B2B Lead Generation, AI Lead Scoring

LLMs and SaaS Tools for Lead Scoring – Global and Polish Approaches

The rise of generative AI has pushed lead scoring into the spotlight. For years, it was treated as a dull exercise in sales math — counting email opens, link clicks, and website visits. Add points, sort the list, move on.

Today it looks very different. Large Language Models (LLMs) can read intent straight from a client’s words: the phrasing of their inquiry, the tone of a reply, even the rhythm of their engagement.

In the U.S. and Western Europe, platforms like 6sense, Clearbit, or ZoomInfo dominate. They merge intent data, predictive scoring, and automated outreach. Integrated with a CRM, they whisper to the sales rep: call this lead today; the other one can wait until next week. AI models scan dozens of signals at once — from LinkedIn posts to website traffic to the way someone answers that very first email. This is no longer scoring. It’s behavioral prediction.

Poland is taking a two-track path. The first track: local SaaS startups building tools for the domestic market. Sellizer.io from Rzeszów started as an offer-tracking app, now it invests heavily in scoring and automated follow-ups. The second track: custom implementations. Development teams, often software houses, stitch together APIs from OpenAI or Claude, creating tailored scoring algorithms. This fits B2B companies with shorter lead lists, where the key challenge is distinguishing who’s just “looking” and who’s preparing to buy.

But there’s a catch. None of this works without quality data. Garbage in – garbage out. If your CRM is sloppy and your contact base outdated, even the smartest model will fail. Take Apollo: after clashing with LinkedIn, it lost access to fresh profiles. Today, one in three prospects there is already stale.

Here smaller Polish players sometimes have the edge: shorter cycles, smaller scales, and the agility to adapt quickly.

The effect? If LLMs analyze intent and SaaS handles execution, your sales reps spend their time with the ones who actually want to buy. Simple, but decisive.

How Scoring Works in SaaS with AI

Thanks to AI, lead scoring is now contextual. SaaS tools don’t just count clicks; they interpret meaning.

An algorithm knows that “we’re interested next quarter” is still a buying signal — but different from “please send the offer today.” It watches the response time, the time of day, and cross-channel activity: webinar attendance, LinkedIn comments, downloads.

In practice, AI scoring runs on three layers:

• Behavioral – clicks, time on site, downloads.
• Semantic – tone and language in emails or chats.
• Predictive – matching lead behavior to past customers who ended up buying.

Example: a SaaS company sells marketing automation. Classic scoring gives points for downloading an e-book. AI scoring goes deeper: if the lead downloaded a RODO compliance e-book and asked about CRM integration, their buying probability is much higher than someone who downloaded three PDFs without ever asking a question.

In Poland, B2B startups are experimenting with such models — including tools that analyze sales calls. Globally, 6sense or HubSpot with AI add-ons push it further: connecting scoring with real-time lead routing. A sales rep gets the alert: “Talk to this person now. Chance of deal: 72%.”*

This changes the game. No more guessing. Sales works on signals AI can read faster and deeper than any human. The tech won’t replace a conversation — but it tells you when and with whom to start it.

Why Outbound Scoring Only Works When It’s Tailored

Inbound scoring is one thing. Outbound is a different story.

On paper it looks simple: upload contacts, assign points, target the “hottest” leads. In practice — it breaks. Outbound is about precision, not mass. If your scoring model doesn’t reflect your business model, your buyers, your sales motion — it’s useless.

A Warsaw software house runs a campaign to industrial machine manufacturers. Standard scoring rewards revenue growth. But rising revenue only proves budget — not intent to buy software. Result? Reps waste time chasing every “growing” firm instead of prospects with clear operational pain.

Change the scoring: add industry-specific signals — participation in Hannover Messe, announcements of new production lines. Suddenly, lead quality jumps. Reps stop wasting calls, deals start closing.

That’s the heart of outbound scoring: not the number of points, but the criteria that actually fit your reality.

• For e-commerce: store traffic, payment integrations.
• For SaaS: trials, API requests, LinkedIn engagement.
• For manufacturing: investments, management changes, contracts in trade media.

You can buy off-the-shelf SaaS scoring, but if you don’t customize it, results will resemble horoscopes — occasionally right, mostly noise. And sales is not luck. It’s process.

So tailor scoring to your business. That’s your edge.

How to Build AI Scoring the Right Way

AI-driven scoring tempts with automation and precision. But without method, it becomes yet another spreadsheet of dead leads. Here’s the path:

1. Start with your clients

The first step is an audit. Make a list of all the clients you’ve actually worked with over the last few years. Not just the “biggest” ones — every single one. Then apply the Pareto principle: identify the 20% who generated 80% of your revenue. The rest? If they came to you by accident, don’t waste resources chasing their “clones.” Those accounts will eat up your budget and time, while the return will be marginal at best. Remember: Pareto works both ways — if you aim at the wrong 80%, you’ll spend 80% of your time and money to generate just 20% of the revenue, if it comes at all.

2. Define lookalikes per industry

Next, create a set of conditions a prospect must meet to qualify as “similar” to your best clients. For example: the number of developers on the team, whether they use a particular technology in their e-commerce store, or even whether they use Google as their mail provider. In e-commerce, this could be annual turnover, number of products in the store, payment integrations. What matters is that these criteria are measurable and easy to verify. Don’t score things that can’t be measured, like “level of digitalization.” That’s just noise.

3. Build and enrich your base

Tools like MeetAlfred will help you collect leads and contact data directly from LinkedIn — still the most up-to-date public database available, even if not perfect. Then use Clay for data enrichment: verifying which entries are current, and adding context from other sources like Crunchbase. And keep this in mind: scoring is not just numbers, it’s also context. AI works best when it gets the full picture of a prospect.

4. Verify with LLMs

Use GPT or Gemini to check if each prospect meets 2–3 of your chosen conditions. For example: does this company have good employer reviews? Is it active in the DACH market? AI can scan sources fast, but here’s the critical warning: don’t trust the results 100%. Assume an error margin of 20–35%. That’s natural. Models are getting better, but true reliability is still at least 3–5 years away.

5. Iterate quarterly

Scoring is not a one-time exercise. Data goes stale, industries shift. That’s why you need to update your criteria every quarter and check whether your top 20% of clients still look the same as they did a year ago.

Not Ready to Start Alone?

Plenty of firms can help you build a real scoring model. Consultancies or Lead generation agencies like SalesMeUp.

If you want feedback on your current scoring, send us your template. You’ll get our take.

Not free, of course. The price is simple: your business email 😉

*https://knowledge.hubspot.com/properties/determine-likelihood-to-close-with-predictive-lead-scoring

LLMs and SaaS Tools for Lead Scoring – Global and Polish Approaches
The rise of generative AI has pushed lead scoring into the spotlight. For years, it was treated as a dry exercise in sales math — counting clicks, email opens, and website visits, then adding points. Simple, mechanical.

Today, things look very different. Large Language Models (LLMs) can analyze a client’s intent not just from actions, but from the content of their inquiry, the tone of their replies, even the frequency of their interactions.

In the West, platforms like 6sense, Clearbit, or ZoomInfo dominate. They combine intent data, predictive scoring, and automated outreach. Integrated with a CRM, they give sales reps a ready answer: “Call this lead today. The other one can wait until next week.” AI models scan dozens of signals at once — from website traffic and LinkedIn activity to how someone responds to the very first email. This is no longer just scoring. It’s behavioral prediction.

Poland is taking a two-track path. The first is local SaaS startups building their own tools for the domestic market. Take Sellizer.io from Rzeszów: it started as a simple offer-tracking tool, now it’s investing heavily in scoring and follow-up automation. The second path is tailored implementations. Here, development teams (often software houses) use APIs from OpenAI or Claude to create custom-made algorithms. This fits B2B companies that don’t need massive lead lists, but do need sharper insight — to tell who is just “browsing” and who is actually ready to buy.

Of course, none of this works without quality data. Garbage in – garbage out. If your contact base is outdated and your CRM is filled in carelessly, even the smartest scoring model will fail. Example: Apollo. After clashing with LinkedIn, they lost access to real-time data. Today, every third prospect in their tool can be outdated.

And here Polish firms sometimes have the edge: smaller scale, shorter decision-making cycles, and the ability to adapt tools quickly to local realities.

The effect? If you let LLMs handle intent analysis and SaaS tools manage execution, your salespeople will spend time talking to the ones who actually want to buy. Simple as that. And in that simplicity lies the real advantage.

 How Scoring Works in SaaS with AI (final with raw link)
Thanks to AI and Large Language Models (LLMs), lead scoring today is far more contextual than ever before. SaaS platforms no longer just count interactions — they interpret them.

An algorithm can “understand” that a reply like “we’re interested next quarter” is still a sales signal, but of a different priority than “please send the offer now.” It doesn’t stop at text. It also looks at the response time, the hour of the day, and whether the lead engages in other channels — joining webinars, or commenting on company posts on LinkedIn.

In practice, AI scoring works on several layers:

Behavioral layer – analyzing actions: clicks, time spent on a website, downloads of materials.

Semantic layer – interpreting language, including the tone of messages in emails or chats.

Predictive layer – comparing a lead’s behavior to the history of past customers, to see which patterns most often led to a deal.

Take an example. A SaaS company selling a marketing automation tool. A classic scoring model would award points for downloading an e-book. But AI-driven scoring “understands” more: a lead that downloads an e-book about RODO and then asks on chat about CRM integration has a much higher probability of buying than someone who downloaded three different files but never started a conversation.

In Poland, the first startups focusing on B2B are already experimenting with such models — for instance, platforms analyzing sales reps’ calls with clients. Globally, leaders like 6sense or HubSpot with AI features go even further, linking scoring to real-time lead routing: a sales rep gets a notification — “Talk to this person now, the chance of a deal is 72%” .

That changes the game. Instead of guessing, sales teams work with signals that AI processes faster than any human ever could. And while no technology will replace an actual sales conversation, AI can give a sharper answer to the most critical question: when, and with whom, should that conversation begin?

Why Outbound Scoring Only Works When It’s Tailored to You
Inbound scoring is one thing. But what about outbound? On paper, outbound lead scoring looks simple: upload a contact list, let the system assign points, and then have sales reps target the “hottest” leads with emails.

In practice, it doesn’t work like that. Why? Because outbound is not about volume. It’s about precision. If the scoring model is not aligned with your business model, your clients, and your sales style — it becomes just another spreadsheet full of numbers.

Take a common market example. A Warsaw-based software company runs a campaign aimed at industrial machine manufacturers. A standard scoring model gives high points to every company whose revenue increased in the last fiscal year. But what does that really tell you? It only confirms the company has budget — not that they plan to invest in software. The result? Sales reps waste time trying to book meetings with every “growing” firm, instead of focusing on prospects with actual problems to solve.

Once they adjust the scoring — adding industry-specific signals, such as participation in Hannover Messe or announcements of new production lines — the quality of leads improves dramatically. Sales reps stop chasing random names, and revenue starts to move.

That is the essence of outbound scoring. What matters is not the number of points, but the criteria that match your reality. For e-commerce, that might be store traffic and payment integrations. For SaaS, trials, API requests, and LinkedIn engagement. For manufacturing, investments in new lines, changes in management, or contracts announced in trade media.

Yes, you can use off-the-shelf SaaS scoring tools. But if you don’t take the time to adapt them to your own process, the results will be closer to a horoscope — sometimes right, more often not. And sales is not about luck. It’s about process.

So if you want outbound scoring to actually work, don’t copy someone else’s rules. Tailor it to your own business. That will be your advantage.

How to Build Proper AI-Driven Scoring
AI-supported scoring promises automation and precision. But if you don’t approach it methodically, you’ll end up with yet another spreadsheet full of “leads” that lead nowhere. Here’s how to build a scoring model that actually works and doesn’t waste your time or budget.

1. Start with yourself – a full list of past clients
 The first step is an audit. Make a list of all the clients you’ve worked with in recent years. Not just the big ones — all of them. Then apply the Pareto principle: identify the 20% that brought you 80% of revenue. The rest? If they found you by accident, don’t invest in chasing their “lookalikes.” They’ll eat up your budget and time while giving little or no return.

2. Define “lookalike” criteria per industry
 Create a set of conditions that a prospect must meet to qualify as similar to your best client. It could be: number of developers, use of a specific technology in their online store, or using Google as their mail provider. For e-commerce — annual turnover, number of products in the store, payment integrations. The key is that the criteria must be measurable and easy to verify. Don’t try to score vague things like “level of digitalization.”

3. Build your database and enrich the data
 Tools like MeetAlfred help you gather leads and contact data directly from LinkedIn — still the most up-to-date public source of contacts, despite its flaws. Clay supports data enrichment — verifying which entries are current and adding context from other sources such as Crunchbase. Remember: scoring is not just numbers, it’s context. AI works best when it sees the full picture of the prospect.

4. Verify criteria with LLMs
 Use GPT or Gemini to check for the presence of 2–3 conditions for each prospect. For example: does the company have good employer reviews, or are they active in the DACH market? AI can scan sources quickly, but here comes the key warning: don’t trust the results 100%. Accept a margin of error of 20–35%. That’s natural — models are improving fast, but full reliability is still at least 3–5 years away.

5. Iterate and correct
 Scoring is not a one-time exercise. Data ages, industries change. That’s why every quarter you need to update your criteria and check whether your top 20% still look the same as they did a year ago.

The simple rule is this: better to have 50 well-chosen prospects than 500 random ones. Outbound only works when it’s tailored to you — AI is a helper here, not a substitute for your strategy.

Don’t Feel Ready to Start on Your Own?
Plenty of companies can help you build a real lead scoring model. You can work with consulting firms like Casbeg, or with lead generation agencies such as SalesMeUp.

If you’re not sure how to approach the topic, or you’d like to hear our take on your current scoring setup, send us your template — and you’ll get our feedback.

Not for free, of course. The price is simple: share your business email address 😉