Intent-basiertes Lead-Gen-System mit n8n: Warme Leads durch Job-Posting-Signale automatisch finden und anschreiben

Haupterkenntnis: Wer nicht nach Jobtiteln filtert, sondern nach aktiven Hiring-Signalen (z.B. SDR-Stellenausschreibungen), erreicht Prospects mit echtem Schmerz – und baut darauf eine vollautomatische Outreach-Pipeline in n8n.

Creator: Clarence Nap
Branche: Marketing
Bereich: Automation

Tags: lead-generation, n8n, cold-email, intent-data, linkedin-scraping, outreach-automation, email-personalization, sdr

Kurzbeschreibung

Clarence Nap zeigt seinen vollautomatisierten n8n-Workflow, mit dem er monatlich über $35.000 Profit erzielt – indem er LinkedIn-Jobpostings als Intent-Signal nutzt, Leads anreichert und personalisierte Cold Emails versendet.

Langbeschreibung

Das Video erklärt Schritt für Schritt einen fünfstufigen n8n-Workflow: (1) Scrapen von LinkedIn-Jobpostings nach bestimmten Rollen (z.B. SDRs) via Apify, (2) Finden von E-Mail-Adressen per EmailFinder-API auf Basis der Unternehmens-Domain, (3) Lead-Anreicherung durch Scrapen der Unternehmenswebsite und LLM-Analyse, (4) Normalisierung des Firmennamens und Extraktion des Vornamens aus der E-Mail-Adresse, (5) Befüllen eines vorbereiteten E-Mail-Templates durch ein LLM. Die zentrale These: Intent-basiertes Targeting (Hiring-Signale) schlägt demografisches Targeting (Jobtitel, Unternehmensgröße) deutlich in Relevanz und Conversion.

Stichpunkte

• Intent-Signal statt Demografie: Firmen mit aktiven SDR-Stellenausschreibungen sind warme Leads für Sales-Agenturen
• 5-stufiger n8n-Workflow: Scrapen → Email finden → Lead anreichern → Namen extrahieren → Email versenden
• Apify als Scraper-Marktplatz für LinkedIn Jobs, Apollo, G2, Crunchbase u.v.m.
• Polling-Loop in n8n nötig, weil Apify-Scraper nicht synchron antwortet (Daten erst nach mehreren Sekunden bereit)
• EmailFinder API liefert alle E-Mail-Adressen einer Domain (bis zu 1.000)
• LLM (GPT-4o mini) analysiert Unternehmens-Homepage auf Testimonials, Wachstumsstories, Erfolge
• LLM bereinigt Firmennamen (entfernt „Ltd.”, „Inc.”, „Limited”) für natürlichere Ansprache
• LLM extrahiert Vornamen aus E-Mail-Adresse für Personalisierung
• Festes E-Mail-Template mit LLM-befüllten Platzhaltern statt vollständig generierter E-Mails – ermöglicht A/B-Testing
• Gleiche Logik auf andere Rollen übertragbar: SEO-Specialist-Hiring → Website-Performance-Problem; CTO-Hiring → Tech-Infrastrukturproblem

Zitate

“Instead of casting a wide net, focus on companies that are already showing signs of a specific need.” “If you constantly let the LLM write the entire email, you can be sure that each and every email is going to be completely different from the one before – so there aren’t really any metrics to test.” “There’s no human that can compete with a flow like this.”

Action Items

1. Apify-Account anlegen und LinkedIn Jobs Scraper (oder Apollo Scraper) für die eigene Zielbranche identifizieren.
2. In n8n: HTTP-Node (POST) zum Triggern des Apify-Scrapers einrichten + Polling-Loop (Code-Node → IF-Node → Wait-Node) für asynchrone Datenabholung bauen.
3. EmailFinder-API-Key besorgen und Node konfigurieren, der auf Basis der gescrapten Domain alle verknüpften E-Mail-Adressen zurückgibt.
4. LinkedIn-Firmenprofil-HTML scrapen, mit Code-Node bereinigen, GPT-4o mini per Prompt die Unternehmens-URL extrahieren lassen.
5. Firmen-Homepage scrapen, HTML bereinigen, LLM-Prompt auf Testimonials / Wachstum / Erfolge ansetzen.
6. Zwei separate LLM-Calls: (a) Firmenname normalisieren (Ltd./Inc. entfernen), (b) Vorname aus E-Mail-Adresse extrahieren.
7. Eigenes Cold-Email-Template mit definierten Platzhaltern ({{first_name}}, {{company_name}}, {{personalization}}) schreiben – LLM nur die Lücken füllen lassen.
8. Intent-Signal auf die eigene Nische anpassen: Welche Stellenausschreibung signalisiert den Schmerz, den du löst?

Full Transcript

Hi, my name is Clarence. I run a lead generation agency that brings in over $35,000inprofiteachmonth,butther{e}^{\prime }sevenakicker.Ido{n}^{\prime }tspendhoursgrindingawayatmydesktomakethishappen.Instead,I^{\prime }vebuiltanautomatedsystemusingN8Nthatfindswarmleads,personalizedoutreach,andbooksclientsformeatscale.Intoda{y}^{\prime }svideo,I^{\prime }mgoingtopullbackthecurtainandshowyouexactlyhowthesystemworksandhowyoucanuseittotransformyourbusinessto.Letmestartwithaquestion.Haveyoueverfeltlikefindingqualityleadsisoneofthehardestpartsofgrowingyourbusiness?Well,yo{u}^{\prime }redefinitelynotalone.Like,manybusinesseswastehours,evenweekschasingcoldprospectswhowereneverreallyinterestedintheirservicestobeginwith.Traditionalmethodslikeblastinggenericemailstomassivelistsjustdo{n}^{\prime }tworkanymore.I^{\prime }mnotsureifthe{y}^{\prime }veeverworked.However,the{y}^{\prime }retime-consuming,inefficient,andle{t}^{\prime }sfaceit,theyoftenleadtosmallresults.I^{\prime }vebeenthere,butthegoodnewsisther{e}^{\prime }samuchsmarterwaytodothings.What{I}^{\prime }venoticedisthatalotofpeopleapproachleadgenerationallwrong.The{y}^{\prime }lltargetverygenericandthe{y}^{\prime }lltargetprospectsbasedonservicelevelmetricslikejobtitlesorcompanysize.Forexample,theymightdecidetoreachouttoallmarketingdirectorsatcompanieswithover500employees.Youmightbewondering,"Well,wha{t}^{\prime }swrongwiththat?"Well,theproblemisthatthisapproachdoes{n}^{\prime }ttakeintoaccountwhetherthesebusinessesactuallyneedyourservices.Meaning,youendupspendingtimeandresourcesonpeoplewhodo{n}^{\prime }tevenknowtheyhaveaproblemorworseare{n}^{\prime }tinterestedinsolvingit.So,ofcourse,yo{u}^{\prime }rewondering,"Well,howdoIfixthis?"Or,"Youfixthisbyflippingthescript."So,insteadofawhitenet,focusoncompaniesthatarealreadyshowingsignsofaspecificneed.I^{\prime }llshowyouwhatImean.Ifyourunasalesagency,justlikeIdo,do{n}^{\prime }tjusttargetdecisionmakers,buttargetbusinessesthatareactivelyhiringforsalesdevelopmentrepresentativeslikeSDRs,becauseyouknowthe{y}^{\prime }reonthelookouttoexpandtheirsalesteam.Whydothis?Well,becausecompaniesinvestingintheserolesareclearlyfocusedonimprovingtheirsalesprocess.The{y}^{\prime }remuchmorelikelytoseevalueinwhatyouoffer.I{t}^{\prime }sallaboutidentifyingpainpoints.Forinstance,acompanyhiringasanSEOspecialistislikelystrugglingwithwebsiteperformance.ThesamegoesforabusinesslookingforanewCTO.Theymayhavetechnicalinfrastructurechallenges,whichiftha{t}^{\prime }swhatyousolve,tha{t}^{\prime }saperfecttimetostepin.ByfocusingonthesesignalslikejobpostingsonLinkedInorindeed,youcantailoryouroutreachtoaddresstheirspecificneeds.Thismakesyourmessagenotjustrelevant,butitmakesitirresistible.Okay,youguysprobablyhavehadenoughofmerambling.Le{t}^{\prime }sdiveintotheworkflow.Whatyoucanseeonscreenisaworkflowthat{I}^{\prime }vepersonallyusedtothisdaytogetmequitealotofclients.BecauseasIsaid,I^{\prime }mtargetingpainpoints.I^{\prime }mtargetingpeoplewhoareactivelylookingtomakeachangeoractivelylookingtohirepeopletodothethingsthatIcandoforthem.Sotha{t}^{\prime }stheperfecttimetostepin.Theworkflowisquitelong.Tha{t}^{\prime }sbecausetherearesometrickyparts.Tha{t}^{\prime }swhy{I}^{\prime }vetriedtobreakitdownintofivedifferentsegments.First,w{e}^{\prime }llscrapecompanieswhoareactivelyhiringforcertainroles.Well,inthiscase,I^{\prime }musingSDRasanexample.Thenw{e}^{\prime }llhavetofindtheiremailaddressbecausethatis{n}^{\prime }tpartofthejobapplication.W{e}^{\prime }llhavetoenrichtheleadanduseARtopersonalizeandthenlastlywriteanemailandsendtheemail.Le{t}^{\prime }sstartatthebeginning.WescrapeforcompanieshiringforSDRs.WescrapeLinkedInbecauseLinkedInisoneoftheplatformsjustlikeindeedwheremostcompaniespostjobapplicationsiftheyhavesome.Butasyoucansee,thereare3000UnitedStateslookingforanSDR.Soplentyofpeopleforustoreachoutto.Theproblemwithdoingthismenulistisgoingtobeverytimeconsuming.Tha{t}^{\prime }swhywemadethispartoftheautomationtoscrapethelistofcompaniesandjobsyoujustsaw.Iuseascraper,whichcanbefoundonApeFi.Forthosewhoare{n}^{\prime }tfamiliar,ApeFiisagiantmarketplaceforscrapers.Yo{u}^{\prime }veseenavideoofmeusingitforanApolloscraper,butyoucanalsohaveanInstagramscraper,Facebookpostscraper,stereoscraper,G2scraper,crunchbasescraper.Younameit.I{t}^{\prime }sprobablyonhere.Sohowtogetthescrapertoworkisactuallyquitesimple.Itshouldhaveonlytakentwonotes,whichisthisHTTPnote,whichissettopostandthisone,whichissettoget.ButtherearesomeotherthingsIhadtoaddtothisflowtomakeitworkwithoutanymanualinput.I^{\prime }llgettothatinasecond.First,letmeshowyouhowyoucanactuallytriggerthescraper.Totriggerthescraper,weuseanAPIforthosewhoare{n}^{\prime }tfamiliar.AnAPIissimplylikeabridgebetweentwosystems,whichletsonesystem,whichisourautomationtalkwithanotherone,whichisthescraper.Forthat,allweneedisaURL.IfoundyoucanfindthisURLontheAPIdocumentation.I{t}^{\prime }sverystraightforward,soIwo{n}^{\prime }tgooveritinanydetail.Athingtonoteisthatwhatyouseehere,I^{\prime }llhighlightitforyou,istheIDofthescraper.SoeachscraperhastheirownID,ofcourse,andthisjusttellstheAPI,whichactorithastocommunicatewith.Soonceyo{u}^{\prime }vesentthetriggertothescrapertostartrunning,allwehavetodoisretrievethedata,retrievethedataset.Forthat,weuseavariable,whichisalsoanID.Youcanseeit,i{t}^{\prime }sthisnumber.I{t}^{\prime }savariablebecauseeachrunofthescraperyoudo,thedatasetIDisgoingtobedifferent.Soitneedstobeavariable.Youcanseethatonlyonthefirstandsecondrun,therewasnoinformationtobeoutput,meaningdatawas{n}^{\prime }treadyyet.Onlythethirdrun,thereactuallywassome.SooncethisHTTPnoteisdone,whichliterallytakesasplitsecondbecauseallithastodoissendatriggertoaURL.Thisnoteisimmediatelyactivatedandistoldtoretrievethedata,butnineoratentimesthedatais{n}^{\prime }tgoingtobereadyinasplitsecondbecausethescraperneedstostartrunningandneedstofinish,whichcantakeawhiledependingonwhatyo{u}^{\prime }reaskingfromit.Tha{t}^{\prime }swhythesefournoteshereareinplace.SoasIjustshowedinsideofthisHTTPnote,youcouldseethatthefirsttworunsweredots.Soiftha{t}^{\prime }sthecase,thiscodenotejustchecksifther{e}^{\prime }sanoutput.Ifthereis{n}^{\prime }tone,i{t}^{\prime }llsendoutnotready.Asyoucansee,thestatusisnotready.Thenitgoestothisifnote,whichchecksifnotreadyispresent.Well,ifitis{n}^{\prime }tpresent,i{t}^{\prime }stoldtowaitandthenreruntheflowuntilther{e}^{\prime }sactuallysomedatatoberetrievedandwecancontinueon.Inthiscase,youcanseeittooktworoundsorthreetriesactuallybeforethedatawasready.SonowyouknowwhyeventhoughtriggeringanAPIandretrievingthedataisverystraightforward,thereweresomenecessaryadditionstothispartoftheflow,whichalsoconcludesthefirstsection.Do{n}^{\prime }tworry,theotheroneswo{n}^{\prime }ttakeaslong.Nowweneedtofindtheemailaddress.WefindtheemailaddressbyusingsomeveryusefulsoftwarecalledEnemailFinder.EnemailFinderisaccessiblealsothroughanAPI,whichiswhywecanlinkittoourworkflow.WhatEnemailFinderdoesisbasedonadomain,itcanfindemailaddresses,whicharelinkedtothatdomain.Sosincewedo{n}^{\prime }thavetheemailaddresstothepersonwhopostedthejobapplicationonLinkedIn,w{e}^{\prime }llhavetojustdoadomainsearchandfindallemailaddresseslinkedtothatcompany.Letmequicklyshowyouwhatweactuallydidretrievebecausetha{t}^{\prime }swhatIforgotjustnow.Sohereyoucanseethedatasetandwhatitcontained.I^{\prime }llshowyouinaschema.Soi{t}^{\prime }sclearerforeveryone.SowehavethepostIDoftheapplicationwhenitwaspublished.Inthiscase,thesalaryis{n}^{\prime }tpresent.Weseethecompanyname.WealsohavethecompanyURL,whichis{n}^{\prime }ttheURLtothecompanywebsite,butthisistheURLtothecompanyLinkedInpage.Well,thejobwaspostedonLinkedIn.Andthenlastlyhere,itsaysposterfullname.Insomecases,thereisanameactuallyhere.Butinmostcases,Iwouldsay9outof10timesthereis{n}^{\prime }taposterfullname.Ithinktha{t}^{\prime }sduetoprivacyreasonsorjustbecauseitis{n}^{\prime }tlikerequired.Andtheposterfullname,notbeingpresentjustmakesourjobalittlebitmoredifficult,buti{t}^{\prime }sstilldoable.And{I}^{\prime }llshowyouhow.SonowyouactuallyknowwhatwasretrievedbythescraperfromtheLinkedInjobpost,whichbringsusontofindingtheemailaddress.Well,tofindtheemailaddressandtotriggertheAPI,asIjustshowed,weneedadomain.Wehave{n}^{\prime }tfoundthedomainyetbecauseitwas{n}^{\prime }tpartoftheretrievedinformation.SowehavetoscrapethecompanyLinkedInprofiletofindadomain.HowwedothatisbyusingalsoHTTPnote.ThisHTTPnotegetsthecompanyLinkedInprofileHTML.Soi{t}^{\prime }sjustItookthisfromtheleftandpasteditoverthere.AnditretrievedustheHTML.Asalways,wewanttocleanthisbecausethiscontainsalotofcodeandalotofunnecessarystuffwhichjustincreasestheamountofinputtokensintotheLM,whichissomethingwedo{n}^{\prime }twant.Tha{t}^{\prime }swhythiscodeispresent.AllthiscodedoesisfilteroutHTML.Asyoucanseeontheleftside,itwasverydirtyasIliketocallitandontheleftontherightside,allthatremainsisplaintextreadableforusandalsoeasierfortheLLM.Thensomewhereonthepage,sosomewherewithinthislineoftext,thereis{n}^{\prime }treallysomewhereinhere.ThereisthedomainnameandURLofthecompany.Sowhat{I}^{\prime }vedoneis{I}^{\prime }vewrittenaprompttokindofanalyzethistextandbasedonthecompanyname,findmethemostlikelyURL.ThepromptisverystraightforwardbasedontheprovidedLinkedIncontent,identifythecompanynameandlocatethewebsiteURL,onlyoutputtheURL.Asinputw{e}^{\prime }vegivenittotheplaintextfromtheleftsideanddragitinonceagain.AndyoucanseeontherightsidetheoutputsidethatithasfoundforustheURLtothecompanyhomepage,whichwecannotonlyusetoenrichbutweactuallyneedtofindemailaddresseslinkedtothisdomain.SoasImentionedbefore,insomecases,theLinkedInapplicationispresent.Inmostcases,itis{n}^{\prime }t.Ifitispresent,itjustmakesyourlifeabiteasierbecauseyoucandoamorespecificsearchusinganemailfinder.Ifitis{n}^{\prime }tpresent,allwehavetoworkwithisthedomain.Sowhatwedoiswetakethedomain,whichwasht.com,whichwastheresultfromthelargelanguagemodel.Youcanseeoverhere,inputitanditreturnsusalloftheemailaddresseswhicharelinkedtoorareusingthisdomain.Youcanseetheentirelist.Ther{e}^{\prime }satotalofathousand.Nowthatw{e}^{\prime }vefoundalloftheseemailaddressesthatkindofconcludesthesecondpartandalltha{t}^{\prime }sleftistoenrichthelead.Sogivethelargelanguagemodelssomeinformationaboutthecompany,writealittlepersonalizationandcleanupthecompanynameifnecessaryandthenlastlysendanemailtotheperson.Howdoweenrichthelead?Well,weenrichtheleadwiththeonlyinformationwehaveatthispoint,whichistheirwebsiteURL.SowefetchalloftheHTMLthistimeinsteadoffromtheLinkedInProvapage.Wedothisfromthecompanywebsitefromtheirhomepage.Thisisthesamecodeas{I}^{\prime }veinputitoverhere.ItjustfilterstheHTMLandreturnsplaintextasyoucansee.Sothisiswha{t}^{\prime }sontheirhomepage.Foranalyzingthehomepage,IjustusechatGPT40mini.Ithinki{t}^{\prime }smorethancapableandi{t}^{\prime }sveryinexpensive.Peoplehavetoldmeaboutdeepseat,butIhave{n}^{\prime }thadthetimetoexperimentwithit.ThisisaverygenericpromptIwrotewiththeassistanceofchatGPT,butyoucanusethisasafoundationbecauseitworksperfectlyfine.Andthenontothequestionsthat{I}^{\prime }maskingittoanswer.So{I}^{\prime }maskingittoextractandorganizethefollowinginformation,testimonials,recentgrowthandsuccessstoriesandontherightsideyoucanseewhattheoutputis.Well,apparentlytherewerenotestimonialsonthehomepage.Therewassomerecentgrowthandthereweresomesuccessstories,whichismorethanenoughforustowriteapersonalization,whichwecanuseastheemailopener.Andthenonceagain,asinput,as{I}^{\prime }veprobablyalreadymentioned,Itakeaplaintextfromtheleft.Soyoumightbewondering,whyareyoucleaningthecompanyname?Andwhatdoesthatmean?Well,insomecases,thejobapplicationpostonLinkedIncontainsthecompanyname,whichislikefollowedbylimitedorbyink,forexample,howthe{y}^{\prime }reregistered.Thethingisyouneverwanttousethatinacalledemailbecauseitdoes{n}^{\prime }tsoundhumanatall.Iwouldneverreferencetoacompanysayingdeslaink,forexample,tha{t}^{\prime }swhyweusethis.Whenthiscasehatchwasalreadycleaned,butifthatis{n}^{\prime }tthecase,itwouldjusttakeitout.Sole{t}^{\prime }ssayLLL,shewaspresentorinkwaspresentorlimited,asIjustsaid,itwouldjustcleanthecompanynamesowecanuseacleanversionofitintheemail.Thenw{e}^{\prime }reontothesecondtolastlargelanguagemodel.Whydo{n}^{\prime }tweneedtogetthefirstname?Well,ifyourememberbacktowherewefoundtheemailaddresses,theemailaddressescontainthefirstname,alastname,andthemainname,wewanttogivethelargelanguagemodeltheeasiestjobpossibletomakeitoutputasconsistentaspossible.Tha{t}^{\prime }swhyweneedthisextrasteptotakewhatwejustgot.Sowetaketheentireemailaddress,whichisonceagaininputfromtheleftside,whichthisvariableactuallytranslatesintothis,andwejustaskittoreturnthefirstname.Ifi{t}^{\prime }spresent,ifitis{n}^{\prime }t,leavethefieldempty,becauseyo{u}^{\prime }drathersayhitosomeone,thensayhiasdumbatwhatever,becausethenyoualreadyknowthe{y}^{\prime }renotgoingtorespondtoyouremail.Andthenyoucanseetheoutputissteward,becausehisemailaddresswassteward.mechiver,sothiswasaveryeasycleanup,whichbringsusontowritingtheemail.Whilewritingtheemailmightbegivingitabitmorecreditthanitdeserves,becauseIalwaysjusttelltheLLMtofillinapre-writtentemplate.WhydoIchoosetopre-writeanemailtemplateinsteadofletchatGPTwriteeverything?Tha{t}^{\prime }sbecauseinmyopinion,chatGPTorclothorwhateveryouuseis{n}^{\prime }tgoodenoughtowriteanentireemail.Tha{t}^{\prime }saprettybigpartofit.Theotherpartofitisthatifyo{u}^{\prime }redoingcoldoutbound,ifyo{u}^{\prime }redoingcoldemailing,orifyo{u}^{\prime }redoinganyoutreachasabusiness,youconstantlywanttoimprove.Andhowdoyouimprove?Youimprovebyswitchingupvariables.Well,ifyouconstantlylettheLLMwritetheentireemail,youcanbesurethateachandeveryemailisgoingtobecompletelydifferentfromtheonebefore.Sothereare{n}^{\prime }treallyanymetricstotest,becausethereare{n}^{\prime }tanyconsistenciesbetweentheemail,soyouca{n}^{\prime }treallytestyouroffer,youca{n}^{\prime }ttestthelayout,becauseyouhavenosayoverit,becausetheLLMisfillingthatinforyou.Tha{t}^{\prime }swhyIwanttotakeawaythatpoweroftheLLM,keepittoourselvesandjustletitwrite,letitfillinthevariablesorletitfillintheblanks,whichitisreallygoodat.SobeforeIgoovertheconclusionofthisentireworkflow,letmeshowyouwhattheinputwas,sotheinputwasthefirstname,thecompanyname,andtheresult.Theresultissimplywhatweextractedfromthehomepage.Andhereistheprompt,asIalreadysaid,Iwantedtofillinatemplate,usingplaceholderforpersonalization,andIjustgaveitsomenotesonthetone,whichbringsustowhatyouguyshaveopenwaitingfor,probablyistheresultofallofourhardwork,whichisafilledinemailtemplate,and{I}^{\prime }lljustreadittoyouguysbriefly.HeyStuart,whichisthefirstnameweretrieved.AfriendmentionedHatchrecently,and{I}^{\prime }vebeenmeetingtoreachout,soundlikeyo{u}^{\prime }remakingbigmoves.Andthenhereitisfollowedbythepersonalization,congratsontheinitiativesrelatedtodecarbonizationandengineeringsolutionsforthebetterlifecycle,bytheway,whichisprobablysomethingHatchisinvolvedwith.Well,itmustbebecauseitwasontheirwebsite.Soyo{u}^{\prime }vegotsomeSDRrollsopenandfigured{I}^{\prime }ddropyouanod.Thisisthebestpartoftheemailbecausetheyactuallyhavesomejobsopenbecausew{e}^{\prime }veverifiedthem.Soweknowthe{y}^{\prime }relookingforthis.Soweknowthisemailisrelevanttothem.Andthenwejusttalksomethingaboutourselves.Irunanagencyspecializingincoldoutbound,andwecanhandleoutreachat50timessincethebaseofit,theQSDR,whichistrue.Ther{e}^{\prime }snohuman.Wecancompetewithaflowlikethis.Furthermore,withresponseratesconsistentlyoutperformingindustryaverages,w{e}^{\prime }vehelpedcompanieslikeBoltandLiverpoolcreateover8millioninpipeline,w{e}^{\prime }dlovetoexploreifwecoulddothesameforyou.Freeforaquickchatthisweek,cheers,Clarence.Thisisoneofthebestemailtemplates{I}^{\prime }veeverwritten,andthisisonewhichworkslikeacharm.Iwouldhighlysuggesttojustcopythisone,useitforyourself,andseethepositiverepliesflowin.Andletmetellyouthatusingthissystemcompletelychangedthegameforme.Usingthisexactsystem,Igrewmyagencyfrom$0 a month to $35,000 in monthly profits within just six months. And the best part of it all is because AI does all the heavy lifting. I now work just three to four hours a day because this system handles everything. It resulted in me not having to worry about where my next lead or my next payment is coming from because I have a consistently lead with this constantly updating, with ready to buy Clarence who are interested in my services. So if you’re tired of outdated methods that waste your time and bring in little to no results, it is time to take action because there are possibilities. You can start by building this automated workflow today and unlock a whole new level of efficiency for your business. If anything was unclear or if you got any questions, drop them in the comments below. I’d love to help you out, dive deeper into any part of the system you’re curious about. And hey, if you found this video helpful, don’t forget to like, subscribe, and turn on the notification bell so you never miss out on actionable strategies to grow your business with automation ever again. I want to thank you guys very much for watching and I’ll see you guys on the next video. Thank you. Bye.

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