[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"\u002Fpodcast\u002Fspezialfolge-159-predictive-analytics-mit-fabian-hadiji":3},{"podcast":4,"pickOfTheDayCount":94,"speakerCount":95,"relatedPodcasts":96,"transcript":145},{"id":5,"published_on":6,"type":7,"number":8,"title":9,"slug":10,"description":11,"transcript":12,"audio_url":13,"apple_url":12,"google_url":12,"spotify_url":12,"youtube_url":12,"cover_image":14,"banner_image":27,"speakers":38,"members":69,"picks_of_the_day":85,"tags":86,"tagsPrepared":91,"speakersPrepared":92},"ce5c3451-1816-45d2-93f5-f054734e8675","2024-08-30T11:07:00.000Z","other","159","Predictive Analytics mit Fabian Hadiji","spezialfolge-159-predictive-analytics-mit-fabian-hadiji","\u003Cp>Herzlich willkommen zu unseren Spezialfolgen zum Thema Business Intelligence, kurz BI. In diesen drei Folgen erhältst du Grundlagen und erlangst essenzielle Kenntnisse zum Thema BI, unterstützt durch unser aktuell dreiköpfiges Team von Lotum.\u003C\u002Fp>\n\u003Cp dir=\"ltr\">Im (vorerst) letzten Teil unserer Business-Intelligence-Serie sprechen wir Fabian Hadiji, Head of Business Intelligence bei Lotum, über das Thema Predictive Analytics.\u003C\u002Fp>\n\u003Cp dir=\"ltr\">Im Gespräch mit Dennis erfahren wir, wie Lotum Daten nutzt, um nicht nur historisches Verhalten von Spieler:innen auszuwerten, sondern auch zukünftiges Verhalten vorherzusagen. Predictive Analytics kann helfen, Kaufverhalten zu verstehen und dem Ausstieg von Nutzer:innen entgegenzuwirken.\u003C\u002Fp>\n\u003Cp dir=\"ltr\">Außerdem sprechen Dennis und Fabian über die Anwendung künstlicher Intelligenz – dem Promotionsfeld von Fabian – im Business-Intelligence-Bereich. Sie erklären, für welche Anwendungsfälle KI besonders gut geeignet ist und für welche (noch) nicht.\u003C\u002Fp>\n\u003Cp dir=\"ltr\">Du bist neugierig auf mehr Insights zum Thema Business Intelligence? Unter \u003Ca href=\"https:\u002F\u002Fprogrammier.bar\">https:\u002F\u002Fprogrammier.bar\u003C\u002Fa> erwarten dich weitere Episoden.\u003C\u002Fp>\n\u003Cp dir=\"ltr\">Gemeinsam mit Wessel, Tobias und Fabian die Datenmengen von Lotums Spielen auszuwerten, klingt für dich nach einer spannenden Mission? Dann hör dir auch die Spezialfolge zu Lotums Stellenausschreibung im Analytics Engineering an und erfahre mehr auf \u003Ca href=\"http:\u002F\u002Flotum.com\u002Fbi\">lotum.com\u002Fbi\u003C\u002Fa>\u003C\u002Fp>",null,"https:\u002F\u002Fwww.buzzsprout.com\u002F176239\u002F15665117-spezialfolge-159-predictive-analytics-mit-fabian-hadiji.mp3",{"id":15,"title":16,"description":12,"tags":12,"location":12,"filename_disk":17,"filename_download":18,"metadata":19,"type":20,"filesize":21,"modified_by":12,"modified_on":22,"embed":12,"uploaded_by":23,"uploaded_on":24,"folder":25,"width":26,"height":26,"charset":12,"duration":12},"0ee3b8e1-8d81-4ae7-98f1-9f98c176eead","159 Ig Fb Fabian Hadiji","0ee3b8e1-8d81-4ae7-98f1-9f98c176eead.png","159_ig_fb_Fabian_Hadiji.png",{},"image\u002Fpng","2353220","2024-07-16T11:06:00.227Z","11a655b6-bcfc-4c6c-a13c-68189e38edb9","2024-07-16T11:06:00.074Z","35140572-5f04-494b-b197-bd9ef1edc33d",1500,{"id":28,"title":29,"description":12,"tags":12,"location":12,"filename_disk":30,"filename_download":31,"metadata":32,"type":20,"filesize":33,"modified_by":12,"modified_on":34,"embed":12,"uploaded_by":35,"uploaded_on":36,"folder":12,"width":37,"height":37,"charset":12,"duration":12},"1815da8f-e51b-4896-a43e-3b6f089ae8dc","Fabian Hadiji Header","1815da8f-e51b-4896-a43e-3b6f089ae8dc.png","Fabian_Hadiji_header.png",{},"1011197","2024-08-13T06:08:00.502Z","e97179d2-51b3-4deb-828f-9524091441f5","2024-08-13T06:07:59.767Z",1024,[39],{"speaker":40},{"id":41,"slug":42,"academic_title":12,"occupation":43,"first_name":44,"last_name":45,"description":46,"event_image":47,"profile_image":59},"834a5de1-235b-4a28-97c4-e072ca1c39d8","fabian-hadiji","Head of BI bei Lotum","Fabian","Hadiji","\u003Cp>Fabian verbindet seine Begeisterung für Daten, Machine Learning und Computerspiele mit seinen beruflichen Tätigkeiten. Neben seiner Rolle als Head of Business Intelligence bei Lotum ist er Lehrbeauftragter an der TH Köln, wo er eine Projektgruppe zum Thema Game Data Science leitet.\u003C\u002Fp>\n\u003Cp>Zuvor hat er im Bereich des Machine Learnings promoviert und war Mitbegründer des Startups \u003Ca class=\"c-link\" href=\"http:\u002F\u002Fgoedle.io\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" data-stringify-link=\"http:\u002F\u002Fgoedle.io\" data-sk=\"tooltip_parent\">goedle.io\u003C\u002Fa>. Als Co-Organisator eines \u003Ca href=\" https:\u002F\u002Fwww.meetup.com\u002Fde-DE\u002FCologne-AI-and-Machine-Learning-Meetup\u002F\" target=\"_blank\" rel=\"noopener\">AI- und Machine Learning-Meetups in Köln\u003C\u002Fa> fördert er aktiv die lokale Machine-Learning-Community.\u003C\u002Fp>",{"id":48,"title":49,"description":12,"tags":12,"location":12,"filename_disk":50,"filename_download":51,"metadata":52,"type":53,"filesize":54,"modified_by":23,"modified_on":55,"embed":12,"uploaded_by":23,"uploaded_on":56,"folder":12,"width":57,"height":58,"charset":12,"duration":12},"2f9d6c94-1a07-4506-89fc-89f6c6975148","Fabian Hadiji Meetup Talk cut","2f9d6c94-1a07-4506-89fc-89f6c6975148.jpg","IMG_6233_VSCO_cut.jpg",{},"image\u002Fjpeg","504407","2024-05-22T16:05:25.599Z","2024-05-22T16:05:09.475Z",1200,918,{"id":60,"title":61,"description":12,"tags":12,"location":12,"filename_disk":62,"filename_download":63,"metadata":64,"type":20,"filesize":65,"modified_by":12,"modified_on":66,"embed":12,"uploaded_by":35,"uploaded_on":67,"folder":12,"width":68,"height":68,"charset":12,"duration":12},"9cc6b9fb-0a20-4b0a-b9da-2e9abd85f29d","Fabian Hadiji Profile","9cc6b9fb-0a20-4b0a-b9da-2e9abd85f29d.png","Fabian_Hadiji_profile.png",{},"5434408","2023-07-26T10:56:04.812Z","2023-07-26T10:56:04.107Z",2048,[70],{"member":71},{"id":72,"first_name":73,"last_name":74,"occupation":75,"description":76,"normal_image":77},"958f6624-c721-4ff9-8a97-69148f540945","Dennis","Becker","\u002F\u002F Podcast-Host","\u003Cp>Dennis ist Head of Development bei Lotum und kümmert sich liebevoll um die Entwickler:innen und deren Weiterentwicklung. \u003Cbr>In den Podcastfolgen übernimmt Dennis gerne die Moderation. Beim täglichen Tischtennis ist er immer am Start und seit wenigen Jahren passionierter Kitesurfer. Mittlerweile ist er auch Vibe Coder mit Leidenschaft und überzeugt, dass seine Kolleg:innen AI unterschätzen. \u003C\u002Fp>",{"id":78,"title":79,"description":12,"tags":12,"location":12,"filename_disk":80,"filename_download":81,"metadata":12,"type":53,"filesize":82,"modified_by":12,"modified_on":83,"embed":12,"uploaded_by":12,"uploaded_on":84,"folder":12,"width":37,"height":37,"charset":12,"duration":12},"7a9fd72e-16b1-4283-8c1f-348a3e7986f9","Dennis Becker","7a9fd72e-16b1-4283-8c1f-348a3e7986f9.jpg","dennis_becker_normal_01e352e682.jpg","45729","2022-01-16T14:26:35.196Z","2021-12-30T14:16:28.647Z",[],[87],{"tag":88},{"id":89,"name":90},"fc7448ba-3fe1-413e-9739-8e12c42dd6de","Business Intelligence",[88],[93],{"first_name":44,"last_name":45,"profile_image":59,"slug":42,"description":46,"event_image":47,"academic_title":12},487,207,[97,113,129],{"id":98,"slug":99,"published_on":100,"type":7,"number":101,"title":102,"audio_url":103,"cover_image":104},"5cc62768-8036-4f45-98ec-e7bfc51d8f3f","spezialfolge-157-data-analytics-mit-tobias-lampert","2024-08-16T15:30:00.000Z","157","Data Analytics mit Tobias Lampert","https:\u002F\u002Fwww.buzzsprout.com\u002F176239\u002F15593898-spezialfolge-157-data-analytics-mit-tobias-lampert.mp3",{"id":105,"title":106,"description":12,"tags":12,"location":12,"filename_disk":107,"filename_download":108,"metadata":109,"type":20,"filesize":110,"modified_by":12,"modified_on":111,"embed":12,"uploaded_by":23,"uploaded_on":112,"folder":25,"width":26,"height":26,"charset":12,"duration":12},"1a7b6299-48b6-442c-abb0-5aabc5d83153","157 Ig Fb Tobias Lampert","1a7b6299-48b6-442c-abb0-5aabc5d83153.png","157_ig_fb_Tobias_Lampert.png",{},"2534515","2024-07-16T11:05:47.877Z","2024-07-16T11:05:47.737Z",{"id":114,"slug":115,"published_on":116,"type":7,"number":117,"title":118,"audio_url":119,"cover_image":120},"ffe2697a-10cf-4e46-99d9-fb39ca142270","spezialfolge-155-data-engineering-mit-wessel-van-de-goor","2024-08-02T15:00:00.000Z","155","Data Engineering mit Wessel van de Goor","https:\u002F\u002Fwww.buzzsprout.com\u002F176239\u002F15513595-spezialfolge-155-data-engineering-mit-wessel-van-de-goor.mp3",{"id":121,"title":122,"description":12,"tags":12,"location":12,"filename_disk":123,"filename_download":124,"metadata":125,"type":20,"filesize":126,"modified_by":12,"modified_on":127,"embed":12,"uploaded_by":23,"uploaded_on":128,"folder":25,"width":26,"height":26,"charset":12,"duration":12},"4e60a88b-aeea-415f-9227-6b983de44c9e","155 Ig Fb Wessel Vande Goor","4e60a88b-aeea-415f-9227-6b983de44c9e.png","155_ig_fb_Wessel_VandeGoor.png",{},"2102536","2024-07-16T11:05:36.348Z","2024-07-16T11:05:36.031Z",{"id":130,"slug":131,"published_on":132,"type":7,"number":133,"title":134,"audio_url":135,"cover_image":136},"81cdea32-e193-451f-a903-a10bb9fee79b","spezialfolge-3-analytics-engineer-w-m-d-wir-suchen-verstaerkung","2024-08-02T14:55:00.000Z","3","Analytics Engineer (w\u002Fm\u002Fd): Wir suchen Verstärkung!","https:\u002F\u002Fwww.buzzsprout.com\u002F176239\u002F15514296-spezialfolge-3-analytics-engineer-w-m-d-wir-suchen-verstarkung.mp3",{"id":137,"title":138,"description":12,"tags":12,"location":12,"filename_disk":139,"filename_download":140,"metadata":141,"type":20,"filesize":142,"modified_by":12,"modified_on":143,"embed":12,"uploaded_by":35,"uploaded_on":144,"folder":25,"width":26,"height":26,"charset":12,"duration":12},"6eb13ab6-c045-41a7-87d5-872ff715c70b","Analytics Engineer","6eb13ab6-c045-41a7-87d5-872ff715c70b.png","Analytics_Engineer.png",{},"995394","2024-08-01T08:12:48.541Z","2024-08-01T08:12:48.319Z",{"id":146,"service":147,"supported_features":148,"speakers":151,"raw_response":156},"d3661a43-8832-49fa-8953-cd1b731f767e","deepgram",[149,150],"timestamps","diarization",[152,154],{"identifier":153,"name":73},"0",{"identifier":155,"name":44},"1",{"metadata":157,"results":171},{"transaction_key":158,"request_id":159,"sha256":160,"created":161,"duration":162,"channels":163,"models":164,"model_info":166},"deprecated","4dfc537e-5891-4dba-badb-4d730eed912b","a9aa81fc0fcb1898abbe7af3f32854e7f75b65e7519f7a58b5cbbc7d81fd93e9","2025-08-08T10:20:03.634Z",1530.6046,1,[165],"91f8eabe-2126-4ed4-9e56-d480032309b3",{"91f8eabe-2126-4ed4-9e56-d480032309b3":167},{"name":168,"version":169,"arch":170},"2-general-nova","2024-05-09.3071","nova-2",{"channels":172,"utterances":13037},[173],{"alternatives":174},[175],{"transcript":176,"confidence":177,"words":178,"paragraphs":12479},"Hallo und herzlich willkommen zum dritten Teil unserer programmierbaren Mini Folge zu Business Intelligence. Ich bin Dennis und hab hier meine Programmier Bieber und die Programmier Barbie mit am Start, wie sie sich eben selbst getauft haben. Wir haben einmal Fabian. Hallo. Wir haben Tobi. Hallihallo. Und wir haben Wessel. Moin. Und genau, wir reden in 3 unterschiedlichen Teilen ein bisschen einen groben Überblick mal so über Business Intelligence bei Lotum. Das heißt, jeder 3 arbeitet bei Lotum. In dem ersten Teil ging es darum, wie kriegen wir eigentlich unsere ganzen Daten irgendwo hin, dass wir damit auch was anfangen können. Im zweiten Teil haben wir damit was angefangen, haben Daten analysiert und ja, funktioniert ihn bereitgestellt, damit Teams einen Mehrwert aus Daten haben und daraus Entscheidungen ableiten können. Und jetzt im dritten Teil soll es Predictive Analytics gehen und dann haben wir diesmal Fabian als unseren Haupt Speaker, der dazu was erzählt. Natürlich auch hier, Wessel und Tobi, ihr seid jederzeit eingeladen. Falls ihr denkt, da fehlt doch was oder da hat der Dennis eine schlechte Frage gestellt, dürft ihr natürlich gerne jederzeit einspringen. Sonst freue ich mich, euch mit den Fragen ein bisschen durch die Folge zu führen. Und bevor wir starten, Fabian, noch an dich erst mal die Frage kurzer Background, was hast Du denn mit Daten zu tun? Ja, also ich hab vor wirklich einigen Jahren schon mal irgendwann Informatik studiert und hab dann gegen Ende des Studiums festgestellt, dass ich mich doch ziemlich für Machine Learning interessiere, was natürlich schon sehr stark auf Daten basiert und hab mich dann im Anschluss an mein Studium auch für eine Promotion im Bereich KI oder Machine Learning entschieden und ja, in den Folgejahren dann in verschiedenen, ja, Data Science Bereichen gearbeitet. Hatte eine Zeit lang mein eigenes Start-up, dann in unterschiedlichen anderen Firmen noch gearbeitet und bin dann vor circa 2 Jahren hier zu Lotum gekommen. Und wer deine Stimme mehr hören möchte, können wir noch kurz Cross Werbung machen. Du warst ja schon durchaus mal in der Programmierbar, unter anderem der Deep Dive 139 GPT Under the Hood. Das war ein, den Du hattest, der Anfang des Jahres. Aber die Zeit vergeht immer so schnell. Denk, das war gerade erst, aber ja, ist auch schon wieder Tatsächlich schon Januar gewesen. Ja. Ja. Verrückt. Predictive Analytics, Einsatz? Ja, also ich meine, wie Du in der letzten Folge schon angeteasert hast, es geht jetzt mehr darum, auch in die Zukunft zu schauen. Also wenn wir vorher viel das Verhalten der Spieler*innen in der App in der Vergangenheit betrachtet haben, geht's jetzt darum zu schauen, was Spieler*innen dann möglicherweise in der Zukunft tun werden, darauf reagieren zu können. Das wär jetzt mehr als ein Satz, aber ja, so kurz und knapp. Sehr gut, cool. Also ja, ist wahrscheinlich daher, ne. Also ich mein, wir haben irgendwie, also in unseren Spielen, das ist ja unser unser Kontext ein bisschen. Wir haben User, die sich unterschiedlich verhalten, die auf unterschiedliche Experimente unterschiedlich reagieren und in der Regel versuchen wir erst mal natürlich auf die die Allgemeinheit, also unsere gesamte Zielgruppe so zu optimieren und das ist viel, was wir machen. Also wie können wir es für das Spiel für alle gleichermaßen ändern und in in Summe besser machen. Aber wenn man eine guten eine gute Datenbasis hat, dann kommt man natürlich auch irgendwie schnell auf die Idee, noch mehr zu machen und zu sagen, okay, ich will einzelnen Usern, ne, eine unterschiedliche Experience geben, ja einfach den möglichst maximalen Spielspaß irgendwie zu zu fördern und das weiß ich gar nicht, ist das also hat das was mit muss das wirklich live sein? Also was ist so ein bisschen die die was sind die Komponenten? Also das geht es darum, wirklich dauerhaft auszuwerten und direkt darauf reagieren zu können, sagen wir mal auf auf Session Basis, auf einzelne Events ist es eher ein ja okay ne nach 3 Monaten haben wir dich in irgend 'nem Bucket einsortiert und dann dann dann können wir was anderes mit dir machen oder sind's all diese ganzen Aspekte? Ja, ich glaub, man kann das Ganze beliebig komplex machen, wenn man wollte und muss dann natürlich auch gucken, wie groß da noch der der Gewinn da durch ist. Aber ich glaub, man kann auch ganz einfach anfangen, dass man sagt, okay, eine Segmentierung der Nutzerbasis muss nicht immer direkt mit Machine Learning gemacht werden, sondern wir können am Anfang erst mal gucken, okay, über was fürn Kanal ist die Spielerin denn überhaupt zu uns gekommen, ne? Das wär sicherlich so die einfachste Segmentierung. Und wenn man jetzt überlegt, dass manche Spieler*innen zum Beispiel durch einen in weit kommen, weil sie von 1 anderen Spieler*innen zum Beispiel schon eingeladen worden sind. Dann gibt's natürlich noch andere Beispiele, wenn wir Marketingkampagnen machen, wo Spieler*innen über diverse Werbenetzwerke kommen können. Und das wär vielleicht 1 so der der leichtesten oder einfachsten Beispiele, wie man am Anfang die Experience der Nutzer*innen schon anpassen kann. Und dann ist sicherlich zum Beispiel, dass man sagen könnt, okay, wenn ich jetzt über irgend son gekommen bin, dann weiß ich vielleicht schon 'n bisschen mehr über das Spiel jetzt mal hypothetisch gesprochen, als wenn ich halt einfach über einen Werbekanal komme und eigentlich nur in das Spiel gekommen bin, weil ich weil ich 'n Video dazu gesehen hab. Und das kannste, kann man dann natürlich dann fortführen, dass man zum Beispiel sagt, okay, man schaut sich das Spielverhalten in der ersten Session an. Was kann man daraus ableiten? Wenn wir jetzt zum Beispiel Beispiel nehmen, wo's ja darum geht, Bildrätsel zu lösen, kann man sich ja anschauen, wie schnell wurden die ersten Rätsel gelöst und daraus dann vielleicht ableiten, ob die neue Spieler*in eher schnell dabei gewesen ist oder langsam und dann das anzupassen, was darauf folgt, dass wir vielleicht die Spieler eben am Anfang nicht überfordern, aber es andererseits auch nicht unterfordern, weil ich würde sagen, dass beides wahrscheinlich zu schnellen Abwandern führen kann, weil es halt einfach dann nicht der Spielspaß ist, den wir erreichen wollen. Mhm. In den ersten Beispielen, die Resonanz ist, man kommt irgendwie über Marketing rein oder vielleicht hat man, ne, durch eine soziale Einladung von 1 Freundin, einem Freund davon mitbekommen. Jetzt im im Teil, okay, teilen, da denke ich so, ja, das können wir ganz kurz rausbekommen vielleicht, weil das sind ja auch irgendwo unsere Daten, also an irgendeine andere Stelle hat ein anderer User was gepostet und da kann man vielleicht Daten dranhängen in irgendeiner Art und Weise. Also kommen wir da gut und schnell an solche Daten und ist das auf der Marketingseite auch so? Kommen wir da auch so live dran, dass wir praktisch sagen können, der kam jetzt in in dieser Kampagne rein und sobald er das Spiel öffnet, wissen wir, der kam über eine Werbung? Genau. Also im Endeffekt, wir haben ja in den ersten Folgen schon mal viel über das Thema SDKs gesprochen, die wir nutzen, überhaupt Daten zu sammeln Mhm. Und in dem Rahmen gibt's halt dann auch noch andere SDKs und Services, die wir nutzen, die uns dann zum Beispiel sagen, das ist die Werbekampagne gewesen oder das Werbenetzwerk, über die eine Spielerin in unser Game gekommen ist, genau. Und das ist mit zusätzlichen Tools möglich und teilweise wird's sogar auch schon von Firebase unterstützt, insbesondere wenn man sich dann wieder im Google Universum bewegt und meinetwegen Werbung auf Google Ads macht. Okay. Die anderen Beispiele, die wir grade hatten, waren son bisschen ja, wir wollen die User Experience anpassen Mhm. Auf Basis von irgendwie etwas, was Du gemacht hast, dann dafür zu sorgen, dass Du noch irgendwie mehr Spaß hast oder das besser auf dich zugeschnitten hast Ja. Letztendlich und was wahrscheinlich trotzdem aus aus Game Lied oder aus aus 1 Sicht 1 erfolgreichen Spiels Spielemachers irgendwie kommt, so. Es wäre ja auch schön, wenn man irgendwie vorhersagen könnte, was Leute dann in der Zukunft machen, ne. Das ist ja noch vielleicht noch mal ein anderer Aspekt, nicht nur das anpassen oder vielleicht ist dann eine passen wir trotzdem das Spiel an, aber sozusagen so die Vorhersage wär da vielleicht sogar man könnte ja auch Vorhersagen machen, wie viel Level noch gespielt werden als Beispiel. Und dann gibt's natürlich auch noch andere Aspekte, die damit zusammenhängen. Einmal, wir haben ja grad ja schon mal über das Abwandern oder Tschören, wie man im Englischen sagt, gesprochen. Grade so Themen wie Tschörenvorhersage sind natürlich interessant. Ich sag mal, das Beispiel kämen die meisten Leute aus der Vergangenheit wahrscheinlich von früher, wenn sie 'n Handyvertrag hatten. Wenn sie gekündigt haben, hat man auf einmal wesentlich mehr Aufmerksamkeit bekommen vom Anbieter, weil der Anbieter versucht, mit allen Mitteln zu verhindern, dass man geht. Eine ähnliche Situation haben wir natürlich auch. Wir haben zwar Millionen von Spieler*innen, aber wir wollen auch verhindern, dass sie gehen. Und wenn wir vorher erkennen könnten, dass meinetwegen das Interesse sinkt oder dass es bestimmte Herausforderungen im Spiel gibt, womit sie nicht zurechtkommen, dann würden wir da auch zum Beispiel gerne entgegensteuern. Und für uns ist es halt 'n bisschen schwieriger bei dem Beispiel vom Mobilfunkvertrag, muss ich früher 3 Monate vorher eine Kündigung schreiben. Bei uns kündigt niemand in den Spielen, weil wir natürlich Free to play Spieler sind. Das heißt, Spieler*innen laden die App runter, fangen an zu spielen und wenn sie keine Lust mehr haben, hören sie einfach auf. Und trotzdem kann man sich dann zum Beispiel die Daten genauer anschauen und daraus dann halt unter Umständen ableiten, ob Spieler*innen mehr oder weniger wahrscheinlich sind, zeitnah abzuwandern. Und da ist es halt auch wieder unterschiedliche Möglichkeiten, die wir haben, jetzt technisch so was anzugehen. Und gibt dann natürlich auch noch diverse andere Beispiele so, ne. Wenn man jetzt so in die Richtung Monetarisierung schaut, sicherlich für uns auch interessant, frühzeitig zu erkennen, ob Spieler*innen eher dazu tendieren, in zu tätigen oder nicht, weil man dann natürlich dann unter Umständen halt auch Angebote zeigen kann oder die Werbelast anpassen kann. Und das sind zum Beispiel weitere Beispiele und können Sie natürlich auch erweitern. Ich meine, sone Wachstum ist für uns immer wichtig. Wir haben jetzt grade schon so über die Social gesprochen. Wenn ich das Gefühl habe, da ist jemand eher, dass er dazu tendiert, vielleicht noch mal jemanden einzuladen, dann könnte ich natürlich halt auch wieder 'n bisschen Werbung dagegen eintauschen, dass ich vielleicht 'n paarmal häufiger die Person dazu auffordere, weil ne, das ist natürlich dann auch ein günstiger Kanal für uns Ja. Die App größer werden zu lassen. Gibt es irgend eine Größenordnung, von der man generell sprechen kann, die man irgendwie braucht an Datengrundlage, so individuell irgendwelche Predictions machen zu können? Also ich glaube, es ist schwierig, wirklich da jetzt eine Daumenregel zu sagen oder zu haben, dass man sagt, okay, man braucht mindestens x Events oder sonst was. Also ich würd schon sagen, dass mit zunehmender Dauer natürlich eine Vorhersage besser wird, aber in gewisser Weise natürlich auch uninteressanter. Also ich sag mal, wenn ich jetzt 'n eine Spielerin 3 Monate beobachten müsste, Mhm. Kauf vorherzusagen, zu Tom Schänden schon zu spät oder die Person hat dann schon Mhm. Was gekauft und da ist so ähnlich dann halt auch wieder mit den mit den Merkmalen oder Featuren, ne. Je mehr wir an Signalen haben, also wir haben ja in den vergangenen Folgen schon darüber gesprochen, dass wir teilweise bis zu 600 Events pro Spiel sammeln, ist natürlich, je mehr Informationen wir haben, desto mehr Merkmale können wir für einen Machine Learning Algorithmus zum Beispiel zusammenstellen, aber das ist trotzdem schwierig zu sagen, was jetzt so die Daumenregel ist. Aber ich würd zum Beispiel sagen, bei bei ist es schon schön, wenn man so die ersten Tage Daten hat und dann halt auch 'n gewisses Bild, was das Spielverhalten abdeckt. Also nur noch mal als Beispiel so vor Picks, da haben wir ja so Elemente wie ein tägliches Rätsel und da könnte man zum Beispiel sagen, dass das Spielen 1 täglichen Rätsels vielleicht stark damit zusammenhängt, ob Spieler*innen abwandern oder nicht, weil es natürlich son gewisser Anreiz ist. Und ich sag mal, wenn man jetzt 3 Tage lang beobachten kann, dass immer wieder das Tag tägliche Rätsel gelöst worden ist, dann hat man vielleicht weniger Bedenken, als wenn man das nur am ersten Tag gesehen hat. Und das sind sicherlich deutlich aussagekräftigere Signale, wo es dann nicht die Masse ausmacht, als wenn ich jetzt jeden Button in der App auswerten würde, weil das natürlich dann sehr viel Rauschen auch, ja, in die Daten einfließen lässt. Ist aber trotzdem wurde vorher machen haben wir uns ja ja viele Gedanken oder machen wir uns Gedanken, welche Events tracken wir und was wollen wir irgendwie damit aussagen und was hat das für einen ne für einen konkreten Bezug zu 'nem Feature oder sowas. Ist das jetzt hier auch so, dass wir uns manuell Gedanken darüber machen, welche Events führen zu 'nem Turn beispielsweise, also dass ein ein ein Spieler einen aufhört oder es ist eher so, wir werfen all unsere Events, die wir haben, in einen und dann wahrscheinlich ist es dort Machine Learning, also als dann wo wir nicht mehr explizit irgendwie was sagen muss, sondern sagen so hier, das sind die Daten und versuche das daraus zu extrahieren. Ich glaub, so die Frage an sich kann man auf verschiedenen Ebenen diskutieren. Also ich glaub, die erste Entscheidung wär erst mal, wollen wir eigentlich das System selber implementieren oder wollen wir einen Drittanbieter dafür nutzen, der sich selbst rum kümmert? Und man kann da sicherlich beide Wege gehen. Da wir eine relativ kleine Firma sind, ist für uns natürlich am Anfang immer der Weg interessant zu sagen, wir testen mal irgendeinen Drittanbieter. Zum Beispiel ist da es halt auch, dass aus dem Google Analytics Universum es halt die Möglichkeiten gibt, Vorhersagen zu machen. Und da ist dann wirklich so, wir schmeißen alle Daten rein und sagen einfach, mach mal. Und Google Analytics weiß ja überhaupt nichts unser über unser Spiel inhaltlich. Und dementsprechend werden die dann dort sicherlich einige Sachen kombinieren, aggregieren, bestimmte Berechnungen machen und dann halt versuchen, daraus automatisch dann Merkmale abzuleiten. Würden wir was implementieren, würden wir sicherlich den anderen Weg gehen. Dann würden wir uns erst mal mit dem Gaming zusammensetzen und noch mal diskutieren, okay, was ist 'n eigentlich dein Gefühl? Was sind aktuell so die größten Pain Points? Ich mein, wir empfangen ja auch Feedback, wir lesen die Reviews, wir schauen jeden Tag in die Daten rein und würden dann vielleicht erst mal mit, sagen wir mal, 'n 'nem Dutzend oder 2 Dutzend Events und Merkmalen anfangen und dann uns iterativ in die Richtung bewegen, dass wir versuchen, immer mehr Merkmale hinzuzufügen, weil wir natürlich viel mehr Wissen über das Spiel haben, über die Mechanik und so was. Jetzt ist das ja, gerade mit dir, der dort ja ein bisschen Hintergrund hat im Bereich Machine Learning, würde man sagen, ne, solche Use Cases gab es letztendlich die letzten Jahre auch. Wie sehr verändert sich das gerade durch das was oder durch GenAI? So gibt es da hat es jetzt schon Sachen einfacher gemacht? Kann man da noch mehr sagen, so hier ist einfach alles, sagen wir mal, was da eine gute Sache ist? Nutzen wir das schon in irgendeiner Art und Weise, was so da der der aktuelle Stand, was AI angeht, wo ja irgendwie Machine Learning ein Teil von ist. Ja. Ja genau, also den letzten Teil vielleicht noch mal son bisschen abzuholen. Ich glaub, wie Du gesagt hast, das sind eigentlich Technologien, die es seit Jahren, locker 10 Jahren gibt, es da bestimmte Anwendungsfälle gibt, man dann doch trotzdem noch mal auch von Unternehmen zu Unternehmen gucken muss, ob es es wirklich wert ist, ne. Wie groß ist der Unterschied, den wir dadurch erreichen können? Und ich glaub, so ähnlich ist es jetzt bei generative AI, das ist natürlich jetzt eine krasse Neuerung, die wir in den letzten 2, 3 Jahren sehen. Und da gibt's natürlich jetzt extrem viele Beispiele, die wir uns anschauen können. Und ich würd sagen, man muss da auch noch mal so auf verschiedenen Ebenen unterscheiden, wie wir's bei Lothum nutzen könnten. Können einerseits sagen, ne, wir nutzen wieder die Tools, die uns zur Verfügung gestellt werden. Also ne, dass wir halt irgendwie mit ChatGPT manche Sachen oder mit Sachen machen lassen, dass wir für die Bildergenerierung vielleicht mit nutzen und so was. Und als Tools würd ich sagen, sind wir als Firma superoffen. Und ich glaub, dass wir schon an vielen Stellen in unseren Arbeitsalltag integriert haben und dass wir davon wieder als kleine Firma sehr stark profitieren, ne. Und vielleicht sollte man ja auch dazusagen, sone Firma wie Lotum sind ja nicht nur die Game Teams. Wir haben dann auch extrem wichtige andere Teams, die es überhaupt nur möglich machen, dass wir solche Spiele produzieren, wenn man jetzt zum Beispiel an das Contentteam denkt oder halt auch, wir haben halt auch 'n Supportteam. Und da gibt's sicherlich viele Anwendungsfälle, wo wir von generative AI profitieren können. Und dann kann man halt sich wieder die Spiele anschauen. Und da würd ich halt sagen, ist es eher aus meiner Sichtweise, meinem Gefühl noch so, dass vielleicht son Ticken zu früh ist, son LLM allein auf son Spiel loszulassen, ne. Wir versuchen da schon auch, was so Feature angeht, uns Gedanken zu machen, wie man meinetwegen Content erstellen könnte, wenn's jetzt zum Beispiel Quizfragen geht oder so was. Aber das ist häufig noch an sonem Punkt, wo man sagen würde, es ist für die Qualität der Spiele, die wir haben, halt noch nicht ausreichend gewesen. Und dann halt auch, ne, da muss man sich wieder Gedanken machen. Also wenn man jetzt zum Beispiel hat, wo dann Leute Inhalte produzieren könnten, kommen bestimmt wieder ein paar wenige wieder auf die Idee, Inhalte produzieren, die wir jetzt mit unserer Brand nicht verknüpfen wollen. Und in anderen Bereichen muss man natürlich auch noch mal sagen, so die Komplexität und die Kosten, die, sagen wir mal so, durch so Machine Learning Algorithmen für die Vorhersage von Join zum Beispiel genutzt werden, da sind die Kosten fürs Berechnen mittlerweile vernachlässigbar. Und wenn man sich jetzt die Kosten anschaut, die durch so was entstehen, wenn man ein LLM fragt, dann ist es immer noch relativ hoch. Wenn man überlegt, dass wir Millionen Spieler haben. Und wenn man das in den Kontext setzt, wie teuer immer noch ein an ein LLM ist im Vergleich dazu, was wir dafür kriegen, eine Werbung auszuspielen, Mhm. Da ist noch eine gewisse Herausforderung. Aber ich seh das durchaus in der Zukunft superspannend an local oder eigene LLMs zu haben, die für unsere Use Cases speziell trainiert sind auf unseren eigenen Daten, spitzere Anwendungsfälle. Und ich glaub, das wird in der Zukunft schon noch was sein, was auch uns noch mal ganz neue Möglichkeiten gibt in der Spieleentwicklung. Mhm. Cool. Gibt es denn schon irgendwelche, also ist ja, glaube ich, generell auch 'n Bereich, in dem wir noch 'n bisschen mehr neu reinkommen, was hat das eben auch schon angesprochen, als er vor 4 Jahren zu Lotum kam, waren wir vielleicht BI technisch noch nicht so aufgestellt, wie wir es heute sind und sind recht stolz, dass wir, sagen wir mal, den den den ganzen Analytics Part mittlerweile ganz gut drauf haben, würde ich sagen und wirklich viel über unsere Spielern und das das Ganze, was da halt zusammenhängt, haben und Predictive Analytics aber eher so ein Bereich ist, wo wir gerade so reinsappen. Das heißt, wir haben wahrscheinlich noch keine Haben wir schon irgendwelche größeren Game Features gehabt, die so darauf basieren, wo wir sagen, dass Ja, ich mein so, aber ich glaub, Du kennst Du aus deiner Vergangenheit als Game Lied von das Thema Difficult Adjustment und das erlebt grade auch noch mal einen Revival sozusagen. Das sind durchaus Sachen, wir drin sind, aber ansonsten würde ich sagen, in den Gameteams ist es sicherlich was, was grade so auch entsteht auch, ne? Die Sachen, wo wir eben drüber gesprochen hatten. Tobi, der meinetwegen dann dort bei The Test 'n bisschen mehr so in die Richtung sagt irgendwie, dass man mal irgendwie jetzt Algorithmen ausm Bereich Machine Learning oder so genutzt hat. Aber sicherlich eher so, dass alleine das Aufstellen, dass das Erarbeiten dieser ganzen Dateninfrastruktur sicherlich so in der Vergangenheit der Fokus war und auch immer noch 'n Großteil unserer Arbeit ist und das eher Themen sind, die wir dann in der Zukunft sicherlich noch mehr explorieren werden und da dann halt auch wirklich wieder gucken, ne, was sind Services, die wir einkaufen? Was sind Services, die wir selber bauen? Und dann halt das Interessante halt noch bei dem Thema ist halt noch mal in andere Teams zu gehen. 'N anderes Beispiel wär zum Beispiel noch, wenn wir uns das Thema Marketing und User Acquisition anschauen, wo ich persönlich halt auch noch mal 'n sehr großen Hebel darin sehe, in die Zukunft schauen zu können, wenn wir uns zum Beispiel das Bewerten von Marketingkampagnen anschauen. Mhm. Können wir natürlich erst mal sagen, okay, gucken wir uns einfach den Status quo an, was es uns kostet, eine Spieler*innen einzukaufen. Aber für uns ist es vom Businessmodell her superinteressant zu sehen, wie bestimmte Gruppen an Spieler*innen sich in der Zukunft verhalten werden, was die Monetarisierung angeht, weil's für uns nur dann Sinn macht, Marketing zu betreiben, wenn wir wissen, dass wir das Geld auch wieder einspielen. Und bei uns ist es halt nicht so, dass wir nach 7 Tagen sagen können, okay, da ist das Geld wieder reingekommen. Wir fahren die Kampagne hoch. Bei uns ist halt eher so die Perspektive, dass wir denken, ja, 180 Tage könnten vielleicht son Zeitraum sein, indem wir erwarten, dass das Geld wieder zurückgespielt wird. Und das macht die Steuerung natürlich von 'ner Werbekampagne extrem schwierig, weil ich ja nicht heute 180 Tage warten kann, dann zu erscheinen, die Entscheidung zu treffen, das Budget zu erhöhen. Und da würd ich sagen, ist da wieder son Beispiel, wo wir sagen, okay, wir glauben, dass diese Technologie für uns sehr wichtig ist. Wir fangen wieder an, am Anfang meinetwegen so was einzukaufen, dass wir Unterstützung bekommen, meinetwegen für bestimmte Kohorten, also Gruppen an Spieler*innen Vorhersagen zu treffen, wie sie sich in der Zukunft von der Monetarisierung her verhalten werden und dann halt die Entscheidung treffen, wie wichtig ist das für uns? Würden wir das selber im Team nachbauen oder was bauen, was für unseren Use Case noch besser passt? Haben wir die Kapazitäten dafür? Und so sehe ich auch anwendungsfälle durchaus noch auch noch in anderen Teams, ja. Okay. Gibt's noch was zu Predictive Analytics? Unsere solche Sachen. Ich könnte sicherlich noch den ganzen Tag darüber reden, aber wir wollten uns ja hier son gewissen zeitlichen Namen nehmen. Ja. Aber vielleicht fällt den anderen noch was ein. Die sind happy. Kopfschütteln ist immer gut im Podcast. Kommt, geht direkt durchs Ohr. Zu unseren Hörer*innen. Nee cool, dann vielen Dank, auch das ist 'n superspannender Part und ich hoffe, unsere Hörer*innen konnten ein bisschen einen Einblick bekommen, ne, was alles unter Business Intelligence letztendlich läuft bei Lotum, also was das für unterschiedliche Bereiche sind, in denen wir da unterwegs sind, was unsere Herausforderungen sind, was wir uns vorstellen, ne? Ich glaube vielleicht auch das, ne, zu so grundsätzlichen Einordnungen so. Es ist 'n Themenbereich, wo wir nicht davon ausgehen, dass er schrumpft, so, sondern das ist eher was was noch wichtiger werden wird, auch in der Zukunft und und von daher einfach 'n sehr fester Bestandteil von von unserer täglichen Arbeit ist in in allen Bereichen, wie Du so sagtest, ne? Also in sehr, sehr vielen Teams, wo das eben datengetrieben unterstützt werden kann und mit Sicherheit auch einen Erfolgsfaktor, ne? Dass man eben auf aufgrund von Daten und Ergebnissen Entscheidungen treffen kann und angereichert mit ein bisschen Bauchgefühl und ein bisschen Erfahrung, die auch dahinter steckt. Ich glaub, das ist auch noch son Punkt, ne? Ich hab noch nicht angesprochen, aber weil wenn man glaube ich, meine meine Sicht so, wenn man Spieleentwicklung sich insgesamt anguckt und wahrscheinlich auch Produktentwicklung, man könnte auch, wenn man nur Zahlen sich anguckt, jedes Spiel und jedes Produkt relativ schnell kaputt kriegen, ne? Selbst wenn man auch immer nur das nimmt, was die besseren Metriken hat oder so, aber es ist dann doch am Ende die Kombination und von daher gefühlt gehört halt eben neben dem auch nur Angucken der Zahlen eben auch das interpretieren der Zahlen und das Kombinieren und letztendlich die Erfahrung dazu, die richtigen Schlüsse dann aus den Zahlen auch am Ende zu ziehen. Ja, und man muss ja auch immer dazu sagen, vieles von dem, wenn wir jetzt über Charilen Prediction so was gesprochen haben, sind ja statistisch Lernverfahren und die geben dir erst mal nur die Korrelation an. Und die Kausalität ist gar nicht so einfach abzuleiten. Und da ist es ja wirklich, wo ja, nicht nur Bauchgefühl, aber auch Erfahrung eine große Rolle spielt und meinetwegen Spiele selber zu spielen, zu verstehen, was die Mechaniken sind. Und ich glaub, da sind wir noch weit davon entfernt, dass uns das AI alles abnimmt und ich glaub, das ist nicht zu unterschätzen, was das ausmacht. Ja. Fantastisch. Dann sind wir am Ende unserer kleinen Business Intelligence Essentials, ich nenne sie jetzt so besser, Serie, angelangt. Schreibt uns gerne mal, wie ihr das Format fandet, ob ihr das cool fandet, so einen Einblick mal in verschiedene Slots aufgeteilt. Wir haben zwischendurch eben schon gesagt, so klar, ne? Es ist ein relativ high level Überblick so über das, was wir machen. Über verschiedenste Bereiche. Es gibt bei Sicherheit reine BI Podcaste draußen, die jede Woche nichts anderes als Thema haben und wenn euch das interessiert und wir irgendwie in einzelne Bereiche noch mal tiefer einsteigen sollen, schreibt uns gerne das Feedback und wir gucken, was wir daraus machen können. Vielen Dank Tobi, vielen Dank Wessel, vielen Dank Fabi, dass ihr die Zeit gefunden habt, das Ganze mit uns aufzunehmen. Schreibt, liebe Hörerinnen, euer Feedback an Podcast at Programmier Punkt Bar oder über unsere Social Media Kanäle oder über Spotify. Es gibt verschiedenste Wege, wie ihr euch wie ihr uns erreichen könnt und sonst alles Gute. Bis zum nächsten Mal. Macht es gut. Tschau ciao. Tschau ciao. 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0: Hallo und herzlich willkommen zum dritten Teil unserer programmierbaren Mini Folge zu Business Intelligence. Ich bin Dennis und hab hier meine Programmier Bieber und die Programmier Barbie mit am Start, wie sie sich eben selbst getauft haben. Wir haben einmal Fabian.\n\nSpeaker 1: Hallo.\n\nSpeaker 0: Wir haben Tobi. Hallihallo. Und wir haben Wessel. Moin. Und genau, wir reden in 3 unterschiedlichen Teilen ein bisschen einen groben Überblick mal so über Business Intelligence bei Lotum.\n\nDas heißt, jeder 3 arbeitet bei Lotum. In dem ersten Teil ging es darum, wie kriegen wir eigentlich unsere ganzen Daten irgendwo hin, dass wir damit auch was anfangen können. Im zweiten Teil haben wir damit was angefangen, haben Daten analysiert und ja, funktioniert ihn bereitgestellt, damit Teams einen Mehrwert aus Daten haben und daraus Entscheidungen ableiten können. Und jetzt im dritten Teil soll es Predictive Analytics gehen und dann haben wir diesmal Fabian als unseren Haupt Speaker, der dazu was erzählt. Natürlich auch hier, Wessel und Tobi, ihr seid jederzeit eingeladen.\n\nFalls ihr denkt, da fehlt doch was oder da hat der Dennis eine schlechte Frage gestellt, dürft ihr natürlich gerne jederzeit einspringen. Sonst freue ich mich, euch mit den Fragen ein bisschen durch die Folge zu führen. Und bevor wir starten, Fabian, noch an dich erst mal die Frage kurzer Background, was hast Du denn mit Daten zu tun?\n\nSpeaker 1: Ja, also ich hab vor wirklich einigen Jahren schon mal irgendwann Informatik studiert und hab dann gegen Ende des Studiums festgestellt, dass ich mich doch ziemlich für Machine Learning interessiere, was natürlich schon sehr stark auf Daten basiert und hab mich dann im Anschluss an mein Studium auch für eine Promotion im Bereich KI oder Machine Learning entschieden und ja, in den Folgejahren dann in verschiedenen, ja, Data Science Bereichen gearbeitet. Hatte eine Zeit lang mein eigenes Start-up, dann in unterschiedlichen anderen Firmen noch gearbeitet und bin dann vor circa 2 Jahren hier zu Lotum gekommen.\n\nSpeaker 0: Und wer deine Stimme mehr hören möchte, können wir noch kurz Cross Werbung machen. Du warst ja schon durchaus mal in der Programmierbar, unter anderem der Deep Dive 139 GPT Under the Hood. Das war ein, den Du hattest, der Anfang des Jahres. Aber die Zeit vergeht immer so schnell. Denk, das war gerade erst, aber ja, ist auch schon wieder\n\nSpeaker 1: Tatsächlich schon Januar gewesen. Ja. Ja. Verrückt.\n\nSpeaker 0: Predictive Analytics, Einsatz?\n\nSpeaker 1: Ja, also ich meine, wie Du in der letzten Folge schon angeteasert hast, es geht jetzt mehr darum, auch in die Zukunft zu schauen. Also wenn wir vorher viel das Verhalten der Spieler*innen in der App in der Vergangenheit betrachtet haben, geht's jetzt darum zu schauen, was Spieler*innen dann möglicherweise in der Zukunft tun werden, darauf reagieren zu können. Das wär jetzt mehr als ein Satz, aber ja, so kurz und knapp.\n\nSpeaker 0: Sehr gut, cool. Also ja, ist wahrscheinlich daher, ne. Also ich mein, wir haben irgendwie, also in unseren Spielen, das ist ja unser unser Kontext ein bisschen. Wir haben User, die sich unterschiedlich verhalten, die auf unterschiedliche Experimente unterschiedlich reagieren und in der Regel versuchen wir erst mal natürlich auf die die Allgemeinheit, also unsere gesamte Zielgruppe so zu optimieren und das ist viel, was wir machen. Also wie können wir es für das Spiel für alle gleichermaßen ändern und in in Summe besser machen.\n\nAber wenn man eine guten eine gute Datenbasis hat, dann kommt man natürlich auch irgendwie schnell auf die Idee, noch mehr zu machen und zu sagen, okay, ich will einzelnen Usern, ne, eine unterschiedliche Experience geben, ja einfach den möglichst maximalen Spielspaß irgendwie zu zu fördern und das weiß ich gar nicht, ist das also hat das was mit muss das wirklich live sein? Also was ist so ein bisschen die die was sind die Komponenten? Also das geht es darum, wirklich dauerhaft auszuwerten und direkt darauf reagieren zu können, sagen wir mal auf auf Session Basis, auf einzelne Events ist es eher ein ja okay ne nach 3 Monaten haben wir dich in irgend 'nem Bucket einsortiert und dann dann dann können wir was anderes mit dir machen oder sind's all diese ganzen Aspekte? Ja, ich glaub, man\n\nSpeaker 1: kann das Ganze beliebig komplex machen, wenn man wollte und muss dann natürlich auch gucken, wie groß da noch der der Gewinn da durch ist. Aber ich glaub, man kann auch ganz einfach anfangen, dass man sagt, okay, eine Segmentierung der Nutzerbasis muss nicht immer direkt mit Machine Learning gemacht werden, sondern wir können am Anfang erst mal gucken, okay, über was fürn Kanal ist die Spielerin denn überhaupt zu uns gekommen, ne? Das wär sicherlich so die einfachste Segmentierung. Und wenn man jetzt überlegt, dass manche Spieler*innen zum Beispiel durch einen in weit kommen, weil sie von 1 anderen Spieler*innen zum Beispiel schon eingeladen worden sind. Dann gibt's natürlich noch andere Beispiele, wenn wir Marketingkampagnen machen, wo Spieler*innen über diverse Werbenetzwerke kommen können.\n\nUnd das wär vielleicht 1 so der der leichtesten oder einfachsten Beispiele, wie man am Anfang die Experience der Nutzer*innen schon anpassen kann. Und dann ist sicherlich zum Beispiel, dass man sagen könnt, okay, wenn ich jetzt über irgend son gekommen bin, dann weiß ich vielleicht schon 'n bisschen mehr über das Spiel jetzt mal hypothetisch gesprochen, als wenn ich halt einfach über einen Werbekanal komme und eigentlich nur in das Spiel gekommen bin, weil ich weil ich 'n Video dazu gesehen hab. Und das kannste, kann man dann natürlich dann fortführen, dass man zum Beispiel sagt, okay, man schaut sich das Spielverhalten in der ersten Session an. Was kann man daraus ableiten? Wenn wir jetzt zum Beispiel Beispiel nehmen, wo's ja darum geht, Bildrätsel zu lösen, kann man sich ja anschauen, wie schnell wurden die ersten Rätsel gelöst und daraus dann vielleicht ableiten, ob die neue Spieler*in eher schnell dabei gewesen ist oder langsam und dann das anzupassen, was darauf folgt, dass wir vielleicht die Spieler eben am Anfang nicht überfordern, aber es andererseits auch nicht unterfordern, weil ich würde sagen, dass beides wahrscheinlich zu schnellen Abwandern führen kann, weil es halt einfach dann nicht der Spielspaß ist, den wir erreichen wollen.\n\nMhm. In den\n\nSpeaker 0: ersten Beispielen, die Resonanz ist, man kommt irgendwie über Marketing rein oder vielleicht hat man, ne, durch eine soziale Einladung von 1 Freundin, einem Freund davon mitbekommen. Jetzt im im Teil, okay, teilen, da denke ich so, ja, das können wir ganz kurz rausbekommen vielleicht, weil das sind ja auch irgendwo unsere Daten, also an irgendeine andere Stelle hat ein anderer User was gepostet und da kann man vielleicht Daten dranhängen in irgendeiner Art und Weise. Also kommen wir da gut und schnell an solche Daten und ist das auf der Marketingseite auch so? Kommen wir da auch so live dran, dass wir praktisch sagen können, der kam jetzt in in dieser Kampagne rein und sobald er das Spiel öffnet, wissen wir, der kam über eine Werbung?\n\nSpeaker 1: Genau. Also im Endeffekt, wir haben ja in den ersten Folgen schon mal viel über das Thema SDKs gesprochen, die wir nutzen, überhaupt Daten zu sammeln Mhm. Und in dem Rahmen gibt's halt dann auch noch andere SDKs und Services, die wir nutzen, die uns dann zum Beispiel sagen, das ist die Werbekampagne gewesen oder das Werbenetzwerk, über die eine Spielerin in unser Game gekommen ist, genau. Und das ist mit zusätzlichen Tools möglich und teilweise wird's sogar auch schon von Firebase unterstützt, insbesondere wenn man sich dann wieder im Google Universum bewegt und meinetwegen Werbung auf Google Ads macht.\n\nSpeaker 0: Okay. Die anderen Beispiele, die wir grade hatten, waren son bisschen ja, wir wollen die User Experience anpassen Mhm. Auf Basis von irgendwie etwas, was Du gemacht hast, dann dafür zu sorgen, dass Du noch irgendwie mehr Spaß hast oder das besser auf dich zugeschnitten hast Ja. Letztendlich und was wahrscheinlich trotzdem aus aus Game Lied oder aus aus 1 Sicht 1 erfolgreichen Spiels Spielemachers irgendwie kommt, so. Es wäre ja auch schön, wenn man irgendwie vorhersagen könnte, was Leute dann in der Zukunft machen, ne.\n\nDas ist ja noch vielleicht noch mal ein anderer Aspekt, nicht nur das anpassen oder vielleicht ist dann eine passen wir trotzdem das Spiel an,\n\nSpeaker 1: aber sozusagen so die Vorhersage wär da vielleicht sogar man könnte ja auch Vorhersagen machen, wie viel Level noch gespielt werden als Beispiel. Und dann gibt's natürlich auch noch andere Aspekte, die damit zusammenhängen. Einmal, wir haben ja grad ja schon mal über das Abwandern oder Tschören, wie man im Englischen sagt, gesprochen. Grade so Themen wie Tschörenvorhersage sind natürlich interessant. Ich sag mal, das Beispiel kämen die meisten Leute aus der Vergangenheit wahrscheinlich von früher, wenn sie 'n Handyvertrag hatten.\n\nWenn sie gekündigt haben, hat man auf einmal wesentlich mehr Aufmerksamkeit bekommen vom Anbieter, weil der Anbieter versucht, mit allen Mitteln zu verhindern, dass man geht. Eine ähnliche Situation haben wir natürlich auch. Wir haben zwar Millionen von Spieler*innen, aber wir wollen auch verhindern, dass sie gehen. Und wenn wir vorher erkennen könnten, dass meinetwegen das Interesse sinkt oder dass es bestimmte Herausforderungen im Spiel gibt, womit sie nicht zurechtkommen, dann würden wir da auch zum Beispiel gerne entgegensteuern. Und für uns ist es halt 'n bisschen schwieriger bei dem Beispiel vom Mobilfunkvertrag, muss ich früher 3 Monate vorher eine Kündigung schreiben.\n\nBei uns kündigt niemand in den Spielen, weil wir natürlich Free to play Spieler sind. Das heißt, Spieler*innen laden die App runter, fangen an zu spielen und wenn sie keine Lust mehr haben, hören sie einfach auf. Und trotzdem kann man sich dann zum Beispiel die Daten genauer anschauen und daraus dann halt unter Umständen ableiten, ob Spieler*innen mehr oder weniger wahrscheinlich sind, zeitnah abzuwandern. Und da ist es halt auch wieder unterschiedliche Möglichkeiten, die wir haben, jetzt technisch so was anzugehen. Und gibt dann natürlich auch noch diverse andere Beispiele so, ne.\n\nWenn man jetzt so in die Richtung Monetarisierung schaut, sicherlich für uns auch interessant, frühzeitig zu erkennen, ob Spieler*innen eher dazu tendieren, in zu tätigen oder nicht, weil man dann natürlich dann unter Umständen halt auch Angebote zeigen kann oder die Werbelast anpassen kann. Und das sind zum Beispiel weitere Beispiele und können Sie natürlich auch erweitern. Ich meine, sone Wachstum ist für uns immer wichtig. Wir haben jetzt grade schon so über die Social gesprochen. Wenn ich das Gefühl habe, da ist jemand eher, dass er dazu tendiert, vielleicht noch mal jemanden einzuladen, dann könnte ich natürlich halt auch wieder 'n bisschen Werbung dagegen eintauschen, dass ich vielleicht 'n paarmal häufiger die Person dazu auffordere, weil ne, das ist natürlich dann auch ein günstiger Kanal für uns Ja.\n\nDie App größer werden zu lassen.\n\nSpeaker 0: Gibt es irgend eine Größenordnung, von der man generell sprechen kann, die man irgendwie braucht an Datengrundlage, so individuell irgendwelche Predictions machen zu können?\n\nSpeaker 1: Also ich glaube, es ist schwierig, wirklich da jetzt eine Daumenregel zu sagen oder zu haben, dass man sagt, okay, man braucht mindestens x Events oder sonst was. Also ich würd schon sagen, dass mit zunehmender Dauer natürlich eine Vorhersage besser wird, aber in gewisser Weise natürlich auch uninteressanter. Also ich sag mal, wenn ich jetzt 'n eine Spielerin 3 Monate beobachten müsste, Mhm. Kauf vorherzusagen, zu Tom Schänden schon zu spät oder die Person hat dann schon Mhm. Was gekauft und da ist so ähnlich dann halt auch wieder mit den mit den Merkmalen oder Featuren, ne.\n\nJe mehr wir an Signalen haben, also wir haben ja in den vergangenen Folgen schon darüber gesprochen, dass wir teilweise bis zu 600 Events pro Spiel sammeln, ist natürlich, je mehr Informationen wir haben, desto mehr Merkmale können wir für einen Machine Learning Algorithmus zum Beispiel zusammenstellen, aber das ist trotzdem schwierig zu sagen, was jetzt so die Daumenregel ist. Aber ich würd zum Beispiel sagen, bei bei ist es schon schön, wenn man so die ersten Tage Daten hat und dann halt auch 'n gewisses Bild, was das Spielverhalten abdeckt. Also nur noch mal als Beispiel so vor Picks, da haben wir ja so Elemente wie ein tägliches Rätsel und da könnte man zum Beispiel sagen, dass das Spielen 1 täglichen Rätsels vielleicht stark damit zusammenhängt, ob Spieler*innen abwandern oder nicht, weil es natürlich son gewisser Anreiz ist. Und ich sag mal, wenn man jetzt 3 Tage lang beobachten kann, dass immer wieder das Tag tägliche Rätsel gelöst worden ist, dann hat man vielleicht weniger Bedenken, als wenn man das nur am ersten Tag gesehen hat. Und das sind sicherlich deutlich aussagekräftigere Signale, wo es dann nicht die Masse ausmacht, als wenn ich jetzt jeden Button in der App auswerten würde, weil das natürlich dann sehr viel Rauschen auch, ja, in die Daten einfließen lässt.\n\nSpeaker 0: Ist aber trotzdem wurde vorher machen haben wir uns ja ja viele Gedanken oder machen wir uns Gedanken, welche Events tracken wir und was wollen wir irgendwie damit aussagen und was hat das für einen ne für einen konkreten Bezug zu 'nem Feature oder sowas. Ist das jetzt hier auch so, dass wir uns manuell Gedanken darüber machen, welche Events führen zu 'nem Turn beispielsweise, also dass ein ein ein Spieler einen aufhört oder es ist eher so, wir werfen all unsere Events, die wir haben, in einen und dann wahrscheinlich ist es dort Machine Learning, also als dann wo wir nicht mehr explizit irgendwie was sagen muss, sondern sagen so hier, das sind die Daten und versuche das daraus zu extrahieren.\n\nSpeaker 1: Ich glaub, so die Frage an sich kann man auf verschiedenen Ebenen diskutieren. Also ich glaub, die erste Entscheidung wär erst mal, wollen wir eigentlich das System selber implementieren oder wollen wir einen Drittanbieter dafür nutzen, der sich selbst rum kümmert? Und man kann da sicherlich beide Wege gehen. Da wir eine relativ kleine Firma sind, ist für uns natürlich am Anfang immer der Weg interessant zu sagen, wir testen mal irgendeinen Drittanbieter. Zum Beispiel ist da es halt auch, dass aus dem Google Analytics Universum es halt die Möglichkeiten gibt, Vorhersagen zu machen.\n\nUnd da ist dann wirklich so, wir schmeißen alle Daten rein und sagen einfach, mach mal. Und Google Analytics weiß ja überhaupt nichts unser über unser Spiel inhaltlich. Und dementsprechend werden die dann dort sicherlich einige Sachen kombinieren, aggregieren, bestimmte Berechnungen machen und dann halt versuchen, daraus automatisch dann Merkmale abzuleiten. Würden wir was implementieren, würden wir sicherlich den anderen Weg gehen. Dann würden wir uns erst mal mit dem Gaming zusammensetzen und noch mal diskutieren, okay, was ist 'n eigentlich dein Gefühl?\n\nWas sind aktuell so die größten Pain Points? Ich mein, wir empfangen ja auch Feedback, wir lesen die Reviews, wir schauen jeden Tag in die Daten rein und würden dann vielleicht erst mal mit, sagen wir mal, 'n 'nem Dutzend oder 2 Dutzend Events und Merkmalen anfangen und dann uns iterativ in die Richtung bewegen, dass wir versuchen, immer mehr Merkmale hinzuzufügen, weil wir natürlich viel mehr Wissen über das Spiel haben, über die Mechanik und so was.\n\nSpeaker 0: Jetzt ist das ja, gerade mit dir, der dort ja ein bisschen Hintergrund hat im Bereich Machine Learning, würde man sagen, ne, solche Use Cases gab es letztendlich die letzten Jahre auch. Wie sehr verändert sich das gerade durch das was oder durch GenAI? So gibt es da hat es jetzt schon Sachen einfacher gemacht? Kann man da noch mehr sagen, so hier ist einfach alles, sagen wir mal, was da eine gute Sache ist? Nutzen wir das schon in irgendeiner Art und Weise, was so da der der aktuelle Stand, was AI angeht, wo ja irgendwie Machine Learning ein Teil von ist.\n\nSpeaker 1: Ja. Ja genau, also den letzten Teil vielleicht noch mal son bisschen abzuholen. Ich glaub, wie Du gesagt hast, das sind eigentlich Technologien, die es seit Jahren, locker 10 Jahren gibt, es da bestimmte Anwendungsfälle gibt, man dann doch trotzdem noch mal auch von Unternehmen zu Unternehmen gucken muss, ob es es wirklich wert ist, ne. Wie groß ist der Unterschied, den wir dadurch erreichen können? Und ich glaub, so ähnlich ist es jetzt bei generative AI, das ist natürlich jetzt eine krasse Neuerung, die wir in den letzten 2, 3 Jahren sehen.\n\nUnd da gibt's natürlich jetzt extrem viele Beispiele, die wir uns anschauen können. Und ich würd sagen, man muss da auch noch mal so auf verschiedenen Ebenen unterscheiden, wie wir's bei Lothum nutzen könnten. Können einerseits sagen, ne, wir nutzen wieder die Tools, die uns zur Verfügung gestellt werden. Also ne, dass wir halt irgendwie mit ChatGPT manche Sachen oder mit Sachen machen lassen, dass wir für die Bildergenerierung vielleicht mit nutzen und so was. Und als Tools würd ich sagen, sind wir als Firma superoffen.\n\nUnd ich glaub, dass wir schon an vielen Stellen in unseren Arbeitsalltag integriert haben und dass wir davon wieder als kleine Firma sehr stark profitieren, ne. Und vielleicht sollte man ja auch dazusagen, sone Firma wie Lotum sind ja nicht nur die Game Teams. Wir haben dann auch extrem wichtige andere Teams, die es überhaupt nur möglich machen, dass wir solche Spiele produzieren, wenn man jetzt zum Beispiel an das Contentteam denkt oder halt auch, wir haben halt auch 'n Supportteam. Und da gibt's sicherlich viele Anwendungsfälle, wo wir von generative AI profitieren können. Und dann kann man halt sich wieder die Spiele anschauen.\n\nUnd da würd ich halt sagen, ist es eher aus meiner Sichtweise, meinem Gefühl noch so, dass vielleicht son Ticken zu früh ist, son LLM allein auf son Spiel loszulassen, ne. Wir versuchen da schon auch, was so Feature angeht, uns Gedanken zu machen, wie man meinetwegen Content erstellen könnte, wenn's jetzt zum Beispiel Quizfragen geht oder so was. Aber das ist häufig noch an sonem Punkt, wo man sagen würde, es ist für die Qualität der Spiele, die wir haben, halt noch nicht ausreichend gewesen. Und dann halt auch, ne, da muss man sich wieder Gedanken machen. Also wenn man jetzt zum Beispiel hat, wo dann Leute Inhalte produzieren könnten, kommen bestimmt wieder ein paar wenige wieder auf die Idee, Inhalte produzieren, die wir jetzt mit unserer Brand nicht verknüpfen wollen.\n\nUnd in anderen Bereichen muss man natürlich auch noch mal sagen, so die Komplexität und die Kosten, die, sagen wir mal so, durch so Machine Learning Algorithmen für die Vorhersage von Join zum Beispiel genutzt werden, da sind die Kosten fürs Berechnen mittlerweile vernachlässigbar. Und wenn man sich jetzt die Kosten anschaut, die durch so was entstehen, wenn man ein LLM fragt, dann ist es immer noch relativ hoch. Wenn man überlegt, dass wir Millionen Spieler haben. Und wenn man das in den Kontext setzt, wie teuer immer noch ein an ein LLM ist im Vergleich dazu, was wir dafür kriegen, eine Werbung auszuspielen, Mhm. Da ist noch eine gewisse Herausforderung.\n\nAber ich seh das durchaus in der Zukunft superspannend an local oder eigene LLMs zu haben, die für unsere Use Cases speziell trainiert sind auf unseren eigenen Daten, spitzere Anwendungsfälle. Und ich glaub, das wird in der Zukunft schon noch was sein, was auch uns noch mal ganz neue Möglichkeiten gibt in der Spieleentwicklung. Mhm.\n\nSpeaker 0: Cool. Gibt es denn schon irgendwelche, also ist ja, glaube ich, generell auch 'n Bereich, in dem wir noch 'n bisschen mehr neu reinkommen, was hat das eben auch schon angesprochen, als er vor 4 Jahren zu Lotum kam, waren wir vielleicht BI technisch noch nicht so aufgestellt, wie wir es heute sind und sind recht stolz, dass wir, sagen wir mal, den den den ganzen Analytics Part mittlerweile ganz gut drauf haben, würde ich sagen und wirklich viel über unsere Spielern und das das Ganze, was da halt zusammenhängt, haben und Predictive Analytics aber eher so ein Bereich ist, wo wir gerade so reinsappen. Das heißt, wir haben wahrscheinlich noch keine Haben wir schon irgendwelche größeren Game Features gehabt, die so darauf basieren, wo wir sagen, dass\n\nSpeaker 1: Ja, ich mein so, aber ich glaub, Du kennst Du aus deiner Vergangenheit als Game Lied von das Thema Difficult Adjustment und das erlebt grade auch noch mal einen Revival sozusagen. Das sind durchaus Sachen, wir drin sind, aber ansonsten würde ich sagen, in den Gameteams ist es sicherlich was, was grade so auch entsteht auch, ne? Die Sachen, wo wir eben drüber gesprochen hatten. Tobi, der meinetwegen dann dort bei The Test 'n bisschen mehr so in die Richtung sagt irgendwie, dass man mal irgendwie jetzt Algorithmen ausm Bereich Machine Learning oder so genutzt hat. Aber sicherlich eher so, dass alleine das Aufstellen, dass das Erarbeiten dieser ganzen Dateninfrastruktur sicherlich so in der Vergangenheit der Fokus war und auch immer noch 'n Großteil unserer Arbeit ist und das eher Themen sind, die wir dann in der Zukunft sicherlich noch mehr explorieren werden und da dann halt auch wirklich wieder gucken, ne, was sind Services, die wir einkaufen?\n\nWas sind Services, die wir selber bauen? Und dann halt das Interessante halt noch bei dem Thema ist halt noch mal in andere Teams zu gehen. 'N anderes Beispiel wär zum Beispiel noch, wenn wir uns das Thema Marketing und User Acquisition anschauen, wo ich persönlich halt auch noch mal 'n sehr großen Hebel darin sehe, in die Zukunft schauen zu können, wenn wir uns zum Beispiel das Bewerten von Marketingkampagnen anschauen. Mhm. Können wir natürlich erst mal sagen, okay, gucken wir uns einfach den Status quo an, was es uns kostet, eine Spieler*innen einzukaufen.\n\nAber für uns ist es vom Businessmodell her superinteressant zu sehen, wie bestimmte Gruppen an Spieler*innen sich in der Zukunft verhalten werden, was die Monetarisierung angeht, weil's für uns nur dann Sinn macht, Marketing zu betreiben, wenn wir wissen, dass wir das Geld auch wieder einspielen. Und bei uns ist es halt nicht so, dass wir nach 7 Tagen sagen können, okay, da ist das Geld wieder reingekommen. Wir fahren die Kampagne hoch. Bei uns ist halt eher so die Perspektive, dass wir denken, ja, 180 Tage könnten vielleicht son Zeitraum sein, indem wir erwarten, dass das Geld wieder zurückgespielt wird. Und das macht die Steuerung natürlich von 'ner Werbekampagne extrem schwierig, weil ich ja nicht heute 180 Tage warten kann, dann zu erscheinen, die Entscheidung zu treffen, das Budget zu erhöhen.\n\nUnd da würd ich sagen, ist da wieder son Beispiel, wo wir sagen, okay, wir glauben, dass diese Technologie für uns sehr wichtig ist. Wir fangen wieder an, am Anfang meinetwegen so was einzukaufen, dass wir Unterstützung bekommen, meinetwegen für bestimmte Kohorten, also Gruppen an Spieler*innen Vorhersagen zu treffen, wie sie sich in der Zukunft von der Monetarisierung her verhalten werden und dann halt die Entscheidung treffen, wie wichtig ist das für uns? Würden wir das selber im Team nachbauen oder was bauen, was für unseren Use Case noch besser passt? Haben wir die Kapazitäten dafür? Und so sehe ich auch anwendungsfälle durchaus noch auch noch in anderen Teams, ja.\n\nSpeaker 0: Okay. Gibt's noch was zu Predictive Analytics? Unsere solche Sachen.\n\nSpeaker 1: Ich könnte sicherlich noch den ganzen Tag darüber reden, aber wir wollten uns ja hier son gewissen zeitlichen Namen nehmen. Ja. Aber vielleicht fällt den anderen noch was ein. Die sind happy. Kopfschütteln ist immer gut im Podcast.\n\nKommt, geht direkt\n\nSpeaker 0: durchs Ohr. Zu unseren Hörer*innen. Nee cool, dann vielen Dank, auch das ist 'n superspannender Part und ich hoffe, unsere Hörer*innen konnten ein bisschen einen Einblick bekommen, ne, was alles unter Business Intelligence letztendlich läuft bei Lotum, also was das für unterschiedliche Bereiche sind, in denen wir da unterwegs sind, was unsere Herausforderungen sind, was wir uns vorstellen, ne? Ich glaube vielleicht auch das, ne, zu so grundsätzlichen Einordnungen so. Es ist 'n Themenbereich, wo wir nicht davon ausgehen, dass er schrumpft, so, sondern das ist eher was was noch wichtiger werden wird, auch in der Zukunft und und von daher einfach 'n sehr fester Bestandteil von von unserer täglichen Arbeit ist in in allen Bereichen, wie Du so sagtest, ne?\n\nAlso in sehr, sehr vielen Teams, wo das eben datengetrieben unterstützt werden kann und mit Sicherheit auch einen Erfolgsfaktor, ne? Dass man eben auf aufgrund von Daten und Ergebnissen Entscheidungen treffen kann und angereichert mit ein bisschen Bauchgefühl und ein bisschen Erfahrung, die auch dahinter steckt. Ich glaub, das ist auch noch son Punkt, ne? Ich hab noch nicht angesprochen, aber weil wenn man glaube ich, meine meine Sicht so, wenn man Spieleentwicklung sich insgesamt anguckt und wahrscheinlich auch Produktentwicklung, man könnte auch, wenn man nur Zahlen sich anguckt, jedes Spiel und jedes Produkt relativ schnell kaputt kriegen, ne? Selbst wenn man auch immer nur das nimmt, was die besseren Metriken hat oder so, aber es ist dann doch am Ende die Kombination und von daher gefühlt gehört halt eben neben dem auch nur Angucken der Zahlen eben auch das interpretieren der Zahlen und das Kombinieren und letztendlich die Erfahrung dazu, die richtigen Schlüsse dann aus den Zahlen auch am Ende zu ziehen.\n\nSpeaker 1: Ja, und man muss ja auch immer dazu sagen, vieles von dem, wenn wir jetzt über Charilen Prediction so was gesprochen haben, sind ja statistisch Lernverfahren und die geben dir erst mal nur die Korrelation an. Und die Kausalität ist gar nicht so einfach abzuleiten. Und da ist es ja wirklich, wo ja, nicht nur Bauchgefühl, aber auch Erfahrung eine große Rolle spielt und meinetwegen Spiele selber zu spielen, zu verstehen, was die Mechaniken sind. Und ich glaub, da sind wir noch weit davon entfernt, dass uns das AI alles abnimmt und ich glaub, das ist nicht zu unterschätzen, was das ausmacht.\n\nSpeaker 0: Ja. Fantastisch. Dann sind wir am Ende unserer kleinen Business Intelligence Essentials, ich nenne\n\nSpeaker 1: sie jetzt so besser,\n\nSpeaker 0: Serie, angelangt. Schreibt uns gerne mal, wie ihr das Format fandet, ob ihr das cool fandet, so einen Einblick mal in verschiedene Slots aufgeteilt. Wir haben zwischendurch eben schon gesagt, so klar, ne? Es ist ein relativ high level Überblick so über das, was wir machen. Über verschiedenste Bereiche.\n\nEs gibt bei Sicherheit reine BI Podcaste draußen, die jede Woche nichts anderes als Thema haben und wenn euch das interessiert und wir irgendwie in einzelne Bereiche noch mal tiefer einsteigen sollen, schreibt uns gerne das Feedback und wir gucken, was wir daraus machen können. Vielen Dank Tobi, vielen Dank Wessel, vielen Dank Fabi, dass ihr die Zeit gefunden habt, das Ganze mit uns aufzunehmen. Schreibt, liebe Hörerinnen, euer Feedback an Podcast at Programmier Punkt Bar oder über unsere Social Media Kanäle oder über Spotify. Es gibt verschiedenste Wege, wie ihr euch wie ihr uns erreichen könnt und sonst alles Gute. Bis zum nächsten Mal.\n\nMacht es gut. Tschau ciao. Tschau ciao. Tschau.",[12482,12491,12494,12505,12518,12527,12534,12547,12555,12560,12569,12582,12593,12606,12619,12624,12635,12645,12657,12662,12675,12688,12701,12714,12719,12724,12737,12750,12757,12770,12783,12790,12803,12814,12826,12839,12852,12865,12873,12881,12894,12906,12918,12931,12939,12950,12954,12967,12980,12990,12997,13002,13015,13028],{"sentences":12483,"speaker":184,"num_words":12490,"start":181,"end":356},[12484,12486,12488],{"text":12485,"start":181,"end":248},"Hallo und herzlich willkommen zum dritten Teil unserer programmierbaren Mini Folge zu Business Intelligence.",{"text":12487,"start":253,"end":339},"Ich bin Dennis und hab hier meine Programmier Bieber und die Programmier Barbie mit am Start, wie sie sich eben selbst getauft haben.",{"text":12489,"start":344,"end":356},"Wir haben einmal Fabian.",41,{"sentences":12492,"speaker":163,"num_words":163,"start":360,"end":361},[12493],{"text":364,"start":360,"end":361},{"sentences":12495,"speaker":184,"num_words":12504,"start":361,"end":472},[12496,12498,12499,12501,12502],{"text":12497,"start":361,"end":372},"Wir haben Tobi.",{"text":379,"start":372,"end":377},{"text":12500,"start":381,"end":392},"Und wir haben Wessel.",{"text":399,"start":392,"end":397},{"text":12503,"start":401,"end":472},"Und genau, wir reden in 3 unterschiedlichen Teilen ein bisschen einen groben Überblick mal so über Business Intelligence bei Lotum.",29,{"sentences":12506,"speaker":184,"num_words":12517,"start":477,"end":784},[12507,12509,12511,12513,12515],{"text":12508,"start":477,"end":498},"Das heißt, jeder 3 arbeitet bei Lotum.",{"text":12510,"start":500,"end":574},"In dem ersten Teil ging es darum, wie kriegen wir eigentlich unsere ganzen Daten irgendwo hin, dass wir damit auch was anfangen können.",{"text":12512,"start":574,"end":668},"Im zweiten Teil haben wir damit was angefangen, haben Daten analysiert und ja, funktioniert ihn bereitgestellt, damit Teams einen Mehrwert aus Daten haben und daraus Entscheidungen ableiten können.",{"text":12514,"start":671,"end":748},"Und jetzt im dritten Teil soll es Predictive Analytics gehen und dann haben wir diesmal Fabian als unseren Haupt Speaker, der dazu was erzählt.",{"text":12516,"start":753,"end":784},"Natürlich auch hier, Wessel und Tobi, ihr seid jederzeit eingeladen.",92,{"sentences":12519,"speaker":184,"num_words":12526,"start":789,"end":979},[12520,12522,12524],{"text":12521,"start":789,"end":858},"Falls ihr denkt, da fehlt doch was oder da hat der Dennis eine schlechte Frage gestellt, dürft ihr natürlich gerne jederzeit einspringen.",{"text":12523,"start":858,"end":907},"Sonst freue ich mich, euch mit den Fragen ein bisschen durch die Folge zu führen.",{"text":12525,"start":911,"end":979},"Und bevor wir starten, Fabian, noch an dich erst mal die Frage kurzer Background, was hast Du denn mit Daten zu tun?",59,{"sentences":12528,"speaker":163,"num_words":12533,"start":982,"end":1289},[12529,12531],{"text":12530,"start":982,"end":1211},"Ja, also ich hab vor wirklich einigen Jahren schon mal irgendwann Informatik studiert und hab dann gegen Ende des Studiums festgestellt, dass ich mich doch ziemlich für Machine Learning interessiere, was natürlich schon sehr stark auf Daten basiert und hab mich dann im Anschluss an mein Studium auch für eine Promotion im Bereich KI oder Machine Learning entschieden und ja, in den Folgejahren dann in verschiedenen, ja, Data Science Bereichen gearbeitet.",{"text":12532,"start":1216,"end":1289},"Hatte eine Zeit lang mein eigenes Start-up, dann in unterschiedlichen anderen Firmen noch gearbeitet und bin dann vor circa 2 Jahren hier zu Lotum gekommen.",96,{"sentences":12535,"speaker":184,"num_words":12546,"start":1293,"end":1501},[12536,12538,12540,12542,12544],{"text":12537,"start":1293,"end":1341},"Und wer deine Stimme mehr hören möchte, können wir noch kurz Cross Werbung machen.",{"text":12539,"start":1341,"end":1406},"Du warst ja schon durchaus mal in der Programmierbar, unter anderem der Deep Dive 139 GPT Under the Hood.",{"text":12541,"start":1410,"end":1439},"Das war ein, den Du hattest, der Anfang des Jahres.",{"text":12543,"start":1444,"end":1464},"Aber die Zeit vergeht immer so schnell.",{"text":12545,"start":1469,"end":1501},"Denk, das war gerade erst, aber ja, ist auch schon wieder",61,{"sentences":12548,"speaker":163,"num_words":12554,"start":1504,"end":1530},[12549,12551,12552,12553],{"text":12550,"start":1504,"end":1517},"Tatsächlich schon Januar gewesen.",{"text":1523,"start":1517,"end":1521},{"text":1523,"start":1521,"end":1525},{"text":1532,"start":1525,"end":1530},7,{"sentences":12556,"speaker":184,"num_words":12559,"start":1534,"end":1546},[12557],{"text":12558,"start":1534,"end":1546},"Predictive Analytics, Einsatz?",3,{"sentences":12561,"speaker":163,"num_words":12568,"start":1550,"end":1758},[12562,12564,12566],{"text":12563,"start":1550,"end":1617},"Ja, also ich meine, wie Du in der letzten Folge schon angeteasert hast, es geht jetzt mehr darum, auch in die Zukunft zu schauen.",{"text":12565,"start":1617,"end":1720},"Also wenn wir vorher viel das Verhalten der Spieler*innen in der App in der Vergangenheit betrachtet haben, geht's jetzt darum zu schauen, was Spieler*innen dann möglicherweise in der Zukunft tun werden, darauf reagieren zu können.",{"text":12567,"start":1720,"end":1758},"Das wär jetzt mehr als ein Satz, aber ja, so kurz und knapp.",72,{"sentences":12570,"speaker":184,"num_words":12581,"start":1761,"end":2022},[12571,12573,12575,12577,12579],{"text":12572,"start":1761,"end":1772},"Sehr gut, cool.",{"text":12574,"start":1776,"end":1794},"Also ja, ist wahrscheinlich daher, ne.",{"text":12576,"start":1794,"end":1850},"Also ich mein, wir haben irgendwie, also in unseren Spielen, das ist ja unser unser Kontext ein bisschen.",{"text":12578,"start":1850,"end":1971},"Wir haben User, die sich unterschiedlich verhalten, die auf unterschiedliche Experimente unterschiedlich reagieren und in der Regel versuchen wir erst mal natürlich auf die die Allgemeinheit, also unsere gesamte Zielgruppe so zu optimieren und das ist viel, was wir machen.",{"text":12580,"start":1971,"end":2022},"Also wie können wir es für das Spiel für alle gleichermaßen ändern und in in Summe besser machen.",85,{"sentences":12583,"speaker":184,"num_words":12592,"start":2025,"end":2457},[12584,12586,12588,12590],{"text":12585,"start":2025,"end":2231},"Aber wenn man eine guten eine gute Datenbasis hat, dann kommt man natürlich auch irgendwie schnell auf die Idee, noch mehr zu machen und zu sagen, okay, ich will einzelnen Usern, ne, eine unterschiedliche Experience geben, ja einfach den möglichst maximalen Spielspaß irgendwie zu zu fördern und das weiß ich gar nicht, ist das also hat das was mit muss das wirklich live sein?",{"text":12587,"start":2231,"end":2266},"Also was ist so ein bisschen die die was sind die Komponenten?",{"text":12589,"start":2266,"end":2442},"Also das geht es darum, wirklich dauerhaft auszuwerten und direkt darauf reagieren zu können, sagen wir mal auf auf Session Basis, auf einzelne Events ist es eher ein ja okay ne nach 3 Monaten haben wir dich in irgend 'nem Bucket einsortiert und dann dann dann können wir was anderes mit dir machen oder sind's all diese ganzen Aspekte?",{"text":12591,"start":2446,"end":2457},"Ja, ich glaub, man",139,{"sentences":12594,"speaker":163,"num_words":12605,"start":2457,"end":2818},[12595,12597,12599,12601,12603],{"text":12596,"start":2457,"end":2531},"kann das Ganze beliebig komplex machen, wenn man wollte und muss dann natürlich auch gucken, wie groß da noch der der Gewinn da durch ist.",{"text":12598,"start":2531,"end":2666},"Aber ich glaub, man kann auch ganz einfach anfangen, dass man sagt, okay, eine Segmentierung der Nutzerbasis muss nicht immer direkt mit Machine Learning gemacht werden, sondern wir können am Anfang erst mal gucken, okay, über was fürn Kanal ist die Spielerin denn überhaupt zu uns gekommen, ne?",{"text":12600,"start":2666,"end":2686},"Das wär sicherlich so die einfachste Segmentierung.",{"text":12602,"start":2690,"end":2766},"Und wenn man jetzt überlegt, dass manche Spieler*innen zum Beispiel durch einen in weit kommen, weil sie von 1 anderen Spieler*innen zum Beispiel schon eingeladen worden sind.",{"text":12604,"start":2766,"end":2818},"Dann gibt's natürlich noch andere Beispiele, wenn wir Marketingkampagnen machen, wo Spieler*innen über diverse Werbenetzwerke kommen können.",124,{"sentences":12607,"speaker":163,"num_words":12618,"start":2818,"end":3365},[12608,12610,12612,12614,12616],{"text":12609,"start":2818,"end":2883},"Und das wär vielleicht 1 so der der leichtesten oder einfachsten Beispiele, wie man am Anfang die Experience der Nutzer*innen schon anpassen kann.",{"text":12611,"start":2883,"end":3040},"Und dann ist sicherlich zum Beispiel, dass man sagen könnt, okay, wenn ich jetzt über irgend son gekommen bin, dann weiß ich vielleicht schon 'n bisschen mehr über das Spiel jetzt mal hypothetisch gesprochen, als wenn ich halt einfach über einen Werbekanal komme und eigentlich nur in das Spiel gekommen bin, weil ich weil ich 'n Video dazu gesehen hab.",{"text":12613,"start":3040,"end":3106},"Und das kannste, kann man dann natürlich dann fortführen, dass man zum Beispiel sagt, okay, man schaut sich das Spielverhalten in der ersten Session an.",{"text":12615,"start":3106,"end":3118},"Was kann man daraus ableiten?",{"text":12617,"start":3118,"end":3365},"Wenn wir jetzt zum Beispiel Beispiel nehmen, wo's ja darum geht, Bildrätsel zu lösen, kann man sich ja anschauen, wie schnell wurden die ersten Rätsel gelöst und daraus dann vielleicht ableiten, ob die neue Spieler*in eher schnell dabei gewesen ist oder langsam und dann das anzupassen, was darauf folgt, dass wir vielleicht die Spieler eben am Anfang nicht überfordern, aber es andererseits auch nicht unterfordern, weil ich würde sagen, dass beides wahrscheinlich zu schnellen Abwandern führen kann, weil es halt einfach dann nicht der Spielspaß ist, den wir erreichen wollen.",203,{"sentences":12620,"speaker":163,"num_words":12559,"start":3369,"end":3380},[12621,12622],{"text":3373,"start":3369,"end":3370},{"text":12623,"start":3375,"end":3380},"In den",{"sentences":12625,"speaker":184,"num_words":12634,"start":3380,"end":3739},[12626,12628,12630,12632],{"text":12627,"start":3380,"end":3462},"ersten Beispielen, die Resonanz ist, man kommt irgendwie über Marketing rein oder vielleicht hat man, ne, durch eine soziale Einladung von 1 Freundin, einem Freund davon mitbekommen.",{"text":12629,"start":3466,"end":3606},"Jetzt im im Teil, okay, teilen, da denke ich so, ja, das können wir ganz kurz rausbekommen vielleicht, weil das sind ja auch irgendwo unsere Daten, also an irgendeine andere Stelle hat ein anderer User was gepostet und da kann man vielleicht Daten dranhängen in irgendeiner Art und Weise.",{"text":12631,"start":3606,"end":3651},"Also kommen wir da gut und schnell an solche Daten und ist das auf der Marketingseite auch so?",{"text":12633,"start":3651,"end":3739},"Kommen wir da auch so live dran, dass wir praktisch sagen können, der kam jetzt in in dieser Kampagne rein und sobald er das Spiel öffnet, wissen wir, der kam über eine Werbung?",127,{"sentences":12636,"speaker":163,"num_words":12644,"start":3742,"end":4016},[12637,12638,12640,12642],{"text":3746,"start":3742,"end":3743},{"text":12639,"start":3743,"end":3818},"Also im Endeffekt, wir haben ja in den ersten Folgen schon mal viel über das Thema SDKs gesprochen, die wir nutzen, überhaupt Daten zu sammeln Mhm.",{"text":12641,"start":3818,"end":3919},"Und in dem Rahmen gibt's halt dann auch noch andere SDKs und Services, die wir nutzen, die uns dann zum Beispiel sagen, das ist die Werbekampagne gewesen oder das Werbenetzwerk, über die eine Spielerin in unser Game gekommen ist, genau.",{"text":12643,"start":3919,"end":4016},"Und das ist mit zusätzlichen Tools möglich und teilweise wird's sogar auch schon von Firebase unterstützt, insbesondere wenn man sich dann wieder im Google Universum bewegt und meinetwegen Werbung auf Google Ads macht.",100,{"sentences":12646,"speaker":184,"num_words":12656,"start":4019,"end":4267},[12647,12648,12650,12652,12654],{"text":4022,"start":4019,"end":4020},{"text":12649,"start":4024,"end":4070},"Die anderen Beispiele, die wir grade hatten, waren son bisschen ja, wir wollen die User Experience anpassen Mhm.",{"text":12651,"start":4073,"end":4143},"Auf Basis von irgendwie etwas, was Du gemacht hast, dann dafür zu sorgen, dass Du noch irgendwie mehr Spaß hast oder das besser auf dich zugeschnitten hast Ja.",{"text":12653,"start":4148,"end":4211},"Letztendlich und was wahrscheinlich trotzdem aus aus Game Lied oder aus aus 1 Sicht 1 erfolgreichen Spiels Spielemachers irgendwie kommt, so.",{"text":12655,"start":4215,"end":4267},"Es wäre ja auch schön, wenn man irgendwie vorhersagen könnte, was Leute dann in der Zukunft machen, ne.",86,{"sentences":12658,"speaker":184,"num_words":12661,"start":4267,"end":4339},[12659],{"text":12660,"start":4267,"end":4339},"Das ist ja noch vielleicht noch mal ein anderer Aspekt, nicht nur das anpassen oder vielleicht ist dann eine passen wir trotzdem das Spiel an,",25,{"sentences":12663,"speaker":163,"num_words":12674,"start":4339,"end":4577},[12664,12666,12668,12670,12672],{"text":12665,"start":4339,"end":4411},"aber sozusagen so die Vorhersage wär da vielleicht sogar man könnte ja auch Vorhersagen machen, wie viel Level noch gespielt werden als Beispiel.",{"text":12667,"start":4411,"end":4442},"Und dann gibt's natürlich auch noch andere Aspekte, die damit zusammenhängen.",{"text":12669,"start":4442,"end":4495},"Einmal, wir haben ja grad ja schon mal über das Abwandern oder Tschören, wie man im Englischen sagt, gesprochen.",{"text":12671,"start":4499,"end":4522},"Grade so Themen wie Tschörenvorhersage sind natürlich interessant.",{"text":12673,"start":4525,"end":4577},"Ich sag mal, das Beispiel kämen die meisten Leute aus der Vergangenheit wahrscheinlich von früher, wenn sie 'n Handyvertrag hatten.",81,{"sentences":12676,"speaker":163,"num_words":12687,"start":4581,"end":4861},[12677,12679,12681,12683,12685],{"text":12678,"start":4581,"end":4652},"Wenn sie gekündigt haben, hat man auf einmal wesentlich mehr Aufmerksamkeit bekommen vom Anbieter, weil der Anbieter versucht, mit allen Mitteln zu verhindern, dass man geht.",{"text":12680,"start":4655,"end":4673},"Eine ähnliche Situation haben wir natürlich auch.",{"text":12682,"start":4673,"end":4708},"Wir haben zwar Millionen von Spieler*innen, aber wir wollen auch verhindern, dass sie gehen.",{"text":12684,"start":4708,"end":4795},"Und wenn wir vorher erkennen könnten, dass meinetwegen das Interesse sinkt oder dass es bestimmte Herausforderungen im Spiel gibt, womit sie nicht zurechtkommen, dann würden wir da auch zum Beispiel gerne entgegensteuern.",{"text":12686,"start":4799,"end":4861},"Und für uns ist es halt 'n bisschen schwieriger bei dem Beispiel vom Mobilfunkvertrag, muss ich früher 3 Monate vorher eine Kündigung schreiben.",102,{"sentences":12689,"speaker":163,"num_words":12700,"start":4864,"end":5102},[12690,12692,12694,12696,12698],{"text":12691,"start":4864,"end":4903},"Bei uns kündigt niemand in den Spielen, weil wir natürlich Free to play Spieler sind.",{"text":12693,"start":4903,"end":4959},"Das heißt, Spieler*innen laden die App runter, fangen an zu spielen und wenn sie keine Lust mehr haben, hören sie einfach auf.",{"text":12695,"start":4959,"end":5030},"Und trotzdem kann man sich dann zum Beispiel die Daten genauer anschauen und daraus dann halt unter Umständen ableiten, ob Spieler*innen mehr oder weniger wahrscheinlich sind, zeitnah abzuwandern.",{"text":12697,"start":5034,"end":5073},"Und da ist es halt auch wieder unterschiedliche Möglichkeiten, die wir haben, jetzt technisch so was anzugehen.",{"text":12699,"start":5077,"end":5102},"Und gibt dann natürlich auch noch diverse andere Beispiele so, ne.",93,{"sentences":12702,"speaker":163,"num_words":12713,"start":5102,"end":5450},[12703,12705,12707,12709,12711],{"text":12704,"start":5102,"end":5223},"Wenn man jetzt so in die Richtung Monetarisierung schaut, sicherlich für uns auch interessant, frühzeitig zu erkennen, ob Spieler*innen eher dazu tendieren, in zu tätigen oder nicht, weil man dann natürlich dann unter Umständen halt auch Angebote zeigen kann oder die Werbelast anpassen kann.",{"text":12706,"start":5223,"end":5257},"Und das sind zum Beispiel weitere Beispiele und können Sie natürlich auch erweitern.",{"text":12708,"start":5257,"end":5283},"Ich meine, sone Wachstum ist für uns immer wichtig.",{"text":12710,"start":5287,"end":5310},"Wir haben jetzt grade schon so über die Social gesprochen.",{"text":12712,"start":5313,"end":5450},"Wenn ich das Gefühl habe, da ist jemand eher, dass er dazu tendiert, vielleicht noch mal jemanden einzuladen, dann könnte ich natürlich halt auch wieder 'n bisschen Werbung dagegen eintauschen, dass ich vielleicht 'n paarmal häufiger die Person dazu auffordere, weil ne, das ist natürlich dann auch ein günstiger Kanal für uns Ja.",129,{"sentences":12715,"speaker":163,"num_words":12718,"start":5452,"end":5466},[12716],{"text":12717,"start":5452,"end":5466},"Die App größer werden zu lassen.",6,{"sentences":12720,"speaker":184,"num_words":12723,"start":5469,"end":5542},[12721],{"text":12722,"start":5469,"end":5542},"Gibt es irgend eine Größenordnung, von der man generell sprechen kann, die man irgendwie braucht an Datengrundlage, so individuell irgendwelche Predictions machen zu können?",24,{"sentences":12725,"speaker":163,"num_words":12736,"start":5545,"end":5818},[12726,12728,12730,12732,12734],{"text":12727,"start":5545,"end":5618},"Also ich glaube, es ist schwierig, wirklich da jetzt eine Daumenregel zu sagen oder zu haben, dass man sagt, okay, man braucht mindestens x Events oder sonst was.",{"text":12729,"start":5618,"end":5676},"Also ich würd schon sagen, dass mit zunehmender Dauer natürlich eine Vorhersage besser wird, aber in gewisser Weise natürlich auch uninteressanter.",{"text":12731,"start":5680,"end":5717},"Also ich sag mal, wenn ich jetzt 'n eine Spielerin 3 Monate beobachten müsste, Mhm.",{"text":12733,"start":5720,"end":5765},"Kauf vorherzusagen, zu Tom Schänden schon zu spät oder die Person hat dann schon Mhm.",{"text":12735,"start":5767,"end":5818},"Was gekauft und da ist so ähnlich dann halt auch wieder mit den mit den Merkmalen oder Featuren, ne.",98,{"sentences":12738,"speaker":163,"num_words":12749,"start":5818,"end":6363},[12739,12741,12743,12745,12747],{"text":12740,"start":5818,"end":5965},"Je mehr wir an Signalen haben, also wir haben ja in den vergangenen Folgen schon darüber gesprochen, dass wir teilweise bis zu 600 Events pro Spiel sammeln, ist natürlich, je mehr Informationen wir haben, desto mehr Merkmale können wir für einen Machine Learning Algorithmus zum Beispiel zusammenstellen, aber das ist trotzdem schwierig zu sagen, was jetzt so die Daumenregel ist.",{"text":12742,"start":5965,"end":6041},"Aber ich würd zum Beispiel sagen, bei bei ist es schon schön, wenn man so die ersten Tage Daten hat und dann halt auch 'n gewisses Bild, was das Spielverhalten abdeckt.",{"text":12744,"start":6041,"end":6169},"Also nur noch mal als Beispiel so vor Picks, da haben wir ja so Elemente wie ein tägliches Rätsel und da könnte man zum Beispiel sagen, dass das Spielen 1 täglichen Rätsels vielleicht stark damit zusammenhängt, ob Spieler*innen abwandern oder nicht, weil es natürlich son gewisser Anreiz ist.",{"text":12746,"start":6169,"end":6254},"Und ich sag mal, wenn man jetzt 3 Tage lang beobachten kann, dass immer wieder das Tag tägliche Rätsel gelöst worden ist, dann hat man vielleicht weniger Bedenken, als wenn man das nur am ersten Tag gesehen hat.",{"text":12748,"start":6254,"end":6363},"Und das sind sicherlich deutlich aussagekräftigere Signale, wo es dann nicht die Masse ausmacht, als wenn ich jetzt jeden Button in der App auswerten würde, weil das natürlich dann sehr viel Rauschen auch, ja, in die Daten einfließen lässt.",216,{"sentences":12751,"speaker":184,"num_words":12756,"start":6367,"end":6692},[12752,12754],{"text":12753,"start":6367,"end":6495},"Ist aber trotzdem wurde vorher machen haben wir uns ja ja viele Gedanken oder machen wir uns Gedanken, welche Events tracken wir und was wollen wir irgendwie damit aussagen und was hat das für einen ne für einen konkreten Bezug zu 'nem Feature oder sowas.",{"text":12755,"start":6499,"end":6692},"Ist das jetzt hier auch so, dass wir uns manuell Gedanken darüber machen, welche Events führen zu 'nem Turn beispielsweise, also dass ein ein ein Spieler einen aufhört oder es ist eher so, wir werfen all unsere Events, die wir haben, in einen und dann wahrscheinlich ist es dort Machine Learning, also als dann wo wir nicht mehr explizit irgendwie was sagen muss, sondern sagen so hier, das sind die Daten und versuche das daraus zu extrahieren.",122,{"sentences":12758,"speaker":163,"num_words":12769,"start":6696,"end":6941},[12759,12761,12763,12765,12767],{"text":12760,"start":6696,"end":6729},"Ich glaub, so die Frage an sich kann man auf verschiedenen Ebenen diskutieren.",{"text":12762,"start":6729,"end":6805},"Also ich glaub, die erste Entscheidung wär erst mal, wollen wir eigentlich das System selber implementieren oder wollen wir einen Drittanbieter dafür nutzen, der sich selbst rum kümmert?",{"text":12764,"start":6805,"end":6826},"Und man kann da sicherlich beide Wege gehen.",{"text":12766,"start":6826,"end":6888},"Da wir eine relativ kleine Firma sind, ist für uns natürlich am Anfang immer der Weg interessant zu sagen, wir testen mal irgendeinen Drittanbieter.",{"text":12768,"start":6892,"end":6941},"Zum Beispiel ist da es halt auch, dass aus dem Google Analytics Universum es halt die Möglichkeiten gibt, Vorhersagen zu machen.",94,{"sentences":12771,"speaker":163,"num_words":12782,"start":6941,"end":7164},[12772,12774,12776,12778,12780],{"text":12773,"start":6941,"end":6982},"Und da ist dann wirklich so, wir schmeißen alle Daten rein und sagen einfach, mach mal.",{"text":12775,"start":6982,"end":7012},"Und Google Analytics weiß ja überhaupt nichts unser über unser Spiel inhaltlich.",{"text":12777,"start":7016,"end":7083},"Und dementsprechend werden die dann dort sicherlich einige Sachen kombinieren, aggregieren, bestimmte Berechnungen machen und dann halt versuchen, daraus automatisch dann Merkmale abzuleiten.",{"text":12779,"start":7086,"end":7111},"Würden wir was implementieren, würden wir sicherlich den anderen Weg gehen.",{"text":12781,"start":7111,"end":7164},"Dann würden wir uns erst mal mit dem Gaming zusammensetzen und noch mal diskutieren, okay, was ist 'n eigentlich dein Gefühl?",83,{"sentences":12784,"speaker":163,"num_words":12789,"start":7168,"end":7361},[12785,12787],{"text":12786,"start":7168,"end":7190},"Was sind aktuell so die größten Pain Points?",{"text":12788,"start":7190,"end":7361},"Ich mein, wir empfangen ja auch Feedback, wir lesen die Reviews, wir schauen jeden Tag in die Daten rein und würden dann vielleicht erst mal mit, sagen wir mal, 'n 'nem Dutzend oder 2 Dutzend Events und Merkmalen anfangen und dann uns iterativ in die Richtung bewegen, dass wir versuchen, immer mehr Merkmale hinzuzufügen, weil wir natürlich viel mehr Wissen über das Spiel haben, über die Mechanik und so was.",78,{"sentences":12791,"speaker":184,"num_words":12802,"start":7363,"end":7640},[12792,12794,12796,12798,12800],{"text":12793,"start":7363,"end":7448},"Jetzt ist das ja, gerade mit dir, der dort ja ein bisschen Hintergrund hat im Bereich Machine Learning, würde man sagen, ne, solche Use Cases gab es letztendlich die letzten Jahre auch.",{"text":12795,"start":7450,"end":7481},"Wie sehr verändert sich das gerade durch das was oder durch GenAI?",{"text":12797,"start":7485,"end":7511},"So gibt es da hat es jetzt schon Sachen einfacher gemacht?",{"text":12799,"start":7511,"end":7565},"Kann man da noch mehr sagen, so hier ist einfach alles, sagen wir mal, was da eine gute Sache ist?",{"text":12801,"start":7565,"end":7640},"Nutzen wir das schon in irgendeiner Art und Weise, was so da der der aktuelle Stand, was AI angeht, wo ja irgendwie Machine Learning ein Teil von ist.",103,{"sentences":12804,"speaker":163,"num_words":12700,"start":7643,"end":7885},[12805,12806,12808,12810,12812],{"text":1523,"start":7643,"end":7644},{"text":12807,"start":7648,"end":7678},"Ja genau, also den letzten Teil vielleicht noch mal son bisschen abzuholen.",{"text":12809,"start":7678,"end":7792},"Ich glaub, wie Du gesagt hast, das sind eigentlich Technologien, die es seit Jahren, locker 10 Jahren gibt, es da bestimmte Anwendungsfälle gibt, man dann doch trotzdem noch mal auch von Unternehmen zu Unternehmen gucken muss, ob es es wirklich wert ist, ne.",{"text":12811,"start":7792,"end":7817},"Wie groß ist der Unterschied, den wir dadurch erreichen können?",{"text":12813,"start":7817,"end":7885},"Und ich glaub, so ähnlich ist es jetzt bei generative AI, das ist natürlich jetzt eine krasse Neuerung, die wir in den letzten 2, 3 Jahren sehen.",{"sentences":12815,"speaker":163,"num_words":12656,"start":7885,"end":8105},[12816,12818,12820,12822,12824],{"text":12817,"start":7885,"end":7916},"Und da gibt's natürlich jetzt extrem viele Beispiele, die wir uns anschauen können.",{"text":12819,"start":7916,"end":7967},"Und ich würd sagen, man muss da auch noch mal so auf verschiedenen Ebenen unterscheiden, wie wir's bei Lothum nutzen könnten.",{"text":12821,"start":7970,"end":8008},"Können einerseits sagen, ne, wir nutzen wieder die Tools, die uns zur Verfügung gestellt werden.",{"text":12823,"start":8012,"end":8079},"Also ne, dass wir halt irgendwie mit ChatGPT manche Sachen oder mit Sachen machen lassen, dass wir für die Bildergenerierung vielleicht mit nutzen und so was.",{"text":12825,"start":8079,"end":8105},"Und als Tools würd ich sagen, sind wir als Firma superoffen.",{"sentences":12827,"speaker":163,"num_words":12838,"start":8109,"end":8366},[12828,12830,12832,12834,12836],{"text":12829,"start":8109,"end":8173},"Und ich glaub, dass wir schon an vielen Stellen in unseren Arbeitsalltag integriert haben und dass wir davon wieder als kleine Firma sehr stark profitieren, ne.",{"text":12831,"start":8173,"end":8216},"Und vielleicht sollte man ja auch dazusagen, sone Firma wie Lotum sind ja nicht nur die Game Teams.",{"text":12833,"start":8220,"end":8312},"Wir haben dann auch extrem wichtige andere Teams, die es überhaupt nur möglich machen, dass wir solche Spiele produzieren, wenn man jetzt zum Beispiel an das Contentteam denkt oder halt auch, wir haben halt auch 'n Supportteam.",{"text":12835,"start":8312,"end":8345},"Und da gibt's sicherlich viele Anwendungsfälle, wo wir von generative AI profitieren können.",{"text":12837,"start":8345,"end":8366},"Und dann kann man halt sich wieder die Spiele anschauen.",104,{"sentences":12840,"speaker":163,"num_words":12851,"start":8369,"end":8706},[12841,12843,12845,12847,12849],{"text":12842,"start":8369,"end":8453},"Und da würd ich halt sagen, ist es eher aus meiner Sichtweise, meinem Gefühl noch so, dass vielleicht son Ticken zu früh ist, son LLM allein auf son Spiel loszulassen, ne.",{"text":12844,"start":8453,"end":8523},"Wir versuchen da schon auch, was so Feature angeht, uns Gedanken zu machen, wie man meinetwegen Content erstellen könnte, wenn's jetzt zum Beispiel Quizfragen geht oder so was.",{"text":12846,"start":8523,"end":8593},"Aber das ist häufig noch an sonem Punkt, wo man sagen würde, es ist für die Qualität der Spiele, die wir haben, halt noch nicht ausreichend gewesen.",{"text":12848,"start":8593,"end":8620},"Und dann halt auch, ne, da muss man sich wieder Gedanken machen.",{"text":12850,"start":8623,"end":8706},"Also wenn man jetzt zum Beispiel hat, wo dann Leute Inhalte produzieren könnten, kommen bestimmt wieder ein paar wenige wieder auf die Idee, Inhalte produzieren, die wir jetzt mit unserer Brand nicht verknüpfen wollen.",132,{"sentences":12853,"speaker":163,"num_words":12864,"start":8708,"end":8997},[12854,12856,12858,12860,12862],{"text":12855,"start":8708,"end":8824},"Und in anderen Bereichen muss man natürlich auch noch mal sagen, so die Komplexität und die Kosten, die, sagen wir mal so, durch so Machine Learning Algorithmen für die Vorhersage von Join zum Beispiel genutzt werden, da sind die Kosten fürs Berechnen mittlerweile vernachlässigbar.",{"text":12857,"start":8828,"end":8891},"Und wenn man sich jetzt die Kosten anschaut, die durch so was entstehen, wenn man ein LLM fragt, dann ist es immer noch relativ hoch.",{"text":12859,"start":8891,"end":8910},"Wenn man überlegt, dass wir Millionen Spieler haben.",{"text":12861,"start":8910,"end":8981},"Und wenn man das in den Kontext setzt, wie teuer immer noch ein an ein LLM ist im Vergleich dazu, was wir dafür kriegen, eine Werbung auszuspielen, Mhm.",{"text":12863,"start":8983,"end":8997},"Da ist noch eine gewisse Herausforderung.",111,{"sentences":12866,"speaker":163,"num_words":12872,"start":8997,"end":9139},[12867,12869,12871],{"text":12868,"start":8997,"end":9078},"Aber ich seh das durchaus in der Zukunft superspannend an local oder eigene LLMs zu haben, die für unsere Use Cases speziell trainiert sind auf unseren eigenen Daten, spitzere Anwendungsfälle.",{"text":12870,"start":9081,"end":9134},"Und ich glaub, das wird in der Zukunft schon noch was sein, was auch uns noch mal ganz neue Möglichkeiten gibt in der Spieleentwicklung.",{"text":3373,"start":9138,"end":9139},55,{"sentences":12874,"speaker":184,"num_words":12880,"start":9142,"end":9486},[12875,12876,12878],{"text":9146,"start":9142,"end":9143},{"text":12877,"start":9143,"end":9421},"Gibt es denn schon irgendwelche, also ist ja, glaube ich, generell auch 'n Bereich, in dem wir noch 'n bisschen mehr neu reinkommen, was hat das eben auch schon angesprochen, als er vor 4 Jahren zu Lotum kam, waren wir vielleicht BI technisch noch nicht so aufgestellt, wie wir es heute sind und sind recht stolz, dass wir, sagen wir mal, den den den ganzen Analytics Part mittlerweile ganz gut drauf haben, würde ich sagen und wirklich viel über unsere Spielern und das das Ganze, was da halt zusammenhängt, haben und Predictive Analytics aber eher so ein Bereich ist, wo wir gerade so reinsappen.",{"text":12879,"start":9424,"end":9486},"Das heißt, wir haben wahrscheinlich noch keine Haben wir schon irgendwelche größeren Game Features gehabt, die so darauf basieren, wo wir sagen, dass",128,{"sentences":12882,"speaker":163,"num_words":12893,"start":9488,"end":9898},[12883,12885,12887,12889,12891],{"text":12884,"start":9488,"end":9579},"Ja, ich mein so, aber ich glaub, Du kennst Du aus deiner Vergangenheit als Game Lied von das Thema Difficult Adjustment und das erlebt grade auch noch mal einen Revival sozusagen.",{"text":12886,"start":9582,"end":9647},"Das sind durchaus Sachen, wir drin sind, aber ansonsten würde ich sagen, in den Gameteams ist es sicherlich was, was grade so auch entsteht auch, ne?",{"text":12888,"start":9647,"end":9665},"Die Sachen, wo wir eben drüber gesprochen hatten.",{"text":12890,"start":9667,"end":9739},"Tobi, der meinetwegen dann dort bei The Test 'n bisschen mehr so in die Richtung sagt irgendwie, dass man mal irgendwie jetzt Algorithmen ausm Bereich Machine Learning oder so genutzt hat.",{"text":12892,"start":9739,"end":9898},"Aber sicherlich eher so, dass alleine das Aufstellen, dass das Erarbeiten dieser ganzen Dateninfrastruktur sicherlich so in der Vergangenheit der Fokus war und auch immer noch 'n Großteil unserer Arbeit ist und das eher Themen sind, die wir dann in der Zukunft sicherlich noch mehr explorieren werden und da dann halt auch wirklich wieder gucken, ne, was sind Services, die wir einkaufen?",158,{"sentences":12895,"speaker":163,"num_words":12905,"start":9901,"end":10137},[12896,12898,12900,12902,12903],{"text":12897,"start":9901,"end":9916},"Was sind Services, die wir selber bauen?",{"text":12899,"start":9919,"end":9961},"Und dann halt das Interessante halt noch bei dem Thema ist halt noch mal in andere Teams zu gehen.",{"text":12901,"start":9961,"end":10075},"'N anderes Beispiel wär zum Beispiel noch, wenn wir uns das Thema Marketing und User Acquisition anschauen, wo ich persönlich halt auch noch mal 'n sehr großen Hebel darin sehe, in die Zukunft schauen zu können, wenn wir uns zum Beispiel das Bewerten von Marketingkampagnen anschauen.",{"text":3373,"start":10075,"end":10078},{"text":12904,"start":10078,"end":10137},"Können wir natürlich erst mal sagen, okay, gucken wir uns einfach den Status quo an, was es uns kostet, eine Spieler*innen einzukaufen.",95,{"sentences":12907,"speaker":163,"num_words":12851,"start":10141,"end":10485},[12908,12910,12912,12914,12916],{"text":12909,"start":10141,"end":10261},"Aber für uns ist es vom Businessmodell her superinteressant zu sehen, wie bestimmte Gruppen an Spieler*innen sich in der Zukunft verhalten werden, was die Monetarisierung angeht, weil's für uns nur dann Sinn macht, Marketing zu betreiben, wenn wir wissen, dass wir das Geld auch wieder einspielen.",{"text":12911,"start":10261,"end":10314},"Und bei uns ist es halt nicht so, dass wir nach 7 Tagen sagen können, okay, da ist das Geld wieder reingekommen.",{"text":12913,"start":10318,"end":10328},"Wir fahren die Kampagne hoch.",{"text":12915,"start":10328,"end":10404},"Bei uns ist halt eher so die Perspektive, dass wir denken, ja, 180 Tage könnten vielleicht son Zeitraum sein, indem wir erwarten, dass das Geld wieder zurückgespielt wird.",{"text":12917,"start":10404,"end":10485},"Und das macht die Steuerung natürlich von 'ner Werbekampagne extrem schwierig, weil ich ja nicht heute 180 Tage warten kann, dann zu erscheinen, die Entscheidung zu treffen, das Budget zu erhöhen.",{"sentences":12919,"speaker":163,"num_words":12930,"start":10485,"end":10752},[12920,12922,12924,12926,12928],{"text":12921,"start":10485,"end":10541},"Und da würd ich sagen, ist da wieder son Beispiel, wo wir sagen, okay, wir glauben, dass diese Technologie für uns sehr wichtig ist.",{"text":12923,"start":10541,"end":10657},"Wir fangen wieder an, am Anfang meinetwegen so was einzukaufen, dass wir Unterstützung bekommen, meinetwegen für bestimmte Kohorten, also Gruppen an Spieler*innen Vorhersagen zu treffen, wie sie sich in der Zukunft von der Monetarisierung her verhalten werden und dann halt die Entscheidung treffen, wie wichtig ist das für uns?",{"text":12925,"start":10657,"end":10703},"Würden wir das selber im Team nachbauen oder was bauen, was für unseren Use Case noch besser passt?",{"text":12927,"start":10703,"end":10717},"Haben wir die Kapazitäten dafür?",{"text":12929,"start":10717,"end":10752},"Und so sehe ich auch anwendungsfälle durchaus noch auch noch in anderen Teams, ja.",110,{"sentences":12932,"speaker":184,"num_words":12938,"start":10756,"end":10786},[12933,12934,12936],{"text":4022,"start":10756,"end":10757},{"text":12935,"start":10760,"end":10774},"Gibt's noch was zu Predictive Analytics?",{"text":12937,"start":10777,"end":10786},"Unsere solche Sachen.",10,{"sentences":12940,"speaker":163,"num_words":451,"start":10786,"end":10888},[12941,12943,12944,12946,12948],{"text":12942,"start":10786,"end":10841},"Ich könnte sicherlich noch den ganzen Tag darüber reden, aber wir wollten uns ja hier son gewissen zeitlichen Namen nehmen.",{"text":1523,"start":10841,"end":10845},{"text":12945,"start":10845,"end":10862},"Aber vielleicht fällt den anderen noch was ein.",{"text":12947,"start":10862,"end":10871},"Die sind happy.",{"text":12949,"start":10871,"end":10888},"Kopfschütteln ist immer gut im Podcast.",{"sentences":12951,"speaker":163,"num_words":12559,"start":10888,"end":10898},[12952],{"text":12953,"start":10888,"end":10898},"Kommt, geht direkt",{"sentences":12955,"speaker":184,"num_words":12966,"start":10898,"end":11238},[12956,12958,12960,12962,12964],{"text":12957,"start":10898,"end":10905},"durchs Ohr.",{"text":12959,"start":10908,"end":10917},"Zu unseren Hörer*innen.",{"text":12961,"start":10921,"end":11073},"Nee cool, dann vielen Dank, auch das ist 'n superspannender Part und ich hoffe, unsere Hörer*innen konnten ein bisschen einen Einblick bekommen, ne, was alles unter Business Intelligence letztendlich läuft bei Lotum, also was das für unterschiedliche Bereiche sind, in denen wir da unterwegs sind, was unsere Herausforderungen sind, was wir uns vorstellen, ne?",{"text":12963,"start":11076,"end":11106},"Ich glaube vielleicht auch das, ne, zu so grundsätzlichen Einordnungen so.",{"text":12965,"start":11109,"end":11238},"Es ist 'n Themenbereich, wo wir nicht davon ausgehen, dass er schrumpft, so, sondern das ist eher was was noch wichtiger werden wird, auch in der Zukunft und und von daher einfach 'n sehr fester Bestandteil von von unserer täglichen Arbeit ist in in allen Bereichen, wie Du so sagtest, ne?",121,{"sentences":12968,"speaker":184,"num_words":12979,"start":11238,"end":11677},[12969,12971,12973,12975,12977],{"text":12970,"start":11238,"end":11295},"Also in sehr, sehr vielen Teams, wo das eben datengetrieben unterstützt werden kann und mit Sicherheit auch einen Erfolgsfaktor, ne?",{"text":12972,"start":11295,"end":11371},"Dass man eben auf aufgrund von Daten und Ergebnissen Entscheidungen treffen kann und angereichert mit ein bisschen Bauchgefühl und ein bisschen Erfahrung, die auch dahinter steckt.",{"text":12974,"start":11371,"end":11393},"Ich glaub, das ist auch noch son Punkt, ne?",{"text":12976,"start":11393,"end":11514},"Ich hab noch nicht angesprochen, aber weil wenn man glaube ich, meine meine Sicht so, wenn man Spieleentwicklung sich insgesamt anguckt und wahrscheinlich auch Produktentwicklung, man könnte auch, wenn man nur Zahlen sich anguckt, jedes Spiel und jedes Produkt relativ schnell kaputt kriegen, ne?",{"text":12978,"start":11514,"end":11677},"Selbst wenn man auch immer nur das nimmt, was die besseren Metriken hat oder so, aber es ist dann doch am Ende die Kombination und von daher gefühlt gehört halt eben neben dem auch nur Angucken der Zahlen eben auch das interpretieren der Zahlen und das Kombinieren und letztendlich die Erfahrung dazu, die richtigen Schlüsse dann aus den Zahlen auch am Ende zu ziehen.",163,{"sentences":12981,"speaker":163,"num_words":12687,"start":11677,"end":11944},[12982,12984,12986,12988],{"text":12983,"start":11677,"end":11774},"Ja, und man muss ja auch immer dazu sagen, vieles von dem, wenn wir jetzt über Charilen Prediction so was gesprochen haben, sind ja statistisch Lernverfahren und die geben dir erst mal nur die Korrelation an.",{"text":12985,"start":11774,"end":11796},"Und die Kausalität ist gar nicht so einfach abzuleiten.",{"text":12987,"start":11796,"end":11877},"Und da ist es ja wirklich, wo ja, nicht nur Bauchgefühl, aber auch Erfahrung eine große Rolle spielt und meinetwegen Spiele selber zu spielen, zu verstehen, was die Mechaniken sind.",{"text":12989,"start":11877,"end":11944},"Und ich glaub, da sind wir noch weit davon entfernt, dass uns das AI alles abnimmt und ich glaub, das ist nicht zu unterschätzen, was das ausmacht.",{"sentences":12991,"speaker":184,"num_words":12996,"start":11947,"end":11991},[12992,12993,12994],{"text":1523,"start":11947,"end":11948},{"text":11956,"start":11953,"end":11954},{"text":12995,"start":11958,"end":11991},"Dann sind wir am Ende unserer kleinen Business Intelligence Essentials, ich nenne",14,{"sentences":12998,"speaker":163,"num_words":13001,"start":11991,"end":12001},[12999],{"text":13000,"start":11991,"end":12001},"sie jetzt so besser,",4,{"sentences":13003,"speaker":184,"num_words":13014,"start":12006,"end":12151},[13004,13006,13008,13010,13012],{"text":13005,"start":12006,"end":12013},"Serie, angelangt.",{"text":13007,"start":12017,"end":12079},"Schreibt uns gerne mal, wie ihr das Format fandet, ob ihr das cool fandet, so einen Einblick mal in verschiedene Slots aufgeteilt.",{"text":13009,"start":12082,"end":12105},"Wir haben zwischendurch eben schon gesagt, so klar, ne?",{"text":13011,"start":12105,"end":12139},"Es ist ein relativ high level Überblick so über das, was wir machen.",{"text":13013,"start":12142,"end":12151},"Über verschiedenste Bereiche.",49,{"sentences":13016,"speaker":184,"num_words":13027,"start":12155,"end":12446},[13017,13019,13021,13023,13025],{"text":13018,"start":12155,"end":12267},"Es gibt bei Sicherheit reine BI Podcaste draußen, die jede Woche nichts anderes als Thema haben und wenn euch das interessiert und wir irgendwie in einzelne Bereiche noch mal tiefer einsteigen sollen, schreibt uns gerne das Feedback und wir gucken, was wir daraus machen können.",{"text":13020,"start":12269,"end":12328},"Vielen Dank Tobi, vielen Dank Wessel, vielen Dank Fabi, dass ihr die Zeit gefunden habt, das Ganze mit uns aufzunehmen.",{"text":13022,"start":12331,"end":12389},"Schreibt, liebe Hörerinnen, euer Feedback an Podcast at Programmier Punkt Bar oder über unsere Social Media Kanäle oder über Spotify.",{"text":13024,"start":12392,"end":12434},"Es gibt verschiedenste Wege, wie ihr euch wie ihr uns erreichen könnt und sonst alles Gute.",{"text":13026,"start":12434,"end":12446},"Bis zum nächsten Mal.",105,{"sentences":13029,"speaker":184,"num_words":13036,"start":12446,"end":12476},[13030,13032,13034,13035],{"text":13031,"start":12446,"end":12456},"Macht es gut.",{"text":13033,"start":12456,"end":12466},"Tschau ciao.",{"text":13033,"start":12466,"end":12473},{"text":12478,"start":12473,"end":12476},8,[13038,13047,13057,13067,13082,13100,13137,13144,13163,13174,13197,13205,13215,13219,13235,13239,13253,13263,13271,13275,13284,13297,13309,13322,13331,13341,13357,13361,13377,13392,13429,13459,13473,13508,13536,13547,13591,13598,13602,13636,13642,13671,13693,13700,13716,13741,13751,13755,13770,13802,13806,13818,13822,13828,13834,13861,13874,13878,13889,13898,13902,13906,13922,13950,13981,14007,14017,14045,14069,14075,14100,14124,14128,14132,14172,14183,14199,14212,14234,14245,14329,14359,14406,14429,14437,14460,14466,14477,14487,14522,14574,14597,14628,14656,14669,14687,14691,14706,14729,14733,14749,14761,14783,14799,14824,14834,14892,14898,14902,14947,14954,15059,15071,15082,15144,15157,15172,15184,15198,15202,15240,15264,15303,15344,15372,15381,15385,15399,15406,15413,15422,15486,15495,15560,15585,15612,15635,15639,15732,15745,15767,15780,15792,15801,15805,15842,15853,15860,15878,15882,15914,15932,15960,15972,15982,16004,16022,16058,16114,16125,16131,16135,16142,16156,16165,16187,16222,16242,16256,16269,16301,16337,16348,16363,16404,16417,16433,16520,16543,16638,16650,16670,16732,16775,16830,16858,16870,16903,16919,16947,16979,16998,17032,17054,17063,17082,17104,17118,17122,17133,17198,17204,17222,17235,17239,17270,17287,17372,17397,17406,17413,17422,17456,17462,17538,17560,17586,17594,17612,17629,17671,17709,17830,17858,17870,17874,17884,17938,17945,17959,17967,17997,18011,18022,18035,18043,18057,18074,18085,18099,18132,18143,18152,18160,18169,18180,18189,18213,18237,18248,18274,18299,18318,18332,18362,18388,18399,18430,18448,18478,18482,18493,18497,18503,18515,18519,18523,18543,18552,18556,18585,18597,18614,18640,18653,18677,18705,18719,18723],{"start":181,"end":198,"confidence":13039,"channel":184,"transcript":13040,"words":13041,"speaker":184,"id":13046},0.8826904,"Hallo und herzlich willkommen",[13042,13043,13044,13045],{"word":180,"start":181,"end":182,"confidence":183,"speaker":184,"speaker_confidence":185,"punctuated_word":186},{"word":188,"start":182,"end":189,"confidence":190,"speaker":184,"speaker_confidence":185,"punctuated_word":188},{"word":192,"start":189,"end":193,"confidence":194,"speaker":184,"speaker_confidence":185,"punctuated_word":192},{"word":196,"start":197,"end":198,"confidence":199,"speaker":184,"speaker_confidence":185,"punctuated_word":196},"9c542b2b-97f3-4fa2-9890-ab3584674ad0",{"start":202,"end":223,"confidence":13048,"channel":184,"transcript":13049,"words":13050,"speaker":184,"id":13056},0.934375,"zum dritten Teil unserer 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