{"id":17625,"date":"2021-06-09T14:55:02","date_gmt":"2021-06-09T12:55:02","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=17625\/"},"modified":"2021-06-09T17:38:11","modified_gmt":"2021-06-09T15:38:11","slug":"navod-ako-vyuzit-konektor-pre-google-analytics-4-v-data-studiu","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/navod-ako-vyuzit-konektor-pre-google-analytics-4-v-data-studiu\/","title":{"rendered":"N\u00e1vod: Ako vyu\u017ei\u0165 konektor pre Google Analytics 4 v Data Studiu"},"content":{"rendered":"

Nov\u00e1 verzia analytick\u00e9ho prostredia, Google Analytics 4<\/a> si postupne od augusta 2020 z\u00edskala ve\u013ek\u00fa \u010das\u0165 pou\u017e\u00edvate\u013eov, ktor\u00fdch pril\u00e1kal najm\u00e4 nov\u00fd d\u00e1tov\u00fd model. Ten je naozaj ve\u013ekou v\u00fdhodou, ke\u010f\u017ee pon\u00faka mo\u017enos\u0165 sledova\u0165 spr\u00e1vanie na web str\u00e1nkach a v aplik\u00e1cii s\u00fa\u010dasne. Okrem toho pon\u00faka aj in\u00e9 v\u00fdhody<\/a>, ako \u013eahk\u00e9 prepojenie s\u00a0Big Query alebo hlb\u0161\u00ed rozbor d\u00e1t v\u00a0Analysis hube.<\/strong><\/p>\n

Ak ste zvyknut\u00ed pou\u017e\u00edva\u0165 na reporty aj n\u00e1stroj Data Studio, ur\u010dite ste si v\u0161imli, \u017ee konektor pre GA4 nepon\u00faka tie ist\u00e9 mo\u017enosti ako star\u0161ia verzia UA. Preto si zhrnieme, ktor\u00e9 dimenzie a metriky s\u00fa aktu\u00e1lne dostupn\u00e9 pre reporty a ak\u00e9 reporty si z nich vieme posklada\u0165.<\/p>\n

Pre\u010do za\u010da\u0165 pou\u017e\u00edva\u0165 GA4<\/h2>\n

Google Analytics 4 postupne nahrad\u00ed aktu\u00e1lnu verziu \u00faplne. Okrem mnoh\u00fdch v\u00fdhod<\/a>, pou\u017e\u00edvan\u00edm novej verzie z\u00edskavame d\u00e1ta, ktor\u00e9 sa inak nebud\u00fa da\u0165 presun\u00fa\u0165 medzi jednotliv\u00fdmi verziami. Preto odpor\u00fa\u010dame jeho nasadenie \u010do najsk\u00f4r a paralelne s\u00a0Universal Analytics sledova\u0165 udalosti na web str\u00e1nkach. Ak chceme skombinova\u0165 \u00fadaje z\u00a0GA4 v reportoch naraz\u00edme na probl\u00e9m v\u00a0podobe ch\u00fdbaj\u00facich metr\u00edk na ktor\u00e9 sme si u\u017e ka\u017edodenne zvykli.<\/p>\n

Pou\u017eitie konektora pre GA4, sa odli\u0161uje najm\u00e4 v\u00a0podobe ch\u00fdbaj\u00faceho n\u00e1h\u013eadu (view) na \u00farovni vlastn\u00edctva (property). Ke\u010f\u017ee nov\u00e1 verzia spracov\u00e1va d\u00e1ta na z\u00e1klade udalost\u00ed, a\u00a0nie rel\u00e1ci\u00ed, v\u00fdsledkom je aj r\u00f4zna dostupnos\u0165 metr\u00edk z\u00a0ktor\u00fdch vieme tvori\u0165 reporty.<\/p>\n

Aby sme vedeli spr\u00e1vne porovna\u0165 v\u00fdsledky medzi Google Analytics 4 a\u00a0Universal Analytics, mus\u00edme n\u00e1js\u0165 spolo\u010dn\u00e9 vlastnosti oboch modelov. K\u00a0tejto \u00falohe n\u00e1m posl\u00fa\u017ei n\u00e1vod, ako si previes\u0165 naj\u010dastej\u0161ie pou\u017e\u00edvan\u00e9 metriky a\u00a0dimenzie cez GA4 do Data Studia.<\/p>\n

Ako reportova\u0165 v Data Studiu v\u00fdsledky z GA4<\/h2>\n

Publikum<\/h3>\n

Za\u010dnime naj\u010dastej\u0161ie pou\u017e\u00edvan\u00fdm reportom v\u00a0Universal Analytics. Preh\u013ead Publika (Audience Overview) je d\u00f4le\u017eitou s\u00fa\u010das\u0165ou merania, ktor\u00e1 zabezpe\u010duje vysok\u00fa \u00farove\u0148 predstavy o\u00a0aktivite na na\u0161ich str\u00e1nkach, aj ke\u010f je najlep\u0161ie hodnoti\u0165 jej v\u00fdsledky v\u00a0kontexte cel\u00e9ho webu.<\/p>\n

Prv\u00fdch \u0161es\u0165 metr\u00edk je celkom jasn\u00fdch, ke\u010f\u017ee s\u00fa dostupn\u00e9 automaticky, alebo prostredn\u00edctvom jednoduchej kalkul\u00e1cie.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Universal Analytics<\/strong><\/td>\nGoogle Analytics 4<\/strong><\/td>\n<\/tr>\n
Users<\/td>\nTotal users<\/td>\n<\/tr>\n
New Users<\/td>\nNew users<\/td>\n<\/tr>\n
Sessions<\/td>\nSessions<\/td>\n<\/tr>\n
Number of Sessions per User<\/td>\nSessions per user<\/td>\n<\/tr>\n
Pageviews<\/td>\nViews<\/td>\n<\/tr>\n
Pages \/ Session<\/td>\nCalculate as:\u00a0Views \/ Sessions<\/td>\n<\/tr>\n
Avg. Session Duration<\/td>\nCalculate\u00a0Avg. Engagement Time\u00a0as:\u00a0User engagement \/ Sessions<\/td>\n<\/tr>\n
Bounce Rate<\/td>\nEngagement rate<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Posledn\u00e9 dve metriky, priemern\u00fd \u010das rel\u00e1cie a bounce rate u\u017e v GA4 nen\u00e1jdete. Namiesto nich sme z\u00edskali engaged sessions a engaged rate. Nov\u00fd model GA4 vyhodnocuje zapojenie pou\u017e\u00edvate\u013eov automaticky, pod\u013ea podmienky \u017ee pou\u017e\u00edvate\u013e str\u00e1vi na str\u00e1nke aspo\u0148 10 sek\u00fand, vykon\u00e1 konverziu alebo spust\u00ed dve zobrazenia str\u00e1nky za sebou. Engagement rate je tak vo v\u00fdsledku o nie\u010do komplexnej\u0161\u00edm ukazovate\u013eom ako klasick\u00fd bounce rate.<\/p>\n

Akviz\u00edcia<\/h3>\n

Preh\u013ead reportov Akviz\u00edcia n\u00e1m ukazuje odkia\u013e pri\u0161li pou\u017e\u00edvatelia na na\u0161u str\u00e1nku. Preto sa aj v GA4 n\u00e1jde \u0161tandardn\u00e1 dimenzia zdroj n\u00e1v\u0161tev (traffic source).<\/p>\n\n\n\n\n\n\n\n
Universal Analytics<\/strong><\/td>\nGoogle Analytics 4<\/strong><\/td>\n<\/tr>\n
Default Channel Grouping<\/td>\nSession default channel grouping<\/td>\n<\/tr>\n
Campaign<\/td>\nSession campaign<\/td>\n<\/tr>\n
Medium<\/td>\nSession medium<\/td>\n<\/tr>\n
Source<\/td>\nSession source<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/strong><\/p>\n

Spr\u00e1vanie<\/h3>\n

Ke\u010f\u017ee model spracovania d\u00e1t v GA4 bol navrhnut\u00fd tak, aby meral interakcie naprie\u010d web str\u00e1nkami a aplik\u00e1ciou, je teraz omnoho jednoduch\u0161ie reportova\u0165 d\u00e1ta oboch vlastn\u00edctiev s\u00fa\u010dasne. Na porovnanie uvediem len, \u017ee koncepty zobrazenia str\u00e1nok a zobrazenia obrazovky, s\u00fa celkom odli\u0161n\u00e9 v Universal Analytics.<\/p>\n\n\n\n\n\n\n
Page Title
\nScreen Name<\/td>\n		Page title and screen name<\/td>\n<\/tr>\n
Pageviews
\nScreenviews<\/td>\n		Views<\/td>\n<\/tr>\n
Event Category
\nEvent Action
\nEvent Label<\/td>\n		Event name
\nCustom dimensions<\/em><\/strong><\/a><\/td>\n<\/tr>\n
Total Events<\/td>\nEvent count<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Navy\u0161e okrem kombinovanej dimenzie N\u00e1zov str\u00e1nky a obrazovky, s\u00fa st\u00e1le dostupn\u00e9 aj dimenzie \u0161pecifick\u00e9 pre web ako Hostname, Page path, a Page title.<\/p>\n

Konverzie<\/h3>\n

Nov\u00fd Google Analytics 4 vymenil koncept cie\u013eov za konverzie. Konverzia je teda ak\u00e1ko\u013evek udalos\u0165 ktor\u00e1 bola ozna\u010den\u00e1 v sekcii konverzie v GA4 \u00fa\u010dte. Aby sme vedeli reportova\u0165 ko\u013eko tak\u00fdchto udalost\u00ed sa uskuto\u010dnilo, pou\u017eijeme dimenziu event_name v spolu s metrikou conversions v Data studiu. Na to aby sme reportovali len \u0161pecifick\u00fa udalos\u0165, m\u00f4\u017eeme pou\u017ei\u0165 filter ktor\u00fd vyberie len konkr\u00e9tny event_name.<\/p>\n\n\n\n\n
Goal XX Completions<\/td>\nConversions\u00a0filtered by\u00a0Event name<\/td>\n<\/tr>\n
Goal XX Conversion Rate<\/td>\nUse data blending to calculate: Conversions\u00a0filtered by\u00a0Event name \/ Sessions<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Z\u00e1ver<\/h2>\n

Ver\u00edm, \u017ee v\u00e1m n\u00e1\u0161 n\u00e1vod v\u00e1m pomohol zorientova\u0165 sa v\u00a0dimenzi\u00e1ch a\u00a0metrik\u00e1ch, z\u00a0ktor\u00fdch m\u00f4\u017eete aktu\u00e1lne sklada\u0165 reporty v\u00a0Data Studiu. Okrem uveden\u00fdch dimenzi\u00ed n\u00e1jdete aj \u010fal\u0161ie, ktor\u00e9 zodpovedaj\u00fa \u0161pecifick\u00fdm situ\u00e1ci\u00e1m a\u00a0preto som ich nespomenul. To u\u017e nech\u00e1m na va\u0161ej zvedavosti. 😊 Konektor pre Google Analytics 4 je navy\u0161e e\u0161te st\u00e1le vo v\u00fdvoji a\u00a0postupne k\u00a0nemu prib\u00fadaj\u00fa \u010fal\u0161ie dimenzie, o\u00a0ktor\u00fdch v\u00e1s budeme ur\u010dite informova\u0165.<\/p>\n","protected":false},"excerpt":{"rendered":"

Nov\u00e1 verzia analytick\u00e9ho prostredia, Google Analytics 4 si postupne od augusta 2020 z\u00edskala ve\u013ek\u00fa \u010das\u0165 pou\u017e\u00edvate\u013eov, ktor\u00fdch pril\u00e1kal…<\/p>\n","protected":false},"author":71,"featured_media":17632,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[803,639],"tags":[603,799,162,798,668,805],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17625"}],"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\/71"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=17625"}],"version-history":[{"count":18,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17625\/revisions"}],"predecessor-version":[{"id":17646,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17625\/revisions\/17646"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/17632"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=17625"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=17625"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=17625"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}