{"id":14247,"date":"2019-04-27T06:42:55","date_gmt":"2019-04-27T04:42:55","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=14247"},"modified":"2021-10-26T15:32:48","modified_gmt":"2021-10-26T13:32:48","slug":"kalkulovane-polia-v-datastudio-ako-nam-vedia-pomoct","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/kalkulovane-polia-v-datastudio-ako-nam-vedia-pomoct\/","title":{"rendered":"Kalkulovan\u00e9 polia v Data Studio: Ako n\u00e1m vedia pom\u00f4c\u0165?"},"content":{"rendered":"

Kalkulovan\u00e9 polia s\u00fa jednou z najv\u00fdkonnej\u0161\u00edch funkci\u00ed n\u00e1stroja Google Data Studio. Tieto polia na z\u00e1klade existuj\u00facich dimenzi\u00ed a metr\u00edk vo va\u0161om zdroji \u00fadajov (data source) umo\u017e\u0148uj\u00fa vytvori\u0165 nov\u00e9 dimenzie a metriky na roz\u0161\u00edrenie preh\u013eadov a anal\u00fdz. <\/b><\/p>\n

\"\"<\/p>\n

\u010co t\u00fdm mysl\u00edm?<\/b><\/p>\n

Predstavme si to na pr\u00edklade <\/span>\u2013 <\/span>\u00a0chceme \u00a0vytvori\u0165 <\/span>vlastn\u00fa skupinu zdrojov n\u00e1v\u0161tevnost\u00ed <\/b>a \u00fadaje chceme vidie\u0165 retroakt\u00edvne, teda aj na <\/span>historick\u00fdch d\u00e1tach<\/b>.<\/span><\/p>\n

Google Analytics m\u00e1 t\u00fa nev\u00fdhodu, \u017ee takmer \u017eiadne nov\u00e9 nastavenia sa neaplikuj\u00fa retroakt\u00edvne na historick\u00e9 d\u00e1ta. To plat\u00ed aj pre prid\u00e1vanie nov\u00fdch dimenzi\u00ed a metr\u00edk, ke\u010f sa \u00fadaje za\u010dn\u00fa objavova\u0165 v reportoch a\u017e od d\u00e1tumu ich pridania do Google Analytics. A presne tu n\u00e1m vie pomocou kalkulovan\u00fdch pol\u00ed pom\u00f4c\u0165 DataStudio. <\/span><\/p>\n

S \u010d\u00edm n\u00e1m vie kalkulovan\u00e9 pole pom\u00f4c\u0165?<\/span><\/p>\n

Data Studio pon\u00faka cel\u00fd rad vzorcov, ktor\u00e9 mo\u017eno pou\u017ei\u0165 na v\u00fdpo\u010det pomerov, extrahovanie a preform\u00e1tovanie textu a aplikovanie logick\u00fdch porovnan\u00ed.<\/span><\/p>\n

Tu je vysvetlenie od Google, ako kalkulovan\u00e9 polia pracuj\u00fa:<\/span><\/p>\n

\u201e<\/span>Kalkulovan\u00e9 pole je vzorec, ktor\u00fd vykon\u00e1va nejak\u00fa akciu na jednom alebo viacer\u00fdch in\u00fdch dimenzi\u00e1ch a metrik\u00e1ch v zdroji \u00fadajov. Vypo\u010d\u00edtan\u00e9 polia m\u00f4\u017eu vykon\u00e1va\u0165 aritmetiku a matematiku, manipulova\u0165 s textom, d\u00e1tumom a geografick\u00fdmi inform\u00e1ciami a pou\u017e\u00edva\u0165 logiku vetvenia na vyhodnotenie va\u0161ich \u00fadajov a n\u00e1vrat r\u00f4znych v\u00fdsledkov. V\u00fdstup vypo\u010d\u00edtan\u00e9ho po\u013ea sa potom m\u00f4\u017ee zobrazi\u0165 pre ka\u017ed\u00fd riadok \u00fadajov v grafoch, ktor\u00e9 toto pole obsahuj\u00fa. Ako sa tieto nov\u00e9 \u00fadaje zobrazuj\u00fa, z\u00e1vis\u00ed od sp\u00f4sobu ich pou\u017eitia.\u201d<\/span><\/i><\/p>\n

Na tomto <\/span>linku<\/span><\/a> si m\u00f4\u017eete pozrie\u0165 cel\u00fd <\/span>zoznam dostupn\u00fdch funkci\u00ed pre kalkulovan\u00e9 polia<\/b>. Na obr\u00e1zku vid\u00edte, kde n\u00e1jdete syntax funkci\u00ed do kalkulovan\u00fdch pol\u00ed.<\/span><\/p>\n

\"\"<\/a><\/p>\n

5 u\u017eito\u010dn\u00fdch pr\u00edkladov, ako za\u010da\u0165 s vypo\u010d\u00edtan\u00fdmi poliami v aplik\u00e1cii Data Studio <\/span><\/p>\n

1. Kombinujte dom\u00e9nu a cestu (page path)<\/strong><\/h2>\n

Ak m\u00e1te na svojej webovej str\u00e1nke viacero dom\u00e9n alebo subdom\u00e9n, m\u00f4\u017ee by\u0165 u\u017eito\u010dn\u00e9 zobrazi\u0165 v preh\u013eadoch cel\u00fa adresu URL str\u00e1nky. Jednoduch\u00e1<\/span> funkcia spojenia dvoch dimenzi\u00ed v\u00e1m umo\u017en\u00ed kombinova\u0165 rozmery dom\u00e9ny (hostname) a str\u00e1nky.<\/b><\/p>\n

Note: Ako vieme, Google Analytics n\u00e1m nepon\u00faka cel\u00fa URL, ale len request URI, respekt\u00edve page path. Z tohto d\u00f4vodu, ak do rovnakej Google Analytics Property zbierame d\u00e1ta z dvoch r\u00f4znych dom\u00e9n(<\/span>www.abc.com<\/span><\/a> a <\/span>www.xyz.com<\/span><\/a>) tak v reporte \u2018V\u0161etky str\u00e1nky\u2019 (All pages) by ste videli ako homepage \/. A nevedeli by ste, na ktor\u00fa dom\u00e9nu vlastne pri\u0161li.<\/span><\/p>\n

Samozrejme, d\u00e1 sa to spravi\u0165 pou\u017eit\u00edm <\/span>advanced filtra<\/b>, ale ten bude fungova\u0165 len od doby implement\u00e1cie toho filtra. Av\u0161ak v DataStudiu to vieme spravi\u0165 ihne\u010f. Ak si chceme pozrie\u0165 content report a m\u00e1me viac dom\u00e9n, takto by sa n\u00e1m na\u010d\u00edtala str\u00e1nka bez oh\u013eadu na dom\u00e9nu. Tak\u00e9to zobrazenie n\u00e1m m\u00f4\u017ee robi\u0165 probl\u00e9m pri anal\u00fdze, ke\u010f\u017ee <\/span>nevieme priradi\u0165, ku ktorej dom\u00e9ne v\u00fdsledky patria<\/b>.<\/span><\/p>\n

\"\"<\/a><\/p>\n

Ako to spravi\u0165 v Data Studiu:<\/span><\/h2>\n

Toto je syntax, ako spoji\u0165 hostname a page path dokopy pomocou funkcie Concat()<\/span><\/p>\n

\"\"<\/a>
\nA tu si pozrite video, ako na to:<\/span><\/p>\n

\"\"<\/a><\/h2>\n

2. Content Grouping<\/strong><\/h2>\n

Povedzme, \u017ee m\u00e1te web so 100 jedine\u010dn\u00fdmi str\u00e1nkami, ktor\u00e9 sedia v 5 k\u013e\u00fa\u010dov\u00fdch sekci\u00e1ch, pri\u010dom pochopenie n\u00e1v\u0161tevnosti ka\u017edej z t\u00fdchto str\u00e1nok je ur\u010dite hodnotn\u00e9. Av\u0161ak je ve\u013emi zlo\u017eit\u00e9 analyzova\u0165 individu\u00e1lne str\u00e1nky po jednom (niekedy je to nutnos\u0165 analyzova\u0165 po individu\u00e1lnej URL), a preto je u\u017eito\u010dn\u00e9 <\/span>vidie\u0165 obsah ako skupinu, kde maj\u00fa jednotliv\u00e9 str\u00e1nky spolo\u010dn\u00e9ho menovate\u013ea.<\/b><\/p>\n

Pr\u00edklad:<\/strong><\/h2>\n

V DASE sa sna\u017e\u00edme p\u00edsa\u0165 \u010dl\u00e1nky na r\u00f4zne t\u00e9my najm\u00e4 v kateg\u00f3ri\u00e1ch <\/span>Google Tag Manager, R Programming, Data Studio alebo Google Analytics<\/b>. Ka\u017ed\u00fd \u010dl\u00e1nok spad\u00e1 pod jednu z t\u00fdchto kateg\u00f3ri\u00ed, a preto by bolo vhodn\u00e9 pozrie\u0165 si dan\u00fd obsah pod \u0161pecifickou kateg\u00f3riou, aby sme zistili, \u010do najviac zauj\u00edma na\u0161ich n\u00e1v\u0161tevn\u00edkov.<\/span><\/p>\n

V slu\u017ebe Google Analytics moment\u00e1lne neexistuje preh\u013ead, ktor\u00fd v\u00e1m to m\u00f4\u017ee uk\u00e1za\u0165. D\u00e1 sa to v\u0161ak dosiahnu\u0165 pomocou <\/span>segmentov, manu\u00e1lnym vytv\u00e1ran\u00edm<\/b> grafov alebo mo\u017eno najlep\u0161ou vo\u013ebou <\/span>\u2013<\/span> definovan\u00edm zoskupen\u00ed vlastn\u00e9ho obsahu<\/b>, ktor\u00e9 si m\u00f4\u017eete nastavi\u0165 bu\u010f cez Google Analytics, alebo Google Tag Manager.<\/span><\/p>\n

Vlastn\u00e9 zoskupenia obsahu v\u00e1m umo\u017e\u0148uj\u00fa prideli\u0165 mno\u017einu str\u00e1nok logick\u00e9mu zoskupeniu a potom ich nahl\u00e1si\u0165 v slu\u017ebe Google Analytics ako kolekt\u00edv. Napr. v\u0161etky str\u00e1nky, na ktor\u00fdch cesta obsahuje \/ blog, ktor\u00fd budeme ozna\u010dova\u0165 ako Blog, v\u0161etky str\u00e1nky, ktor\u00e9 obsahuj\u00fa \/ nakupujeme, sa zoskupia ako Obchod. Potom m\u00f4\u017eete zobrazi\u0165 \u0161tandardn\u00e9 preh\u013eady slu\u017eby Google Analytics pomocou tohto zoskupenia ako dimenzie.<\/span><\/p>\n

Jednou z hlavn\u00fdch nev\u00fdhod zoskupen\u00ed vlastn\u00e9ho obsahu je to, \u017ee <\/span>nadobudn\u00fa \u00fa\u010dinnos\u0165 a\u017e od d\u00e1tumu, ke\u010f ich definujete<\/b>. Ak sa teda chcete vr\u00e1ti\u0165 sp\u00e4\u0165 a skontrolova\u0165 historick\u00e9 \u00fadaje v r\u00e1mci skup\u00edn vlastn\u00fdch kan\u00e1lov, m\u00e1te smolu. Slu\u017eba Google Data Studio m\u00e1 v\u0161ak rie\u0161enie, ktor\u00e9 v\u00e1m umo\u017en\u00ed definova\u0165 obsahov\u00e9 skupiny a aplikova\u0165 ich na v\u0161etky historick\u00e9 \u00fadaje.<\/span><\/p>\n

\"\"<\/a><\/p>\n

3. Nekonzistentn\u00e9 pomenovanie kampane<\/strong><\/h2>\n

V ide\u00e1lnom svete by sme toto nemuseli rie\u0161i\u0165. Av\u0161ak pri auditoch r\u00f4znych \u00fa\u010dtov vid\u00edme \u010dasto <\/span>nekonzistentn\u00e9 pomenovania kampan\u00ed pomocou utm_ parametrov<\/b>.<\/span><\/p>\n

Faktom je, \u017ee <\/span>Google Analytics je case sensitive<\/b>. Ak sa n\u00e1zov kampane odli\u0161uje (napriklad n\u00e1zov m\u00e9dia je cpc a nabud\u00face pri tvorbe utm_ linkov sa pomenuje Cpc), tak v Google Analytics sa to zatriedi \u00a0ako dve odli\u0161n\u00e9 m\u00e9dia. To zna\u010dne komplikuje anal\u00fdzu, lebo obe m\u00e9dia maj\u00fa by\u0165 agregovan\u00e9 v jednej dimenzii a hodnote.<\/span><\/p>\n

Note: V ide\u00e1lnom pr\u00edpade by ste mali vytvori\u0165 <\/span>konzistentn\u00e9 ozna\u010dovanie kampan\u00ed <\/b>vo va\u0161ej organiz\u00e1cii. Pou\u017eit\u00edm spolo\u010dn\u00fdch n\u00e1zvov\u00fdch konvenci\u00ed medzi marketingov\u00fdmi t\u00edmami a partnersk\u00fdmi agent\u00farami bude proces pod\u00e1vania spr\u00e1v a anal\u00fdzy ove\u013ea efekt\u00edvnej\u0161\u00ed. Ak v\u0161ak potrebujete <\/span>vy\u010disti\u0165 historick\u00e9 \u00fadaje alebo nekonzistentne ozna\u010den\u00fa kampa\u0148<\/b>, Data Studio bude va\u0161\u00edm priate\u013eom:<\/span><\/p>\n

Naj\u010dastej\u0161\u00edm nes\u00faladom je zvy\u010dajne pou\u017eitie in\u00e9ho pomenovania, ako napr. \u201ecpc\u201c, \u201eCPC\u201c a \u201eCpc\u201c. Toto sa d\u00e1 \u013eahko vyrie\u0161i\u0165 tak, \u017ee sa <\/span>v\u0161etky rozmery kampane (m\u00e9dia, at\u010f) zmen\u00edme funkciou LOWER() na mal\u00e9 p\u00edsmen\u00e1<\/b>. <\/span><\/p>\n

T\u00fdm docielime to, \u017ee sa n\u00e1m agreguj\u00fa v\u0161etky m\u00e9dia do jednej hodnoty, lebo s\u00fa rovnako uveden\u00e9 mal\u00fdmi p\u00edsmenami.<\/span><\/p>\n

LOWER (Medium)<\/span><\/p>\n

Podobne m\u00f4\u017eete pou\u017ei\u0165 LOWER (Kampa\u0148) a LOWER (Zdroj).<\/span><\/p>\n

Ke\u010f pou\u017eijete vypo\u010d\u00edtan\u00fa dimenziu, Data Studio potom pre v\u00e1s agreguje nekonzistentn\u00e9 hodnoty.<\/span><\/p>\n

4. Vytvorte vlastn\u00e9 geografick\u00e9 oblasti<\/strong><\/h2>\n

Slu\u017eba Google Analytics v\u00e1m <\/span>poskytuje \u0161tandardn\u00e9 geografick\u00e9 rozmery mesta, regi\u00f3nu a krajiny<\/b>. Ale \u010do ke\u010f va\u0161a organiz\u00e1cia pou\u017e\u00edva vlastn\u00e9 region\u00e1lne zoskupenie? Ni\u017e\u0161ie si uk\u00e1\u017ekovo nazna\u010d\u00edme, ako vieme hodnoty dimenzie zoskupi\u0165 do zmysluplnej\u0161\u00edch kateg\u00f3ri\u00ed.<\/span><\/p>\n

Toto je funkcia, ktor\u00fa m\u00f4\u017eete pou\u017ei\u0165 na <\/span>definovanie vlastn\u00fdch vlastn\u00fdch regi\u00f3nov na z\u00e1klade \u0161tandardn\u00fdch dimenzi\u00ed:<\/b><\/p>\n

My pou\u017e\u00edvame dimenziu City (Mesto) a zatrie\u010fujeme mest\u00e1 pod\u013ea lokality na Z\u00e1pad, Stred a V\u00fdchod.<\/span><\/i><\/p>\n

Note: V tejto formulke m\u00f4\u017eeme pou\u017ei\u0165 hocijak\u00fa dimenziu a jej hodnoty rovnak\u00fdm sp\u00f4sobom zatriedi\u0165 do kateg\u00f3ri\u00ed, ktor\u00e9 v\u00e1m d\u00e1vaj\u00fa zmysel a podporia va\u0161u anal\u00fdzu.<\/span><\/i><\/p>\n

\"\"<\/a><\/p>\n

Info: Data Studio nepodporuje vyhl\u00e1senia IF, tak\u017ee namiesto toho mus\u00edte pou\u017ei\u0165 funkciu CASE. <\/span><\/p>\n

V\u0161eobecn\u00fd form\u00e1t je: <\/span>CASE WHEN X THEN Y1 ELSE Y2 END, kde X je boolean v\u00fdraz a Y1 a Y2 s\u00fa textov\u00e9, \u010d\u00edseln\u00e9 alebo boolean hodnoty<\/b>.<\/span><\/p>\n

5. Vytvorte vlastn\u00e9 zoskupenie kan\u00e1lov<\/strong><\/h2>\n

\u017diadne vlastn\u00e9 skupiny kan\u00e1lov, ktor\u00e9 vytvor\u00edte v slu\u017ebe Google Analytics, nebud\u00fa v Data Studio k dispoz\u00edcii. Defin\u00edcie kan\u00e1lov v\u0161ak m\u00f4\u017eete znova vytvori\u0165 v aplik\u00e1cii Data Studio <\/span>pomocou kon\u0161trukcie CASE s radom klauz\u00fal WHEN<\/b>.<\/span><\/p>\n

Tu je predvolen\u00e9 zoskupenie kan\u00e1lov slu\u017eby Google Analytics reprezentovan\u00e9 ako vyhl\u00e1senie CASE. Pou\u017eite ho ako z\u00e1klad pre vytvorenie vlastn\u00fdch defin\u00edci\u00ed kan\u00e1lov:<\/span><\/p>\n

\"\"<\/a><\/p>\n

Z\u00e1ver<\/strong><\/h1>\n

Pou\u017e\u00edvanie kalkulovan\u00fdch pol\u00ed je zru\u010dnos\u0165, ktor\u00fa by mal ovl\u00e1da\u0165 ka\u017ed\u00fd pou\u017e\u00edvate\u013e programu Data Studio. <\/b>V\u010faka tomu bude v\u00e1\u0161 n\u00e1stroj na \u010distenie \u00fadajov relevantnej\u0161\u00ed a u\u013eah\u010d\u00ed v\u00e1m \u017eivot.<\/span><\/p>\n

Ak m\u00e1te z\u00e1ujem dozvedie\u0165 sa viac o Data Studio a neviete si rady, pokojne n\u00e1m nap\u00ed\u0161te alebo si rezervujte workshop a radi v\u00e1s to nau\u010d\u00edme.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Kalkulovan\u00e9 polia s\u00fa jednou z najv\u00fdkonnej\u0161\u00edch funkci\u00ed n\u00e1stroja Google Data Studio. Tieto polia na z\u00e1klade existuj\u00facich dimenzi\u00ed a…<\/p>\n","protected":false},"author":66,"featured_media":14258,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[640],"tags":[],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14247"}],"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\/66"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=14247"}],"version-history":[{"count":8,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14247\/revisions"}],"predecessor-version":[{"id":17938,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14247\/revisions\/17938"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/14258"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=14247"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=14247"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=14247"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}