{"id":14365,"date":"2019-07-04T11:01:55","date_gmt":"2019-07-04T09:01:55","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=14365"},"modified":"2019-07-04T11:01:55","modified_gmt":"2019-07-04T09:01:55","slug":"sekvencne-segmenty-v-google-analytics","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/sekvencne-segmenty-v-google-analytics\/","title":{"rendered":"Sekven\u010dn\u00e9 segmenty v Google Analytics"},"content":{"rendered":"<p>Vytvorenie segmentov v sekvenci\u00e1ch v r\u00e1mci slu\u017eby Google Analytics v\u00e1m m\u00f4\u017ee pon\u00faknu\u0165 \u010fal\u0161\u00ed preh\u013ead o tom, ako n\u00e1v\u0161tevn\u00edci pou\u017e\u00edvaj\u00fa va\u0161e str\u00e1nky.<\/p>\n<p>\u0160tandardn\u00fd segment v Google Analytics v\u00e1m umo\u017en\u00ed vybra\u0165 si ur\u010dit\u00e9 krit\u00e9ri\u00e1. Napr\u00edklad n\u00e1v\u0161tevn\u00edci z Bratislavy, ktor\u00ed pou\u017e\u00edvali desktop a nak\u00fapili. Kombin\u00e1ciou r\u00f4znych krit\u00e9ri\u00ed m\u00f4\u017eete z\u00edska\u0165 inform\u00e1cie o svojich str\u00e1nkach a u\u017e\u00edvateloch, \u010di samotn\u00fdch rel\u00e1ci\u00e1ch. Av\u0161ak, ch\u00fdba tomu definovanie ur\u010ditej postupnosti, ako sa dan\u00e9 interakcie stali.<\/p>\n<h2>Pr\u00edklad:<\/h2>\n<p>U\u017e\u00edvatel pri\u0161iel na ecommerce str\u00e1nku www.exampleshop.sk a vykonal nasleduj\u00face interakcie.<\/p>\n<ol>\n<li>Otvoril si detail produktu xyz.<\/li>\n<li>Zv\u00e4\u010d\u0161il si fotku produktu.<\/li>\n<li>Rozklikol si preferencie produktu.<\/li>\n<li>Ale nenak\u00fapil.<\/li>\n<\/ol>\n<p><strong>NOTE:<\/strong> Samozrejme, je d\u00f4le\u017eit\u00e9, aby ste tieto veci merali. V z\u00e1kladnom nastaven\u00ed Google Analytics meria len zobrazenia str\u00e1nky (Pageviews). Zv\u00e4\u010d\u0161enie fotky, ako aj prezretie preferenci\u00ed produktu viete mera\u0165 pomocou udalost\u00ed (Events).<\/p>\n<p>Ak by sme chceli vysegmentova\u0165 vy\u0161\u0161ie spomenut\u00fd postup interakci\u00ed, vyzeralo by to nasledovne:<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter size-full wp-image-14368\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-17.png\" alt=\"\" width=\"742\" height=\"310\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-17.png 742w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image2-17-300x125.png 300w\" sizes=\"(max-width: 742px) 100vw, 742px\" \/><\/p>\n<p>Probl\u00e9m s t\u00fdmto segmentom je tak\u00fd, \u017ee n\u00e1m neovplyv\u0148uje postupnos\u0165, v akej sa udalosti stali. Jednoducho len vysegmentuje rel\u00e1cie, kde v\u0161etky tieto podmienky boli v danej rel\u00e1cii splnen\u00e9.<\/p>\n<p>A tu n\u00e1m vedia pom\u00f4c\u0165 sekven\u010dn\u00e9 segmenty!<\/p>\n<p>Segmentovanie sekvenci\u00ed v\u00e1m umo\u017e\u0148uje vytv\u00e1ra\u0165 segmenty nielen kombin\u00e1ciou krit\u00e9ri\u00ed, ale aj definovan\u00edm, v akom porad\u00ed ich va\u0161i n\u00e1v\u0161tevn\u00edci vykonali.<\/p>\n<p>M\u00f4\u017eete napr\u00edklad zobrazi\u0165 v\u0161etk\u00fdch u\u017e\u00edvate\u013eov, ktor\u00ed pri\u0161li na str\u00e1nku kampane, potom pre\u0161li na str\u00e1nku produktu, a potom nav\u0161t\u00edvili str\u00e1nku podpory &#8211; v tomto porad\u00ed. M\u00f4\u017eete tie\u017e sledova\u0165, \u010do urobili pred a po \u0161pecifickej sekvencii.<\/p>\n<p>Sekven\u010dn\u00e9 segmenty s\u00fa skvel\u00e9 napriklad na segmentovanie va\u0161eho lievika (funnelu).<\/p>\n<p>Ak sa vr\u00e1time sp\u00e4\u0165 k na\u0161emu pr\u00edkladu hore, pomocou sekven\u010dn\u00fdch segmentov vysklad\u00e1me n\u00e1\u0161 segment v porad\u00ed v akom chceme.<\/p>\n<p>Prv\u00e1 interakcia je s produktovou str\u00e1nkou, hne\u010f po nej nasleduje event so zv\u00e4\u010d\u0161en\u00edm fotky produktu, n\u00e1sledne (nemus\u00ed by\u0165 hne\u010f) n\u00e1v\u0161evn\u00edk nenak\u00fapi.<\/p>\n<p>Podmienka <strong>\u2018is immediately followed\u2019<\/strong> hovori, \u017ee nasleduj\u00faci event mus\u00ed by\u0165 hne\u010f po prvej podmienke, inak nebude zaraden\u00fd do segmentu.<\/p>\n<p>Podmienka<strong> \u2018is followed by\u2019<\/strong> zase hovor\u00ed o tom, \u017ee nasleduj\u00faca podmienka mus\u00ed by\u0165 splnen\u00e1 v tejto rel\u00e1cii (av\u0161ak predch\u00e1dzaj\u00faca podmienka mus\u00ed by\u0165 splnen\u00e1), ale nemus\u00ed to by\u0165 okam\u017eite.<\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter size-full wp-image-14367\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-17.png\" alt=\"\" width=\"797\" height=\"433\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-17.png 797w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/image1-17-300x163.png 300w\" sizes=\"(max-width: 797px) 100vw, 797px\" \/><\/p>\n<h2>Z\u00e1ver<\/h2>\n<p>Vyu\u017eit\u00edm tejto funkcie v slu\u017ebe Google Analytics m\u00f4\u017eete z\u00edska\u0165 nov\u00fd a ove\u013ea presnej\u0161\u00ed preh\u013ead vo svojich anal\u00fdzach, \u010do vedie k lep\u0161iemu pochopeniu toho, ako va\u0161i n\u00e1v\u0161tevn\u00edci pou\u017e\u00edvaj\u00fa va\u0161u str\u00e1nku, a ako ju m\u00f4\u017eete zlep\u0161i\u0165 a zv\u00fd\u0161i\u0165 konverzie. \u201c<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Vytvorenie segmentov v sekvenci\u00e1ch v r\u00e1mci slu\u017eby Google Analytics v\u00e1m m\u00f4\u017ee pon\u00faknu\u0165 \u010fal\u0161\u00ed preh\u013ead o tom, ako n\u00e1v\u0161tevn\u00edci&#8230;<\/p>\n","protected":false},"author":66,"featured_media":14366,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14365"}],"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=14365"}],"version-history":[{"count":1,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14365\/revisions"}],"predecessor-version":[{"id":14369,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/14365\/revisions\/14369"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/14366"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=14365"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=14365"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=14365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}