{"id":21347,"date":"2026-03-04T15:13:18","date_gmt":"2026-03-04T13:13:18","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=21347\/"},"modified":"2026-03-04T15:13:18","modified_gmt":"2026-03-04T13:13:18","slug":"rfm-a-rfe-analyza-jednoduchy-sposob-ako-segmentovat-zakaznikov","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/rfm-a-rfe-analyza-jednoduchy-sposob-ako-segmentovat-zakaznikov\/","title":{"rendered":"RFM a RFE anal\u00fdza: Jednoduch\u00fd sp\u00f4sob, ako segmentova\u0165 z\u00e1kazn\u00edkov"},"content":{"rendered":"<p><span style=\"font-weight: 400;\"><strong>Ak pracujete v performance marketingu, sk\u00f4r \u010di nesk\u00f4r naraz\u00edte na ot\u00e1zku segment\u00e1cie z\u00e1kazn\u00edkov.<\/strong> <\/span><\/p>\n<p><span style=\"font-weight: 400;\">K\u00fdm pri desiatkach z\u00e1kazn\u00edkov sa d\u00e1 e\u0161te aplikova\u0165 met\u00f3da \u201c<strong>one size fits all<\/strong>\u201d, pri stovk\u00e1ch a\u017e tis\u00edcoch je dobr\u00e9 pou\u017ei\u0165 jednoduch\u00fd syst\u00e9m, ktor\u00fd r\u00fdchlo uk\u00e1\u017ee, komu sa oplat\u00ed venova\u0165 viac pozornosti, koho treba udr\u017ea\u0165 a koho <strong>zmysluplne reaktivova\u0165.\u00a0<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Presne na to sl\u00fa\u017ei <strong>model RFM<\/strong> (recency, frequency, monetary) a jeho variant <strong>RFE<\/strong> (recency, frequency, engagement). Vych\u00e1dzaj\u00fa z historick\u00e9ho spr\u00e1vania, priradia ka\u017ed\u00e9mu pou\u017e\u00edvate\u013eovi sk\u00f3re a v\u00fdsledok viete premeni\u0165 na segmenty typu <strong>Champions, Loyal alebo At-risk. <\/strong><\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\"><strong>A \u010do je k\u013e\u00fa\u010dov\u00e9<\/strong> &#8211; tieto segmenty sa daj\u00fa prenies\u0165 do v\u00e1\u0161ho CRM syst\u00e9mu alebo Google Analytics a pou\u017ei\u0165 na tvorbu publ\u00edk, ktor\u00e9 n\u00e1sledne aktivujete.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Aby ste nemuseli zosta\u0165 len pri te\u00f3rii, <strong>v \u010dl\u00e1nku n\u00e1jdete aj odkaz na na\u0161u webov\u00fa aplik\u00e1ciu. V nej si m\u00f4\u017eete RFM anal\u00fdzu vysk\u00fa\u0161a\u0165 na vlastn\u00fdch d\u00e1tach alebo si ju preklika\u0165 na pripravenom demo datasete.<\/strong><\/span><\/p>\n<h2><strong>\u010co je RFM anal\u00fdza\u00a0<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">RFM anal\u00fdza je technika segment\u00e1cie z\u00e1kazn\u00edkov na z\u00e1klade transak\u010dnej (alebo inej) hist\u00f3rie v troch dimenzi\u00e1ch:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Recency (R)<\/strong> \u2013 kedy z\u00e1kazn\u00edk nak\u00fapil.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Frequency (F)<\/strong> \u2013 ako \u010dasto nakupuje.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Monetary (M)<\/strong> \u2013 ak\u00e1 bola hodnota objedn\u00e1vky.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">V praxi t\u00fdm z\u00edskate konzistentn\u00fd sp\u00f4sob, ako odpoveda\u0165 na ot\u00e1zky ako <strong>\u201cKto s\u00fa moji najlep\u0161\u00ed z\u00e1kazn\u00edci?\u201d<\/strong>, <strong>\u201cKto mal hodnotu, ale \u201eodch\u00e1dza\u201c?<\/strong>\u201d, alebo <strong>\u201cKto m\u00e1 potenci\u00e1l sta\u0165 sa loj\u00e1lnym?\u201d.\u00a0<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">V\u00fdhoda RFM je, \u017ee nejde o subjekt\u00edvne kateg\u00f3rie, ale o sk\u00f3rovanie <strong>zalo\u017een\u00e9 na d\u00e1tach.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Nev\u00fdhodou je, \u017ee zjednodu\u0161uje z\u00e1kazn\u00edkov na historick\u00e9 spr\u00e1vanie <strong>bez kontextu<\/strong>, tak\u017ee v\u00fdsledky silno z\u00e1visia od zvolen\u00fdch hran\u00edc a <strong>nemusia<\/strong> spo\u013eahlivo vysvetli\u0165 \u201epre\u010do\u201c ani predpoveda\u0165, \u010do z\u00e1kazn\u00edk urob\u00ed \u010falej.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-new.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21351 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-new.png\" alt=\"RFM RFE analyza\" width=\"1200\" height=\"628\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-new.png 1200w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-new-300x157.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-new-1024x536.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-new-600x314.png 600w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h2><strong>Nem\u00e1te tr\u017eby z online predaja? Pou\u017eite RFE<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Nie ka\u017ed\u00fd biznis m\u00e1 prirodzene <strong>\u201eMonetary\u201c<\/strong> hodnotu, ktor\u00fa m\u00f4\u017ee pou\u017ei\u0165 (napr\u00edklad obsahov\u00e9 weby). Vtedy d\u00e1va zmysel varianta RFE, kde sa<strong> Monetary nahr\u00e1dza metrikou Engagement (E).<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Engagement m\u00f4\u017ee by\u0165 vyjadren\u00fd r\u00f4zne, napr\u00edklad:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">\u010dasom na str\u00e1nke,<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">po\u010dtom zobrazen\u00fdch str\u00e1nok,<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">splnen\u00fdmi cie\u013emi (goal completions).<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><strong>Podstatn\u00e9 je, aby \u201evalue\u201c reprezentovalo engagement konzistentne<\/strong> \u2013 teda rovnak\u00fdm sp\u00f4sobom pre v\u0161etk\u00fdch pou\u017e\u00edvate\u013eov. V\u010faka tomu bude aj v\u00fdsledn\u00e1 segment\u00e1cia stabiln\u00e1 a porovnate\u013en\u00e1.<\/span><\/p>\n<h2><strong>Ako funguje sk\u00f3rovanie RFM\/RFE<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\"><strong>Dobr\u00e1 spr\u00e1va?<\/strong> Na v\u00fdpo\u010det RFM nepotrebujete doktor\u00e1t z d\u00e1tovej vedy. Sta\u010d\u00ed v\u00e1m z\u00e1kladn\u00e1 matematika. Pre ka\u017ed\u00e9ho pou\u017e\u00edvate\u013ea jednoducho vypo\u010d\u00edtate R-F-M sk\u00f3re na \u0161k\u00e1le od 1 po 5.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Typicky sa to rob\u00ed cez <strong>kvintily<\/strong> (teda jednoduch\u00e9 rozdelenie pod\u013ea poradia), pri\u010dom najlep\u0161ia skupina dostane sk\u00f3re 5 a najslab\u0161ia skupina dostane sk\u00f3re 1.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">V\u00fdsledkom s\u00fa tri \u010diastkov\u00e9 sk\u00f3re: <strong>R score<\/strong>, <strong>F score<\/strong>, <strong>M score<\/strong> (alebo E score pri RFE). V tejto f\u00e1ze m\u00e1 ka\u017ed\u00fd pou\u017e\u00edvate\u013e tri \u010d\u00edsla. Aby ste z toho spravili nie\u010do pou\u017eite\u013en\u00e9 pre kampane, tieto hodnoty spoj\u00edte do jednej segmenta\u010dnej kateg\u00f3rie.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64.png\" data-rel=\"lightbox-image-1\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21350 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64.png\" alt=\"RFM a RFE anal\u00fdza v praxi 2\" width=\"1600\" height=\"767\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64.png 1600w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64-300x144.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64-1024x491.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64-1536x736.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-64-600x288.png 600w\" sizes=\"(max-width: 1600px) 100vw, 1600px\" \/><\/a><\/p>\n<h2><strong>Sp\u00e1janie sk\u00f3re do fin\u00e1lneho segmentu<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Vo\u013eba met\u00f3dy ur\u010duje, \u010di budete ma\u0165 ve\u013ea jemn\u00fdch mikrosegmentov, alebo menej \u0161ir\u0161\u00edch skup\u00edn, a tie\u017e to, ako citlivo sa v\u00fdsledn\u00e9 hodnotenie men\u00ed pri zhor\u0161en\u00ed jednej dimenzie.<\/span><\/p>\n<p><strong>Tu s\u00fa 4 z\u00e1kladn\u00e9 met\u00f3dy, ktor\u00e9 m\u00f4\u017eete pou\u017ei\u0165:<\/strong><\/p>\n<h3><b>1. Concatenation (zre\u0165azenie sk\u00f3re)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">R, F a M (alebo E) sk\u00f3re <\/span><b>spoj\u00edte<\/b><span style=\"font-weight: 400;\"> do jedn\u00e9ho k\u00f3du bez \u010fal\u0161ieho prepo\u010dtu. Pr\u00edklad: R=5, F=4, M=3 \u2192 <\/span><b>\u201e543\u201c<\/b><span style=\"font-weight: 400;\">.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">V\u00fdhoda je vysok\u00e1 granularita \u2013 viete presne rozl\u00ed\u0161i\u0165 profil spr\u00e1vania (kto je napr. ve\u013emi \u201erecent\u201c, ale len priemern\u00fd vo frekvencii). Pri \u0161k\u00e1le 1\u20135 vznikne 125 kombin\u00e1ci\u00ed (5\u00d75\u00d75). Nev\u00fdhoda je, \u017ee tak\u00fdchto mikrosegmentov je ve\u013ea, tak\u017ee pomenovanie a aktiv\u00e1cia v kampaniach b\u00fdva pracnej\u0161ia.<\/span><\/p>\n<h3><b>2. Addition (s\u00fa\u010det sk\u00f3re)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Fin\u00e1lne sk\u00f3re vznikne <\/span><b>s\u010d\u00edtan\u00edm<\/b><span style=\"font-weight: 400;\"> jednotliv\u00fdch dimenzi\u00ed: R + F + M. Pri \u0161k\u00e1le 1\u20135 je teda v\u00fdsledn\u00e9 sk\u00f3re \u010d\u00edslo medzi <\/span><b>3 a\u017e 15<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/21-2.png\" data-rel=\"lightbox-image-2\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21354 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/21-2.png\" alt=\"RFM RFE analyza aditivny model\" width=\"1200\" height=\"628\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/21-2.png 1200w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/21-2-300x157.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/21-2-1024x536.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/21-2-600x314.png 600w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Je to asi naj\u013eah\u0161ie interpretovate\u013en\u00fd pr\u00edstup. Ka\u017ed\u00e1 dimenzia prispieva \u201esvoj\u00edm dielom\u201c do celku. Je tie\u017e tolerantnej\u0161\u00ed vo\u010di tomu, ak m\u00e1 z\u00e1kazn\u00edk jednu dimenziu slab\u0161iu, ale in\u00e9 ve\u013emi siln\u00e9. Nev\u00fdhodou je, \u017ee nemus\u00ed zachyti\u0165 interakcie medzi dimenziami (napr. n\u00edzka recency m\u00f4\u017ee by\u0165 d\u00f4le\u017eitej\u0161\u00ed sign\u00e1l ako v\u00fd\u0161ka objedn\u00e1vky).<\/span><\/p>\n<h3><b>3. Multiplication (n\u00e1sobenie sk\u00f3re)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Sk\u00f3re vznikne <\/span><b>n\u00e1soben\u00edm<\/b><span style=\"font-weight: 400;\">: R \u00d7 F \u00d7 M. Pri \u0161k\u00e1le 1\u20135 je teda rozsah <\/span><b>1 a\u017e 125<\/b><span style=\"font-weight: 400;\">, podobne ako pri zre\u0165azen\u00ed sk\u00f3re.<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/20-2.png\" data-rel=\"lightbox-image-3\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21353 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/20-2.png\" alt=\"RFM RFE analyza multiplikacny model\" width=\"1200\" height=\"628\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/20-2.png 1200w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/20-2-300x157.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/20-2-1024x536.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/20-2-600x314.png 600w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Tento sp\u00f4sob je citlivej\u0161\u00ed na slab\u00e9 miesta &#8211; n\u00edzke sk\u00f3re v jednej dimenzii v\u00fdrazne zn\u00ed\u017ei celkov\u00e9 sk\u00f3re. Je vhodn\u00fd, ke\u010f chcete, aby \u201ezlyhanie\u201c v recency\/frequency\/monetary automaticky \u0165ahalo z\u00e1kazn\u00edka nadol. Treba si v\u0161ak da\u0165 pozor, preto\u017ee n\u00edzke sk\u00f3re v jednej dimenzii m\u00f4\u017ee zn\u00ed\u017ei\u0165 celkov\u00e9 sk\u00f3re viac, ne\u017e d\u00e1va zmysel.<\/span><\/p>\n<h3><b>4. Weighted addition (v\u00e1\u017een\u00fd s\u00fa\u010det)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Ka\u017edej dimenzii prirad\u00edte <\/span><b>v\u00e1hu pod\u013ea d\u00f4le\u017eitosti<\/b><span style=\"font-weight: 400;\"> a vypo\u010d\u00edtate v\u00e1\u017een\u00e9 sk\u00f3re, napr. <\/span><b>wR\u00b7R + wF\u00b7F + wM\u00b7M<\/b><span style=\"font-weight: 400;\"> (alebo podobn\u00e1 v\u00e1\u017een\u00e1 varianta).<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/19-2.png\" data-rel=\"lightbox-image-4\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21352 size-full\" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/19-2.png\" alt=\"RFM RFE analyza vazeny model\" width=\"1200\" height=\"628\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/19-2.png 1200w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/19-2-300x157.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/19-2-1024x536.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/19-2-600x314.png 600w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">V\u010faka tomu viete model prisp\u00f4sobi\u0165 biznisu. Ak je napr\u00edklad <\/span><b>recency<\/b><span style=\"font-weight: 400;\"> silnej\u0161\u00ed prediktor \u010fal\u0161ieho n\u00e1kupu, d\u00e1te jej vy\u0161\u0161iu v\u00e1hu. V\u00fdhoda je flexibilita a lep\u0161ie zladenie s cie\u013emi. Nev\u00fdhodou je vy\u0161\u0161ia komplexita. V\u00e1hy treba rozumne nastavi\u0165 a priebe\u017ene validova\u0165, \u010di st\u00e1le d\u00e1vaj\u00fa zmysel a koreluj\u00fa s re\u00e1lnymi v\u00fdsledkami.<\/span><\/p>\n<h2><strong>RFM\/RFE v\u00fdpo\u010det \u201ena po\u010dkanie\u201c<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Ak si chcete RFM\/RFE anal\u00fdzu vysk\u00fa\u0161a\u0165, m\u00f4\u017eete pou\u017ei\u0165 na\u0161u webov\u00fa aplik\u00e1ciu. <strong>Je zdarma a potrebujete na to iba CSV s\u00fabor s pou\u017e\u00edvate\u013esk\u00fdm ID, d\u00e1tumom aktivity a hodnotou. <\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">&#x1f449;<a href=\"https:\/\/rfm-rfe-customer-analysis.streamlit.app\/\" target=\"_blank\" rel=\"noopener\"> Link na aplik\u00e1ciu<\/a><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Podrobnej\u0161ie vysvetlenie v\u00fdznamu jednotliv\u00fdch segmentov n\u00e1jdete priamo v aplik\u00e1cii v \u010dasti <strong>\u201esegments explanation\u201c.<\/strong> V tejto sekcii uvid\u00edte, \u010do konkr\u00e9tne ka\u017ed\u00fd segment znamen\u00e1, ak\u00e9 spr\u00e1vanie je pre\u0148 typick\u00e9 a na ak\u00fd typ aktiv\u00e1cie sa hod\u00ed (napr\u00edklad retencia, reaktiv\u00e1cia alebo upsell).<\/span><\/p>\n<h2><strong>Aktiv\u00e1cia segmentov<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Segmenty v tabu\u013eke v\u00e1m tr\u017eby nevytvoria. Skuto\u010dn\u00e1 hodnota RFM anal\u00fdzy vznik\u00e1 a\u017e v momente, ke\u010f t\u00fdmito d\u00e1tami \u201enak\u0155mite\u201c svoje marketingov\u00e9 n\u00e1strojov a za\u010dnete s nimi re\u00e1lne pracova\u0165:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Personaliz\u00e1cia v CRM a e-mailingu &#8211; neposielajte rovnak\u00fd newsletter v\u0161etk\u00fdm. Napr\u00edklad segment <\/span><b>\u201eChampions\u201c<\/b><span style=\"font-weight: 400;\"> si zasl\u00fa\u017ei VIP po\u010fakovanie alebo skor\u0161\u00ed pr\u00edstup k novink\u00e1m (bez nutnosti zliav), zatia\u013e \u010do skupinu <\/span><b>\u201eAt-risk\u201c<\/b><span style=\"font-weight: 400;\"><span style=\"font-weight: 400;\"> m\u00f4\u017eete sk\u00fasi\u0165 motivova\u0165 k n\u00e1vratu win-back kup\u00f3nom.<\/span><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Inteligentn\u00e9 publik\u00e1 v GA4 a Google Ads <\/b><span style=\"font-weight: 400;\">&#8211; segmenty m\u00f4\u017eete importova\u0165 do GA4 ako vlastn\u00e9 dimenzie (<\/span><i><span style=\"font-weight: 400;\">User Properties<\/span><\/i><span style=\"font-weight: 400;\">). To v\u00e1m umo\u017en\u00ed vytvori\u0165 relevantn\u00e9 publik\u00e1 pre remarketing. Namiesto triafania naslepo pod\u013ea demografie tak cielite na pou\u017e\u00edvate\u013eov pod\u013ea ich skuto\u010dnej hodnoty a n\u00e1kupn\u00e9ho spr\u00e1vania.<\/span><\/li>\n<\/ul>\n<h2><strong>Stru\u010dn\u00e9 zhrnutie krokov<\/strong><\/h2>\n<table>\n<tbody>\n<tr>\n<td><b>Krok<\/b><\/td>\n<td><b>Aktivita<\/b><\/td>\n<td><b>V\u00fdstup (Pr\u00edklad)<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>1. Pr\u00edprava<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Export transak\u010dn\u00fdch d\u00e1t z CRM\/GA4<\/span><\/td>\n<td><span style=\"font-weight: 400;\">customer_id<\/span><span style=\"font-weight: 400;\">, <\/span><span style=\"font-weight: 400;\">timestamp<\/span><span style=\"font-weight: 400;\">, <\/span><span style=\"font-weight: 400;\">value<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>2. Sk\u00f3rovanie<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Rozdelenie do kvantilov (1\u20135)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">R: 5, F: 2, M: 4<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>3. Segment\u00e1cia<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Spojenie sk\u00f3re zvolenou met\u00f3dou<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Segment: <\/span><b>\u201eLoyal \u2013 At risk\u201c<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>4. Aktiv\u00e1cia<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Import do Google Ads \/ Mailchimp<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Cielen\u00e1 kampa\u0148 na mieru<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><strong>Z\u00e1ver<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">RFM a RFE s\u00fa jednoduch\u00e9, no v praxi ve\u013emi \u00fa\u010dinn\u00e9 sp\u00f4soby, ako r\u00fdchlo rozdeli\u0165 pou\u017e\u00edvate\u013eov pod\u013ea historick\u00e9ho spr\u00e1vania a t\u00fato logiku prenies\u0165 <strong>do marketingovej aktiv\u00e1cie.<\/strong> Pracujete s tromi dimenziami (R, F, M\/E), prirad\u00edte im sk\u00f3re (naj\u010dastej\u0161ie na \u0161k\u00e1le 1\u20135) <strong>a n\u00e1sledne ich spoj\u00edte do jednej segmenta\u010dnej kateg\u00f3rie.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">V\u00fdsledkom s\u00fa segmenty, ktor\u00e9 sa daj\u00fa zrozumite\u013ene interpretova\u0165 a hlavne okam\u017eite vyu\u017ei\u0165 v kampaniach. <\/span><strong>Nezabudnite pri tom na jednoduch\u00e9 pravidlo &#8211; \u010d\u00edm konzistentnej\u0161ie a \u010distej\u0161ie d\u00e1ta (d\u00e1tumy, identifik\u00e1tory, hodnota), t\u00fdm lep\u0161ie v\u00fdsledky &#x1f642;<\/strong><\/p>\n<p>A samozrejme, ak m\u00e1te ot\u00e1zky, alebo si neviete s nie\u010d\u00edm pom\u00f4c\u0165 &#8211;<a href=\"https:\/\/calendar.google.com\/calendar\/appointments\/schedules\/AcZssZ1y_TEM0euCqh2XawEbJiRNyB0ni-NhyaWZ7TWX5eePhw1F8aQZHS2nfIEuNNyBAQWHxY1embTe\" target=\"_blank\" rel=\"noopener\"><strong> booknite si bezplatn\u00fa 30m konzult\u00e1ciu s na\u0161im Marekom.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ak pracujete v performance marketingu, sk\u00f4r \u010di nesk\u00f4r naraz\u00edte na ot\u00e1zku segment\u00e1cie z\u00e1kazn\u00edkov. K\u00fdm pri desiatkach z\u00e1kazn\u00edkov sa&#8230;<\/p>\n","protected":false},"author":78,"featured_media":21356,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[637],"tags":[992,1082,1081,776],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/21347"}],"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\/78"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/comments?post=21347"}],"version-history":[{"count":8,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/21347\/revisions"}],"predecessor-version":[{"id":21362,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/21347\/revisions\/21362"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/21356"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=21347"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=21347"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=21347"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}