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schiefgehen","2026-10-07T18:11:36.849Z","2026-10-08T10:30:49.033Z","2026-10-08T10:30:49.050Z",228176268736688,"1c87403f-d98f-4402-aa9e-92b73cff2577",{"seo":1699,"_uid":1702,"body":1703,"text":1701,"image":1998,"footer":30,"header":30,"authors":2002,"headline":1692,"component":2004,"categories":2005,"bottom_head":2037,"breadcrumbs":2038,"seo_noindex":51,"sidebar_cta":2058,"reading_time":1675,"seo_nofollow":51,"seo_canonical":30,"bottom_buttons":2059,"additional_slot":2060,"sidebar_cta_enabled":81,"sidebar_toc_enabled":81},{"title":1700,"plugin":912,"og_image":30,"og_title":1700,"description":1701,"twitter_image":30,"twitter_title":1700,"og_description":1701,"twitter_description":1701},"Black Week: A/B-Tests laufen lassen oder einfrieren?","Viele Shops stoppen ihre A/B-Tests vor Black Friday aus Angst vor Fehlern. Warum das teuer ist, was wirklich riskant ist und wie du die Black Week zum Lernen nutzt.","31217cd1-1a4d-4746-9478-82a50f30e016",[1704,1721,1756,1810,1855,1868,1893,1918,1981],{"_uid":1705,"text":1706,"component":1720},"aeab8520-e97e-4908-ba5f-e968d82300a4",{"type":39,"content":1707},[1708,1712,1716],{"type":42,"content":1709},[1710],{"text":1711,"type":513},"Die schlechte Nachricht kommt meistens im Oktober, und sie kommt höflich. Der Head of E-Commerce oder der Head of CRO meldet sich bei uns: In der Black Week könne man leider nicht testen. Jeder Fehler koste da schließlich Millionen.",{"type":42,"content":1713},[1714],{"text":1715,"type":513},"Gegen diesen Satz ist schwer anzukommen. Er klingt nach Verantwortung. Wer ihm widerspricht, trägt im Zweifel den Millionenschaden gleich mit. Viel Vergnügen.",{"type":42,"content":1717},[1718],{"text":1719,"type":513},"Ich halte diesen Reflex für einen der teuersten im E-Commerce. Hier ist der Grund:","richtext-text",{"_uid":1722,"text":1723,"component":1720},"7cb4e835-e730-426e-a63d-caf32f19add5",{"type":39,"content":1724},[1725,1730,1734,1738,1752],{"type":925,"attrs":1726,"content":1727},{"level":1069},[1728],{"text":1729,"type":513},"Freeze heißt nicht Sicherheit",{"type":42,"content":1731},[1732],{"text":1733,"type":513},"Freeze klingt nach Vorsicht. Freeze heißt, in der Woche mit dem meisten Traffic des Jahres zu beschließen, nichts zu lernen.",{"type":42,"content":1735},[1736],{"text":1737,"type":513},"Der Handelsverband HDE rechnete für Black Friday und Cyber Monday 2025 mit 5,8 Milliarden Euro Umsatz. Das ist ein Fenster, in dem so viele kaufwillige Menschen gleichzeitig auf deiner Seite sind wie sonst nie. Genau dann schaltet man das Labor ab.",{"type":42,"content":1739},[1740,1742,1750],{"text":1741,"type":513},"Für dieses Verhalten gibt es sogar einen Namen: den ",{"text":1743,"type":513,"marks":1744},"Zero-Risk Bias",[1745],{"type":1746,"attrs":1747},"link",{"href":1748,"uuid":118,"anchor":118,"target":1749,"linktype":136},"https://www.konversionskraft.de/behavior-patterns/zero-risk-bias","_self",{"text":1751,"type":513},". Menschen beseitigen lieber ein kleines Risiko komplett, als ein größeres zu verringern. Hier ist das kleine Risiko der kaputte Test: sichtbar, eskalierbar, mit Namen dran. Das große Risiko ist eine Woche ohne Erkenntnis. Das sieht niemand, denn es kommt nie eine Mail mit dem Betreff „Wir haben diese Woche nichts gelernt“.",{"type":42,"content":1753},[1754],{"text":1755,"type":513},"Das ist keine Experimentierkultur. Das ist Fehlerkultur. Eine Kultur, die nur testet, solange nichts auf dem Spiel steht, testet nicht. Sie dekoriert.",{"_uid":1757,"text":1758,"component":1720},"08431f9e-0eab-4bf5-adc6-3e45d0c87af0",{"type":39,"content":1759},[1760,1765,1769,1786,1794,1802],{"type":925,"attrs":1761,"content":1762},{"level":1069},[1763],{"text":1764,"type":513},"Die Einwände, und wo sie recht haben",{"type":42,"content":1766},[1767],{"text":1768,"type":513},"An diesem Punkt taucht meistens ein Einwand auf. Eigentlich sind es vier. Alle haben ein Stück Wahrheit.",{"type":42,"content":1770},[1771,1775,1777,1784],{"text":1772,"type":513,"marks":1773},"„Der Traffic in der Black Week ist doch gar nicht typisch.“",[1774],{"type":932},{"text":1776,"type":513}," Stimmt. Rabattjäger kaufen anders als Stammkunden an einem Dienstag im März. In der Fachsprache heißt das fehlende externe Validität: Ein Gewinner aus der Black Week muss im Februar nicht gewinnen, wie ",{"text":1778,"type":513,"marks":1779},"dieser Artikel",[1780],{"type":1746,"attrs":1781},{"href":1782,"uuid":118,"anchor":118,"target":1783,"linktype":136},"https://atticusli.com/blog/posts/ab-testing-external-validity-threats/","_blank",{"text":1785,"type":513}," zu Recht warnt. Daraus folgt aber nur, dass du die Ergebnisse anders auswertest und große Änderungen später noch einmal bestätigst. Ein Grund zum Aufhören ist es nicht.",{"type":42,"content":1787},[1788,1792],{"text":1789,"type":513,"marks":1790},"„Jeder Fehler kostet in dieser Woche Millionen.“",[1791],{"type":932},{"text":1793,"type":513}," Auch wahr, zumindest für einen Fehler, den niemand bemerkt. Millionen ist aber eine Zahl, und Zahlen kann man prüfen. Was kostet ein Fehler, der nur einen Teil des Traffics trifft und sich in Minuten abschalten lässt? Was kostet dagegen eine Woche, in der du nichts lernst? Gegen den ersten Fall hilft ein Kill-Switch. Gegen den zweiten hilft kein Freeze, er verursacht ihn.",{"type":42,"content":1795},[1796,1800],{"text":1797,"type":513,"marks":1798},"„Die IT hat Code-Freeze.“",[1799],{"type":932},{"text":1801,"type":513}," Code-Freeze und Learning-Freeze sind zwei verschiedene Dinge, die gern in derselben Mail bestellt werden. Je nach Setup braucht ein Test keinen Release. Klär das mit der IT, bevor du den Freeze einfach hinnimmst.",{"type":42,"content":1803},[1804,1808],{"text":1805,"type":513,"marks":1806},"„Die Laufzeit ist zu kurz.“",[1807],{"type":932},{"text":1809,"type":513}," Das ist der stärkste Einwand. Die Black Week 2026 geht vom Montag, 23. November, bis zum Cyber Monday am 30. November. Wer erst am Montag startet, hat genau einen Wochenzyklus, und der reicht für manche Fragen, für andere dagegen nicht, weil er bei schwankendem Traffic wenig Spielraum für Ausreißer lässt. Deshalb gilt: Starte früher. Dann läuft der Test schon, wenn die Black Week beginnt, und geht einfach durch sie hindurch.",{"_uid":1811,"text":1812,"component":1720},"54466905-5599-4bcf-a227-4280d3982595",{"type":39,"attrs":1813,"content":1814},{"backgroundColor":118},[1815,1820,1825,1850],{"type":925,"attrs":1816,"content":1817},{"level":1069,"textAlign":118},[1818],{"text":1819,"type":513},"Was in der Black Week wirklich riskant ist",{"type":42,"attrs":1821,"content":1822},{"textAlign":118},[1823],{"text":1824,"type":513},"Damit das hier kein Dogma wird, die ehrliche Liste:",{"type":1826,"content":1827},"bullet_list",[1828,1836,1843],{"type":1829,"content":1830},"list_item",[1831],{"type":42,"attrs":1832,"content":1833},{"textAlign":118},[1834],{"text":1835,"type":513},"Alles mögliche an der Kampagne ändern",{"type":1829,"content":1837},[1838],{"type":42,"attrs":1839,"content":1840},{"textAlign":118},[1841],{"text":1842,"type":513},"Preistests mit Folgen für Marge und Retouren",{"type":1829,"content":1844},[1845],{"type":42,"attrs":1846,"content":1847},{"textAlign":118},[1848],{"text":1849,"type":513},"Tests, die du nur mit einem Deployment wieder abschalten kannst",{"type":42,"attrs":1851,"content":1852},{"textAlign":118},[1853],{"text":1854,"type":513},"Alles andere hat in dieser Woche mehr Testfläche, als die meisten Teams nutzen.",{"_uid":1856,"text":1857,"component":1720},"a0bef922-e241-4e5c-b2d1-00554991044b",{"type":39,"content":1858},[1859,1864],{"type":925,"attrs":1860,"content":1861},{"level":1069},[1862],{"text":1863,"type":513},"Was du nur jetzt lernen kannst",{"type":42,"content":1865},[1866],{"text":1867,"type":513},"Wie Kunden auf Rabatte reagieren, wenn sie wirklich kaufen wollen. Ob ein Bundle zieht, wenn alle nach Schnäppchen suchen. Wo deine Versandkostenschwelle plötzlich zu hoch oder zu niedrig wirkt, weil der Warenkorb in dieser Woche ganz anders aussieht als im Rest des Jahres. Ob Knappheit und Dringlichkeit funktionieren, wenn der Kunde den Zeitdruck ohnehin schon spürt. Diese Fragen kannst du im Februar nicht beantworten. Dort fehlt der Kaufdruck.",{"_uid":1869,"text":1870,"component":1720},"8a642d4c-03e0-4081-9b83-f34930265783",{"type":39,"content":1871},[1872,1877,1881,1885,1889],{"type":925,"attrs":1873,"content":1874},{"level":1069},[1875],{"text":1876,"type":513},"Der Test, der im Sale nichts brachte",{"type":42,"content":1878},[1879],{"text":1880,"type":513},"Ein Beispiel aus dem Winter-Sale 2014/15, nicht aus der Black Week, aber mit derselben Mechanik. Tom Tailor testete drei Varianten gegen die Kontrolle: Stimulanz, Verknappung und beides zusammen.",{"type":42,"content":1882},[1883],{"text":1884,"type":513},"Im normalen Sale-Zeitraum kam wenig dabei heraus. Die Stimulanz steigerte die Conversion Rate um 2,5 Prozent, die Kombination aus beidem blieb unter der Signifikanzschwelle, und die Verknappung landete mit minus 0,5 Prozent bei null. Ernüchternd. Der Bericht hält fest: Verknappung ohne zeitliche Begrenzung zeigt keinen Impact.",{"type":42,"content":1886},[1887],{"text":1888,"type":513},"Wer nach diesem Ergebnis die Akte schließt, schreibt in die Auswertung: Knappheit funktioniert bei uns nicht. Das Team passte den Test an und wiederholte ihn im Final Sale, dem Schlussspurt mit echter Frist. Das Ergebnis war eindeutig. Die Kombination gewann mit 12,6 Prozent mehr Conversion Rate und rund 15 Prozent mehr Umsatz pro Besucher. Alle drei Varianten waren signifikant.",{"type":42,"content":1890},[1891],{"text":1892,"type":513},"Der erste Test war also kein Fehlschlag. Er hat gezeigt, dass Knappheit ohne Frist nicht trägt, und die Black Week bringt diese Frist von Natur aus mit, ohne dass du dafür etwas inszenieren musst. Du musst nur testen, was daraus folgt.",{"_uid":1894,"text":1895,"component":1720},"b9b5f62b-c80d-4949-907a-9f92e3f9a5d4",{"type":39,"content":1896},[1897,1902,1914],{"type":925,"attrs":1898,"content":1899},{"level":1069},[1900],{"text":1901,"type":513},"Der Fall, der vor schönen Zahlen warnt",{"type":42,"content":1903},[1904,1906,1912],{"text":1905,"type":513},"Ein Fashion-Händler testete für ein Schuhmodell drei Varianten der Produktseite gegen die Kontrolle. Die Gewinnervariante steigerte die Add-to-Cart-Rate um 244 Prozent und die Bestellungen um 43 Prozent. Gleichzeitig kamen fast dreimal so viele Retouren zurück. Übrig blieben 22 Prozent mehr Deckungsbeitrag. Alle Zahlen stehen in der ",{"text":1907,"type":513,"marks":1908},"Case Study zu Conversion, Retouren und Deckungsbeitrag",[1909],{"type":1746,"attrs":1910},{"href":1911,"uuid":118,"anchor":118,"target":1749,"linktype":136},"https://www.konversionskraft.de/experimentation/conversion-deckungsbeitrag-case-study",{"text":1913,"type":513},".",{"type":42,"content":1915},[1916],{"text":1917,"type":513},"Wer in der Black Week mit Rabatten arbeitet, sollte wissen, was nach dem Rabatt zurückkommt. Wer nur auf die Conversion Rate schaut, bekommt ein schönes Ergebnis und eine falsche Antwort.",{"_uid":1919,"text":1920,"component":1720},"6f007fb5-d8fe-47c7-8a68-01bde001b49a",{"type":39,"content":1921},[1922,1927],{"type":925,"attrs":1923,"content":1924},{"level":1069},[1925],{"text":1926,"type":513},"Das Spielbuch in fünf Punkten",{"type":1928,"attrs":1929,"content":1930},"ordered_list",{"order":927,"tight":51},[1931,1941,1951,1961,1971],{"type":1829,"content":1932},[1933],{"type":42,"content":1934},[1935,1939],{"text":1936,"type":513,"marks":1937},"Guardrail-Metriken vor dem Start festlegen.",[1938],{"type":932},{"text":1940,"type":513}," Retourenquote, Deckungsbeitrag, Ladezeit. Wer die Grenze erst im Test zieht, zieht sie dahin, wo das Ergebnis schön aussieht.",{"type":1829,"content":1942},[1943],{"type":42,"content":1944},[1945,1949],{"text":1946,"type":513,"marks":1947},"Den Kill-Switch testen, bevor der Traffic kommt.",[1948],{"type":932},{"text":1950,"type":513}," Ein Kill-Switch, den noch nie jemand gedrückt hat, ist eine Vermutung.",{"type":1829,"content":1952},[1953],{"type":42,"content":1954},[1955,1959],{"text":1956,"type":513,"marks":1957},"In drei Phasen auswerten:",[1958],{"type":932},{"text":1960,"type":513}," Vorlauf, Black Week, Nachlauf. Ein Gesamtergebnis ohne diese Trennung mittelt Rabattjäger und Stammkunden zu einer Zahl, die niemand versteht.",{"type":1829,"content":1962},[1963],{"type":42,"content":1964},[1965,1969],{"text":1966,"type":513,"marks":1967},"Neukunden und Bestandskunden getrennt ansehen.",[1968],{"type":932},{"text":1970,"type":513}," Sie verhalten sich in dieser Woche kaum gleich.",{"type":1829,"content":1972},[1973],{"type":42,"content":1974},[1975,1979],{"text":1976,"type":513,"marks":1977},"Den Testplan Ende Oktober einfrieren.",[1978],{"type":932},{"text":1980,"type":513}," Den Testplan, nicht die Tests.",{"_uid":1982,"text":1983,"component":1720},"f529ed1a-0042-4f5b-872b-cd30a85a8ea1",{"type":39,"content":1984},[1985,1990,1994],{"type":925,"attrs":1986,"content":1987},{"level":1069},[1988],{"text":1989,"type":513},"Die eine Frage fürs Montagsmeeting",{"type":42,"content":1991},[1992],{"text":1993,"type":513},"Stell am Montag vor der Black Week eine einzige Frage: Was wollen wir bis Cyber Monday wissen, was wir heute noch nicht wissen?",{"type":42,"content":1995},[1996],{"text":1997,"type":513},"Wenn niemandem etwas einfällt, weißt du wenigstens, warum der Freeze im Oktober so schnell durchging.",{"id":1999,"alt":30,"name":30,"focus":30,"title":30,"source":30,"filename":2000,"copyright":30,"fieldtype":49,"meta_data":2001,"is_external_url":51},228224662068305,"https://a.storyblok.com/f/328018/1672x941/77ea00b808/frenzied-black-friday-warehouse-fulfillment.jpg",{},[2003],"491d42eb-e892-4c21-bba7-75e8ea5c0bce","page-blog-post",[2006,2022],{"name":2007,"created_at":2008,"published_at":2009,"updated_at":2010,"id":2011,"uuid":1490,"content":2012,"slug":2015,"full_slug":2016,"sort_by_date":118,"position":2017,"tag_list":2018,"is_startpage":51,"parent_id":2019,"meta_data":118,"group_id":2020,"first_published_at":2009,"release_id":118,"lang":124,"path":118,"alternates":2021,"default_full_slug":118,"translated_slugs":118,"_stopResolving":81},"Online Marketing","2026-09-28T07:21:35.489Z","2026-09-28T07:21:35.545Z","2026-09-28T07:21:35.558Z",224831469555576,{"_uid":2013,"component":2014},"f9009758-fc7c-42dd-bc65-264e7fc2f361","document-blog-category","online-marketing","de/_categories/online-marketing",-490,[],101158655217801,"1b4c64e8-b799-4842-ae0b-6eb9a4c165e5",[],{"name":2023,"created_at":2024,"published_at":2025,"updated_at":2026,"id":2027,"uuid":2028,"content":2029,"slug":2031,"full_slug":2032,"sort_by_date":118,"position":119,"tag_list":2033,"is_startpage":51,"parent_id":2019,"meta_data":118,"group_id":2034,"first_published_at":2035,"release_id":118,"lang":124,"path":118,"alternates":2036,"default_full_slug":118,"translated_slugs":118,"_stopResolving":81},"A/B-Testing","2025-10-13T20:20:36.242Z","2026-10-08T05:55:32.229Z","2026-10-08T05:55:32.241Z",101159879683477,"430ab7d7-8d03-4995-856e-478aa05dd33e",{"_uid":2030,"component":2014},"f510a510-df14-4dd3-b179-909017021494","experimentation","de/_categories/experimentation",[],"27b82645-0fb7-46c1-85f9-4056d28d1b36","2025-11-17T10:34:46.494Z",[],[],[2039,2045,2052],{"_uid":2040,"link":2041,"text":2044,"component":59},"9f1c2b7a-3d4e-4a51-8c60-1b2a7e5d0a11",{"id":2042,"url":2043,"linktype":31,"fieldtype":32,"cached_url":2043},632563185,"de","Home",{"_uid":2046,"link":2047,"text":442,"component":59},"9f1c2b7a-3d4e-4a51-8c60-1b2a7e5d0a12",{"id":2048,"url":2049,"linktype":31,"fieldtype":32,"cached_url":2049,"story":2050},"7cc26d60-883e-42a6-8af9-9c1241ebd4eb","de/blog",{"name":442,"id":2051,"uuid":2048,"slug":440,"url":2049,"full_slug":2049,"_stopResolving":81},99774720228859,{"_uid":2053,"link":2054,"text":1692,"component":59},"9f1c2b7a-3d4e-4a51-8c60-1b2a7e5d0a13",{"id":1697,"url":2055,"linktype":31,"fieldtype":32,"cached_url":2055,"story":2056},"de/online-marketing/testing-freeze-black-week",{"name":1692,"id":1696,"uuid":1697,"slug":2057,"url":2055,"full_slug":2055,"_stopResolving":81},"testing-freeze-black-week",[],[],[],-100,[],224831483482113,"5074556c-2551-4f99-ae0e-f3262a274f21","2026-10-07T18:21:08.316Z",[],{"name":2068,"created_at":2069,"published_at":2070,"updated_at":2071,"id":2072,"uuid":2073,"content":2074,"slug":2734,"full_slug":2735,"sort_by_date":118,"position":2740,"tag_list":2741,"is_startpage":51,"parent_id":2063,"meta_data":118,"group_id":2742,"first_published_at":2743,"release_id":118,"lang":124,"path":118,"alternates":2744,"default_full_slug":118,"translated_slugs":118},"Marketingkanäle richtig bewerten: wesentliche Schritte zur Multi-Touch-Attribution","2025-10-15T16:23:21.662Z","2026-09-28T11:51:22.577Z","2026-09-28T11:51:22.605Z",101809363636879,"d56edf7a-4f9f-45ad-997c-a936b4b1ee46",{"seo":2075,"_uid":2077,"body":2078,"text":2076,"image":2696,"footer":30,"header":30,"authors":2699,"category":1490,"headline":2068,"component":2004,"categories":2701,"bottom_head":2720,"breadcrumbs":2721,"seo_noindex":51,"sidebar_cta":2736,"reading_time":2737,"seo_nofollow":51,"seo_canonical":30,"bottom_buttons":2738,"additional_slot":2739,"sidebar_cta_enabled":81,"sidebar_toc_enabled":81},{"title":2068,"plugin":912,"og_image":30,"og_title":2068,"description":2076,"twitter_image":30,"twitter_title":2068,"og_description":2076,"twitter_description":2076},"Last Click Bewertungen sind unrealistisch und verfälschen die Budgetallokation. Erfahre, wie du mit dynamischen Attributionsmodellen und Markow Ketten Marketingkanäle richtig bewertest.","7a1eeae2-dfe5-4232-bc44-803468364204",[2079,2165,2177,2238,2249,2262,2273,2363,2375,2440,2451,2629,2640],{"_uid":2080,"text":2081,"component":1720},"a2350ee8-7ee2-47d4-a22f-ec39f3b8b4b2",{"type":39,"content":2082},[2083,2093,2097,2101,2105,2115,2123,2131,2137,2142,2149,2153,2157,2161],{"type":2084,"content":2085},"blockquote",[2086],{"type":42,"content":2087},[2088],{"text":2089,"type":513,"marks":2090},"„Ich weiß, die Hälfte meiner Werbung ist hinausgeworfenes Geld. Ich weiß nur nicht, welche Hälfte.“",[2091],{"type":2092},"italic",{"type":42,"content":2094},[2095],{"text":2096,"type":513},"Dieses Zitat von Henry Ford ist dir bestimmt bekannt.",{"type":42,"content":2098},[2099],{"text":2100,"type":513},"Seitdem hat sich jedoch einiges in der Werbung und Analyse von Werbemaßnahmen getan.",{"type":42,"content":2102},[2103],{"text":2104,"type":513},"Theoretisch.",{"type":42,"content":2106},[2107,2109,2113],{"text":2108,"type":513},"Auch wenn viele Organisationen heutzutage eigens entwickelte ",{"text":2110,"type":513,"marks":2111},"Attributionsmodelle",[2112],{"type":932},{"text":2114,"type":513}," einsetzen, wird das Last Click Modell noch viel zu häufig genutzt.",{"type":42,"content":2116},[2117,2121],{"text":2118,"type":513,"marks":2119},"Das Problem:",[2120],{"type":932},{"text":2122,"type":513}," Last Click Bewertungen sind unrealistisch und entsprechen nicht deiner tatsächlichen Werberealität.",{"type":42,"content":2124},[2125,2129],{"text":2126,"type":513,"marks":2127},"In einem aktuellen Projekt",[2128],{"type":932},{"text":2130,"type":513}," wollten wir wissen, ob das genutzte Retargeting-Tool sein Geld auch wirklich Wert ist. Hätten wir uns nur auf den letzten Klick fokussiert, wäre das wohl nach hinten losgegangen.",{"type":42,"content":2132},[2133],{"text":2134,"type":513,"marks":2135},"Daher zeige ich dir in diesem Beitrag eine Möglichkeit, den Impact deiner Werbemaßnahmen realistisch und ohne erheblichen Aufwand zu analysieren: Multi-Touch-Attribution.",[2136],{"type":932},{"type":925,"attrs":2138,"content":2139},{"level":1069},[2140],{"text":2141,"type":513},"Marketingkanäle bewerten: Attributionsmodelle und die Customer Journey",{"type":42,"content":2143},[2144,2147],{"text":2110,"type":513,"marks":2145},[2146],{"type":932},{"text":2148,"type":513}," helfen dir herauszufinden, welchen Impact deine Marketingmaßnahmen haben. Sie zeigen dir, welche Kanäle bzw. Maßnahmen im Laufe der Customer Journey zu einer Conversion geführt haben. Beispiele für Attributionsmodelle sind Last Click- oder First Click Modelle, aber auch datenbasierte Modelle kommen in der Praxis zum Einsatz.",{"type":42,"content":2150},[2151],{"text":2152,"type":513},"Heutzutage können wir insbesondere im digitalen Raum die Werbewirkung unserer Marketingkanäle aufschlussreich analysieren. Häufig liegt aber das Problem viel mehr in der korrekten Bewertung der jeweiligen Kanäle.",{"type":42,"content":2154},[2155],{"text":2156,"type":513},"Marketer geben monatlich erhebliche Beträge für Ads aus, andere schwören auf Service- oder Retargeting-Tools – doch was passiert, wenn du eine dieser Maßnahmen abschaltest oder Budget verschiebst?",{"type":42,"content":2158},[2159],{"text":2160,"type":513},"Sieh dir die nachfolgende Abbildung an.",{"type":42,"content":2162},[2163],{"text":2164,"type":513},"Wem schreibst du nach erfolgreicher Conversion den Erfolg zu?",{"_uid":2166,"image":2167,"caption":2171,"component":2176},"b83ac644-3bb3-46d5-a03f-737089d091ae",{"alt":2168,"src":2169,"name":30,"focus":30,"title":118,"source":30,"filename":2169,"copyright":30,"fieldtype":49,"meta_data":2170,"is_external_url":81},"Beispielhafte Touchpoints entlang der Customer Journey – wem schreibst du den Conversion-Erfolg zu?","https://storage.googleapis.com/kk-website-assets/bGhx2XMf_TouchpointsCustomerJourney-820x450.png",{"alt":2168,"title":118,"source":30,"copyright":30},{"type":39,"content":2172},[2173],{"type":42,"content":2174},[2175],{"text":2168,"type":513},"richtext-image",{"_uid":2178,"text":2179,"component":1720},"26a9933b-5b81-44dc-99c0-9b280d0f2ca6",{"type":39,"content":2180},[2181,2185,2189,2195,2199,2203,2207,2213,2228,2232],{"type":42,"content":2182},[2183],{"text":2184,"type":513},"Gar nicht so einfach, oder?",{"type":42,"content":2186},[2187],{"text":2188,"type":513},"So auch in einem aktuellen Praxisfall, den ich in einem Kundenprojekt erlebte. In einem Projekt kam die Frage auf, ob das eingesetzte Retargeting-Tool seinen Preis auch wirklich wert sei.",{"type":925,"attrs":2190,"content":2192},{"level":2191},3,[2193],{"text":2194,"type":513},"Faire Umsatzverteilung? Darum sind Last Click Modelle unrealistisch",{"type":42,"content":2196},[2197],{"text":2198,"type":513},"Warum ist es wichtig, sich nicht zu sehr auf den letzten Klick zu fokussieren?",{"type":42,"content":2200},[2201],{"text":2202,"type":513},"Noch immer nutzen viele Organisationen einfache Attributionsmodelle wie das Last Click Modell. Häufig wissen sie auch nicht, welche anderen Möglichkeiten sie alternativ einsetzen könnten. Deshalb geht ungeahntes Potenzial verloren, da du mit dem Last Click Modell, bezogen auf deine Daten, ein stark verzerrtes Bild der Realität erhältst. Anschließend schreibst du dem letzten Touchpoint den ganzen Umsatz zu – andere Touchpoints gehen leer aus.",{"type":42,"content":2204},[2205],{"text":2206,"type":513},"In manchen Situationen mag so eine Betrachtungsweise sinnvoll sein, meistens aber möchte man möglichst nah an die Realität kommen. So ein Last Click Modell ist nicht nur unfair, sondern auch fernab der Realität.",{"type":42,"content":2208},[2209],{"text":2210,"type":513,"marks":2211},"Last Click Modelle (LCM) führen noch zu weiteren Problemen:",[2212],{"type":932},{"type":1826,"attrs":2214,"content":2215},{"tight":51},[2216,2222],{"type":1829,"content":2217},[2218],{"type":42,"content":2219},[2220],{"text":2221,"type":513},"Last Touch Marketing Attribution ignoriert den Conversion Funnel",{"type":1829,"content":2223},[2224],{"type":42,"content":2225},[2226],{"text":2227,"type":513},"sie liefern nur ein begrenztes Verständnis deiner Käufergruppen",{"type":42,"content":2229},[2230],{"text":2231,"type":513},"Oftmals sorgen auch organisatorische Hemmnisse dafür, dass LCM zum Einsatz kommen.",{"type":42,"content":2233},[2234],{"text":2235,"type":513,"marks":2236},"Doch es gibt eine gute Lösung, diese Probleme aufzulösen: datengetriebene und dynamische Attributionsmodelle.",[2237],{"type":932},{"_uid":2239,"image":2240,"caption":2244,"component":2176},"c683ecd7-8e26-4b91-9922-e517fa7bc98c",{"alt":2241,"src":2242,"name":30,"focus":30,"title":118,"source":30,"filename":2242,"copyright":30,"fieldtype":49,"meta_data":2243,"is_external_url":81},"Vergleich statische Modelle vs. datengetriebene Modelle","https://storage.googleapis.com/kk-website-assets/AnMLNTqj_statischeunddatengetriebenemodelle-820x525.png",{"alt":2241,"title":118,"source":30,"copyright":30},{"type":39,"content":2245},[2246],{"type":42,"content":2247},[2248],{"text":2241,"type":513},{"_uid":2250,"text":2251,"component":1720},"a1cafa2b-4b23-4854-98b3-5f46609fa799",{"type":39,"content":2252},[2253,2258],{"type":925,"attrs":2254,"content":2255},{"level":1069},[2256],{"text":2257,"type":513},"Multi-Touch-Attribution: datengetriebene Attributionsmodelle mittels Algorithmus aufbauen",{"type":42,"content":2259},[2260],{"text":2261,"type":513},"Im Kern geht es bei datengetriebenen Attributionsmodellen natürlich um Daten.",{"_uid":2263,"image":2264,"caption":2268,"component":2176},"4023c99b-f30d-4252-b3ba-825044c8faa9",{"alt":2265,"src":2266,"name":30,"focus":30,"title":118,"source":30,"filename":2266,"copyright":30,"fieldtype":49,"meta_data":2267,"is_external_url":81},"Attributionsmodelle helfen dir, herauszufinden, welchen Impact deine Marketingmaßnahmen tatsächlich haben.","https://storage.googleapis.com/kk-website-assets/dM1nuHvW_Attributionsanalyse-820x578.png",{"alt":2265,"title":118,"source":30,"copyright":30},{"type":39,"content":2269},[2270],{"type":42,"content":2271},[2272],{"text":2265,"type":513},{"_uid":2274,"text":2275,"component":1720},"3745cac7-2997-4da4-920a-847009e4626b",{"type":39,"content":2276},[2277,2281,2286,2290,2294,2315,2319,2323,2344],{"type":42,"content":2278},[2279],{"text":2280,"type":513},"Im Beispiel von Markow Ketten (siehe nächster Abschnitt), berechnest du für jede Marketingaktivität die Wichtigkeit, indem du simulierst, was passieren würde, wenn du diese Marketingaktivität weglassen würdest.",{"type":925,"attrs":2282,"content":2283},{"level":2191},[2284],{"text":2285,"type":513},"Funktionsweise des Algorithmus",{"type":42,"content":2287},[2288],{"text":2289,"type":513},"Um dir die Funktionsweise des Algorithmus möglichst einfach zu veranschaulichen, schauen wir uns ein Beispiel an.",{"type":42,"content":2291},[2292],{"text":2293,"type":513},"Folgende drei Customer Journeys haben wir:",{"type":1928,"attrs":2295,"content":2296},{"order":927,"tight":51},[2297,2303,2309],{"type":1829,"content":2298},[2299],{"type":42,"content":2300},[2301],{"text":2302,"type":513},"Display ➔ E-Mail ➔ SEA",{"type":1829,"content":2304},[2305],{"type":42,"content":2306},[2307],{"text":2308,"type":513},"Display",{"type":1829,"content":2310},[2311],{"type":42,"content":2312},[2313],{"text":2314,"type":513},"E-Mail ➔ SEA",{"type":42,"content":2316},[2317],{"text":2318,"type":513},"Die erste Customer Journey hat eine Bestellung ausgelöst, die anderen beiden gehen leer aus.",{"type":42,"content":2320},[2321],{"text":2322,"type":513},"Der Algorithmus betrachtet jeden Besuch bzw. jeden Touchpoint als Zustand. In unserem Beispiel sind das die Marketingkanäle. Zusätzlich zu unseren Kanälen, fügen wir noch Hilfszustände hinzu, damit der Algorithmus auch weiß, wann es zu einer Bestellung kam und wann nicht.",{"type":1928,"attrs":2324,"content":2325},{"order":927,"tight":51},[2326,2332,2338],{"type":1829,"content":2327},[2328],{"type":42,"content":2329},[2330],{"text":2331,"type":513},"(Start) ➔ Display ➔ E-Mail ➔ SEA ➔ (Conversion)",{"type":1829,"content":2333},[2334],{"type":42,"content":2335},[2336],{"text":2337,"type":513},"(Start) ➔ Display ➔ (null)",{"type":1829,"content":2339},[2340],{"type":42,"content":2341},[2342],{"text":2343,"type":513},"(Start) ➔ E-Mail ➔ SEA ➔ (null)",{"type":42,"content":2345},[2346,2348,2352,2354,2358,2359,2361],{"text":2347,"type":513},"(null) bedeutet: ",{"text":2349,"type":513,"marks":2350},"Keine Bestellung aufgegeben",[2351],{"type":2092},{"text":2353,"type":513}," und (Conversion) bedeutet: ",{"text":2355,"type":513,"marks":2356},"Bestellung aufgegeben",[2357],{"type":2092},{"text":1913,"type":513},{"type":2360},"hard_break",{"text":2362,"type":513},"Diese Customer Journeys werden nun als Netzwerk dargestellt.",{"_uid":2364,"image":2365,"caption":2369,"component":2176},"cc9cedaf-d3fa-4dba-8b9a-11083648d1e2",{"alt":2366,"src":2367,"name":30,"focus":30,"title":2366,"source":30,"filename":2367,"copyright":30,"fieldtype":49,"meta_data":2368,"is_external_url":81},"Schaubild einer Customer Journey mit Multi-Touch-Attribution","https://storage.googleapis.com/kk-website-assets/Gfuur4Uo_DarstellungCustomerJourney-820x389.png",{"alt":2366,"title":2366,"source":30,"copyright":30},{"type":39,"content":2370},[2371],{"type":42,"content":2372},[2373],{"text":2374,"type":513},"Beispielhafte Darstellung der Customer Journeys inkl. Übergangswahrscheinlichkeiten.",{"_uid":2376,"text":2377,"component":1720},"7d0af51c-1aa9-439d-9152-e21dd2453984",{"type":39,"content":2378},[2379,2389,2399,2414,2418,2422,2428,2432,2436],{"type":42,"content":2380},[2381,2383,2387],{"text":2382,"type":513},"Es werden dabei nur die Zustände miteinander verbunden, die tatsächlich in den Customer Journeys vorkommen. Die Prozente geben die ",{"text":2384,"type":513,"marks":2385},"Übergangswahrscheinlichkeit",[2386],{"type":932},{"text":2388,"type":513}," an, also wie wahrscheinlich es ist, von einem Zustand in den nächsten zu wechseln. Beispielsweise geht die Hälfte (50 %) aller Zustände von SEA zur Bestellung über, die andere Hälfte (50 %) führt zu keiner Bestellung.",{"type":42,"content":2390},[2391,2393,2397],{"text":2392,"type":513},"Im nächsten Schritt wird berechnet, wie hoch die ",{"text":2394,"type":513,"marks":2395},"Gesamtwahrscheinlichkeit",[2396],{"type":932},{"text":2398,"type":513}," einer Bestellung ist, indem die Übergangswahrscheinlichkeiten eines Bestellpfades multipliziert und mit anderen Pfaden addiert werden. In unserem Beispiel existieren zwei Bestellpfade. Diese sind:",{"type":1928,"attrs":2400,"content":2401},{"order":927,"tight":51},[2402,2408],{"type":1829,"content":2403},[2404],{"type":42,"content":2405},[2406],{"text":2407,"type":513},"Start) ➔ Display ➔ E-Mail ➔ SEA ➔ (Conversion)",{"type":1829,"content":2409},[2410],{"type":42,"content":2411},[2412],{"text":2413,"type":513},"(Start) ➔ E-Mail ➔ SEA ➔ (Conversion)",{"type":42,"content":2415},[2416],{"text":2417,"type":513},"Multipliziert man nun die Übergangswahrscheinlichkeiten miteinander und addiert anschließend die Ergebnisse aller Pfade, so erhält man die Gesamtwahrscheinlichkeit von 33 %:",{"type":42,"content":2419},[2420],{"text":2421,"type":513},"66,66 % * 50 % * 100 % * 50 % = 16 %",{"type":1826,"attrs":2423,"content":2424},{"tight":51},[2425],{"type":1829,"content":2426},[2427],{"type":42},{"type":42,"content":2429},[2430],{"text":2431,"type":513},"33,33 % * 10 0% * 50 % = 16 %",{"type":42,"content":2433},[2434],{"text":2435,"type":513},"= 33 %",{"type":42,"content":2437},[2438],{"text":2439,"type":513},"Um jetzt die Wichtigkeit eines Kanals ausrechnen zu können, wird simuliert, was passieren würde, wenn ein Kanal aus dem Netzwerk entfernt wird.",{"_uid":2441,"image":2442,"caption":2446,"component":2176},"df7a926d-a43d-4c79-82a7-bc6a3988de5d",{"alt":2443,"src":2444,"name":30,"focus":30,"title":118,"source":30,"filename":2444,"copyright":30,"fieldtype":49,"meta_data":2445,"is_external_url":81},"Attributionsberechnung bzw. Wichtigkeit eines Kanals, sobald ein anderer entfernt wird.","https://storage.googleapis.com/kk-website-assets/-EUpohep_Attributionsberechnung-820x377.png",{"alt":2443,"title":118,"source":30,"copyright":30},{"type":39,"content":2447},[2448],{"type":42,"content":2449},[2450],{"text":2443,"type":513},{"_uid":2452,"text":2453,"component":1720},"b74709f4-f6e1-4fbb-b9f6-769a1f837cc1",{"type":39,"content":2454},[2455,2459,2462,2466,2470,2474,2478,2482,2490,2494,2498,2503,2507,2516,2520,2524,2530,2557,2561,2565,2569,2574,2578,2582,2586,2590,2594,2598,2608,2612,2617,2621,2625],{"type":42,"content":2456},[2457],{"text":2458,"type":513},"In unserem Beispiel wird nun der Display-Kanal entfernt. Dadurch wird ein Bestellpfad gelöscht und es existiert nur noch folgender:",{"type":42,"content":2460},[2461],{"text":2413,"type":513},{"type":42,"content":2463},[2464],{"text":2465,"type":513},"Demnach beträgt die Gesamtwahrscheinlichkeit einer Conversion nur noch 16 %:",{"type":42,"content":2467},[2468],{"text":2469,"type":513},"33,33 % * 100 % * 50 % = 16 %",{"type":42,"content":2471},[2472],{"text":2473,"type":513},"Verglichen mit der Gesamtwahrscheinlichkeit davor, fällt diese um 50 % (0,5) (33 % ➔ 16 %). Dieser relative Unterschied spiegelt nun die Wichtigkeit des Display-Kanals wider und beträgt 0,5.",{"type":42,"content":2475},[2476],{"text":2477,"type":513},"Diese Simulation wird auch für die restlichen Kanäle durchgeführt, um so die Wichtigkeit der Kanäle berechnen zu können. Mit diesen Werten werden anschließend die Bestellungen bzw. Umsätze neu kalkuliert.",{"type":42,"content":2479},[2480],{"text":2481,"type":513},"Nach dem Last-Click Modell hätte nur SEA die Bestellung zugewiesen bekommen. Nach der Zuweisung durch den Algorithmus, erhält SEA und E-Mail jeweils 0,4 und Display 0,2 der Bestellung.",{"type":42,"content":2483},[2484,2488],{"text":2485,"type":513,"marks":2486},"Das bisherige Beispiel zeigt dir die Markow-Ketten der 1. Ordnung.",[2487],{"type":932},{"text":2489,"type":513}," Eine Markow-Kette ist ein stochastischer Prozess mit dem Ziel, Wahrscheinlichkeiten für das Eintreten zukünftiger Ereignisse festzustellen. Die Ordnung gibt an, wie viele Zustände für die Berechnung der Übergangswahrscheinlichkeiten mit einbezogen werden sollen.",{"type":42,"content":2491},[2492],{"text":2493,"type":513},"1. Ordnung bedeutet, dass nur der aktuelle Zustand benutzt wird. Die 2. Ordnung bezieht auch den Zustand davor mit ein.",{"type":42,"content":2495},[2496],{"text":2497,"type":513},"So werden auch Interaktionseffekte zwischen den Kanälen bzw. Marketingaktivitäten berücksichtigt. Möglicherweise verhält sich ein Nutzer anders, wenn er zuerst Display und dann SEA sieht, als ein anderer, der zuerst E-Mail und dann SEA sieht.",{"type":925,"attrs":2499,"content":2500},{"level":2191},[2501],{"text":2502,"type":513},"Budgetallokation: Der Nutzen einer Multi-Touch-Attributionsanalyse",{"type":42,"content":2504},[2505],{"text":2506,"type":513},"Durch den Einsatz eines datengetriebenen Attributionsmodells kommst du deutlich näher an die Realität und damit an die tatsächliche Performance deiner Marketingmaßnahme.",{"type":42,"content":2508},[2509,2511,2515],{"text":2510,"type":513},"Es stellt sich auch direkt die Frage, was du mit dieser gemessenen Performance machst: ",{"text":2512,"type":513,"marks":2513},"Budgetallokation",[2514],{"type":932},{"text":1913,"type":513},{"type":42,"content":2517},[2518],{"text":2519,"type":513},"Bei der Budgetallokation wird das verfügbare Marketingbudget auf die verschiedenen Marketingkanäle bzw. Maßnahmen aufgeteilt. Als Verantwortlicher stellt sich dir die Frage, wie das Marketingbudget aufgeteilt werden soll. Hier kommt nun unsere gemessene Performance aus der datengetriebenen Attributionsanalyse zum Einsatz.",{"type":42,"content":2521},[2522],{"text":2523,"type":513},"Diese verrät uns anhand des ROIs, wie „gut“ eine Marketingaktivität funktioniert. Ist diese weit unter dem durchschnittlichen ROI, sollte hier eventuell weniger Budget investiert werden. Ist dagegen der ROI einer Marketingaktivität sehr hoch, könnte in Betracht gezogen werden, hier mehr Budget zu investieren.",{"type":42,"content":2525},[2526],{"text":2527,"type":513,"marks":2528},"Die Vorteile von dynamischen Modellen liegen auf der Hand:",[2529],{"type":932},{"type":1826,"attrs":2531,"content":2532},{"tight":51},[2533,2539,2545,2551],{"type":1829,"content":2534},[2535],{"type":42,"content":2536},[2537],{"text":2538,"type":513},"du erhältst eine realistische Bewertung der Marketingaktivität",{"type":1829,"content":2540},[2541],{"type":42,"content":2542},[2543],{"text":2544,"type":513},"du kannst eine viel bessere Budgetierung der Marketingaktivitäten vornehmen",{"type":1829,"content":2546},[2547],{"type":42,"content":2548},[2549],{"text":2550,"type":513},"du erreichst eine effizientere Budgetallokation",{"type":1829,"content":2552},[2553],{"type":42,"content":2554},[2555],{"text":2556,"type":513},"und du erzielst letztlich einen höheren Nutzen bei gleichen Kosten",{"type":42,"content":2558},[2559],{"text":2560,"type":513},"Um beurteilen zu können, ob die Performance durch die neue Budgetallokation besser geworden ist, heißt es zunächst, je nach Länge der durchschnittlichen Customer Journey abzuwarten.",{"type":42,"content":2562},[2563],{"text":2564,"type":513},"Stellst du eine Veränderung fest, kannst du nun erneut das Budget gemäß der Performance verteilen. Dieses Spiel geht immer weiter, bis du annähernd ein Optimum für jede Marketingaktivität gefunden hast.",{"type":42,"content":2566},[2567],{"text":2568,"type":513},"Warum es dann doch nicht ganz so einfach ist, zeige ich dir im Kapitel Herausforderungen.",{"type":925,"attrs":2570,"content":2571},{"level":2191},[2572],{"text":2573,"type":513},"Herausforderungen der datengetriebenen Attributionsanalyse",{"type":42,"content":2575},[2576],{"text":2577,"type":513},"Auch wenn das datengetriebene Modell ein deutlich besseres Abbild des tatsächlichen Impacts einer Marketingaktivität als eine Last Click Betrachtung aufweist, so kann dieses trotzdem die Realität nicht zu 100 % abbilden.",{"type":42,"content":2579},[2580],{"text":2581,"type":513},"Empfehlungen von Freunden, persönliche Wünsche, Werte und Charakterzüge der Kunden werden in der Customer Journey nicht abgebildet, um nur einige zu nennen. Denn die „Reise deiner Kunden“ mit allen qualitativen Aspekten ist sehr individuell und lässt sich nicht immer in Zahlen fassen.",{"type":42,"content":2583},[2584],{"text":2585,"type":513},"Auch auf technischer Seite existieren Herausforderungen. Unterstützt das Web-Analyse-Tool Cross Device Tracking? Was passiert, wenn jemand seine Cookies löscht oder den Cookie Consent nicht annimmt?",{"type":42,"content":2587},[2588],{"text":2589,"type":513},"Auch bei der Budgetallokation gibt es Herausforderungen.",{"type":42,"content":2591},[2592],{"text":2593,"type":513},"Jede Marketingaktivität hat ihr individuelles Limit. Der E-Mail-Kanal ist hierfür ein anschauliches Beispiel. Da die Erstellung von E-Mail-Inhalten verhältnismäßig günstig ist, ist es nicht überraschend, dass hier häufig ein sehr hoher ROI beobachtet wird.",{"type":42,"content":2595},[2596],{"text":2597,"type":513},"Gehst du nach dem oben erwähnten Schema vor, so könntest du einfach mehr E-Mails versenden. Denn mehr E-Mails = mehr Sales = mehr Umsatz, oder?",{"type":42,"content":2599},[2600,2602,2606],{"text":2601,"type":513},"Dass diese Vorgehensweise nicht ins Unendliche skalierbar ist, ist klar. Irgendwann nervst du deine Nutzer mit den E-Mails und erreichst genau das Gegenteil deines ursprünglichen Ziels, nämlich ",{"text":2603,"type":513,"marks":2604},"weniger",[2605],{"type":932},{"text":2607,"type":513}," Abonnenten.",{"type":42,"content":2609},[2610],{"text":2611,"type":513},"Hier könnte beispielsweise ein Ziel sein, die optimale Versandfrequenz zu finden. Dabei haben wir noch nicht über die Inhalte gesprochen. Die Suche nach diesem Optimum gilt es auch für alle anderen Marketingaktivitäten durchzuführen, am besten duch ein Herantasten mittels „Trial-and-Error“.",{"type":925,"attrs":2613,"content":2614},{"level":1069},[2615],{"text":2616,"type":513},"Attribution-Monitoring: dauerhafte Performance-Transparenz durch Machine Learning",{"type":42,"content":2618},[2619],{"text":2620,"type":513},"Einzelne Analysen mit besagtem Modell sind schon sehr hilfreich.",{"type":42,"content":2622},[2623],{"text":2624,"type":513},"Aber da geht noch mehr. Durch ein einfaches Monitoring machst du dir die ganze Power eines datengetriebenen Attributionsmodells zunutze.",{"type":42,"content":2626},[2627],{"text":2628,"type":513},"In der folgenden Abbildung ist an einem Beispiel erkennbar, wie anschließend die berechneten Daten veranschaulicht werden könnten.",{"_uid":2630,"image":2631,"caption":2635,"component":2176},"9cc94e32-ce22-45c7-91ca-88b0e580ebfb",{"alt":2632,"src":2633,"name":30,"focus":30,"title":118,"source":30,"filename":2633,"copyright":30,"fieldtype":49,"meta_data":2634,"is_external_url":81},"Beispielhaftes Dashboard, welches dir den Nutzen deiner Kanäle aufschlüsselt.","https://storage.googleapis.com/kk-website-assets/prDSq0v1_DashboardKana%CC%88le-820x383.png",{"alt":2632,"title":118,"source":30,"copyright":30},{"type":39,"content":2636},[2637],{"type":42,"content":2638},[2639],{"text":2632,"type":513},{"_uid":2641,"text":2642,"component":1720},"f0214a3c-1200-42a7-89ce-581d04bf7a47",{"type":39,"content":2643},[2644,2649,2654,2659,2664,2669,2685],{"type":42,"attrs":2645,"content":2646},{"textAlign":118},[2647],{"text":2648,"type":513},"Ein einfaches Dashboard zeigt dir die jeweilige Performance, indem es sich die vorher definierten Daten zieht, den Machine Learning Algorithmus trainiert, um dir anschließend den ROI auszurechnen.",{"type":42,"attrs":2650,"content":2651},{"textAlign":118},[2652],{"text":2653,"type":513},"Gleichzeitig kannst du Empfehlungen erhalten, wie die derzeitige Budgetverteilung vonstatten gehen sollte, welche Kanäle du mehr oder welche weniger bespielen solltest, und bei welchen Kanälen du vielleicht ein Abschalten in Erwägung ziehen kannst. Bitte schau dir dazu unbedingt nochmal in Ruhe den Abschnitt „Herausforderungen“ weiter oben an.",{"type":42,"attrs":2655,"content":2656},{"textAlign":118},[2657],{"text":2658,"type":513},"Eine faire Aufteilung der Conversions bzw. des Umsatzes auf die beteiligten Kanäle ist eine komplexe Angelegenheit. 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