{"id":17876,"date":"2021-10-07T09:35:54","date_gmt":"2021-10-07T07:35:54","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=17876\/"},"modified":"2021-10-07T09:36:24","modified_gmt":"2021-10-07T07:36:24","slug":"vyhodnocujete-kvalitu-vasich-clankov-spravne","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/vyhodnocujete-kvalitu-vasich-clankov-spravne\/","title":{"rendered":"Vyhodnocujete kvalitu va\u0161ich \u010dl\u00e1nkov spr\u00e1vne?"},"content":{"rendered":"<p><strong>Tvorba blogu a \u010dl\u00e1nkov je z\u00e1kladom mnoh\u00fdch online marketingov\u00fdch strat\u00e9gii (vr\u00e1tane tej na\u0161ej). Ak to tak m\u00e1te aj vy, ur\u010dite viete, \u017ee tvorba skuto\u010dne kvalitn\u00e9ho obsahu si vy\u017eaduje ve\u013ea \u010dasu, \u00fasilia a pe\u0148az\u00ed. Preto by va\u0161ou prioritou malo by\u0165 aj vyhodnocovanie \u00faspe\u0161nosti a kvality jednotliv\u00fdch \u010dl\u00e1nkov.<\/strong><\/p>\n<p>T\u00fato t\u00e9mu m\u00e1me spracovan\u00fa aj vo videoforme, tak\u017ee ak rad\u0161ej pozer\u00e1te video ako \u010d\u00edtate, nech sa p\u00e1\u010di. \ud83d\ude42<\/p>\n<p><iframe loading=\"lazy\" title=\"Ako mera\u0165 kvalitu \u010dl\u00e1nku? | Z\u00e1klady Analytiky | DASE\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/GfaerOz77RI?feature=oembed&#038;enablejsapi=1&#038;origin=https:\/\/www.dase-analytics.com\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<p>&nbsp;<\/p>\n<h2>Automaticky nastaven\u00fd Google Analytics nesta\u010d\u00ed<\/h2>\n<p>Ka\u017ed\u00fa na\u0161u spolupr\u00e1cu s nov\u00fdm klientom, za\u010d\u00edname tvorbou analytickej strat\u00e9gie. V r\u00e1mci nej sa p\u00fdtame aj na to, \u010do klienti meraj\u00fa doposia\u013e. V\u00e4\u010d\u0161inou, ke\u010f sa bav\u00edme o vyhodnocovan\u00ed \u00faspe\u0161nosti \u010dl\u00e1nkov, tak sa dozvieme, \u017ee sa meraj\u00fa <em>pageviews<\/em> (zobrazenia \u010dl\u00e1nku), <em>time on page<\/em> (\u010das str\u00e1ven\u00fd na str\u00e1nke), pr\u00edpadne <em>scroll depth<\/em> (h\u013abka zoscrollovania). Ak v\u0161ak tieto metriky sledujeme samostatne, ned\u00e1vaj\u00fa v\u00e1m re\u00e1lny obraz o kvalite \u010dl\u00e1nku.<\/p>\n<p>Ak sa napr\u00edklad pozer\u00e1te len na \u010das str\u00e1ven\u00fd na str\u00e1nke, tak v realite to m\u00f4\u017ee znamena\u0165, \u017ee niekto pri\u0161iel na \u010dl\u00e1nok, nechal si ho otvoren\u00fd a potom pokra\u010doval v browsovan\u00ed na druhej z\u00e1lo\u017eke. Do pol hodiny sa vr\u00e1til sp\u00e4\u0165 na \u010dl\u00e1nok a zatvoril ho. Re\u00e1lne teda \u010dl\u00e1nok ne\u010d\u00edtal.<\/p>\n<p>Ak sa pozer\u00e1te len na scroll depth, m\u00f4\u017ee nasta\u0165 situ\u00e1cia, kedy pou\u017e\u00edvate\u013e pr\u00edde na \u010dl\u00e1nok, za 2 sekundy len prescrolluje \u010dl\u00e1nok hore-dole a op\u00e4\u0165 si ho v\u00f4bec nepre\u010d\u00edta.<\/p>\n<h2>Re\u00e1lne pre\u010d\u00edtan\u00fd \u010dl\u00e1nok<\/h2>\n<p>Preto v DASE pou\u017e\u00edvame kalkulovan\u00fa metriku, ktor\u00fa sme si nazvali \u201c<em>Real consumed content<\/em>\u201d (re\u00e1lne skonzumovan\u00fd obsah). Ide o kombin\u00e1ciu dvoch vy\u0161\u0161ie spomenut\u00fdch metr\u00edk. To znamen\u00e1, \u017ee na to, aby bol \u010dl\u00e1nok ozna\u010den\u00fd ako \u201c<em>real consumed<\/em>\u201d mus\u00ed sa pou\u017e\u00edvate\u013e doscrollova\u0165 do ur\u010ditej h\u013abky \u010dl\u00e1nku a z\u00e1rove\u0148 na \u010dl\u00e1nku str\u00e1vi\u0165 ur\u010dit\u00fd \u010das.<\/p>\n<p>\u010co v tomto pr\u00edpade znamen\u00e1 slovo \u201cur\u010dit\u00fd\u201d? \u010cas sa vypo\u010d\u00edtava pod\u013ea po\u010dtu znakov v danom \u010dl\u00e1nku. Script vyhodnot\u00ed, za ko\u013eko v priemere sa m\u00e1 \u010dl\u00e1nok pre\u010d\u00edta\u0165. \u010co sa h\u013abky scrollovania t\u00fdka, tak je v podstate o dohode s klientom. Niekedy sta\u010d\u00ed 80%, in\u00ed chc\u00fa 100%.<\/p>\n<h2>Pln\u00ed \u010dl\u00e1nok svoj \u00fa\u010del?<\/h2>\n<p>Druh\u00fd pr\u00edstup k vyhodnocovaniu kvality \u010dl\u00e1nku je cez sledovanie toho, \u010di pln\u00ed svoj \u00fa\u010del. To, \u017ee si \u010dl\u00e1nok niekto pre\u010d\u00edta je s\u00edce pekn\u00e9, ale kraj\u0161ie by bolo, keby urob\u00ed po\u017eadovan\u00fa akciu. Ak pod \u010dl\u00e1nkom pou\u017e\u00edvate relevantn\u00fd call-to-action prvok, m\u00f4\u017eete sledova\u0165, ko\u013eko \u013eud\u00ed na\u0148 klikne. Okrem absol\u00fatneho \u010d\u00edsla by v\u00e1s mal zauj\u00edma\u0165 aj konverzn\u00fd pomer. To znamen\u00e1 pomer medzi kliknutiami verzus celkov\u00fdm pageviews (zobrazeniami) \u010dl\u00e1nku.<\/p>\n<p>V praxi sa v\u00e1m potom m\u00f4\u017ee sta\u0165 to, \u017ee budete ma\u0165 \u010dl\u00e1nok, ktor\u00fd bude ma\u0165 napr\u00edklad 1500 pageviews. Druh\u00fd \u010dl\u00e1nok bude ma\u0165 len 500 pageviews. Kedy ste sa pozerali len na t\u00fato metriku, za \u00faspe\u0161nej\u0161\u00ed vyhodnot\u00edte ten prv\u00fd. Ke\u010f sa v\u0161ak pozriete na po\u010det kliknut\u00ed, zist\u00edte, \u017ee oba \u010dl\u00e1nky maj\u00fa rovnak\u00fd po\u010det 50 kliknut\u00ed. To znamen\u00e1, \u017ee:<\/p>\n<ol>\n<li>\u010dl\u00e1nok m\u00e1 1500 pageviews, 50 klinukt\u00ed na CTA a teda konverzn\u00fd pomer je 3%.<\/li>\n<li>\u010dl\u00e1nok m\u00e1 500 pageviews, 50 kliknut\u00ed a teda konverzn\u00fd pomer 10%.<\/li>\n<\/ol>\n<p>Ke\u010f sa na to pozriete z toho poh\u013eadu, ktor\u00fd \u010dl\u00e1nok je kvalitnej\u0161\u00ed? \ud83d\ude09<\/p>\n<p>Ak skombinujete \u201c<em>real consumed content<\/em>\u201d s t\u00fdmto pr\u00edstupom, viete si dokonca vytv\u00e1ra\u0165 <em>funnel<\/em> (lievik), kedy si pri ka\u017edom \u010dl\u00e1nku vyhodnot\u00edte, ko\u013eko pou\u017e\u00edvate\u013eov prech\u00e1dzalo t\u00fdmito krokmi lievika:<\/p>\n<ol>\n<li>Pageview<\/li>\n<li>Real consumed content<\/li>\n<li>Click na CTA (pr\u00edpadne tu m\u00f4\u017ee by\u0165 share \u010dl\u00e1nku, koment\u00e1r, alebo in\u00e1 relevantn\u00e1 metrika).<\/li>\n<\/ol>\n<h2>Article interaction rate<\/h2>\n<p>Ned\u00e1vno sme s jedn\u00fdm z na\u0161ich dlhoro\u010dn\u00fdch klientov vymysleli \u00faplne nov\u00fa metriku. Jej n\u00e1zov je \u201c<em>Article interaction rate<\/em>\u201d (AIR- pomer interakci\u00ed s \u010dl\u00e1nkom).\u00a0 T\u00fato metriku sme si zadefinovali nasledovne:<\/p>\n<p>Ide o percentu\u00e1lnu hodnotu a je definovan\u00e1 ako podiel <em>pageviews<\/em> s interakciou (ak\u00e1ko\u013evek z 8 ni\u017e\u0161ie vymenovan\u00fdch interakci\u00ed) verzus celkov\u00fd po\u010det <em>pageviews<\/em>.<\/p>\n<ol>\n<li>Kliknutie na link v \u010dl\u00e1nku<\/li>\n<li>Pou\u017eitie CTA<\/li>\n<li>Pou\u017eitie social buttonov<\/li>\n<li>Prehratie videa<\/li>\n<li>Prezretie gal\u00e9rie<\/li>\n<li>Kliknutie na s\u00favisiaci \u010dl\u00e1nok<\/li>\n<li>Kliknutie na s\u00favisiaci projekt<\/li>\n<li>Komentovanie<\/li>\n<\/ol>\n<p>Takto viete porovn\u00e1va\u0165, ktor\u00fd \u010dl\u00e1nok m\u00e1 vy\u0161\u0161\u00ed podiel <em>pageviews<\/em> s interakciami. V reporte v Data Studiu si to predstavte ako tabu\u013eku, kde bud\u00fa v riadkoch jednotliv\u00e9 \u010dl\u00e1nky a v st\u013apcoch:<\/p>\n<ul>\n<li>Article interaction rate.<\/li>\n<li>Celkov\u00fd po\u010det pageviews- v\u0161etky d\u00e1ta toti\u017e treba vyhodnocova\u0165 v kontexte. Preto ak bude ma\u0165 niektor\u00fd \u010dl\u00e1nok pr\u00edli\u0161 m\u00e1lo pageview, tak je nerelevantn\u00e9 porovn\u00e1va\u0165 jeho AIR s \u010dl\u00e1nkom, ktor\u00fd m\u00e1 ve\u013ea pageviews.<\/li>\n<li>Celkov\u00fd po\u010det interakci\u00ed s dan\u00fdm \u010dl\u00e1nkom.<\/li>\n<li>Ka\u017ed\u00e1 z 8 interakci\u00ed bude ma\u0165 svoj st\u013apec a po\u010det ko\u013ekokr\u00e1t nastala.<\/li>\n<\/ul>\n<p>V reporte to potom m\u00f4\u017ee vyzera\u0165 nasledovne:<\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-17872 size-large\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate-1024x358.png\" alt=\"\" width=\"1024\" height=\"358\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate-1024x358.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate-300x105.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate-1536x537.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate-600x210.png 600w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate.png 1580w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a> <a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2.png\" data-rel=\"lightbox-image-1\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-17873 size-large\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2-1024x413.png\" alt=\"\" width=\"1024\" height=\"413\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2-1024x413.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2-300x121.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2-1536x620.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2-600x242.png 600w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/articel-intection-rate2.png 1585w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/p>\n<p>M\u00e1te e\u0161te in\u00fd sp\u00f4sob ako vyhodnocujete kvalitu resp. \u00faspe\u0161nos\u0165 va\u0161ich \u010dl\u00e1nkov? Dajte n\u00e1m vedie\u0165 v koment\u00e1roch.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tvorba blogu a \u010dl\u00e1nkov je z\u00e1kladom mnoh\u00fdch online marketingov\u00fdch strat\u00e9gii (vr\u00e1tane tej na\u0161ej). Ak to tak m\u00e1te aj&#8230;<\/p>\n","protected":false},"author":4,"featured_media":17866,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[637],"tags":[603,643,794,666,372,641],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17876"}],"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\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=17876"}],"version-history":[{"count":2,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17876\/revisions"}],"predecessor-version":[{"id":17878,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/17876\/revisions\/17878"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/17866"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=17876"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=17876"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=17876"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}