Adding a "How Did You Hear About Us" Field

Analytics tools track clicks, but they miss much of how buyers actually discover products. Podcasts, word of mouth, private communities, AI assistants and social posts often shape a buying decision without leaving a clean tracking trail. By the time someone visits your site, the influence that mattered most may be weeks old and completely invisible to your dashboards.

A simple "How did you hear about us?" field captures this self-reported attribution. It costs almost nothing to add, and it is one of the cheapest ways to understand what is really driving awareness. This guide covers where to put the field, how to design it, how to clean and analyze the answers, and the mistakes that make the data useless.

Why self-reported attribution matters

Software attribution usually credits the last trackable click before a conversion. For many B2B SaaS companies, that last click is a branded Google search or a direct visit. On paper it looks like search is your best channel. In reality, that search often happened because someone heard about you on a podcast, saw a founder's post on LinkedIn, or got a recommendation from a colleague in a private Slack group. The search engine gets the credit, and the channel that created the demand gets none.

Self-reported attribution fills that gap by asking the buyer directly. The answers are imperfect, since people forget and simplify, but they reveal influences that no tracking script can see. It is especially valuable for channels that are hard or impossible to track by design. Word of mouth leaves no referrer. Private communities and messaging apps strip out link data, which is why this traffic is often called dark social. Podcasts and events produce awareness that turns into a search days later. And recommendations from AI assistants increasingly send buyers to a brand without any referral data at all, or with referral data that only captures a fraction of the influence.

For a small team deciding where to spend limited time and money, this matters a great deal. If you cut a podcast sponsorship because analytics shows little podcast traffic, you may be cutting the channel that drives your branded searches.

Where to add the field

The most common placement is the signup or demo request form, since that is where you have the buyer's attention at the moment of conversion. Demo forms are a natural fit because buyers expect a few qualifying questions and the extra field adds little friction. Self-serve signup forms are more sensitive, since every additional field can reduce completion.

If you are worried about signup friction, move the question into an onboarding survey shown immediately after signup. Users who have already created an account are generally willing to answer one or two quick questions, especially if the survey also asks something that personalizes their setup. You lose a small share of responses from people who skip the survey, but you protect the signup rate.

Sales teams can also ask during discovery calls. Conversation produces richer answers than a form, because a salesperson can follow up when someone says "I think I saw you somewhere" and help them remember the specific newsletter or event. The trade-off is consistency. Unless reps record the answer in a defined CRM field, those insights stay in call notes and never make it into analysis.

Free text or dropdown

The design choice that shapes your data most is whether to use free text or a fixed list of options.

Free text

A free-text field captures specific detail that a dropdown cannot: the exact podcast episode, the name of the colleague who recommended you, or the particular community thread where someone mentioned your product. That detail is often the most actionable part of the data, because it tells you which specific sources to invest in. The cost is cleanup. Answers arrive in every possible format, and you need to categorize them before you can count anything.

Dropdown

A dropdown is easy to analyze from day one, because every response already belongs to a category. The downside is that it limits detail and can bias answers. People pick whatever option is closest, even if it is not quite right, and options near the top of the list tend to get chosen more often. A dropdown also cannot reveal a channel you did not think to include.

Combination

Many teams settle on a combination. One approach is a dropdown followed by an optional "Tell us more" text field, which keeps analysis simple while still collecting specifics from people willing to share them. Another is to use free text only and categorize answers later. Free text tends to produce more honest answers, since people describe what actually happened rather than choosing from a menu, and modern tools make categorization much less painful than it used to be.

Choosing your options

If you use a dropdown, build the list from the channels you actually use plus the ones you suspect matter. A typical B2B SaaS list includes search engines, AI assistants such as ChatGPT or Perplexity, LinkedIn, other social media, podcasts, newsletters, a friend or colleague, online communities such as Slack groups or Reddit, review sites, events or webinars, ads, blog posts or articles, and an "Other" option.

Keep the list short enough to scan quickly, which usually means ten to fourteen options at most. Separate options only when you would make a different decision based on the difference. Splitting "LinkedIn" into organic posts and ads is worthwhile if you run both and need to compare them; splitting "social media" into six platforms you never use is not. Revisit the list occasionally, but avoid changing it often, because frequent changes make it hard to compare one period with another.

Required or optional

Making the field required increases the response rate but adds friction, and some people will pick a random option just to get past it. Making it optional reduces friction and tends to produce more truthful answers, but you will have gaps. There is no universal right answer. If the field sits on a demo form where buyers already expect questions, required usually works fine. On a self-serve signup form, optional is the safer starting point. Whichever you choose, watch both conversion rate and the share of junk answers for a few weeks and adjust.

Analyzing responses

Review the data monthly. Look at the share of responses by channel, how that mix changes over time and whether it differs between free signups and paying customers. The detailed text answers are often the most useful part, because they point to specific sources, such as a particular podcast or community, that you can deliberately invest in.

The most revealing exercise is comparing self-reported data with your analytics. If many people say they heard about you on a podcast but analytics shows almost no podcast traffic, the podcast is probably driving branded searches and direct visits rather than clicks. The same pattern often appears with LinkedIn, where people see a post, remember the name and search for it later. Neither source is complete on its own, but together they tell a much clearer story than either does separately.

Cleaning free-text responses

If you collect free text, set up a small set of categories that match your channels and group variations under them. "LI," "Linkedin," "saw a post on LinkedIn" and "your CEO's posts" all belong in the same bucket. Keep a separate note of specific sources, such as podcast names, newsletter names, community names and individual referrers, since these are what you will act on.

AI tools can categorize large volumes of responses quickly and are generally good at it, but check a sample of their output by hand. They can misread ambiguous answers, such as treating "Google" as search when the person meant a Google partner directory, or confuse two similarly named podcasts. A quick monthly review keeps the categories honest.

Connecting responses to revenue

Self-reported answers become far more valuable once they are attached to customer records. Store the response in your CRM or product database alongside the account, so that you can later see which channels produce paying customers, which produce larger deals and which bring customers who stay. A channel that drives many signups but few paying accounts is very different from one that drives fewer signups with strong retention.

This step is often skipped because the answer gets collected in a form tool and never makes it into the system where revenue lives. Check that the field is mapped correctly before you launch, and test it with a real submission.

A worked example (illustrative)

Imagine a small analytics product that adds a free-text field to its demo form. After two months, its analytics still credits most demos to organic search, but the free-text answers tell a different story: a noticeable group of buyers mention one particular industry newsletter, and several name a specific practitioner community. The team checks the CRM and finds that the newsletter readers close at a higher rate than average. Instead of cutting the newsletter sponsorship for showing almost no tracked clicks, they renew it and start participating more actively in the community. None of that would have been visible from click data alone.

Combining with other attribution methods

Self-reported attribution is not perfect. People forget where they first heard about you, give the most recent influence instead of the first, or choose the simplest answer. Treat it as one source among several rather than the final word. Combine it with analytics data, UTM tracking, CRM records and notes from sales conversations. When several sources point the same way, you can act with confidence. When they disagree, that disagreement is itself useful, because it often reveals a channel creating demand that another channel is collecting.

Common mistakes

The most common mistake is offering too many options, which overwhelms people and produces noisy data. A close second is missing an important channel entirely, so that answers get forced into "Other" and the signal disappears. Many teams also collect the data and never analyze it, which makes the field pure friction with no benefit.

Another frequent problem is failing to connect responses to customer outcomes, which leaves you counting signups by source without knowing which sources produce revenue. Finally, changing the options too often makes trends impossible to compare across months. Settle on a list, keep it stable for at least a quarter and make changes deliberately.

Start simple

Add a free-text or short dropdown field to your signup or demo form this week, and make sure the answer is stored alongside the customer record. After a month, sit down with the responses and compare them with your analytics. You may find channels driving more awareness than your dashboards suggest, and a few specific sources worth doubling down on.