give people space to write a sentence, the responses transform. You stop getting channel labels. You start getting stories.

"My colleague Sarah mentioned you after your talk at SaaStock. I then listened to your episode on Lenny's Podcast and started following you on LinkedIn."

— An actual HDYHAU response. Try getting this from a dropdown.

That single response tells you more than a thousand dropdown selections. It tells you the conference talk created awareness, the podcast deepened trust, LinkedIn maintained presence, and a peer recommendation was the catalyst. No attribution model in the world would have connected those dots. But the buyer just told you, for free, in fifteen seconds.

The key principles for making HDYHAU work: ask about first awareness, not last touch. Use open text, not dropdowns. Frame it as "What first put us on your radar?" Follow up for specifics — which podcast, which person, which event. And actually analyse the responses. That last part is where most companies fall down, and where AI changes everything. More on that shortly.

When To Ask: The Timing Question

Where you place the HDYHAU question matters almost as much as how you phrase it. There are three viable options, each with trade-offs. The right choice depends on your sales motion, your conversion volume, and how much friction your form can tolerate.

A demo request or signup form is a practical starting point because it captures fresh recall. Any extra field can add friction, so make it optional when appropriate and monitor form completion alongside the usefulness of the responses.

How AI Transforms HDYHAU Analysis

Open-text HDYHAU responses are messy. People misspell podcast names, omit context, or combine several channels in one sentence. Manual review may work at low volume, but it becomes increasingly time-consuming as the dataset grows.

Dropdowns produce data that is already categorised, but predefined options can omit an influence or collapse several touchpoints into one answer. Open text preserves more detail at the cost of additional analysis.

Language models can help structure a response like "my mate Dave who runs ops at Grafton mentioned you at drinks, then I heard the founder on Pavillion's podcast" into candidate sources such as peer recommendation and podcast. Across a larger dataset, the same process can surface recurring entities or time-based correlations for a person to review; it does not establish causation on its own.

This is what First Signals does. It turns open-text responses into suggested channels and sources that can be reviewed and tracked over time. The result combines the detail of customer language with a structured dashboard workflow.

A meaningful gap between HDYHAU responses and analytics is a prompt to investigate. Review classification quality, cohort performance and revenue evidence before moving budget; customer memory and click data answer different questions and both have limitations.

The Dark Funnel Problem

There is a name for the channels that create demand but don't show up in your analytics: the dark funnel. It includes every touchpoint that happens outside your tracking — conversations between peers, podcast episodes listened to on a morning commute, Slack community threads, conference hallway conversations, LinkedIn posts read but never clicked.

The dark funnel can be material in B2B buying, but its size is company-specific. When you ask buyers what made them believe your company could solve their problem, some will name influences that click tracking cannot observe: a trusted person, a podcast, a community thread or an event conversation.

Your attribution dashboard shows you who came to the party. HDYHAU can help reveal who sent the invitation. In B2B, that earlier influence may be more useful than the final door they walked through.

HDYHAU is one scalable method for investigating the dark funnel. It is not perfect — buyers have imperfect memories, some will still default to the most recent touchpoint, and the data requires interpretation. But it can add decision-useful context to precise click data that covers only part of the journey.

Implementation: A Practical Checklist

If you are ready to implement or overhaul your HDYHAU survey, the following sequence is a practical starting framework. Adapt it to your form, response volume, sales motion and review process.

Frequently Asked Questions

The Question You Should Be Asking Tomorrow

You do not need a six-month attribution project. You do not need to replace your analytics stack. You do not need to hire a data scientist. You need to add one open-text field to one form and start reading what your buyers tell you.

Teams that do this consistently can test whether their picture of the pipeline is incomplete. They may find overlooked channels that create demand, or heavily funded channels that mainly capture demand already in motion. That evidence is a stronger basis for deciding what to test, fund, or stop.

Start with the question. Make it open text. Frame it around first awareness. Follow up for specifics. And when the volume gets too high to read manually, let AI do the categorisation. The data is already there, sitting in the heads of every person who fills in your form. You just have to ask for it properly.