Can I make the RB2B prospect firehose useful in a weekend?

If you’ve ever watched a firehose of RB2B leads pour into your CRM, only to realize you have no idea who to contact or what to say: this one’s for you.

Most posts about RB2B data are either salesy or overly technical. This one’s neither. Nor is it a step-by-step tutorial — it’s a “follow along” as I spent a weekend figuring out how to make the RB2B firehose of prospect data actually useful, instead of just spamming every lead with a 0% success rate.

I wanted to see if I could turn a noisy stream of anonymous website visits into qualified opportunities and better user experiences. The key was making website deanonymization actionable, layering context like Ideal Customer Profile (ICP) fit, buying persona, and buyer journey stage, and then triggering the right next action (which, more often than not, meant education, not “Got 15 minutes?”).

This idea came from a recurring question I kept hearing: “If I know who visited my site, how do I actually make that useful?” Since we’ve built a business around solving this exact problem, I decided to take a weekend to see it from the customer’s perspective.

Who’s this for?
If you’re a growth marketer trying to automate your B2B pipeline, you’ve probably looked into visitor identification or deanonymization tools like RB2B. These tools tell you which companies (not necessarily people) are on your site. But the real question is: what do you do with that data? Simply knowing a company name isn’t enough and this post explores how to bridge that gap.

Executive summary

The Problem

Most website deanonymization tools (like RB2B, Clearbit Reveal, or 6sense) flood your CRM with a stream of visiting companies and guessed contacts.

Without real context - such as ICP fit, persona, problem awareness, or buyer journey stage - that firehose isn’t truly actionable. The result: outreach thrash, wasted time, and loss of trust in the data.

The Approach

For this project, I imagined my (fake) business selling an “agentic QA” product. My goal was to automate how I handled RB2B’s firehose: not to blast everyone with a “Got 15 mins?” email, but to guide only the right people toward buying, based on who they were and what they engaged with.

I kept the following principles in mind when approaching the problem:

• Actionability = context + prioritization: The data’s not valuable until you know why it matters now.

• Deanonymization ≠ personalization: You don’t need to overreach; intent context does the heavy lifting.

• Buying signals ≠ buying intent: Educate first, sell later.

What I Built

I built a lightweight data pipeline that did more than just lookup provided LinkedIn contacts via tools like BetterContact. It:

1. Ranked companies for ICP fit.

2. Assessed contacts against my buyer persona (if available).

3. Inferred buyer stage from on-site behavior — were they exploring the problem, the solution, or evaluating vendors?

4. Filled buyer gaps by finding the right contact on LinkedIn if the visitor wasn’t the decision-maker.

5. Drafted tailored outreach based on stage — focused on helping, not selling.

The logic was simple:

• Don’t chase non-ICP companies (kindergartens don’t need enterprise QA tools).

• Match content and messaging to role (QA engineers need docs; VPs need ROI).

• Target the real buying committee, not just whoever clicked the link.

After a few iterations, the result was a working prototype that turned RB2B’s noisy data stream into prioritized, context-rich opportunities — and more importantly, a playbook for how to make deanonymized data actionable.

You can see the implementation here: github.com/customeros/automate-rb2b.

What I Found

1. I was able to drastically reduce “spray and pray” outreach to non-buyers by ignoring companies that were never realistic targets for my product, reducing costs of data enrichment and mitigating the negative effects of spamming

2. You need to have a clear separation between educate, evaluate, and purchase pages, which doing so allowed me to tag each visitor’s intent and tailor how I engaged with them.

3. I also discovered that for my fictitious product, actual buying personas rarely visit my site directly, meaning the real opportunity is to use website activity as a trigger to prospect the right people inside those companies. Though this was fictitiously generated data as well, but generated based on the content of my fictitious site… So this may not be the same for your product.

All of these findings are backed by real-world data: focusing outreach only on high-fit, high-intent visitors has delivered 17%+ reply rates and 10% meeting booking rates in tests.

So can you make the RB2B firehose useful in a weekend?

Absolutely, if you stop thinking of it as a list of leads and start treating it as a signal system. Once you layer ICP, persona, and intent context, the noise becomes a roadmap: who to educate, who to nurture, and who’s actually ready to buy.

HOWEVER - would I recommend doing so? Absolutely not. This is a whole product on it's own, not something you want to build and maintain in house. Youu are better off going with someone who will do all of this for you. Wink wink.

At least now, when someone asks how to make a website identification firehose useful, I’ve got a solid answer and a working example to point them to.

Though, it's not for the faint of heart.

1. Why raw data firehoses fail (common pitfalls)

Let's quickly run through why users of RB2B churn in such great numbers.

It's always the same story: a surge of visitor data with no clear way to prioritize it.

Here are the common reasons why a raw firehose of RB2B data fails to translate into actionable pipeline:

1. Volume ≠ clarity: “Who do we reach out to?” The stream lacks the specific team or application inside a big logo, often leading to wild goose chases.

2. ICP drift: Many of the identified visitors don’t match your ideal customer profile or economic buyer segment. (For example, you see a lot of junior/legacy job titles or sub-industries that aren’t in your target.)

3. Persona ambiguity: RB2B and other tools often only return a suspected contact at the company (e.g., an CFO that definitely isn't on your technical docs site), whereas the actual person engaging might be a different persona entirely (e.g., backend dev vs platform engineer vs QA lead).

4. Journey blindness: The data doesn’t reveal whether a visitor is just learning or actually evaluating your solution – the same company could have both, with very different intent. Without this context, you risk treating everyone as “hot” or everyone as “cold” incorrectly.

5. Content/CTA mismatch: The follow-up offer is misaligned with their needs. For example, asking for a sales call (“schedule a demo?”) when the person actually needs a “how it works” guide, technical documentation, or proof points first.

6. Anonymous silo: When someone does raise their hand (signs up for a trial or fills a form), their prior anonymous activity isn’t connected to their now-known profile. You lose the context of what they did pre‑signup, so sales starts from scratch.

7. Data integrity & nav issues: Data quality problems (analytics events not firing, broken tracking) or site navigation issues (unclear documentation paths, confusion between free vs paid features) muddy the waters. If the data is wrong or the user journey is confusing, your team can’t interpret the intent correctly.

2. What actionable RB2B deanonymization data looks like

To make deanonymized visitor data useful (actionable), each account needs to be enriched have the following context fields filled in near real-time:

• ICP Fit (A/B/C/D): firmographics/technographics, size-by-segment, region, cloud or K8s usage, compliance needs, buying power.

• Problem Theme: mapped to your solution’s use-case areas (e.g., load/performance issues, regression testing, non‑functional validation needs, trial activation bottlenecks).

• Journey Stage: is this visit about Understanding their problem → Understanding the solutions → Evaluating the solutions → Committing to solving. Each stage implies a different CTA.

• Relevant people: Not only do you need to understand the companies buying stage, you need to then go find all the people you actually need to reach out to who are your actual buyers.

• Recommended Next Action: route + message + content pack + owner (sales vs marketing) appropriate for that persona/stage.

3. The 7‑Part Operating System for Actionable Identified Web Visitor Data

I broke down the steps I wanted to take with the data (though wouldn't necessarily implement in v1) as follows:

1. Map — content and personas

   • I wanted to define what content actually exists across web, docs, product trial, and content downloads, what type of content it was, as well as the personas who this content targets. Then I would build a database with content metadata as context to feed LLMs context about my visitors.

2. Enrich — company & tech context

   • If I were going the whole hog, I would take the visitor’s company domain to enrich with publically available firmographics (company size, industry), and then add more data like technographics (cloud provider, app usage), and whatever other data points are useful for identifying your ICP after I have confirmed company fit in the next step.

3. Fit — ICP check

   • This step is critical. I use all the data I have to check that the company fits my ICP - if it doesn't immediately stop processing and save myself time and money.

4. Infer — persona + problem + stage

   • For ICP fit companies I next apply a light LLM query on the visitors page visit patterns, content depth (if available), and sequence (e.g., Docs → Integrations → Pricing) to infer the buying stage of the customer (that example sequence may well be an evaluator of my product).

5. Route — to the right motion

   • Branch into the right play: an Educate track (for the curious), an Evaluate track (hands‑on technical buyers), or Purchase track (economic buyers). Each track has appropriate channels and SLAs.

6. Feedback — close the loop

   • Capture outcomes (e.g., qualified? demo completed? closed-won?), then recalibrate scores and rules regularly. Also fix any content gaps or navigation issues discovered.

Seems like a lot of complex work? Sadly it is.

4. Fit & Intent Scoring Template

With the approach in place, I had to define two scoring models to gate non-ICP fits (Fit), and classify buying stage (Intent).

Fit Score (ICP fit):

• 4 = Ideal fit: target segment, uses the right tech (AWS + EKS), relevant team detected.

• 3 = Acceptable fit: adjacent segment or partial tech match.

• 2 = Weak fit: very small or the visiting person is likely a user but not a buyer.

• 1 = Poor fit: hobbyist, student, agency, or clearly outside target.

• 0 = Suppress entirely: bots, EDU domains, competitors, etc.

Intent Score (Journey stage):

• Evaluation stage: multiple pricing page views, deep docs dive (≥3 pages & >3 minutes), integration/setup docs viewed, trial started.

• Solution stage: looked at solution pages + a case study and maybe one doc, possibly a repeat visit, first pricing view.

• Education stage: top-funnel only (e.g., blog or one docs page skimmed), no pricing or product pages yet.

• Ambient interest: just a quick homepage peruse, careers page, or very brief visit.

• Noise: irrelevant page hits (e.g., purely accidental or bounces).

5. So here's what I ended up with:

Ok that's enough waffle.

Let me show you where I got to in a weekend.

We start with webhook ingestion for RB2B (and Ngrok support for running it locally):

Next we can see the initial companies that have been added via the webhook (or in this case a demo set of leads that I seeded):

And if we click into that company we get the leads:

I added a LinkedIn prospecting tool that demonstrates how I would go about the prospecting of economic buyers of companies that are meaningfully engaged on our website, and also for fun generated some (god awful) emails that you might send (sorry fictitious Sarah Johnson from Stripe):

Looks simple - but all the edge cases, and backend data handling to prune a firehose of non-relevant leads to an inbound GTM motion are not that straightforward.

6. Journey‑Aware Playbooks

Clearly by now you should realize that not all prospects should get the same follow-up to a website visit. If I were to operationalize this, I would setup distinct plays that I could A/B test, rather than just let an LLM go wild with putting nonsense together.

However do as I say not as I do, so here is a quick selection of LLM generated examples of how I would framework it:

A) Educate Researcher (technical individual contributor, e.g. SDET/QA or backend dev)

• Signals: Visits blog posts → documentation pages; no visits to pricing; compares tools; spends a long time on “how-to” guides.

• Primary need: Education and problem framing – they’re learning, not looking to be sold.

• CTA: Send helpful content (2–3 email/tutorial sequence): e.g. “How other teams are automating non‑functional requirement validation,” “latest failure we've seen that was completely avoidable,” “Common testing issue we've seen a lot more with vibe coding.”

• Offer: Invite them to an interactive sandbox or provide a hands-on walkthrough guide, but do not ask for a meeting until they show signs of moving into evaluation.

B) Evaluator (hands‑on technical buyer, likely Platform/DevOps engineer)

• Signals: Repeated pricing page views; checking integrations; started a trial or activation; reading troubleshooting/installation docs.

• Primary need: Help to unblock and accelerate their evaluation, and prove time-to-value.

• CTA: Offer an “Evaluation fast-track” package: e.g. quick start install instructions, success metrics or checklist, sample data sets, a scale-up/down test script. Optionally offer a 20-min technical session to assist.

C) Economic Buyer (e.g. VP Engineering/CTO or Product leader)

• Signals: Looks at ROI/TCO pages, case studies of customers, security/compliance info, maybe pricing comparison; only light engagement with docs.

• Primary need: A compelling business case and risk mitigation. They care about outcomes, ROI, and assurances.

• CTA: Provide a one-pager highlighting outcomes (e.g. how similar teams achieved X), total cost of ownership, and 2–3 short customer success vignettes. Then suggest a call or meeting to discuss detailed evaluation criteria (once they’ve digested the materials).

7. Gotchas and Reporting I would consider:

A few practical rules I would implement to ensure you don’t misuse the data or burn prospects:

• Don’t be creepy: Avoid explicitly saying "we saw you visited our site" when reaching out. Use the insight to personalize and prioritize, but calling it out can scare off prospects.

• Suppress:

  • Non‑ICP segments (education, micro-startups, etc.) → perhaps give them the option to opt-in to your (high-value) newsletter.

  • Careers page viewers → exclude from sales outreach entirely.

  • Free-tool or free-tier seekers → put into an educational track; do not push them to sales/demo prematurely.

• Disambiguate big logos: If a huge company (e.g. Cisco) hits your site, don’t assume the whole company is interested. Require a clue about which team or product line they represent before routing. If uncertain, hold off in a research queue or use ad retargeting rather than spamming the wrong people.

Example metrics to refine the system:

• Leading indicators – Number of Evaluate-stage accounts identified per week, trial activation rate, time-to-first-value (how quickly a new trial user reaches a key milestone).

• Lagging indicators – Opportunity creation rate by Intent segment, win rate, average deal size (ASP), sales cycle length.

• Quality indicators – SDR no-show rate pivoted against every relevant data point (are we setting meetings with the wrong people?), reply/response rates by persona or stage, content engagement/completion rates (for nurtures).

• Process indicators – % of anonymous sessions successfully deanonymized and stitched to accounts, average docs pages per visit (depth), any 404 or error pages hit, time spent on “getting started” content, etc.

Use these to close the loop: if a metric looks off (e.g., lots of Evaluate-stage leads but low conversion), adjust your scoring thresholds or content offers accordingly.

Even with a system in place, it’s easy to slip into old habits. Avoid these mistakes:

• Treating all visitors as equal and blasting out generic “Quick call?” emails to everyone.

• Counting interest in a free tool or free tier as if it were sales intent (they’re exploring, not ready to buy).

• Ignoring account hierarchy – remember, the specific team inside a big company matters more than the logo itself.

• Having opaque pricing or an unclear free vs paid path, causing self-serve users to stall (and then mislabeling their intent).

• Setting initial scoring rules and never revisiting them – failing to incorporate feedback means the model will drift or miss new patterns.

8. Future Tooling & Integrations

If I were to take this further, this is what I would look at integrating:

• Enrichment – e.g. BetterContact, TheirStack, firmographic/technographic databases.

• Engagement/Outreach – e.g. Apollo, Outreach, Salesloft, Instantly, Smartleads, etc.

• Data Pipeline – a database (Postgres will do) to store all this, plus a reverse-ETL tool to push derived fields (scores, persona) back into CRM for the team to use (vibe coding time am I right?).

9. The bottom line

More anonymous visitor data isn’t the answer — actionable context is.

Of course - feel like this is too much just to make your website deanonymization data actionable?

CustomerOS takes all of this one step further and tells you how to improve your website content to give even more signal to drown out the noise. And it does all of this out of the box by fully understanding your business and your customers from day 1 - so that you only deal with the signal and not the noise.

Book a call with us today so we can walk through your business and guide you through the best way to turn your anonymous inbound traffic into leads - even if that means going with RB2B!