{"id":19296,"date":"2023-04-21T09:49:23","date_gmt":"2023-04-21T07:49:23","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=19296\/"},"modified":"2023-04-25T10:23:53","modified_gmt":"2023-04-25T08:23:53","slug":"uvod-do-atribucie-v-google-analytics-4","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/uvod-do-atribucie-v-google-analytics-4\/","title":{"rendered":"\u00davod do atrib\u00facie v Google Analytics 4 (UA vs. GA4)"},"content":{"rendered":"<p><strong>Atrib\u00facia a priradenie konverzi\u00ed k zdrojom patria medzi d\u00f4le\u017eit\u00e9 faktory, ktor\u00e9 n\u00e1m pon\u00fakaj\u00fa \u0161ir\u0161\u00ed poh\u013ead na vyhodnotenie marketingov\u00fdch a reklamn\u00fdch kampan\u00ed. <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/atribucia-attribution\/\" target=\"_blank\" rel=\"noopener\">Atrib\u00facia<\/a> predstavuje priradenie kreditu jednotliv\u00fdm reklam\u00e1m, kliknutiam a ostatn\u00fdm faktorom, ktor\u00e9 viedli ku <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/konverzia-conversion\/\" target=\"_blank\" rel=\"noopener\">konverzii<\/a>.<\/strong><\/p>\n<p><strong><a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/atribucny-model-attribution-model\/\" target=\"_blank\" rel=\"noopener\">Atribu\u010dn\u00fd model<\/a> si potom m\u00f4\u017eeme predstavi\u0165 ako pravidlo alebo algoritmus, ktor\u00fd ur\u010duje sp\u00f4sob pripisovania kreditu za konverzie bodom kontaktu na konverznej ceste.<\/strong><\/p>\n<p>V Universal Analytics sa be\u017en\u00fd u\u017e\u00edvate\u013e stretol iba s jedn\u00fdm typom Atribu\u010dn\u00e9ho modelu &#8211; model <strong>Posledn\u00e9 kliknutie naprie\u010d kan\u00e1lmi \/ Posledn\u00e9 nepriame kliknutie (Last non direct click model)<\/strong>. Tento model ignoruje priame (direct) n\u00e1v\u0161tevy a prira\u010fuje cel\u00fd podiel splnenia cie\u013eu pr\u00e1ve posledn\u00e9mu zdroju, ktor\u00fd viedol ku konverzii.<\/p>\n<p>Pr\u00edchod Google Analytics 4 so sebou priniesol aj \u0161ir\u0161iu mno\u017einu atribu\u010dn\u00fdch modelov, ktor\u00e9 m\u00e1me ako u\u017e\u00edvatelia na v\u00fdber. Pri v\u00fdbere Atribu\u010dn\u00e9ho modelu treba zv\u00e1\u017ei\u0165 rozsah na\u0161ich reportov. <strong>V Google Analytics 4 pozn\u00e1me tri druhy rozsahov:<\/strong><\/p>\n<ol>\n<li><strong>Zdroj rel\u00e1cie<\/strong> (session source),<\/li>\n<li><strong>Zdroj akviz\u00edcie<\/strong> pou\u017e\u00edvate\u013eov (user acquisition source),<\/li>\n<li><strong>Zdroj udalosti<\/strong> (event source).<\/li>\n<\/ol>\n<p>V Universal Analytics sa pou\u017e\u00edval iba jedin\u00fd typ &#8211; zdroj rel\u00e1cie (session source).<\/p>\n<h2>1. Zdroj rel\u00e1cie (session source)<\/h2>\n<p>Atrib\u00facia pod\u013ea zdroja rel\u00e1cie je zalo\u017een\u00e1 na zdroji, z ktor\u00e9ho pri\u0161la <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/relacia-session\/\" target=\"_blank\" rel=\"noopener\"><strong>rel\u00e1cia<\/strong><\/a>. Dimenzia tohto rozsahu je vo ve\u013ekom pou\u017e\u00edvan\u00e1 v reportoch <strong>Akviz\u00edcie n\u00e1v\u0161tevnosti<\/strong> (Traffic acquisition).<\/p>\n<p><strong>Zdroj rel\u00e1cie<\/strong> je zdroj, ktor\u00fd rel\u00e1ciu za\u010dal (napr. sprostredkovane zo soci\u00e1lnych m\u00e9di\u00ed alebo ako v\u00fdsledok organick\u00e9ho vyh\u013ead\u00e1vania). Ak rel\u00e1cia za\u010dala priamou n\u00e1v\u0161tevou (direct), tak sa zdroj rel\u00e1cie prirad\u00ed k zdroju predch\u00e1dzaj\u00facej rel\u00e1cie (ak nejak\u00e1 bola).<\/p>\n<p><strong>Priama n\u00e1v\u0161teva<\/strong> znamen\u00e1, \u017ee Analytics nevie, odkia\u013e pou\u017e\u00edvate\u013e pri\u0161iel, preto\u017ee kliknutie nepre\u0161lo cez parameter sprostredkovate\u013ea, gclid, alebo UTM.<\/p>\n<p>Zdroj rel\u00e1cie je priamy iba vtedy, ak Analytics nevid\u00ed \u017eiadny in\u00fd zdroj n\u00e1v\u0161tevy dan\u00e9ho pou\u017e\u00edvate\u013ea v r\u00e1mci <strong>sp\u00e4tn\u00e9ho n\u00e1h\u013eadu<\/strong> (Lookback window). Predvolen\u00e1 d\u013a\u017eka sp\u00e4tn\u00e9ho n\u00e1h\u013eadu v GA4 je 90 dn\u00ed, zatia\u013e \u010do v UA to bolo 6 mesiacov.<\/p>\n<h2>2. Prv\u00fd zdroj pou\u017e\u00edvate\u013ea (First user source)<\/h2>\n<p>Dimenzia naz\u00fdvan\u00e1 <strong>prv\u00fd<\/strong> <strong>zdroj pou\u017e\u00edvate\u013ea<\/strong> (zdroj prvej n\u00e1v\u0161tevy pou\u017e\u00edvate\u013ea) je novinka uveden\u00e1 s GA4. Predstavuje zdroj, z ktor\u00e9ho pou\u017e\u00edvate\u013e pri\u0161iel prv\u00fdkr\u00e1t na web \/ do aplik\u00e1cie.<\/p>\n<p>T\u00e1to nov\u00e1 dimenzia je s\u00fa\u010das\u0165ou nov\u00e9ho pr\u00edstupu Googlu k meraniu v online marketingu, ktor\u00fd sa u\u017e nezameriava len na ROAS (revenues vs. costs &#8211; v\u00fdnosy vs. n\u00e1klady), ale analyzuje aj CAC vs. LTV (customer acquisition cost vs. lifetime value &#8211; n\u00e1klady na akviz\u00edciu z\u00e1kazn\u00edka vs. jeho celo\u017eivotn\u00e1 hodnota).<\/p>\n<p>Pri tomto pr\u00edstupe sa pou\u017e\u00edva logika, pri ktorej mus\u00edme najprv pou\u017e\u00edvate\u013ea z\u00edska\u0165 a po in\u0161tal\u00e1cii aplik\u00e1cie \/ n\u00e1v\u0161teve webu je potrebn\u00e9 vytvori\u0165 \u010fal\u0161ie marketingov\u00e9 \u00fasilie, aby sme pou\u017e\u00edvate\u013ea aj spe\u0148a\u017eili.<\/p>\n<p>V GA4 sa prv\u00e1 n\u00e1v\u0161teva pou\u017e\u00edvate\u013ea zaznamen\u00e1va udalos\u0165ou <strong>first_visit<\/strong> pre web alebo udalos\u0165ou <strong>first_open<\/strong> pre aplik\u00e1ciu.<\/p>\n<p>Zdroj prvej n\u00e1v\u0161tevy sa pripisuje pomocou modelu <strong>Posledn\u00e9ho nepriameho kliknutia<\/strong> (Last non direct click). T\u00e1to atrib\u00facia zdroja sa vz\u0165ahuje iba na interakcie pred prvou n\u00e1v\u0161tevou webu \/ prv\u00fdm otvoren\u00edm aplik\u00e1cie.<\/p>\n<p>Po priraden\u00ed zost\u00e1va Zdroj prvej n\u00e1v\u0161tevy nezmenen\u00fd. Zmeni\u0165 sa m\u00f4\u017ee iba v pr\u00edpade, \u017ee Google Analytics dok\u00e1\u017ee technicky prepoji\u0165 aktivitu pou\u017e\u00edvate\u013ea na webe a v aplik\u00e1cii.<\/p>\n<p>Resetovanie tohto zdroja m\u00f4\u017ee nasta\u0165 v pr\u00edpade straty sledovania pou\u017e\u00edvate\u013ea, \u010di\u017ee napr\u00edklad ak pou\u017e\u00edvate\u013e nenav\u0161t\u00edvil webov\u00fa str\u00e1nku dlh\u0161ie ako je d\u00e1tum vypr\u0161ania platnosti s\u00faboru cookie pre Analytics.<\/p>\n<h2>3. Zdroj udalosti (Event source)<\/h2>\n<p>Spolo\u010dne s GA4 pri\u0161la ve\u013ek\u00e1 zmena ke\u010f sa za\u010dali pou\u017e\u00edva\u0165 udalosti ako z\u00e1klad zhroma\u017e\u010fovania \u00fadajov a vytv\u00e1rania preh\u013eadov a nahradili tak rel\u00e1cie, ktor\u00e9 boli z\u00e1kladom meran\u00ed v UA. T\u00e1to zmena mala zna\u010dn\u00fd vplyv na predstavenie nov\u00fdch Atribu\u010dn\u00fdch modelov, z ktor\u00fdch si m\u00f4\u017eeme v nastaveniach GA4 zvoli\u0165. V prostred\u00ed GA4 si v\u010faka tomu vieme vytv\u00e1ra\u0165 atribu\u010dn\u00e9 preh\u013eady pou\u017eit\u00edm nami vybran\u00e9ho atribu\u010dn\u00e9ho modelu. Toto nastavenie plat\u00ed <strong>iba na konverzn\u00e9 udalosti<\/strong>.<\/p>\n<p>Pou\u017e\u00edvan\u00fd model si m\u00f4\u017eeme nastavi\u0165 v sekcii <strong>Spr\u00e1vca<\/strong> (Admin) v \u010dasti <strong>Vlastn\u00edctvo<\/strong> (Property) &gt; <strong>Nastavenie atrib\u00facie<\/strong> &gt; <strong>Atribu\u010dn\u00fd model preh\u013eadov<\/strong> (vi\u010f screenshot).<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-19307 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0.png\" alt=\"atribucia v Ga4 vs UA DASE BLOG\" width=\"2048\" height=\"1196\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0.png 2048w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0-300x175.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0-1024x598.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0-1536x897.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/atribucia-obr-0-600x350.png 600w\" sizes=\"(max-width: 2048px) 100vw, 2048px\" \/><\/a><\/p>\n<p>Preddefinovan\u00fd pou\u017e\u00edvan\u00fd model je v GA4 nastaven\u00fd na<strong> model na z\u00e1klade d\u00e1t naprie\u010d kan\u00e1lmi (Cross-channel data-driven model)<\/strong>. Tento model je mo\u017en\u00e9 kedyko\u013evek zmeni\u0165 a zmena je retroakt\u00edvna a teda zmen\u00ed aj historick\u00e9 \u00fadaje.<\/p>\n<p>\u017diadny atribu\u010dn\u00fd model nepripisuje atribu\u010dn\u00fd kredit priamym (direct) n\u00e1v\u0161tev\u00e1m, ak cesta ku konverzii nepozost\u00e1va v\u00fdhradne z priamych n\u00e1v\u0161tev.<\/p>\n<h1>Atribu\u010dn\u00e9 modely<\/h1>\n<p>GA4 n\u00e1m na v\u00fdber pon\u00faka (v \u010dase p\u00edsania \u010dl\u00e1nku) 7 atribu\u010dn\u00fdch modelov:<\/p>\n<ol>\n<li><strong>Model na z\u00e1klade d\u00e1t naprie\u010d kan\u00e1lmi <\/strong>(Cross-channel data-driven model)<\/li>\n<li><strong>Model posledn\u00e9ho kliknutia naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel last click model)<\/li>\n<li><strong>Model prv\u00e9ho kliknutia naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel first click model)<\/li>\n<li><strong>Line\u00e1rny model naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel linear model)<\/li>\n<li><strong>Model na z\u00e1klade poz\u00edcie naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel position-based model)<\/li>\n<li><strong>Model rastu naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel time decay model)<\/li>\n<li><strong>Model na z\u00e1klade preferovan\u00e9ho posledn\u00e9ho kliknutia v slu\u017ebe Google Ads <\/strong>(Ads-preferred last click model)<\/li>\n<\/ol>\n<p>Av\u0161ak v novink\u00e1ch tohto mesiaca Google <a href=\"https:\/\/support.google.com\/analytics\/answer\/9164320?hl=en&amp;dark=1#040623\" target=\"_blank\" rel=\"noopener\">ozn\u00e1mil<\/a>, \u017ee 4 z nich pl\u00e1nuje postupne odstavi\u0165 kv\u00f4li postupn\u00e9mu vyp\u00ednaniu slu\u017eby Universal Analytics.<\/p>\n<p><strong>Jedn\u00e1 sa o modely:<\/strong><\/p>\n<ul>\n<li><strong>Model prv\u00e9ho kliknutia naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel first click model)<\/li>\n<li><strong>Line\u00e1rny model naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel linear model)<\/li>\n<li><strong>Model rastu naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel time decay model)<\/li>\n<li><strong>Model na z\u00e1klade poz\u00edcie naprie\u010d kan\u00e1lmi<\/strong> (Cross-channel position-based model)<\/li>\n<\/ul>\n<p>T\u00e1to zmena za\u010dne plati\u0165 <strong>v m\u00e1ji <\/strong>tohto roka od kedy novovytvoren\u00e9 vlastn\u00edctva GA4 nebud\u00fa dan\u00e9 4 modely v nastaveniach v\u00f4bec pon\u00faka\u0165 a n\u00e1sledne <strong>v septembri<\/strong> tieto modely \u00faplne zmizn\u00fa plne aj zo v\u0161etk\u00fdch ostatn\u00fdch GA4 vlastn\u00edctiev.<\/p>\n<p>Preto si aspo\u0148 v skratke vysvetl\u00edme ako pracuj\u00fa 3 modely, ktor\u00e9 bud\u00fa pou\u017e\u00edvan\u00e9 aj v bud\u00facnosti bez Universal Analytics. Detailnej\u0161ie inform\u00e1cie a inform\u00e1cie o ostatn\u00fdch modeloch n\u00e1jdete v <a href=\"https:\/\/support.google.com\/analytics\/answer\/10596866?hl=en&amp;utm_id=ad&amp;sjid=15472006207641279264-EU#zippy=%2Cobsah-tohto-%C4%8Dl%C3%A1nku%2Cin-this-article%2Cthe-methodology-behind-data-driven-attribution-advanced\">ofici\u00e1lnej dokument\u00e1cii od Googlu.<\/a><\/p>\n<h3>Model na z\u00e1klade d\u00e1t naprie\u010d kan\u00e1lmi (Cross-channel data-driven model)<\/h3>\n<p>Ako napoved\u00e1 n\u00e1zov, tak tento model pripisuje kredit za konverziu <strong>na z\u00e1klade d\u00e1t<\/strong> jednotliv\u00fdch konverzn\u00fdch udalost\u00ed. Ka\u017ed\u00fd model na z\u00e1klade \u00fadajov je preto \u0161pecifick\u00fd pre ka\u017ed\u00e9ho inzerenta a konverzn\u00fa udalos\u0165.<\/p>\n<p>Tento model na atrib\u00faciu pou\u017e\u00edva <strong>algoritmy strojov\u00e9ho u\u010denia<\/strong>, ktor\u00e9 vyhodnocuj\u00fa konverzn\u00e9 aj nekonverzn\u00e9 cesty. V\u00fdsledn\u00fd model sa u\u010d\u00ed, ako r\u00f4zne body kontaktu ovplyv\u0148uj\u00fa v\u00fdsledky konverzi\u00ed. S\u00fa v \u0148om zahrnut\u00e9 faktory ako s\u00fa \u010das od konverzie, typ zariadenia, po\u010det interakci\u00ed s reklamou a podobne.<\/p>\n<p>Model pomocou pr\u00edstupu protichodn\u00fdch situ\u00e1ci\u00ed porovn\u00e1, \u010do sa re\u00e1lne stalo, s t\u00fdm, \u010do sa mohlo sta\u0165. N\u00e1sledne ur\u010d\u00ed, ktor\u00e9 body kontaktu <strong>najpravdepodobnej\u0161ie poved\u00fa ku konverzi\u00e1m<\/strong>. Na z\u00e1klade tejto pravdepodobnosti model pripisuje kredit za konverziu t\u00fdmto bodom kontaktu.<\/p>\n<p>V z\u00e1vislosti od dostupnosti \u00fadajov m\u00f4\u017ee tento model vyu\u017e\u00edva\u0165 s\u00fahrnn\u00e9 d\u00e1ta z <a href=\"https:\/\/support.google.com\/analytics\/answer\/1011397?sjid=15472006207641279264-EU#zippy=%2Cobsah-tohto-%C4%8Dl%C3%A1nku\" target=\"_blank\" rel=\"noopener\">nastaven\u00ed zdie\u013eania \u00fadajov<\/a>.<\/p>\n<h3>Model posledn\u00e9ho kliknutia naprie\u010d kan\u00e1lmi <strong>(Cross-channel last click model)<\/strong><\/h3>\n<p>Podobne aj tu n\u00e1zov presne vystihuje dan\u00fd model, preto\u017ee tento model pripisuje 100 % hodnoty konverzie posledn\u00e9mu kan\u00e1lu, z ktor\u00e9ho sa z\u00e1kazn\u00edk preklikol pred konverziou a ignoruje pritom priamu n\u00e1v\u0161tevnos\u0165.<\/p>\n<h3>Model na z\u00e1klade preferovan\u00e9ho posledn\u00e9ho kliknutia v slu\u017ebe Google Ads (Ads-preferred last click model)<\/h3>\n<p>V tomto modeli sa 100 % hodnoty konverzie sa pripisuje posledn\u00e9mu kan\u00e1lu slu\u017eby Google Ads, cez ktor\u00fd sa z\u00e1kazn\u00edk preklikol pred konverziou. Ak t\u00e1to cesta nezah\u0155\u0148a \u017eiadne kliknutie v slu\u017ebe Google Ads, tak sa atribu\u010dn\u00fd model zmen\u00ed na posledn\u00e9 kliknutie naprie\u010d kan\u00e1lmi.<\/p>\n<h2>Nezhoduj\u00fa sa v\u00e1m konverzie v Google Ads a Google Analytics 4?<\/h2>\n<p>To sa m\u00f4\u017ee dia\u0165 aj pr\u00e1ve kv\u00f4li atribu\u010dn\u00fdm modelom. Okrem atrib\u00fa\u010dn\u00e9ho modelu existuj\u00fa aj \u010fal\u0161ie d\u00f4vody. Tie n\u00e1jdete v tomto \u010dl\u00e1nku. &#x1f447;<\/p>\n\n\t<section class=\"post shortcode\">\n\t  <div class=\"row\">\n\t\t<a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/?post_type=post&p=19275\/\" class=\"col-lg-7 col-sm-6\">\n\t\t  <div class=\"post-img\" style=\"background-image:url(https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/nezhoduju-sa-konverzie-1024x536.jpg)\"><\/div>\n\t\t<\/a>\n\t\t<div class=\"col-lg-5 col-sm-6\">\n\t\t  <header>\n\t\t\t<a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/?post_type=post&p=19275\/\">\n    \t\t  <span class=\"title\">Nezhoduj\u00fa sa v\u00e1m konverzie? Google Ads vs. Google Analytics 4<\/span>\t\n\t\t\t<\/a>\n\t\t\t<div class=\"post-meta text-primary\">\n\t\t\t  <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/category\/analytika-a-biznis\/\">Analytika a biznis<\/a>  | \n\t\t\t  <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/author\/branislavbalala\/\">Branislav Balala<\/a> |\n\t\t\t  <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/2023\/04\">\n\t\t\t  \t<time datetime=\"2023-04-03\" itemprop=\"datePublished\">03. apr 2023<\/time>\n\t\t\t  <\/a>\n\t\t\t<\/div>\n\t\t  <\/header>\n\t\t  <article>\n\t\t  \t<p>Aj vy ste sa u\u017e zam\u00fd\u0161\u013eali nad t\u00fdm, \u010di je lep\u0161ie posiela\u0165 konverzie do Google Ads cez Google...<\/p>\n\t\t  <\/article>\n\t\t<\/div>\n\t  <\/div>\n\t<\/section>\n\t\n","protected":false},"excerpt":{"rendered":"<p>Atrib\u00facia a priradenie konverzi\u00ed k zdrojom patria medzi d\u00f4le\u017eit\u00e9 faktory, ktor\u00e9 n\u00e1m pon\u00fakaj\u00fa \u0161ir\u0161\u00ed poh\u013ead na vyhodnotenie marketingov\u00fdch&#8230;<\/p>\n","protected":false},"author":76,"featured_media":19302,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[803,640,637],"tags":[618,887,885,799,798,888,886],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/19296"}],"collection":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/users\/76"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=19296"}],"version-history":[{"count":8,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/19296\/revisions"}],"predecessor-version":[{"id":19310,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/19296\/revisions\/19310"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/19302"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=19296"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=19296"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=19296"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}