{"id":21410,"date":"2026-04-10T13:36:06","date_gmt":"2026-04-10T11:36:06","guid":{"rendered":"https:\/\/www.dase-analytics.com\/blog\/?p=21410\/"},"modified":"2026-04-10T13:46:18","modified_gmt":"2026-04-10T11:46:18","slug":"prestante-merat-minulost-zacnite-predvidat-zakaznikov","status":"publish","type":"post","link":"https:\/\/www.dase-analytics.com\/blog\/sk\/prestante-merat-minulost-zacnite-predvidat-zakaznikov\/","title":{"rendered":"Presta\u0148te mera\u0165 minulos\u0165. Za\u010dnite predv\u00edda\u0165 z\u00e1kazn\u00edkov."},"content":{"rendered":"<p><span style=\"font-weight: 400;\">V\u00e4\u010d\u0161ina marketingov\u00fdch reportov odpoved\u00e1 najm\u00e4 na ot\u00e1zku,<strong> \u010do sa u\u017e stalo.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Uk\u00e1\u017ee v\u00e1m n\u00e1v\u0161tevnos\u0165, konverzie, \u010di v\u00fdkonnos\u0165 kampan\u00ed v minulosti. <strong>Predikt\u00edvna analytika ide o krok \u010falej.<\/strong> Vyu\u017e\u00edva <strong>historick\u00e9 a aktu\u00e1lne d\u00e1ta<\/strong> na odhad bud\u00faceho spr\u00e1vania pou\u017e\u00edvate\u013eov.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">V marketingu to znamen\u00e1, \u017ee sa nemus\u00edte p\u00fdta\u0165 len na to, <strong>kto nak\u00fapil v\u010dera.<\/strong> M\u00f4\u017eete sa p\u00fdta\u0165 aj na to, <strong>kto pravdepodobne nak\u00fapi zajtra,<\/strong> <strong>kto m\u00f4\u017ee od\u00eds\u0165 a ktor\u00e9 publikum bude ma\u0165 vy\u0161\u0161iu hodnotu.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Dobrou spr\u00e1vou je, \u017ee predikt\u00edvna analytika u\u017e <strong>nie je<\/strong> v\u00fdhradne dom\u00e9nou d\u00e1tov\u00fdch vedcov. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pre analytikov, alebo aj market\u00e9rov existuj\u00fa <strong>tri realistick\u00e9 cesty, ako s \u0148ou za\u010da\u0165.<\/strong> Prvou s\u00fa nat\u00edvne predikcie <strong>v GA4.<\/strong> Druhou s\u00fa vlastn\u00e9 modely<strong> v BigQuery ML<\/strong>. Tre\u0165ou je pr\u00e1ca <strong>v Pythone<\/strong>, napr\u00edklad cez Google Colab.<\/span><\/p>\n<p><strong>Ka\u017ed\u00e1 z t\u00fdchto ciest m\u00e1 in\u00e9 n\u00e1roky, in\u00fa mieru flexibility a in\u00fd typ vyu\u017eitia.<\/strong><\/p>\n<h2><strong>1. GA4 | najjednoduch\u0161\u00ed vstup do predikt\u00edvnej analytiky<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Ak u\u017e pou\u017e\u00edvate Google Analytics 4, GA4 je prirodzen\u00e9 miesto, kde za\u010da\u0165. Je to najdostupnej\u0161ia cesta, preto\u017ee predikt\u00edvne metriky s\u00fa vstavan\u00e9 priamo v n\u00e1stroji. Google ich generuje automaticky na z\u00e1klade d\u00e1t vo va\u0161om vlastn\u00edctve (property).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">GA4 pon\u00faka tri hlavn\u00e9 predikt\u00edvne metriky:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Purchase probability<\/strong> &#8211; pravdepodobnos\u0165, \u017ee pou\u017e\u00edvate\u013e akt\u00edvny za posledn\u00fdch 28 dn\u00ed uskuto\u010dn\u00ed n\u00e1kup v nasleduj\u00facich 7 d\u0148och.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Churn probability<\/strong> &#8211; pravdepodobnos\u0165, \u017ee pou\u017e\u00edvate\u013e akt\u00edvny za posledn\u00fdch 7 dn\u00ed nebude akt\u00edvny v nasleduj\u00facich 7 d\u0148och.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Predicted revenue<\/strong> &#8211; o\u010dak\u00e1van\u00fd v\u00fdnos zo v\u0161etk\u00fdch n\u00e1kupn\u00fdch konverzi\u00ed v nasleduj\u00facich 28 d\u0148och od pou\u017e\u00edvate\u013ea, ktor\u00fd bol akt\u00edvny za posledn\u00fdch 28 dn\u00ed.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Najv\u00e4\u010d\u0161ia v\u00fdhoda GA4 je v tom, \u017ee tieto metriky viete pou\u017ei\u0165 v <strong>Audience builder.<\/strong> To znamen\u00e1, \u017ee predikcia sa d\u00e1 priamo prepoji\u0165 s marketingovou aktiv\u00e1ciou.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Predstavte si, \u017ee m\u00e1te e shop a chcete oslovi\u0165 \u013eud\u00ed, ktor\u00ed s vysokou pravdepodobnos\u0165ou nak\u00fapia v najbli\u017e\u0161\u00edch d\u0148och. V GA4 si vytvor\u00edte publikum typu \u201c<\/span><i><span style=\"font-weight: 400;\"><strong>Likely 7 day purchasers<\/strong>\u201d<\/span><\/i><span style=\"font-weight: 400;\"> a exportujete ho do\u00a0<\/span><strong>Google Ads.\u00a0<\/strong><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69.png\" data-rel=\"lightbox-image-0\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21411 \" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69.png\" alt=\"prediktivna analytika DASE blog 1\" width=\"1055\" height=\"739\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69.png 1600w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69-300x210.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69-1024x717.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69-1536x1075.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/1-69-600x420.png 600w\" sizes=\"(max-width: 1055px) 100vw, 1055px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Podobne m\u00f4\u017eete vytvori\u0165 publikum pou\u017e\u00edvate\u013eov s vy\u0161\u0161ou pravdepodobnos\u0165ou odchodu a pripravi\u0165 pre nich <strong>re-engagement kampa\u0148.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Treba v\u0161ak po\u010d\u00edta\u0165 aj s limitmi. Predikt\u00edvne metriky nie s\u00fa dostupn\u00e9 automaticky pre ka\u017ed\u00e9ho. Mus\u00edte sp\u013a\u0148a\u0165 minim\u00e1lne podmienky objemu d\u00e1t a trackingu. Potrebuje aspo\u0148:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><strong>1 000 vracaj\u00facich sa pou\u017e\u00edvate\u013eov, ktor\u00ed uskuto\u010dnili n\u00e1kup<\/strong><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><strong>1 000 vracaj\u00facich sa pou\u017e\u00edvate\u013eov, ktor\u00ed n\u00e1kup neuskuto\u010dnili<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Tieto podmienky musia by\u0165 splnen\u00e9 konzistentne po\u010das 28 dn\u00ed, aby modely zostali akt\u00edvne. Rovnako plat\u00ed, \u017ee kvalita predikci\u00ed z\u00e1vis\u00ed od kvality a objemu historick\u00fdch d\u00e1t.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Viac info o predikt\u00edvnych metrik\u00e1ch v GA4 sa dozviete v tomto \u010dl\u00e1nku:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><a href=\"https:\/\/www.dase-analytics.com\/blog\/sk\/ked-analytika-zacina-citat-buducnost-prediktivne-metriky\/\"><span style=\"font-weight: 400;\">https:\/\/www.dase-analytics.com\/blog\/sk\/ked-analytika-zacina-citat-buducnost-prediktivne-metriky\/<\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">GA4 je teda najr\u00fdchlej\u0161\u00ed sp\u00f4sob ak chcete predikcie r\u00fdchlo premeni\u0165 na publikum alebo aktiv\u00e1ciu kampane. <strong>Ak v\u0161ak potrebujete vlastn\u00fa logiku modelu, mus\u00edte siahnu\u0165 po inom n\u00e1stroji.<\/strong><\/span><\/p>\n<h2><strong>2. BigQuery ML | viac kontroly priamo nad d\u00e1tami<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">Ke\u010f GA4 nesta\u010d\u00ed, \u010fal\u0161\u00edm krokom m\u00f4\u017ee by\u0165 <strong>BigQuery ML.<\/strong> Ide o funkcionalitu v Google BigQuery, ktor\u00e1 umo\u017e\u0148uje vytv\u00e1ra\u0165 a sp\u00fa\u0161\u0165a\u0165 <strong>machine learning modely<\/strong> priamo v BigQuery pomocou \u0161tandardn\u00fdch SQL dopytov.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Je to v\u00fdhodn\u00e9 najm\u00e4 vtedy, ke\u010f u\u017e m\u00e1te marketingov\u00e9 alebo webov\u00e9 d\u00e1ta v BigQuery. Nemus\u00edte ich pres\u00fava\u0165 do in\u00e9ho prostredia a pritom z\u00edskate viac kontroly nad t\u00fdm, \u010do presne predikujete a na z\u00e1klade \u010doho.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hlavn\u00e1 v\u00fdhoda BigQuery ML je teda kombin\u00e1ciou <strong>troch vec\u00ed:<\/strong><\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><strong>d\u00e1ta zost\u00e1vaj\u00fa v BigQuery\u00a0<\/strong><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><strong>model sa tvor\u00ed cez SQL\u00a0\u00a0<\/strong><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><strong>nemus\u00edte hne\u010f prejs\u0165 do Pythonu alebo in\u00e9ho d\u00e1tov\u00e9ho prostredia.<\/strong><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">BigQuery ML podporuje r\u00f4zne typy modelov, tak\u017ee nie je probl\u00e9m vybra\u0165 si vhodn\u00fd pre konkr\u00e9tny typ \u00falohy.\u00a0<\/span><\/p>\n<h4><strong>Pr\u00edklad: predikcia revenue pod\u013ea po\u010dtu sessions<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">Jednoduch\u00fdm pr\u00edkladom je <strong>predikcia revenue<\/strong> na z\u00e1klade <strong>po\u010dtu sessions<\/strong> pomocou <strong>line\u00e1rnej regresie.<\/strong> V BigQuery m\u00e1te historick\u00e9 d\u00e1ta o n\u00e1v\u0161tevnosti a tr\u017eb\u00e1ch z GA4 BigQuery exportu.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Model sa na z\u00e1klade t\u00fdchto d\u00e1t nau\u010d\u00ed vz\u0165ah medzi sessions a revenue a n\u00e1sledne vie odhadn\u00fa\u0165 v\u00fdsledok pri bud\u00facich hodnot\u00e1ch. Tieto predikcie m\u00f4\u017eu n\u00e1sledne sl\u00fa\u017ei\u0165 ako podklad pri pl\u00e1novan\u00ed o\u010dak\u00e1van\u00fdch v\u00fdsledkov alebo data-driven rozhodovan\u00ed.\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65.png\" data-rel=\"lightbox-image-1\" data-rl_title=\"\" data-rl_caption=\"\" title=\"\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21412 \" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65.png\" alt=\"prediktivna analytika DASE blog 2\" width=\"927\" height=\"362\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65.png 1600w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65-300x117.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65-1024x399.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65-1536x599.png 1536w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/2-65-600x234.png 600w\" sizes=\"(max-width: 927px) 100vw, 927px\" \/><\/a><\/p>\n<p><strong>D\u00f4le\u017eit\u00e9 je, \u017ee v BQML si sami ur\u010dujete:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">\u010do bude cie\u013eov\u00e1 premenn\u00e1<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ktor\u00e9 vstupy do modelu zahrniete<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">na ak\u00fdch d\u00e1tach bude model tr\u00e9novan\u00fd<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\"><strong>Pr\u00e1ve tu sa ukazuje rozdiel oproti GA4.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">K\u00fdm v GA4 pracujete s predpripraven\u00fdmi metrikami od Googlu, v BigQuery ML si viete model prisp\u00f4sobi\u0165 vlastn\u00fdm potreb\u00e1m. Vy\u017eaduje si to v\u0161ak viac technickej expert\u00edzy ne\u017e GA4. Mus\u00edte si da\u0165 viac z\u00e1le\u017ea\u0165 na tom, ako s\u00fa d\u00e1ta pripraven\u00e9, ke\u010f\u017ee<strong> kvalita vstupov priamo ovplyvn\u00ed kvalitu predikcie.<\/strong><\/span><\/p>\n<h2><strong>3. Python a Google Colab: najv\u00e4\u010d\u0161ia flexibilita pre vlastn\u00fd workflow<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\"><strong>Python<\/strong> pon\u00faka z poh\u013eadu marketingovej analytiky najv\u00e4\u010d\u0161iu flexibilitu, preto\u017ee <strong>d\u00e1va kontrolu<\/strong> nad d\u00e1tami, pr\u00edpravou, modelom aj vyhodnoten\u00edm v\u00fdsledkov.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Medzi \u010dasto pou\u017e\u00edvan\u00e9 kni\u017enice patria <strong>Pandas, Numpy <\/strong>a<strong> Scikit Learn.<\/strong> Pr\u00e1ve na nich stoj\u00ed ve\u013ek\u00e1 \u010das\u0165 be\u017en\u00fdch analytick\u00fdch a predikt\u00edvnych workflow. <strong>Google Colab<\/strong> je v tomto kontexte praktick\u00e9 prostredie, kde mo\u017eno s Pythonom pracova\u0165 bez potreby zlo\u017eit\u00e9ho nastavovania.<\/span><\/p>\n<p><strong>V Pythone sa predikt\u00edvna analytika zvy\u010dajne sklad\u00e1 z viacer\u00fdch krokov:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">pr\u00edprava d\u00e1t<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">vizualiz\u00e1cia<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">vytvorenie modelu<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">vyhodnotenie modelu<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">predikcia<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">To je podstatn\u00fd rozdiel oproti GA4 a do istej miery aj oproti BQML. V Pythone nepracujete len s hotov\u00fdm v\u00fdstupom alebo jedn\u00fdm SQL modelom. <strong>Rie\u0161ite cel\u00fd proces od \u010distenia d\u00e1t a\u017e po evalu\u00e1ciu.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\">Aby ste si line\u00e1rnu regresiu v Google Colab mohli vysk\u00fa\u0161a\u0165 sami, pripravili sme pre v\u00e1s <a href=\"https:\/\/colab.research.google.com\/drive\/14QSa5qSeT6UzuuiXc_f88x8jmugLSUF2?usp=sharing\" target=\"_blank\" rel=\"noopener\"><strong>uka\u017ekov\u00fd notebook. &#x1f447;<\/strong><\/a><\/span><\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-21413 \" src=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/3-55.png\" alt=\"prediktivna analytika DASE blog 3\" width=\"866\" height=\"587\" srcset=\"https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/3-55.png 1167w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/3-55-300x203.png 300w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/3-55-1024x694.png 1024w, https:\/\/www.dase-analytics.com\/blog\/wp-content\/uploads\/3-55-600x407.png 600w\" sizes=\"(max-width: 866px) 100vw, 866px\" \/><\/p>\n<h2><strong>Ktor\u00fa cestu si vybra\u0165?<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">V\u00fdber z\u00e1vis\u00ed najm\u00e4 od troch vec\u00ed: ak\u00e9 d\u00e1ta m\u00e1te, ak\u00e9 ot\u00e1zky rie\u0161ite a ak\u00e9 technick\u00e9 zru\u010dnosti m\u00e1 v\u00e1\u0161 t\u00edm.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pre r\u00fdchlu orient\u00e1ciu pom\u00f4\u017ee<strong> jednoduch\u00e9 porovnanie:<\/strong><\/span><\/p>\n<table style=\"height: 181px;\" width=\"1006\">\n<tbody>\n<tr>\n<td><strong>Pr\u00edstup<\/strong><\/td>\n<td><strong>Najv\u00e4\u010d\u0161ia v\u00fdhoda<\/strong><\/td>\n<td><strong>Kedy d\u00e1va zmysel<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>GA4<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">r\u00fdchly \u0161tart a priama aktiv\u00e1cia publ\u00edk<\/span><\/td>\n<td><span style=\"font-weight: 400;\">ke\u010f chcete vyu\u017ei\u0165 hotov\u00e9 predikt\u00edvne metriky bez vlastn\u00e9ho modelovania<\/span><\/td>\n<\/tr>\n<tr>\n<td><strong>BigQuery ML<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">vlastn\u00e9 modely priamo cez SQL nad d\u00e1tami<\/span><\/td>\n<td><span style=\"font-weight: 400;\">ke\u010f m\u00e1te d\u00e1ta v BigQuery a potrebujete viac kontroly<\/span><\/td>\n<\/tr>\n<tr>\n<td><strong>Python a Google Colab<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">najv\u00e4\u010d\u0161ia flexibilita a vlastn\u00fd workflow<\/span><\/td>\n<td><span style=\"font-weight: 400;\">ke\u010f chcete \u00eds\u0165 do v\u00e4\u010d\u0161ej h\u013abky a pracova\u0165 detailne s d\u00e1tami<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">V praxi tieto pr\u00edstupy \u010dasto funguj\u00fa ved\u013ea seba. <strong>GA4 m\u00f4\u017ee by\u0165 najr\u00fdchlej\u0161\u00ed sp\u00f4sob, ako za\u010da\u0165 s predikt\u00edvnymi metrikami.<\/strong><\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>BigQuery ML<\/strong> m\u00f4\u017ee nadviaza\u0165 tam, kde potrebujete vlastn\u00e9 modelovanie nad va\u0161imi d\u00e1tami. A Python sa hod\u00ed vtedy, ke\u010f chcete \u00eds\u0165 jhlb\u0161ie a ma\u0165 pln\u00fa kontrolu nad cel\u00fdm procesom.<\/span><\/p>\n<h2><strong>Z\u00e1ver<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\"><strong>Posun od deskript\u00edvnej k predikt\u00edvnej analytike nie je len technologick\u00e1 zmena.<\/strong> Je to zmena v tom, ako prem\u00fd\u0161\u013eate o marketingov\u00fdch rozhodnutiach. Namiesto \u010disto sp\u00e4tn\u00e9ho hodnotenia minulosti za\u010d\u00ednate pracova\u0165 s odhadom bud\u00faceho spr\u00e1vania.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ak m\u00e1te <strong>ak\u00e9ko\u013evek ot\u00e1zky<\/strong> alebo h\u013ead\u00e1te sp\u00f4sob, ako predikt\u00edvnu analytiku efekt\u00edvne zapoji\u0165 do va\u0161ich procesov, nev\u00e1hajte n\u00e1m nap\u00edsa\u0165 na <strong>cibula@dase.sk<\/strong>, alebo si rovno <strong><a href=\"https:\/\/calendar.google.com\/calendar\/appointments\/schedules\/AcZssZ1y_TEM0euCqh2XawEbJiRNyB0ni-NhyaWZ7TWX5eePhw1F8aQZHS2nfIEuNNyBAQWHxY1embTe\" target=\"_blank\" rel=\"noopener\">booknite s na\u0161im Marekom bezplatn\u00fa 30m konzult\u00e1ciu tu.<\/a><\/strong><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>V\u00e4\u010d\u0161ina marketingov\u00fdch reportov odpoved\u00e1 najm\u00e4 na ot\u00e1zku, \u010do sa u\u017e stalo. Uk\u00e1\u017ee v\u00e1m n\u00e1v\u0161tevnos\u0165, konverzie, \u010di v\u00fdkonnos\u0165 kampan\u00ed&#8230;<\/p>\n","protected":false},"author":78,"featured_media":21415,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[639,637],"tags":[1097,1098,1095,1096],"_links":{"self":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/21410"}],"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=21410"}],"version-history":[{"count":4,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/21410\/revisions"}],"predecessor-version":[{"id":21418,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/posts\/21410\/revisions\/21418"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media\/21415"}],"wp:attachment":[{"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/media?parent=21410"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/categories?post=21410"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dase-analytics.com\/blog\/sk\/wp-json\/wp\/v2\/tags?post=21410"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}