This AI Finds Your Lost Customers

The Invisible Goldmine on Your Website

Most B2B websites operate like billboards on a highway: 100 people drive past, two stop, 98 disappear. Across SaaS, agencies, and enterprise vendors, roughly 98% of visitors never convert on the first visit, never fill out a form, and never book a demo. Analytics tools log the session, marketing reports the traffic, and then those visitors vanish into the ether.

That “lost” traffic is not random noise. Someone who found your pricing page, scrolled your case studies, or spent six minutes on a product tour already signaled high intent. They compared you to competitors, checked whether you integrate with their stack, and maybe even screenshotted a feature for an internal Slack thread.

Traditional funnels treat those visitors as a rounding error because they did not submit a form or click “Contact sales.” You end up with a leaky bucket where ad spend, SEO, and content budgets pour in at the top, and barely measurable leads drip out the bottom. Marketing calls it “brand awareness”; finance calls it wasted CAC.

A different mental model treats that anonymous audience as a dormant asset: an invisible goldmine of people who already know you exist and already care enough to browse. Instead of chasing ever-colder lists on LinkedIn or buying third-party intent data, you start by mining the intent happening on your own domain. Your website becomes less of a brochure and more of a sensor network.

That shift unlocks a new category of system: a lead machine that actively captures and acts on intent in real time. Rather than waiting for the 2% who are ready to talk today, the machine spots the 98% who are just early, distracted, or cautious about handing over their email. It treats every serious visit as the start of a conversation, not a missed opportunity.

Modern lead machines stitch together visitor identification, automation, and AI outreach. They watch who lands on your site, infer who they are, then trigger personalized follow-ups on channels like LinkedIn while interest is still warm. The result is a pipeline powered by behavior, not just hope that someone eventually fills out a form.

Meet the AI 'Lead Machine' Stack

Call it a lead machine, call it an AI sales intern—under the hood, it is three very specific tools wired together: RB2B for identification, n8n for orchestration, and Prospera for outreach. Each one handles a different layer of the funnel, from “who is this person?” to “what should we say?” to “send it on LinkedIn right now.”

RB2B acts as the eyes of the stack. You drop a small JavaScript snippet on your site, and RB2B starts using cookies and its data graph to resolve anonymous visitors into real people: work email addresses, LinkedIn profiles, and company details. Brendan Jowett reports RB2B can identify roughly 70–80% of website traffic, surfacing both person‑level and company‑level visitors in its dashboard.

n8n sits in the middle as the brain. It watches RB2B for new identified visitors, then runs them through whatever logic you encode: qualify with an AI agent, check against HubSpot or your CRM, filter out existing clients, and decide whether they are worth outreach. All of this happens in a visual workflow, so non‑developers can drag nodes instead of writing glue code.

Prospera becomes the voice. Once n8n approves a contact, it passes the visitor’s LinkedIn URL, role, and context into Prospera, which generates and schedules AI‑personalized LinkedIn connection requests and follow‑ups. Because LinkedIn reply rates routinely beat cold email, this channel-first choice matters as much as the AI copy itself.

At a high level, the data flow looks like this:

• Website visit
• RB2B identification
• n8n decision logic
• Prospera LinkedIn message sequence

Everything here is composable. RB2B could be swapped for another visitor ID tool, n8n for Make or Zapier, Prospera for any LinkedIn automation platform. But this specific trio has been battle‑tested in agencies like Jowett’s, where once configured, the system runs on autopilot—quietly converting yesterday’s anonymous traffic into tomorrow’s pipeline.

RB2B: Unmasking Your Anonymous Visitors

RB2B turns your anonymous traffic into a living contact database with a single line of JavaScript. You drop a lightweight script into your site’s header, and from that moment every visit can be tied to a persistent cookie, cross‑referenced against RB2B’s identity graph, and resolved into a real person with a real job at a real company.

Setup feels more like adding Google Analytics than deploying a CDP. You create an RB2B account, grab the auto‑generated code snippet, and paste it into your global `<head>`—via your CMS, tag manager, or template. The final step: update your privacy policy to disclose this kind of visitor identification and contact enrichment, which your legal team will care about as much as your sales team loves the leads.

Once live, RB2B’s dashboard starts to populate with two core data views: profiles and companies. Profiles surface individual visitors, while companies aggregate all traffic from a given domain so you can see, for example, 9 people from the same SaaS vendor checking your pricing page over 48 hours.

For each identified visitor, RB2B exposes an unusually rich record. Typical fields include: - Full name - Job title and seniority - Company name and website - Work email address - Country and sometimes city - Most importantly, LinkedIn profile URL

That LinkedIn URL is the magic key for the rest of the AI lead machine. Tools like Prospera and automation platforms such as n8n – Workflow Automation and AI Agent Orchestration can grab that profile, send a connection request, and trigger multi‑step outreach that feels targeted because it is based on what that person actually did on your site.

RB2B’s headline claim is blunt: identify 70–80% of your B2B website traffic. For a site pulling 10,000 monthly visits from business audiences, that can translate into 7,000–8,000 enriched sessions and hundreds or thousands of unique contacts, depending on repeat visits. Even if only 10% of those become qualified leads, you are suddenly dealing with a firehose compared to the handful of form fills you used to get.

Practically, that means sales teams stop guessing who might be in market and start prioritizing visitors who just hit high‑intent pages. A pricing‑page view from a Head of Operations, with a verified work email and LinkedIn profile, becomes an immediate candidate for fast, personalized outreach instead of a lost bounce in your analytics.

Building the Brain: Your n8n Workflow

Building the automation brain starts with a single Webhook node in n8n. RB2B fires data to this endpoint in real time every time it identifies a new visitor, pushing payloads that include name, company, email, and LinkedIn profile. That Webhook becomes the entry point for every downstream decision, from CRM checks to AI outreach.

Once n8n receives the RB2B payload, the next move is a CRM lookup. In Brendan Jowett’s build, that means a HubSpot node configured to search contacts by a mix of fields, typically: - Email address - First and last name - Company domain

HubSpot’s search API returns either a matching contact record or nothing. n8n stores that response as structured JSON, which you can branch on later without writing a line of code. For other CRMs, you swap the node but keep the same pattern: search by identity, not just email.

The real intelligence shows up in n8n’s Switch node. This node inspects the HubSpot response and routes each visitor down one of two paths based on a simple condition: “contact found” or “no contact found.” In practice, you point the Switch at a field like `total` or `results.length` from the HubSpot output.

On the “contact found” branch, you typically short‑circuit any new outreach. The workflow might: - Update the existing record with latest visit data - Add an internal task for the account owner - Drop an event into a Slack or email notification channel

On the “no contact found” branch, the workflow treats the visitor as net‑new. n8n can create a fresh contact in HubSpot, tag it with source metadata (RB2B, page visited, timestamp), and then pass the record to Prospera for LinkedIn connection requests and nurturing. That path is where the AI agent behavior really kicks in.

This CRM‑check gate prevents embarrassing misfires that tank trust. Without it, the system would blast cold‑style outreach to existing customers, active deals, and even churned accounts who already told sales “no.” For B2B teams running dozens or hundreds of touches per day, that guardrail is the difference between a smart AI lead machine and an automated reputation problem.

The 'New Prospect' Automation Path

New visitors who clear the CRM check hit a separate n8n branch: the “new prospect” path. At this point, RB2B has already done the heavy lifting, handing over a payload that usually includes name, company, work email, and a LinkedIn URL. n8n’s job is to turn that JSON blob into a structured, outreach‑ready contact.

First step is data hygiene. An n8n Function node can normalize fields (split full names, standardize job titles, strip tracking params from LinkedIn URLs) and tag the lead with metadata such as “source=RB2B” and “intent=website_visitor.” Another node can map this clean schema into whatever Prospera expects: firstName, lastName, companyName, profileUrl, and context fields.

Forwarding to Prospera usually happens via HTTP Request or a native node, depending on how quickly the integration stack catches up. The workflow packages the enriched visitor into a payload that can trigger: - A new prospect record - Enrollment into a LinkedIn connection sequence - An initial AI‑generated message draft

Before n8n hands the lead off, you can insert an enrichment and qualification layer. A call to Clearbit, Apollo, or a similar API can append company size, industry, and funding. A parallel n8n node can hit an LLM endpoint (OpenAI, Anthropic, or local) with a prompt like: “Given this job title and company description, is this a decision‑maker for B2B marketing services? Reply with yes/no and a 0–100 fit score.”

That AI response becomes a gatekeeper. If the fit score falls below, say, 70, n8n can route the contact into a low‑priority list or suppress outreach entirely. High‑fit leads get tagged “qualified_by_ai=true” and move straight into Prospera with their score and reasoning attached as context for message personalization.

End result: this branch quietly manufactures a “warm” lead out of an anonymous pageview. By the time Prospera sees the contact, the system already knows who they are, where they work, whether they’re a realistic buyer, and which LinkedIn sequence should start the conversation.

The 'Existing Contact' Safeguard

Existing contacts hitting your site trigger the quieter, but equally critical, side of the n8n Switch node. When RB2B flags a visitor and your CRM lookup finds a match, the workflow branches into an “existing contact” path designed to protect relationships, not blast more outreach.

This path matters because aggressive automation can easily spam your best customers. That VP of Revenue who’s already mid‑deal with your sales team should not suddenly receive a cold LinkedIn pitch from Prospera just because they checked your pricing page again.

Inside n8n, the “existing contact” branch can stay simple or become its own micro‑workflow. The most conservative option: do nothing beyond logging the event in n8n, ensuring no new outreach fires from Prospera or email. That alone prevents embarrassing double‑touches and keeps your AI agent from stepping on your account executives’ toes.

More sophisticated teams use this branch to enrich context. You can push a “visited website on {{date}}” event into HubSpot or Salesforce, updating the contact record with page URLs, UTM parameters, and session counts. Over time, those touchpoints power better lead scoring and help reps see who is quietly warming up ahead of renewal or expansion.

Internal alerts are where this becomes a real revenue radar. When a high‑value account returns, n8n can post to Slack with: - Contact name and role - Company and account owner - Pages viewed and timestamp - Suggested next step for the rep

That Slack ping turns anonymous browsing into a same‑day, human follow‑up instead of an automated drip. Your AI stack acts as an intelligent filter, routing net‑new visitors into Prospera while shielding existing relationships from robotic outreach.

Used this way, RB2B – B2B Website Visitor Identification Platform plus n8n does more than generate leads. It upgrades your CRM hygiene, your timing, and the professionalism of every touchpoint your customers experience.

Prospera: Your Automated LinkedIn Closer

Prospera AI acts as the closer in this stack, the execution layer that actually talks to your prospects on LinkedIn. While RB2B and n8n figure out who visited and what they did, Prospera is the agent that turns that context into conversations, not just contact records.

Once n8n finishes its checks—new lead, not already in your CRM, meets your filters—it pushes a payload of structured data into Prospera. That payload typically includes the visitor’s name, role, company, LinkedIn URL, pages viewed, and any scoring or notes your n8n workflow adds along the way.

Prospera ingests that data and drops the lead into a predefined LinkedIn sequence. You design the sequence once, then every qualified visitor that flows through n8n hits the same playbook, with dynamic personalization filled in from the RB2B profile and your website analytics.

First move is a connection request that reads like a human wrote it after browsing the prospect’s site. Prospera uses AI prompts and templates to reference the company, job title, or even the specific product page they viewed, avoiding the generic “I’d like to add you to my network” spam that dominates LinkedIn.

After the connection lands, Prospera schedules a series of follow-up messages directly inside LinkedIn DMs. These messages are AI-written, but grounded in your positioning and offer, with delays and branching logic you control: day 1, day 3, day 7, or only after a reply.

Sequences do more than pitch; they qualify. Messages can ask targeted questions about current tools, budgets, or timelines, and Prospera tracks responses to move people between branches—warm, not interested, or ready to talk.

All of this runs without leaving LinkedIn. Prospects never touch a landing page or email thread; the entire journey from “anonymous visitor” to “qualified opportunity” happens inside their inbox, where response rates routinely beat cold email.

End goal is simple: book meetings. Prospera’s later steps push for a specific next action—a calendar link, a demo slot, or a short intro call—so that by the time someone hits your calendar, they already know who you are and why they’re talking to you.

Why AI-Personalized Outreach Wins

Cold outbound still behaves like a numbers game: scrape a list, blast a generic pitch, hope a fraction of a percent bites. This AI-driven system flips that script by starting with intent. Someone already visited your pricing page or n8n integration docs; you’re not interrupting their day, you’re following up on a signal.

RB2B and n8n quietly assemble that signal into a profile: company, role, LinkedIn URL, and the exact pages they touched. Instead of guessing who might care, you know this specific operations lead spent 7 minutes on your “HubSpot automation” page. Outreach stops being speculative and starts looking like customer support arriving late.

Prospera’s AI agents then weaponize context. They pull a prospect’s LinkedIn headline, work history, and recent activity, and merge it with the on-site behavior RB2B captured. The result is a message that sounds like a sharp SDR who did 5 minutes of research, written in 5 seconds by a model.

A first touch might read: “Saw you were checking out our n8n integration page and the HubSpot workflow examples—given your role as RevOps Manager at Acme, I thought I’d share how agencies are using similar setups to recover 20–30% of lost inbound.” That line does three things at once: proves you know who they are, proves you know what they looked at, and hints at a concrete outcome. No “quick question” subject lines, no vague “synergies.”

Performance follows that relevance. Teams running high-intent, AI-personalized LinkedIn flows routinely report: - 40–70% connection acceptance rates instead of sub-20% from cold lists - 15–30% reply rates, versus the 1–5% typical of templated blasts - Shorter time-to-meeting because prospects already evaluated you on-site

Contextual hooks are the superpower. “Noticed you were reviewing our pricing calculator…” or “Saw you comparing our n8n recipes for Salesforce versus HubSpot…” immediately anchor the conversation to something the buyer actually did. Combined with Prospera’s ability to mirror tone and seniority—C-suite brevity vs. practitioner detail—you get outreach that feels human, timely, and earned, not automated and desperate.

The Ethics and Compliance Tightrope

Privacy law catches up fast when your AI lead machine starts unmasking anonymous visitors. GDPR in the EU and CCPA/CPRA in California both treat persistent identifiers, cookies, and cross-site matching as personal data, even in a B2B context. If RB2B is resolving a browser session to a specific work email and LinkedIn profile, you are squarely in regulated territory.

Most B2B marketers lean on “legitimate interest” under GDPR to justify this kind of tracking and outreach. The argument: someone from a company actively browsed your pricing page or case studies, so contacting them about that exact product is reasonable and expected. That can fly with regulators, but only if you balance your interests against the individual’s rights and document that assessment.

Compliance starts with radical clarity. Your privacy policy should explicitly state that you: - Use third-party tools to identify website visitors - Enrich that data with company and professional profile information - Use it for B2B marketing, including LinkedIn outreach

A generic “we use cookies” paragraph will not cut it when you are piping those identities into n8n and Prospera for automated sequences. You need a cookie consent banner that distinguishes strictly necessary cookies from marketing and tracking cookies, and only fires RB2B after a user opts in where local law requires. Granular controls and a clear “reject all” button are rapidly becoming table stakes in the EU and UK.

Opt-out cannot be a dark pattern buried three clicks deep. Every outreach touchpoint—email, LinkedIn message, or sequence powered by Prospera AI – AI-Powered LinkedIn Outreach—should offer an obvious way to stop future contact. For email, that means a one-click unsubscribe. For LinkedIn, that means honoring a “not interested” reply and suppressing that contact in your workflow.

Trust is the real currency here. Use this stack to follow up on high-intent signals—multiple visits to your pricing page, a deep dive into your docs—not to blast anyone who glanced at your homepage. If your “AI lead machine” feels like surveillance plus spam, you will burn brand equity faster than you book meetings. Used thoughtfully, it can feel like a timely, relevant nudge rather than a privacy violation.

The Composable Future of Business Ops

Composable automation quietly turns one-off hacks like this “lead machine” into a repeatable operating system. Instead of buying a monolithic platform, teams stitch together RB2B, n8n, and Prospera into a stack that behaves like a custom internal tool, without a 10-person dev team or a six‑figure implementation project.

Tools like n8n are effectively low-code integration buses with an AI layer. You drag nodes instead of writing boilerplate, then drop in LLM calls for classification, enrichment, or copywriting. That means a marketing ops lead can ship what used to require backend engineers, QA, and a sprint review.

The same pattern that rescues anonymous visitors can power a fleet of small, specialized agents. Think: - AI email responders that draft replies based on CRM history and product docs - Automated lead research agents that enrich prospects with firmographics and recent news - Dynamic content personalization that rewrites page copy per visitor segment in real time

Each agent becomes a node in your business graph, all orchestrated by n8n, Zapier, or Make. RB2B might feed product-qualified leads into one branch, while another flow monitors churn risk and triggers a different outreach track. You’re not buying “a platform”; you’re composing one from interoperable services.

For lean B2B teams, this stack-based approach is a force multiplier. A single ops generalist can design, test, and iterate on revenue workflows that outmaneuver slower incumbents still trapped in rigid CRMs and quarterly IT roadmaps. The constraint shifts from “What can we afford to build?” to “What should we automate next?”

Frequently Asked Questions

What is RB2B and how does it identify website visitors?

RB2B is a B2B visitor identification tool. It uses a JavaScript snippet and cookie data to match anonymous website traffic against a large identity database, resolving visitors to their work email, company, and LinkedIn profile.

What is the role of n8n in this automation stack?

n8n acts as the central automation engine or 'brain'. It receives visitor data from RB2B, orchestrates the logic (like checking a CRM), and then sends the qualified lead to an outreach tool like Prospera AI to initiate contact.

Is identifying visitors and contacting them on LinkedIn legal?

While powerful, this technique requires careful handling of privacy regulations like GDPR and CCPA. B2B marketing often falls under 'legitimate interest', but you must update your privacy policy, use proper cookie consent, and provide opt-out options.

Can this system integrate with my existing CRM like Salesforce or HubSpot?

Yes. A key step in the workflow is cross-referencing identified visitors with your CRM. This prevents you from sending outreach to existing customers or active leads, ensuring a better experience and focusing efforts on new prospects.