Identify website visitors: from anonymous visit to customer profile

No analytics tool will hand you the names of anonymous visitors. A person reading your pricing page appears as a browser, a location, and a device — no name, no email, no identity. That's by design: browsers restrict cross-site tracking, privacy law treats de-anonymizing strangers as a violation, and the products that claim otherwise are either limited to company names on office traffic or operating in territory you don't want to follow them into.

But the thing people are usually reaching for when they ask who visited is something else: knowing which engaged visitors turned into customers, which research journey led to a purchase, and what someone did before they signed up. Those are real questions with real answers — just not by unmasking strangers.

First, the default: analytics counts visitors anonymously, and tools that support identification identify nobody until you decide otherwise. Turning it on is a deliberate choice you make for your own users, usually at signup or login — the moment someone tells you who they are anyway. This is standard across the category: product analytics tools like Mixpanel and Amplitude work this way, and so does Bigdelta on the web analytics side.

When you do turn it on, the interesting part happens: everything that person did before signing up joins their profile. The twelve pricing-page visits from last month, the campaign that first brought them in, the form they abandoned and came back to finish — the whole story attaches to the person, and in tools with replays, any visit on the timeline opens as a session recording. If they browsed on a phone and signed up on a laptop, those meet in the same profile too.

Setting it up is a one-time job: when someone signs in, your site tells the analytics tool who they are — a user ID is all it takes — and everything after that is automatic (in Bigdelta it's one line in the site snippet). Nothing is inferred, bought, or looked up. The visitor introduced themselves to you, on your site; the merge just stops their earlier history from being orphaned.

For a creator with a newsletter, the question is which posts produce subscribers — and pageviews can't answer it, because they count page loads, not people. A popular post might bring thousands of readers and five signups; a niche post fifty readers and ten.

With pre-signup history attached to each subscriber's profile, the picture flips. You can see which articles each subscriber actually read before subscribing, how many visits it took, and which source started the journey. Did the Reddit spike produce subscribers, or just traffic? Do people subscribe on the first visit or the third? The answers come straight from the profiles of people who subscribed — their journeys started before they introduced themselves, and now that part is visible.

DTC sites live on repeat customers, and the interesting questions sit before the first order: did the buyer stumble in and check out, or visit the product page four times, read reviews, and compare pricing first? When they came back for a second order, did they go straight to the cart or browse new arrivals?

Profiles show that full timeline — anonymous research visits, first purchase, repeat visits. With billing connected, every profile also shows lifetime value and purchase history, so “this customer bought twice, is worth $240, and hasn't visited in 45 days” replaces “we have repeat customers.” Segment by revenue and work backwards: which landing pages and which traffic sources produce repeat buyers rather than one-time orders, and what do loyal customers do differently before their first purchase?

For SaaS, identification turns a statistic into an account. A trial signup's profile shows the whole pre-signup arc: which pages they read, how deep they went into docs, what brought them in. Sales can open a profile before a call and see the actual research path. Product can segment a funnel by source and see which channels produce signups that activate and stick, not just signups.

With billing connected, the same profile carries MRR and plan, and usage drops become visible before renewal. For B2B, profiles roll up into accounts by domain or account ID: “someone visited pricing” becomes “six people at Acme viewed the feature page, and two read the billing FAQ.” Seat activity shows whether a trial is one curious person or a team spreading the product — which is the difference between a long shot and an expansion candidate.

Everything described above is anonymous by default. Nobody is identified unless you choose to identify them: profiles stay anonymous and unlinked until your site sends an identifier, and you choose exactly which attributes are stored — a user ID alone is enough, and anything beyond it (an email, a plan) is up to you. Whatever tool you use, these two properties are the ones to check for before adopting it.

In Bigdelta's case: any profile can be deleted or anonymized on request, and profile data is stored on EU servers and never sold. Retention follows the plan — Forever Free keeps profile data for one year, Growth extends it to five — which matters because the best profile questions span time, like how many of last spring's subscribers are still active.

Profiles bridge the gap between aggregate analytics and individual people. Traffic metrics answer whether people are coming; profiles answer what each person did before and after they identified themselves. Turn on identification at signup and login, connect billing if revenue matters to your questions, and the full timeline — anonymous research included — becomes something sales, support, and product can actually read.