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How to fix your database using LLMs and AI. Lessons from 11 years of lead generation experience.

Let's push the slogans aside and talk about hard work, data, and how to actually help your sales team close more deals. Your reps are out there calling and emailing people who have never even heard of your company. It is time to improve their odds.Most B2B companies operate in a gray zone. Between 95% and 98% of your website visitors browse your pricing, read your case studies, and compare you to competitors before leaving without a trace. You know nothing about them, and they quickly forget you. That is a pure waste of resources.How do you change this? You need to invest in intent data. This means building or buying tools that show you more than just what users are doing on your site. You need to know exactly who they are. Specifically, you need to identify which companies are visiting your pages and what they are looking for.What follows is a practical breakdown of intent data tools that make this process possible. We will look at what works, where the flaws are, what to watch out for, and how to turn this information into revenue.Table of Contents:<ol><li>The 84% Problem: Why your sales team finds out about clients too late.</li><li>Technical tool overview: just the facts</li><li>Snitcher: Identifying companies and syncing with your CRM.</li><li>HubSpot: When you want data and execution in one place.</li><li>Dealfront: The best option for Europe (and why that matters).</li><li>Albacross: How to close the funnel using LinkedIn.</li><li>Apollo: 275 million records and the issue of data quality.&nbsp;</li><li>The SMU Methodology: From anonymous visits to paid invoices (BANT and Persistence).&nbsp;</li><li>Q&amp;A: Law, money, and common sense.</li><li>Data table: Statistics you can verify.</li></ol><h3>The 84% Problem: Why is your sales rep finding out about the lead too late?</h3>Recent studies from 6Sense, backed by consistent year-over-year data from Gartner, Forrester, and McKinsey, show that 81% of B2B buyers define their requirements before they even consider speaking to your sales team. On top of that, 84% of inquiries coming through website forms are sent solely to get a secondary quote for a deal where the supplier has already been chosen.<blockquote>84% of B2B Deals Are Decided Before Marketers Even Know About Them</blockquote>This means that by the time a potential client calls or writes to you, they already have a set list of demands and have finished talking to several competitors. You aren't actually fighting for a contract. Instead, you are wasting time on a deal that is already lost, or you end up cutting your margins so thin just to win that the project becomes unprofitable.The average B2B sales cycle dropped from 11.3 months in 2024 to 10.1 months in 2025. If you don't engage with the customer while they are doing their initial research, known as the Solution Exploration stage, you simply won't make the shortlist. The statistics are clear: the company that holds the top spot on a buyer's preference list before any direct contact is made wins the deal 80% of the time.Intent Data is essentially a signal that a company has stopped just looking around and started searching for a real solution. It is your final warning to reach out before the buying window closes for good.<h3>A technical review of the tools: just the facts</h3>Snitcher: Identifying companies and syncing with your CRMSnitcher doesn't try to be everything to everyone. It is a tool designed to identify the highest number of business entities worldwide.<ul><li>How it works: You install a script that identifies companies and, more importantly, pushes that data to HubSpot in real time.</li><li>The result: One Snitcher client saw a 28.6x ROI in the first six weeks by catching up-selling opportunities they didn't even know existed.</li><li>The catch: Snitcher only identifies the company. If ten people from Microsoft visit your site, Snitcher tells you Microsoft was there. It won't tell you if it was an intern or the purchasing director. Identifying the right contact person is still a necessary part of the process when working with intent data.</li></ul>HubSpot: Data and execution in one placeHubSpot has a built-in "Prospects" tool. Its main advantage is that your entire sales and marketing process already lives there.<ul><li>The upside: After a year of using the full ecosystem, clients report an average of 129% more leads. The system can automatically link a website visit to an existing contact in your CRM if that person has ever filled out one of your forms.</li><li>The downside: It is expensive and complex. If all you need is identification, paying for the full Enterprise tier is a waste of money. Crucially, HubSpot works well for sales opportunities already identified in your CRM, but it is only mediocre at handling the rest of the traffic hitting your site.</li></ul>Dealfront: European data and complianceFor companies operating in Poland, Germany, or Scandinavia, this is often the only choice that makes sense.<ul><li>Here is why: Dealfront, created from the merger of Echobot and Leadfeeder, has the largest database of IP addresses assigned to European companies. While Apollo or ZoomInfo work great in the US, their effectiveness in Europe drops by dozens of percentage points.</li><li>Results: The company Actito reports that thanks to Dealfront, 61% of identified visits convert into open sales opportunities. This is not magic. It is simply a matter of calling people while they are actively reading about you.</li><li>The downsides: In head-to-head tests, it performs worse than Snitcher, and it is also significantly more expensive.</li></ul>Albacross: Retargeting and automationAlbacross goes a step further by letting you automatically create LinkedIn audiences from the companies that visited your website.<ul><li>How it is used: If you see that 50 logistics companies visited your site this week, Albacross automatically adds them to your LinkedIn ad campaign.</li><li>The benefits: Spendesk increased their conversion rate by 33% using this solution. Albacross is estimated to be 1.7x more effective at identification than standard trackers thanks to their proprietary IP-to-company database.</li><li>The downsides: Their identification database is the smallest among the providers I have worked with.</li></ul>Apollo: A database of 275 million records (and its traps)Apollo is currently the largest prospecting engine on the market.The potential: 275 million contacts and over 65 filters, including intent data from Bombora.<ul><li>The reality: The database accuracy is around 65%. This means every third email might bounce, and every third phone number could be out of date. In Europe, effectiveness is often even lower. This is a tool for those who prioritize scale over precision.</li><li>The benefits: Once a company is identified, you get immediate access to data on the people who potentially visited your site, along with a CRM and sales system that offers massive automation capabilities.</li><li>The downsides: Very poor performance when it comes to identifying traffic from outside the US.</li></ul><h3>The SalesMeUp Methodology: How to make money from this?</h3>At SalesMeUp, we don’t believe in the "sell right now" approach. Only 2% of the market is ready to buy at any given second. The rest requires groundwork.We follow the BANT framework (Budget, Authority, Need, Time) and segment leads accordingly:<ul><li>Prospect: Anyone who visited the site and was identified. We don't pester them.</li><li>Lead: Someone who has engaged in a dialogue, even if they said "not right now."</li><li>Opportunity: Someone with a real problem to solve within a specific timeframe.</li></ul>Our longest conversion took 2 years and 7 months. If we had given up after the first "no," that deal never would have happened. Success in B2B belongs to the persistent, not the "fast and furious."<h3>Q&amp;A: Common Sense</h3><ol><li>Q: Is this GDPR compliant?&nbsp; &nbsp;A: Yes, as long as you are identifying companies via IP addresses (B2B) and not collecting private data from individuals without their consent. Companies like Dealfront and Albacross build their entire business models around full compliance with European law.</li><li>Q: How much does it cost?&nbsp; &nbsp; A: Anywhere from $59 to $1,000 per month for the tools alone. On top of that, you have to factor in the cost of labor for data verification. External agencies that handle this professionally charge from around 40,800 PLN net per year.</li><li>Q: Is this data 100% accurate?&nbsp; &nbsp; A: No. No tool is 100% effective. Treat this as an indicator, not the absolute truth.</li></ol><h3>Data Table: our sources </h3><table style="border-collapse: collapse; width: 100%;" border="1" data-mce-selected="1" data-mce-style="border-collapse: collapse; width: 100%;"><tbody><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Source</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Link</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">&nbsp;Key stats</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">6Sense report</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;"><a href="https://www.google.com/search?q=https://6sense.com/blog/b2b-buying-behavior-statistics/" data-mce-href="https://www.google.com/search?q=https://6sense.com/blog/b2b-buying-behavior-statistics/">https://www.google.com/search?q=https://6sense.com/blog/b2b-buying-behavior-statistics/</a> https://6sense.com/newsroom/84-of-b2b-deals-are-decided-before-marketers-even-know-about-them/</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">84% of buyers define requirements before talking to sales; 80% chance of winning for the company in the top spot on the shortlist.</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">HubSpot</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">https://www.google.com/search?q=https://hubspot.com/products/crm/intent</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">129% average increase in lead generation after one year of use.</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Snitcher (Case Study)</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">https://snitcher.com/customers/influential-software</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">28.6x ROI within six weeks of implementation; win rate improved by 5% over 12 months.</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Albacross</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">https://www.google.com/search?q=https://albacross.com/customer-stories/spendesk</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">33% higher conversion rate on accounts identified via intent data.</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Dealfront (2026 Trends)</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">https://www.google.com/search?q=https://dealfront.com/blog/demand-generation-trends/</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">Outbound MQL to SQL conversion is typically 10-25%.</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Apollo.io (Sacra Research)</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">https://sacra.com/c/apollo/</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">Database of 275 million contacts; 40% year-over-year revenue growth; data accuracy around 65%.</td></tr><tr><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;">Gartner</td><td style="width: 33.3325%;" data-mce-style="width: 33.3325%;"><a href="https://www.gartner.com/en/sales/insights/b2b-buying-journey" data-mce-href="https://www.gartner.com/en/sales/insights/b2b-buying-journey">https://www.gartner.com/en/sales/insights/b2b-buying-journey</a> (download the report)</td><td style="width: 33.335%;" data-mce-style="width: 33.335%;">Customers spend only 17% of their time meeting with suppliers during the entire buying process.</td></tr></tbody></table>P.S. If your sales reps are still wasting time calling people who were "just looking," let’s talk. We can show you how to turn anonymous visits into actual revenue.

Intent Data Tools in LLMs era

Table of Contents &nbsp; &nbsp;<ol><li>The Return of the Philosophy Major&nbsp;<ol><li>Why logic is the new interface for AI&nbsp;</li><li>Thinking vs. Programming in B2B sales&nbsp; </li></ol></li><li>Why Your Developer Will Ruin Your Prospecting&nbsp;<ol><li>&nbsp;The trap of "if X, then Y" logic in human relationships&nbsp;</li><li>&nbsp;The "Thermomix" myth of fully automated sales&nbsp;</li><li>&nbsp;Why conceptual precision is the secret to non-generic content&nbsp; </li></ol></li><li>Intent Engineering: Syllogisms in the Service of Sales&nbsp;<ol><li>&nbsp;Prompting as formalized argumentation&nbsp;</li><li>&nbsp;The Socratic "Why?" in customer research&nbsp;</li><li>&nbsp;A philosophical definition of a "lead"&nbsp;</li></ol></li><li>&nbsp;Locality: Your Secret Weapon That Bots Do Not Grasp&nbsp;<ol><li>&nbsp;Why AI fails at regional nuances&nbsp;</li><li>&nbsp;The "Midsommar" test: Human judgment over global scale&nbsp;</li></ol></li><li>&nbsp;The Seneca and "Persistent" Strategy&nbsp;<ol><li>&nbsp;Omnia adversa exercitationes putat: Adversity as training&nbsp;</li><li>&nbsp;Patience over binary results: The 31-month conversion cycle&nbsp;</li></ol></li><li>&nbsp;HubSpot vs. Apollo: Why Your CRM Needs an Exorcist&nbsp;<ol><li>&nbsp;The "for your review" dilemma in AI agents&nbsp;</li><li>&nbsp;Data flow vs. cognitive value&nbsp;</li><li>&nbsp;The ethics of quality over quantity in lead scoring&nbsp;</li></ol></li><li>&nbsp;SOutreach: Beyond Developer Algorithms&nbsp;<ol><li>&nbsp;Ontology vs. Scale: Why standard databases fail&nbsp;</li><li>&nbsp;Human-centric data analysis for 77% higher accuracy&nbsp;</li><li>&nbsp;The cost of getting it wrong: "Hey Buddy" vs. the €500k contract&nbsp;</li></ol></li><li>&nbsp;Summary: Logic as the Ultimate Sales Strateg</li></ol><h2> The Return of the Philosophy Major (With Logic in Tow)</h2> For the past two decades, humanities graduates, especially philosophy students, were the punchline of jokes about using their degrees to work in fast food. Today, those jokes are just stale relics from the 90s used by law, med, and tech students to make themselves feel better. We have entered the era of Generative AI, where language has shifted from a tool for describing the world to an interface for controlling the most powerful technology of our time. Consequently, a philosophy graduate with a top grade in logic now finds themselves on a level playing field with specialists who spent years studying law, technology, or medicine. This does not mean specialists are no longer needed, but they are certainly no longer indispensable in many fields. If you sell in a B2B model, you must understand this: AI does not need a programmer to function. AI needs someone who knows how to think.<h3>Why Your Developer Will Ruin Your Prospecting</h3> Developers are trained to think in terms of syntax and functions: if X, then Y. This approach is excellent for building a CRM but disastrous for building human relationships. When a developer writes a tool or a prompt for a salesperson, it usually results in something that functions but does not necessarily align with the salesperson's goals. For example, in Apollo.io, you can use custom prompts for every row in a database. You can ask if a lead's company offers B2B or B2C services or generate a message for a specific person.<figure id="figure-gx0k4id0skml99rgrv"><img id="gx0k4id0skml99rgrv" src="https://cdn.durable.co/blocks/1bAlGILCCFgZQk6n5pBKQxuZBdnPEXrVbhjMxu0rnsHV5dHWxtfw3JaWxsUajEaM.png" alt="" title="" class="object-cover bg-gray-100 rounded-2xl md:rounded-3xl lg:rounded-4xl" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/1bAlGILCCFgZQk6n5pBKQxuZBdnPEXrVbhjMxu0rnsHV5dHWxtfw3JaWxsUajEaM.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure>Looks powerful, doesn't it? But how does it actually work? Well, in the campaign preview, you might find that the dynamically generated content doesn't match what you see in your contact table because it gets generated during the campaign itself.Classic developer logic, right? The function exists, it generates content, and it allows you to insert that content into a campaign. It doesn't matter that it becomes risky for a user who lacks proper training. And Apollo is just one of many systems that has implemented Agentic AI for sales.If you believe companies promising that their AI Sales SDR or Prospector will find leads and handle prospecting all on its own, you probably also bought a Thermomix thinking it cooks soup by itself. After you chop the vegetables, measure the ingredients, and toss them into the machine, you hit start instead of turning a dial on an induction stove. That does not mean the soup made itself. I assume, however, that you are not the kind of person I can charge €50 to explain why they do not need to spend €50. It is surprising how often we let ourselves be used by companies that claim they will do something for us as long as we do A, B, C, and D first.A philosopher and any sharp salesperson with a humanities background knows that conceptual precision is 90% of the battle. AI does not guess your intentions. If you do not define a customer's problem to its very core, the way Plato or Kant taught, the bot will give you generic fluff that nobody will read. At SalesMeUp, we see this every day. We have generated $1,000,000 in revenue for our clients from LinkedIn leads, a platform many claim is too spammed to work. This success comes from someone knowing how to ask AI about the essence of a persona rather than just their database parameters.<h3>Intent Engineering: Syllogisms in the Service of Sales</h3>Most people think Prompt Engineering is some magic art of typing in keywords. It is not. It is simply formalized argumentation.<ul><li>Logic over code: If you can build a syllogism, you can program AI to personalize cold emails so they do not look like spam.</li><li>&nbsp;The Socratic "Why?": Instead of asking AI how to write an email, the salesperson-philosopher asks about the practical concerns a logistics director in the UK has when considering a change in CRM providers.</li><li>&nbsp;Defining a "lead": Most companies struggle with this. We define it through specific interest and a scheduled meeting. This philosophical approach ensures we do not waste time on prospects that will never become opportunities.</li></ul><h3>Locality – Your Secret Weapon That Bots Do Not Grasp</h3>AI is excellent at a global scale but fails when it comes to locality. This is where the human salesperson comes in. You can use AI to appear local, from using virtual addresses to adjusting the language. However, you are the one who has to judge if it is wise to send emails in Scandinavia during Midsommar. Hint: it is not, as everyone is out celebrating. AI does not feel these nuances, so you have to be the one to tell it.<blockquote><h3>Omnia aduersa exercitationes putat&nbsp; (He regards all adversity as training - abou the presistent of the Wise).</h3></blockquote><h3>The Seneka and Persistent Strategy.&nbsp;</h3>In B2B sales, the longest conversion we have recorded lasted 2 years and 7 months. Programmers want results right now, in binary terms. A philosopher knows that building relationships is a process that spans quarters. That is why our Lead Nurturing is not an intrusive newsletter, but personal communication that builds a relationship step by step.<h3>&nbsp;HubSpot vs Apollo, or why your CRM needs an exorcist (and a philosopher)</h3>In the world of B2B sales, there is a cult of tools. The most common myth I hear is: "We'll buy an AI SDR, plug it into HubSpot, and leads will just fall into the funnel". The reality? An AI SDR requires implementation. How is it that a powerful multi-platform designed to work for you still needs you? Well, it is simple:&nbsp;<blockquote>&nbsp; &nbsp; Artisan’s Ava is an AI sales agent that automates the entire outbound sales process. Ava helps sales teams generate more qualified leads faster—without the need to grow the headcount. Ava uses conversational intelligence to gather data about your ICP and your goals through a simple chat interface. As you interact with her, Ava taps into her database of over 300 million contacts to identify leads that match your ICP. Then, she crafts personalized emails for each lead and presents them to you for review.</blockquote>&nbsp; &nbsp; That last sentence is telling: "for your review". What happens if you do not have the knowledge needed to approve them?&nbsp;&nbsp; A programmer will tell you: "The API is working, data is flowing". A philosopher will ask: "But does this data have any cognitive value?". At SalesMeUp, we know that integration is just the beginning. The real work starts with scoring and segmentation. If you mindlessly pump thousands of records into your CRM, all you achieve is clogging the funnel with "trash" that your sales department will have to manually clean up for five hours a day. We use logic to separate prospects from real opportunities. In sales, much like in ethics, it is not the quantity that matters, but the quality of the actions taken.<h3> &nbsp;SOutreach – Because Developer Algorithms Can't Tell a "Hey Buddy" From a "Dear Sir"</h3>This brings us to the heart of the matter. Why did we build our own application, SOutreach? Because the public data available on the market is often about as accurate as a morning newspaper horoscope. The statistics are brutal: 9 out of 49 records from popular databases don't even match the job title, and industry error rates reach 43%.&nbsp; &nbsp; Developers creating mass prospecting tools focus on scale. We focused on data ontology. Thanks to SOutreach, our databases are anywhere from 20% to 77% more accurate than what you can buy "off the shelf". Why? Because we taught the system to look at a company the way a human does—analyzing not just tags, but the actual website content and market context.&nbsp; In outbound sales, the "rule of locality" is sacred. If you are sending an email to a Swede from an office in Katowice, you have to sound like someone who understands their market, not like a bot from across the ocean. SOutreach allows us to catch these nuances that an AI SDR misses. It is the difference between a "Hey buddy!" sent to a bank president and a precisely tailored message that opens the door to a 500,000 euro contract.To sum up: if you have someone on your team who can challenge AI with logic rather than just code—hold onto them. They are the ones who will write the sales strategy while others wonder why their generic bots aren't delivering results.&nbsp; &nbsp;Want to see how a "philosophical" approach to data delivers leads in your industry? Let us know—we will create a sample for you that proves the difference Sources: <a href="https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future" data-mce-href="https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future">https://hbr.org/2023/06/ai-prompt-engineering-isnt-the-future&nbsp;</a> <a href="https://www.thelatinlibrary.com/sen/sen.prov.shtml" data-mce-href="https://www.thelatinlibrary.com/sen/sen.prov.shtml">https://www.thelatinlibrary.com/sen/sen.prov.shtml</a> <h3>Q: Why is SOutreach better than standard B2B databases?</h3>A: Standard databases generate errors between 18% and 43% in critical fields such as job titles or industries. SOutreach uses more advanced persona matching, which improves data accuracy by as much as 77%<h3>Q: Which tools are best for identifying anonymous website visitors in Europe?</h3>A: The choice depends on your specific needs: Snitcher integrates well with HubSpot, Dealfront is a leader in the European market, and Albacross is helpful for marketing automation. However, the tool itself is not the key factor; the way a human verifies that data is what matters most.<h3>Q: How long does it take to acquire the first customer in a new foreign market?</h3>A: Realistically, you should expect it to take between two and six quarters. The first year is generally focused on gathering feedback and validating your value proposition rather than achieving mass sales.<h3>Q: Is it worth automating prospecting on LinkedIn?&nbsp;</h3>A: Yes, but only if the automation supports a personal relationship. Without human oversight and a proper lead nurturing scenario, automation simply becomes a spam generator that damages your reputation in the market.<h3>Q: What is the difference between Prompt Engineering for a salesperson versus a programmer?</h3>A: A salesperson-philosopher focuses on psychology and the logic of argumentation, whereas a programmer focuses on syntax and data structure. In B2B sales, it is the understanding of customer intent that drives higher conversion rates rather than pure technology.<h3>Q: How does a humanities education impact database quality?</h3>A: It allows for better verification. Automated databases often have error rates between 18% and 43%. A humanist knows that without manual verification and a "human eye," you are simply paying for junk records.<h3>Q: Is it worth investing in cold leads in 2026?&nbsp;</h3>A: Yes, because 81% of companies buy from the provider they established contact with first. Properly "warmed up" cold leads eventually yield conversion rates similar to those of referrals.<h3>Q: How can you ensure a company is treated as a local entity abroad?</h3>A: AI can help you with translation, but you must take care of a local domain, a phone number, and—most importantly—case studies from that specific market. Locality is not just about language; it is about business context.

Philosophy vs. Code: Why Salespeople Matter in the Race for the Best Prompts

Test Turinga w Twoim Inboxie: Dlaczego AI wywala się na pierwszym zdaniu? Znasz to uczucie? Otwierasz maila i po trzech słowach wiesz, że nie pisał go człowiek, tylko algorytm. Większość kampanii outboundowych oblewa test Turinga już w przedbiegach. Dlaczego? Bo AI kocha „lać wodę”, używać słów, których nikt normalny nie wypowiada przy kawie, i – co najgorsze – opiera się na danych, które często są po prostu śmieciowe.<h3>&nbsp; &nbsp; Oto dlaczego Twój klient od razu wyczuwa bota i jak to zmienić.</h3>&nbsp; 1. „AI-izmy”, czyli słownik, który Cię zdradza Modele językowe mają swoje ulubione frazy. Jeśli Twój mail zaczyna się od „W dzisiejszym dynamicznym świecie” albo obiecuje „płynną integrację” (seamless integration) i „maksymalizację potencjału”, to gratulacje – właśnie trafiłeś do folderu „Inne”. Ludzie w biznesie nie mówią tak do siebie. Prawdziwy handlowiec pisze konkretnie, czasem skrótowo, a przede wszystkim – po ludzku.&nbsp; &nbsp; 2. Brak „brudu” w danych AI jest tak dobre, jak dane, które mu podasz. A z bazami danych w internecie jest problem: testy pokazują, że nawet 43% rekordów może być niezgodnych z branżą, a 18% ze stanowiskiem. Jeśli wyślesz „spersonalizowaną” przez AI wiadomość do kogoś, kto od dwóch lat nie pracuje w danej firmie albo zajmuje się czymś zupełnie innym, żadna technologia Cię nie uratuje. To jest moment, w którym „automat” przegrywa z ręczną weryfikacją. Choć nie jest prawdą, że z takiej sytuacji nie można wybrnać....&nbsp; &nbsp; 3. Pułapka „Gorącego Leada” Wiele firm chce używać AI tylko po to, by łowić „hot leady” – tych 2% rynku gotowych do zakupu tu i teraz. Problem w tym, że boty nie potrafią budować relacji w pozostałych 98% przypadków. Prawdziwy test Turinga przechodzisz wtedy, gdy potrafisz pociągnąć dyskusję z kimś, kto mówi „nie teraz”, i wrócić do niego za 3 miesiące z czymś sensownym, a nie kolejnym generycznym follow-upem.<h3>&nbsp; Jak sprawić, by klient nie miał pewności, czy piszesz Ty, czy maszyna? &nbsp;</h3><ol><li>Zasada 80/20: Pozwól AI zrobić research i szkielet, ale „mięso” i styl muszą być Twoje. Jeśli coś brzmi zbyt mądrze – skasuj to.</li><li>Lokalność zamiast globalnego bełkotu: Nic tak nie uwiarygadnia handlowca jak znajomość lokalnego rynku, specyficznych problemów branży czy nawet posiadanie wspólnych znajomych w portfolio. AI tego nie „poczuje”, dopóki mu tego nie wskażesz.</li><li>Weryfikuj bazy przed wysyłką: Nie ufaj algorytmom na słowo. Czasem 5 godzin ręcznej pracy nad bazą daje lepszy zwrot niż 5 minut klikania w „automatyczny prospecting”.</li><li>Nurturing zamiast spamu: Buduj relację długofalowo. Rekordziści w SalesMeUp konwertowali po dwóch latach regularnego (ale ludzkiego!) kontaktu.</li></ol>&nbsp; &nbsp;AI to świetny asystent, ale fatalny szef sprzedaży. Jeśli chcesz wygrywać w inboxie, przestań brzmieć jak instrukcja obsługi pralki, a zacznij jak partner w biznesie Źródła: https://www.grammarly.com/blog/ai/common-ai-words/https://www.contentbeta.com/blog/list-of-words-overused-by-ai/

Wiadomości generowane przez AI w cold outreach

Lead scoring written in a notebook with charts. Symbolizes evaluation and analytics.

<h2>LLMs and SaaS Tools for Lead Scoring – Global and Polish Approaches </h2>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.<h3>How Scoring Works in SaaS with AI</h3> 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:<ul><li>Behavioral – clicks, time on site, downloads.</li><li>Semantic – tone and language in emails or chats.</li><li>Predictive – matching lead behavior to past customers who ended up buying.</li></ul> 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. <h2>Why Outbound Scoring Only Works When It’s Tailored</h2> 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.<ul><li>For e-commerce: store traffic, payment integrations.</li><li>For SaaS: trials, API requests, LinkedIn engagement.</li><li>For manufacturing: investments, management changes, contracts in trade media.</li></ul> 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. <h2> How to Build AI Scoring the Right Way </h2> AI-driven scoring tempts with automation and precision. But without method, it becomes yet another spreadsheet of dead leads. Here’s the path: <h3>1. Start with your clients</h3>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.<h3>2. Define lookalikes per industry</h3>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.<h3>3. Build and enrich your base</h3>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.<h3>4. Verify with LLMs</h3>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.<h3>5. Iterate quarterly</h3>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.<h3>Not Ready to Start Alone?</h3>Plenty of firms can help you build a real scoring model. Consultancies or Lead generation agencies like <a data-mce-href="https://salesmeup.de" href="https://salesmeup.de" title="Lead Generation B2B Agency" target="_blank" rel="noopener" data-mce-selected="inline-boundary">SalesMeUp</a>.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.&nbsp;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 &nbsp;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 &nbsp;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 &nbsp;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 &nbsp;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 &nbsp;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 😉 &nbsp;

B2B Lead Generation, AI Lead Scoring

<h2>LLMy i SaaS toole do lead scoringu – światowe i polskie podejście</h2> Wzrost popularności generatywnej AI sprawił, że lead scoring – temat przez lata traktowany jak nieco nudna matematyka sprzedażowa – nagle znalazł się w centrum uwagi. Kiedyś scoring opierał się na prostych regułach: otworzył maila, kliknął w link, odwiedził stronę – dostawał punkty. Dziś na scenę wchodzą modele językowe (LLM-y), które potrafią oceniać intencję klienta na podstawie treści jego zapytania, tonu odpowiedzi czy nawet częstotliwości interakcji.Na Zachodzie prym wiodą rozwiązania takie jak 6sense, Clearbit czy ZoomInfo – platformy, które łączą intent data, scoring predykcyjny i automatyzację outreachu. Dzięki integracji z CRMem handlowiec dostaje gotową podpowiedź: ten lead warto zadzwonić dziś, ten drugi może poczekać do przyszłego tygodnia. Modele AI analizują dziesiątki sygnałów jednocześnie – od ruchu na stronie, przez wpisy na LinkedIn, aż po sposób odpowiadania na pierwsze maile. To już nie jest tylko scoring, to predykcja zachowań.W Polsce widać dwie ścieżki. Pierwsza – lokalne startupy SaaS, które rozwijają własne narzędzia pod potrzeby rodzimych firm. Przykład? Sellizer.io z Rzeszowa, które zaczynało od trackingu ofert, a dziś mocno inwestuje w scoring i automatyzację follow-upów. Druga – implementacje na miarę klienta, gdzie zespoły developerskie (często software house’y) korzystają z API OpenAI czy Claude’a, by budować szyte na miarę algorytmy. Takie podejście stosują np. firmy B2B, które mają krótsze listy potencjalnych klientów, ale potrzebują lepszego rozróżnienia: kto tylko „ogląda”, a kto faktycznie planuje zakup.Choć brzmi to futurystycznie, praktyka pokazuje, że największym wyzwaniem nie jest samo wdrożenie LLM-ów, ale jakość danych wejściowych. Garbage in – garbage out. Jeśli baza kontaktów jest nieaktualna, a CRM wypełniany byle jak, nawet najlepszy scoring nie ma sensu. Np. firma Apollo, po skonfliktowaniu się z portalem LinkedIn straciła możliwość pobierania najaktualniejszych danych. W tej chwili nawet co 3ci prospect w ich narzędziu jest już nieaktualny. &nbsp;Tu właśnie polskie firmy mają przewagę: mniejsze skale, krótsze procesy decyzyjne i możliwość szybkiego dostosowania narzędzia do realiów rynku.Efekt? Jeśli wykorzystasz LLM do analizy intencji, a SaaS do egzekucji, Twoi handlowcy spędzą czas na rozmowach z tymi, którzy naprawdę chcą kupić. To proste, ale właśnie w tej prostocie tkwi przewaga. <h3>W jaki sposób prowadzony jest scoring w SaaS z AI</h3> Dziś, dzięki AI i dużym modelom językowym (LLM), scoring staje się znacznie bardziej kontekstowy. SaaS-y nie tylko zliczają interakcje, ale interpretują ich znaczenie. Algorytm potrafi „zrozumieć”, że odpowiedź typu „jesteśmy zainteresowani w przyszłym kwartale” to wciąż sygnał sprzedażowy, ale innego priorytetu niż krótkie „proszę o ofertę teraz”. Co więcej, analizuje nie tylko treść, ale też tempo reakcji, porę dnia, a nawet to, czy lead angażuje się w inne kanały – np. ogląda webinary albo komentuje posty firmowe na LinkedInie.W praktyce scoring AI działa na kilku warstwach:<ul><li>Warstwa behawioralna – analiza zachowań: kliknięcia, czas spędzony na stronie, pobrane materiały.</li><li>Warstwa semantyczna – interpretacja języka, w tym tonu wypowiedzi w mailach czy czatach.</li><li>Warstwa predykcyjna – porównanie leadów z historią wcześniejszych klientów, aby ocenić, które zachowania najczęściej kończyły się sprzedażą.</li></ul> Weźmy przykład: SaaS sprzedający narzędzie do automatyzacji marketingu. Zwykły scoring da punkty za pobranie e-booka. Ale scoring AI „zrozumie”, że lead, który pobrał e-book o RODO i zadał pytanie na czacie o integrację z CRM-em, ma znacznie większe prawdopodobieństwo zakupu niż ktoś, kto pobrał trzy różne materiały, ale nie wszedł w dialog.W Polsce pierwsze rozwiązania tego typu wdrażają startupy skupione na B2B, np. platformy analizujące rozmowy handlowców z klientami. Na świecie liderzy – jak 6sense czy HubSpot z funkcjami AI – idą jeszcze dalej, łącząc scoring z real-time routingiem leadów: handlowiec dostaje notyfikację „rozmawiaj z nim teraz, szansa na deal wynosi 72%” *.To zmienia grę. Zamiast zgadywać, handlowcy działają w oparciu o sygnały, które AI przetwarza szybciej niż jakikolwiek człowiek. I choć technologia nie zastąpi rozmowy, to precyzyjniej podpowie, kiedy i z kim warto ją zacząć.<h2>Scoring działa tylko, gdy jest ściśle dopasowany do Ciebie</h2> To tyle jeżeli chodzi o scoring inboundowy, a co ze scoringiem dla outboundu? Scoring leadów w outboundzie brzmi na papierze prosto. Wrzucasz bazę kontaktów, system nadaje punkty, handlowcy biorą „najgorętsze” leady i puszczają maile w ruch. Ale w praktyce… to nie działa w taki sposób. Dlaczego? Bo outbound to nie masówka. To precyzja. Jeśli scoring nie jest dopasowany do Twojego modelu biznesowego, do Twoich klientów i do Twojego stylu sprzedaży – stanie się bezużyteczną tabelką z liczbami.Weźmy typowy rynkowy przykład. Firma software’owa z Warszawy prowadzi kampanię do producentów maszyn przemysłowych. Standardowy scoring daje wysokie punkty każdemu, o kim było wiadomo, że jego przychody urosły od ostatniego roku rozliczeniowego. &nbsp;Tyle że to żaden wyznacznik, jeżeli chodzi o możliwość inwestycji w oprogramowanie. Zwiększony przychód to zaledwie warunek, który pozwala nam określić, że klienta będzie stać na usługi software house’u. Efekt? Handlowcy próbują umówić spotkania w każdej firmie z odpowiednim przychodem, zamiast zająć się opracowaniem metody na określenie które z ich prospektów mają realne problemy do rozwiązania.Dopiero gdy zmienią scoring – uwzględniając branżowe sygnały, np. udział w targach Hannover Messe czy ogłoszenia o nowych liniach produkcyjnych – nagle leady zaczną być jakościowo lepsze. Handlowcy przestaną marnować czas, a sprzedaż ruszy.To jest właśnie sedno outboundowego scoringu. Liczy się nie ilość punktów, ale kryteria, które naprawdę pasują do Twojej rzeczywistości. Dla e-commerce będzie to ruch w sklepie i integracje płatności. Dla SaaS-u – triale, zapytania o API i zaangażowanie na LinkedIn. Dla produkcji – inwestycje w nowe linie, zmiany w zarządzie, kontrakty ogłaszane w mediach branżowych.Możesz korzystać z gotowych SaaS-ów do scoringu, ale jeśli nie zadasz sobie trudu, by dopasować model do siebie, wyniki będą przypominały horoskop – czasem się sprawdzą, częściej nie. A w sprzedaży nie chodzi o przypadek, tylko o proces.Dlatego jeśli chcesz, żeby outboundowy scoring faktycznie działał, nie kopiuj cudzych regułek. Skrój go pod własny biznes. To będzie Twoja przewaga.<h2> Jak stworzyć poprawny scoring z AI</h2> Scoring wspierany przez AI kusi obietnicą automatyzacji i precyzji. Ale jeśli nie podejdziesz do tego procesu metodycznie, skończysz z kolejną tabelą pełną „leadów”, które nie prowadzą do niczego. Oto jak zbudować model scoringowy, który naprawdę działa i nie marnuje Twojego czasu ani budżetu.<h3>1. Zacznij od siebie – pełna lista obsłużonych klientów</h3> &nbsp;Pierwszy krok to audyt. Zrób listę klientów, z którymi faktycznie pracowałeś w ostatnich latach. Nie tylko tych „największych”, ale wszystkich. Potem zastosuj zasadę Pareto – poszukaj tych 20%, którzy wygenerowali 80% przychodów. Reszta? Jeśli trafili do Ciebie przypadkiem, nie inwestuj w ich „klony”. To one będą zjadały Twój budżet i czas, a zwrot będzie marginalny. zasada pareto - nie poszukuj wszystkich, szukaj 20% najlepszych, jeżeli reszta trafiła do Ciebie przypadkowo to nie warto inwestować w pozostałe 80% ponieważ, zje to 80% Twoich wydatków i czasu a przyniesie tylko 20% przychodów, o ile w ogóle się zwróci.<h3>2. Definiuj „lookalike” per branża</h3> &nbsp;Stwórz zestaw kryteriów, które musi spełnić prospect, aby można go było uznać za podobnego do Twojego najlepszego klienta. Może to być: liczba programistów, stosowanie konkretnej technologii na stronie sklepu internetowego, Google jako operator skrzynki pocztowej. W e-commerce – obroty roczne, liczba produktów w sklepie, integracje płatności. Ważne, by były to warunki mierzalne i łatwe do zweryfikowania. Nie mierz takich rzeczy jak np. “poziom cyfryzacji przedsiębiorstwa”<h3>3. Zbuduj bazę i uzupełnij dane</h3> &nbsp;Narzędzia takie jak MeetAlfred pomogą Ci zebrać leady i dane kontaktowe, na bieżąco z LinkedIn - niedoskonałej ale cały czas najbardziej aktualnej bazy danych kontaktów dostępnej publicznie. Clay pozwoli na data enrichment – zweryfikowaniu które dane są aktualne, oraz dodaniu danych z innych źródeł, takich jak np. Crunchbase. Pamiętaj, że scoring to nie tylko liczby, ale też kontekst! AI najlepiej działa, gdy dostaje pełny obraz prospecta.<h3>4. Zweryfikuj kryteria w LLM</h3>&nbsp;Wykorzystaj GPT czy Gemini, aby sprawdzić występowanie 2–3 warunków na prospecta. Przykład: czy firma ma dobre opinie jako pracodawca, czy działa na rynku DACH. AI szybko przeskanuje źródła, ale tu kluczowe ostrzeżenie: nie ufaj wynikom w 100%. Przyjmij margines błędu 20–35%. To naturalny etap – modele stają się coraz lepsze, ale do pełnej niezawodności potrzebują jeszcze co najmniej 3–5 lat.<h3>5. Iteruj i koryguj</h3> &nbsp;Scoring nie jest jednorazowym ćwiczeniem. Dane się starzeją, a branże zmieniają. Dlatego co kwartał aktualizuj listę warunków i sprawdzaj, czy Twoje 20% najlepszych klientów wciąż wygląda tak samo, jak rok temu.To prosta zasada: lepiej mieć 50 dobrze wybranych prospectów niż 500 przypadkowych. Outbound działa tylko wtedy, gdy jest dopasowany do Ciebie – AI jest tu pomocnikiem, ale nie zastąpi Twojej strategii. <h3>Nie czujesz się na siłach żeby zacząć samodzielnie?</h3> Wiele firm może Ci pomóc zbudować lead scoring z prawdziwego zdarzenia. Możesz skorzystać zarówno ze spółek konsultingowych, jak i agencji generujących leady&nbsp; (<a data-mce-href="https://salesmeup.de" href="https://salesmeup.de" title="agencja generująca leady b2b" target="_blank" data-mce-selected="inline-boundary">SalesMeUp</a>). Jeżeli nie wiesz jak się zabrać za zagadnienia albo chcesz poznać naszą opinię o Twoim scoringu, prześlij nam twoją formatkę i otrzymaj nasz komentarz. Nie nie za darmo, musisz w zamian podać swój służbowy adres e-mail ;) *https://knowledge.hubspot.com/properties/determine-likelihood-to-close-with-predictive-lead-scoring

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