{"id":17307,"date":"2021-04-26T15:31:39","date_gmt":"2021-04-26T13:31:39","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=17307\/"},"modified":"2021-10-26T14:35:35","modified_gmt":"2021-10-26T12:35:35","slug":"vyber-metriky-pomocou-parametrov","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/vyber-metriky-pomocou-parametrov\/","title":{"rendered":"Data Studio hack: v\u00fdber metriky pomocou parametrov"},"content":{"rendered":"<div id=\"toc_container\" class=\"toc_wrap_right toc_white no_bullets\"><p class=\"toc_title\">Obsah \u010dl&aacute;nku<\/p><ul class=\"toc_list\"><li><ul><li><\/li><li><\/li><li><\/li><li><\/li><\/ul><\/li><li><ul><li><\/li><li><\/li><li><\/li><\/ul><\/li><li><\/li><\/ul><\/div>\n\n<p><strong>Po minulomesa\u010dnom \u010dl\u00e1nku o <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/parametre-v-data-studiu-nova-ficura-ktoru-si-zamilujete\/\" target=\"_blank\" rel=\"noopener noreferrer\">parametroch v Data Studiu<\/a> sa mi ozvalo hne\u010f nieko\u013eko \u010ditate\u013eov, ktor\u00fdch t\u00e1to nov\u00e1 fi\u010d\u00fara zaujala a chceli by o parametroch vedie\u0165 viac. Dnes sa teda pozrieme na to, ako e\u0161te viac zjednodu\u0161i\u0165 va\u0161e reporty a ako poskytn\u00fa\u0165 u\u017e\u00edvate\u013eovi mo\u017enos\u0165 v\u00fdberu metriky, ktor\u00e1 ho zauj\u00edma.<\/strong><\/p>\n<p>Ak ste chceli vizualizova\u0165 d\u00e1ta z piatich cie\u013eov, doteraz ste museli vytv\u00e1ra\u0165 5 samostatn\u00fdch reportov, pre ka\u017ed\u00fd jeden cie\u013e zvl\u00e1\u0161\u0165. S pou\u017eit\u00edm parametra v\u00e1m bude sta\u010di\u0165 jeden report a filter pre v\u00fdber cie\u013ea. V\u00fdsledkom n\u00e1\u0161ho sna\u017eenia bude tak\u00fdto interakt\u00edvny report:<\/p>\n<p><a href=\"https:\/\/datastudio.google.com\/reporting\/616e8ed3-86e8-4238-ba48-f49ffa230f95\" target=\"_blank\" rel=\"noopener noreferrer\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-17335 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameters-gds-report.png\" alt=\"parametre v data studiu - v\u00fdber metr\u00edk\" width=\"1194\" height=\"587\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameters-gds-report.png 1194w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameters-gds-report-300x147.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameters-gds-report-1024x503.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameters-gds-report-600x295.png 600w\" sizes=\"(max-width: 1194px) 100vw, 1194px\" \/><\/a><\/p>\n<h2><span id=\"Priprava_dat\">Pr\u00edprava d\u00e1t<\/span><\/h2>\n<h3><span id=\"1_Prepojenie_Data_Studia_s_Google_Analytics_cez_Extract_Data_konektor\">1. Prepojenie Data Studia s Google Analytics cez Extract Data konektor<\/span><\/h3>\n<p>Aby sme v\u00f4bec mohli pracova\u0165 s tak\u00fdmto filtrom, je nutn\u00e9 prepoji\u0165 <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/google-data-studio\/\" target=\"_blank\" rel=\"noopener noreferrer\">Data Studio<\/a> s va\u0161\u00edm Google Analytics cez Extract Data konektor. Je to z toho d\u00f4vodu, \u017ee vo vlastnom kalkulovanom poli budeme musie\u0165 &#8222;pomixova\u0165&#8220; <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/dimenzia\/\" target=\"_blank\" rel=\"noopener noreferrer\">dimenziu<\/a> s agregovan\u00fdmi <a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/vyraz\/metrika-metric\/\" target=\"_blank\" rel=\"noopener noreferrer\">metrikami<\/a>. Toto v\u0161ak nie je mo\u017en\u00e9 s klasick\u00fdm Google Analytics konektorom, preto budeme musie\u0165 pou\u017ei\u0165 Extract Data konektor:<\/p>\n<ol>\n<li>V sekcii <strong>Resource &gt; Manage added data sources &gt; ADD A DATA SOURCE<\/strong> n\u00e1jdite konektor <strong>Extract Data<\/strong><\/li>\n<li>Zvo\u013ete si Google Analytics view, ktor\u00e9 chcete pou\u017ei\u0165 na reportovanie<\/li>\n<li>Navo\u013ete si v\u0161etky dimenzie a metriky, ktor\u00e9 chcete pri reportingu pou\u017e\u00edva\u0165<\/li>\n<li>Nastavte \u010dasov\u00e9 okno, pre ktor\u00e9 chcete zbiera\u0165 d\u00e1ta (napr. posledn\u00fdch 365 dn\u00ed)<\/li>\n<li>Nastavte automatick\u00fd update na dennej b\u00e1ze (ide\u00e1lne v skor\u00fdch rann\u00fdch hodin\u00e1ch)<\/li>\n<\/ol>\n<p>V\u00fdsledn\u00e9 nastavenie Extract Data konektora bude potom vyzera\u0165 takto:<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/extract-data-gds.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"img-shadow aligncenter wp-image-17314 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/extract-data-gds.png\" alt=\"extract data google data studio\" width=\"820\" height=\"749\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/extract-data-gds.png 820w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/extract-data-gds-300x274.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/extract-data-gds-600x548.png 600w\" sizes=\"(max-width: 820px) 100vw, 820px\" \/><\/a><\/p>\n<h3><span id=\"2_Vytvorenie_parametra_so_zoznamom_cielov\">2. Vytvorenie parametra so zoznamom cie\u013eov<\/span><\/h3>\n<p>V druhom kroku si vytvor\u00edte parameter, ktor\u00fd nesk\u00f4r pou\u017eijete ako filter. Parameter vytvor\u00edte vo va\u0161om d\u00e1tovom zdroji: <strong>Resource &gt; Manage added data sources<\/strong>, kde n\u00e1jdete v pravom hornom rohu tla\u010didlo\u00a0<strong>+ ADD A PARAMETER<\/strong>. Naj\u013eah\u0161ie sa v\u00e1m bude pracova\u0165 ak v prvom st\u013apci Value bude presn\u00fd n\u00e1zov dimenzie, alebo metriky, v na\u0161om pr\u00edpade n\u00e1zvy cie\u013eov. Prav\u00fd st\u013apec Label obsahuje hodnoty, ktor\u00e9 bud\u00fa zobrazen\u00e9 v samotnom filtri.<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameter-list-of-goals.png\" data-rel=\"lightbox-image-1\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"img-shadow aligncenter wp-image-17308 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameter-list-of-goals.png\" alt=\"\" width=\"494\" height=\"614\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameter-list-of-goals.png 494w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/parameter-list-of-goals-241x300.png 241w\" sizes=\"(max-width: 494px) 100vw, 494px\" \/><\/a><\/p>\n<h3><span id=\"3_Vytvorenie_kalkulovaneho_pola_s_hodnotou_parametra\">3. Vytvorenie kalkulovan\u00e9ho po\u013ea s hodnotou parametra<\/span><\/h3>\n<p>Tret\u00edm krokom bude vytvorenie akejsi premennej, ktorej \u00falohou je nies\u0165 nies\u0165 hodnotu parametra. T\u00e1to &#8222;pseudopremenn\u00e1&#8220; bude vyzera\u0165 ve\u013emi jednoducho a vytvor\u00edte ju vo va\u0161om d\u00e1tovom zdroji: <strong>Resource &gt; Manage added data sources<\/strong>, kde n\u00e1jdete v pravom hornom rohu tla\u010didlo\u00a0<strong>+ ADD A FIELD<\/strong>.<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/selected-goal-data-studio-hack.png\" data-rel=\"lightbox-image-2\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"img-shadow aligncenter wp-image-17310 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/selected-goal-data-studio-hack.png\" alt=\"custom field data studio\" width=\"593\" height=\"410\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/selected-goal-data-studio-hack.png 593w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/selected-goal-data-studio-hack-300x207.png 300w\" sizes=\"(max-width: 593px) 100vw, 593px\" \/><\/a><\/p>\n<h3><span id=\"4_Vytvorenie_kalkulovaneho_pola_pre_vyber_metriky\">4. Vytvorenie kalkulovan\u00e9ho po\u013ea pre v\u00fdber metriky<\/span><\/h3>\n<p>Vo \u0161tvrtom kroku vytvor\u00edme \u010fal\u0161ie kalkulovan\u00e9 pole, v ktorom pomocou CASE funkcie budeme vybera\u0165 jednotliv\u00e9 metriky na z\u00e1klade vo\u013eby parametra. Na to pou\u017eijeme na\u0161u pseudopremenn\u00fa z predch\u00e1dzaj\u00faceho kroku. Kalkulovan\u00e9 pole bude potom vyzera\u0165 nasledovne:<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/data-studio-case.png\" data-rel=\"lightbox-image-3\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"img-shadow aligncenter wp-image-17321 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/data-studio-case.png\" alt=\"CASE custom field Data Studio\" width=\"854\" height=\"521\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/data-studio-case.png 854w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/data-studio-case-300x183.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/data-studio-case-600x366.png 600w\" sizes=\"(max-width: 854px) 100vw, 854px\" \/><\/a><\/p>\n<p>Toto kalkulovan\u00e9 pole bude na v\u00fdstupe prezentova\u0165 hodnotu Goal Completions. Ak by ste chceli vo svojom reporte pou\u017ei\u0165 napr. metriku Goal Conversion Rate, vytvor\u00edte si \u010fal\u0161ie kalkulovan\u00e9 pole s pr\u00edslu\u0161n\u00fdmi metrikami pod\u013ea tohto vzorca.<\/p>\n<pre>CASE Selected Goal\r\nWHEN 'G01: Article Fully Read - Blog' THEN G01: Article Fully Read - Blog (Goal 1 Completions)\r\nWHEN 'G11: Ebook Download' THEN G11: Ebook Download (Goal 11 Completions)\r\nWHEN 'G04: Newsletter Subscription - Ebook' THEN G04: Newsletter Subscription - Ebook (Goal 4 Completions)\r\nWHEN 'G03: Newsletter Subscription - Blog' THEN G03: Newsletter Subscription - Blog (Goal 3 Completions)\r\nWHEN 'G05: Contact - Form Submit' THEN G05: Contact - Form Submit (Goal 5 Completions)\r\nEND<\/pre>\n<h2><span id=\"Vytvorenie_reportu_v_Data_Studiu\">Vytvorenie reportu v Data Studiu<\/span><\/h2>\n<h3><span id=\"5_Vytvorenie_prehladov_s_pouzitim_kalkulovaneho_pola\">5. Vytvorenie preh\u013eadov s pou\u017eit\u00edm kalkulovan\u00e9ho po\u013ea<\/span><\/h3>\n<p>S vlastn\u00fdm kalkulovan\u00fdm po\u013eom, ktor\u00e9 ste si vytvorili v predo\u0161lom kroku, pracujete norm\u00e1lne ako s hociktorou inou metrikou. Vyskladajte si report z tabuliek, grafov \u010di scorecards pod\u013ea toho ako potrebujete.<\/p>\n<h3><span id=\"6_Vlozenie_filtra_pre_vyber_ciela\">6. Vlo\u017eenie filtra pre v\u00fdber cie\u013ea<\/span><\/h3>\n<p>Teraz potrebujete do reportu vlo\u017ei\u0165 filter pre v\u00fdber cie\u013ea, ide\u00e1lne typu drop-down list. V nastaven\u00ed filtra si zvol\u00edte v sekcii Control field v\u00e1\u0161 parameter z prv\u00e9ho kroku, v tomto pr\u00edpade List of Goals. Samozrejme, m\u00f4\u017eete prida\u0165 aj \u010fal\u0161ie filtre, napr. d\u00e1tumov\u00fd filter, alebo filter pre Source\/Medium.<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/filter-parameter-data-studio.png\" data-rel=\"lightbox-image-4\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"img-shadow aligncenter wp-image-17330 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/filter-parameter-data-studio.png\" alt=\"filter parameter data studio\" width=\"719\" height=\"293\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/filter-parameter-data-studio.png 719w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/filter-parameter-data-studio-300x122.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/filter-parameter-data-studio-600x245.png 600w\" sizes=\"(max-width: 719px) 100vw, 719px\" \/><\/a><\/p>\n<h3><span id=\"7_Vlozenie_dynamickeho_nadpisu\">7. Vlo\u017eenie dynamick\u00e9ho nadpisu<\/span><\/h3>\n<p>\u010cere\u0161ni\u010dkou na pomyselnej torte bude dynamick\u00fd nadpis, ktor\u00fd sa bude meni\u0165 v z\u00e1vislosti od v\u00fdberu metriky vo filtri. Tu si v\u0161ak mus\u00edte pom\u00f4c\u0165 men\u0161\u00edm trikom. Nadpis nie je ni\u010d in\u00e9 ako riadok tabu\u013eky s na\u0161ou pseudopremennou. Hodnotu metriky jednoducho vytiahnete mimo plochu dashboardu a tabu\u013eku na\u0161t\u00fdlujete pod\u013ea potreby &#8211; odstr\u00e1nite \u010d\u00edslovanie riadkov, sumariza\u010dn\u00fd riadok, hlavi\u010dku a zv\u00e4\u010d\u0161\u00edte ve\u013ekos\u0165 p\u00edsma.<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds.png\" data-rel=\"lightbox-image-5\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"img-shadow aligncenter wp-image-17332 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds.png\" alt=\"table parameter google data studio\" width=\"1744\" height=\"643\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds.png 1744w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds-300x111.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds-1024x378.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds-1536x566.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/table-parameter-gds-600x221.png 600w\" sizes=\"(max-width: 1744px) 100vw, 1744px\" \/><\/a><\/p>\n<h2><span id=\"Dynamicky_vyber_metrik_a_dimenzii\">Dynamick\u00fd v\u00fdber metr\u00edk a dimenzi\u00ed<\/span><\/h2>\n<p>Ak u\u017e m\u00e1te s Data Studiom sk\u00fasenosti, ur\u010dite si kladiete ot\u00e1zku, pre\u010do jednoducho nepou\u017ei\u0165 volite\u013en\u00e9 metriky, ktor\u00e9 sa daj\u00fa ve\u013emi jednoducho nastavi\u0165 pri ka\u017edom prvku reportu. \u00c1no daj\u00fa, ale u\u017e\u00edvate\u013e si tak zmen\u00ed metriku len pre ten jeden konkr\u00e9tny prvok. Navy\u0161e pou\u017e\u00edvanie volite\u013en\u00fdch metr\u00edk nie je pr\u00edli\u0161 user friendly.<\/p>\n<p>Ako vid\u00edte, s parametrami sa toho d\u00e1 urobi\u0165 ove\u013ea viac, prakticky cel\u00fd report sa m\u00f4\u017ee prekresli\u0165 po jednej zmene vo\u013eby vo filtri. <strong>Podobn\u00fdm sp\u00f4sobom m\u00f4\u017eete pripravi\u0165 dynamick\u00fd v\u00fdber dimenzi\u00ed<\/strong> &#8211; na ten nepotrebujete Extract Data konektor, ale m\u00f4\u017eete pracova\u0165 priamo s d\u00e1tami z Google Analytics. Podobne, ako v pr\u00edpade volite\u013en\u00fdch metr\u00edk, Data Studio pon\u00faka aj mo\u017enos\u0165 Drill down dimenzi\u00ed. Op\u00e4\u0165 v\u0161ak plat\u00ed to, \u010do som p\u00edsal vy\u0161\u0161ie. Dynamick\u00fd v\u00fdber cez filter sa vz\u0165ahuje na cel\u00fd report a nie len na jeden konkr\u00e9tny prvok.<\/p>\n<p>Budem r\u00e1d ak tento \u010dl\u00e1nok niekoho in\u0161piruje. K\u013eudne sa va\u0161\u00edm vyu\u017eit\u00edm parametrov pochv\u00e1\u013ete dole v koment\u00e1roch.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Po minulomesa\u010dnom \u010dl\u00e1nku o parametroch v Data Studiu sa mi ozvalo hne\u010f nieko\u013eko \u010ditate\u013eov, ktor\u00fdch t\u00e1to nov\u00e1 fi\u010d\u00fara&#8230;<\/p>\n","protected":false},"author":70,"featured_media":17342,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[667,639],"tags":[668],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17307"}],"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\/70"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=17307"}],"version-history":[{"count":31,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17307\/revisions"}],"predecessor-version":[{"id":17925,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17307\/revisions\/17925"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/17342"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=17307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=17307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=17307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}