{"id":4958,"date":"2017-07-17T15:00:50","date_gmt":"2017-07-17T13:00:50","guid":{"rendered":"https:\/\/igloonet.cz\/blog\/?p=4958"},"modified":"2020-07-23T13:52:22","modified_gmt":"2020-07-23T11:52:22","slug":"lesk-bida-atribuci","status":"publish","type":"post","link":"https:\/\/igloonet.cz\/blog\/lesk-bida-atribuci\/","title":{"rendered":"Lesk a b\u00edda atribuc\u00ed"},"content":{"rendered":"
<\/div>\n

Jak vyhodnocujeme \u00fasp\u011b\u0161nost a pro\u010d si mysl\u00edme, \u017ee se od atribu\u010dn\u00edch model\u016f o\u010dek\u00e1v\u00e1 nesplniteln\u00e9? P\u0159esta\u0148te hledat svat\u00fd gr\u00e1l a za\u010dn\u011bte experimentovat. <\/span>Anti-clickbait alert!<\/em> Prvn\u00ed dva odstavce jsou stru\u010dn\u00fd exkurz do historie atribu\u010dn\u00edho modelov\u00e1n\u00ed.<\/p>\n

<\/p>\n

Historie atribuce v online marketingu<\/h1>\n

P\u0159ed d\u00e1vn\u00fdmi \u010dasy lid\u00e9 za\u010dali p\u0159em\u00fd\u0161let, jak\u00fdm zp\u016fsobem prodat firm\u00e1m reklamu na internetu. Netrvalo dlouho a p\u0159i\u0161li s revolu\u010dn\u00ed novinkou. Na internetu zvl\u00e1dneme zm\u011b\u0159it, kolik reklama opravdu vyd\u011bl\u00e1. U\u017e \u017e\u00e1dn\u00e9 zbyte\u010dn\u00e9 pl\u00fdtv\u00e1n\u00ed rozpo\u010dty! I zaj\u00e1sali v\u0161ichni market\u00e9\u0159i a online marketing za\u010dal vzkv\u00e9tat. Teze vyhodnotitelnosti proto byla a st\u00e1le z\u016fst\u00e1v\u00e1 hlavn\u00ed mantrou online marketingu p\u0159i vymezov\u00e1n\u00ed v\u016f\u010di tradi\u010dn\u011bj\u0161\u00edm form\u00e1m propagace.<\/p>\n

Historicky byly v\u0161echny online analytick\u00e9 n\u00e1stroje i reklamn\u00ed syst\u00e9my zalo\u017eeny na Single Source Attribution<\/em> - ve\u0161ker\u00e9 z\u00e1sluhy za \u00fasp\u011b\u0161n\u00e9 spln\u011bn\u00ed c\u00edle (v\u011bt\u0161inou prodej) z\u00edsk\u00e1 pouze jedna kampa\u0148 \/ jeden zdroj. V Google Analytics proto uvid\u00edme jako zodpov\u011bdn\u00fd za konverzi posledn\u00ed zdroj n\u00e1v\u0161t\u011bvy, ze kter\u00e9ho z\u00e1kazn\u00edk p\u0159i\u0161el. V reportu Skliku, kde chyb\u00ed informaci o dal\u0161\u00edch kan\u00e1lech, pot\u00e9 uvid\u00edme jako p\u016fvodce n\u00e1kupu posledn\u00ed Sklik kampa\u0148 v cest\u011b z\u00e1kazn\u00edka.<\/p>\n

Last click - p\u016fvodce v\u0161eho zla<\/h1>\n

Postupem \u010dasu za\u010dali jak market\u00e9\u0159i, tak klienti ch\u00e1pat, \u017ee situace je o n\u011bco slo\u017eit\u011bj\u0161\u00ed. O\u010dek\u00e1vat, \u017ee prodej nastal pouze d\u00edky posledn\u00edmu v \u0159ad\u011b p\u0159\u00edstup\u016f, nejsp\u00ed\u0161 nen\u00ed \u00fapln\u011b korektn\u00ed popis reality.<\/p>\n

Na druhou stranu d\u0159\u00edv tento probl\u00e9m nebyl tak z\u00e1sadn\u00ed. N\u00e1kupn\u00ed cesta z\u00e1kazn\u00edka byla daleko krat\u0161\u00ed ne\u017e dnes - na internetu nebylo tolik konkurence, zbo\u017eov\u00e9 srovn\u00e1va\u010de teprve za\u010d\u00ednaly, nebo o nich je\u0161t\u011b nikdo nesly\u0161el, a remarketing se pou\u017e\u00edval ve sv\u00e9m p\u016fvodn\u00edm v\u00fdznamu<\/a>. Last click realitu tolik nek\u0159ivil.<\/p>\n

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

Atribuce a dal\u0161\u00ed v\u00fdvoj<\/h1>\n

K Single Source<\/em> brzy vznikly alternativy v podob\u011b Fractional<\/em> model\u016f. Ty na z\u00e1klad\u011b pevn\u00e9ho pravidla rozpo\u010d\u00edtaj\u00ed vliv na v\u00fdslednou konverzi mezi v\u00edce zdroj\u016f (line\u00e1rn\u00edm, \u010di jin\u00fdm zp\u016fsobem).<\/p>\n

Nejnov\u011bj\u0161\u00edm p\u0159\u00edstupem jsou v posledn\u00ed dob\u011b Algorithmic \/ Probabilistic \/ Data-driven<\/em> modely. Nevych\u00e1zej\u00ed z pevn\u011b nastaven\u00fdch pravidel, ale pr\u016fb\u011b\u017en\u011b se sna\u017e\u00ed dopo\u010d\u00edt\u00e1vat, jak velk\u00fd vliv m\u00e1 kter\u00e1 kampa\u0148 na v\u00fdsledn\u00e9m prodeji. Dva nejb\u011b\u017en\u011bji pou\u017e\u00edvan\u00e9 vych\u00e1zej\u00ed z Shapleyho hodnoty zn\u00e1m\u00e9 z teorie her, \u010di Markovov\u00fdch \u0159et\u011bzc\u016f.<\/p>\n

Shapley<\/a> se sna\u017e\u00ed dopo\u010d\u00edtat, o kolik v\u011bt\u0161\u00ed je celkov\u00e1 hodnota (celkov\u00e9 tr\u017eby), pokud potenci\u00e1ln\u00ed z\u00e1kazn\u00edci p\u0159ijdou i p\u0159es danou kampa\u0148 (nehled\u011b na to, kde v cest\u011b k n\u00e1kupu byla). Markovovy \u0159et\u011bzce<\/a> po\u010d\u00edtaj\u00ed, jak se zm\u011bn\u00ed celkov\u00e1 hodnota, pokud bude dal\u0161\u00ed p\u0159\u00edstup potenci\u00e1ln\u00edho z\u00e1kazn\u00edka p\u0159es tuto kampa\u0148.<\/p>\n

Pro zjednodu\u0161en\u00fd, ale hezk\u00fd p\u0159ehled doporu\u010duji Pavl\u016fv \u010dl\u00e1nek<\/a>.<\/p>\n

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

RTB a data-driven modely<\/h1>\n

Pou\u017e\u00edv\u00e1te data-driven modely a u\u017e jste se setkali s t\u00edm, \u017ee sofistikovan\u00fd RTB remarketing (RTB House, Criteo) vych\u00e1z\u00ed n\u011bjak podez\u0159ele skv\u011ble? My\u0161leno je\u0161t\u011b l\u00e9pe ne\u017e last click skv\u011ble.<\/p>\n

Ono to nen\u00ed zase tak p\u0159ekvapiv\u00e9. Jejich k\u00f3dy na str\u00e1nce toti\u017e sb\u00edraj\u00ed data o ve\u0161ker\u00e9 p\u0159\u00edchoz\u00ed n\u00e1v\u0161t\u011bvnosti a sna\u017e\u00ed se dopo\u010d\u00edtat pravd\u011bpodobnost, \u017ee dan\u00e1 cookie nakoup\u00ed. \u010c\u00edm v\u011bt\u0161\u00ed pravd\u011bpodobnost, t\u00edm v\u00edce se n\u00e1v\u0161t\u011bvn\u00edkovi bude remarketing zobrazovat a sna\u017eit se dostat do n\u00e1kupn\u00ed cesty.<\/p>\n

Na tento v\u00fdpo\u010det pou\u017e\u00edvaj\u00ed v\u011bt\u0161inou tak\u00e9 Shapleyeho, \u010di Markovovy \u0159et\u011bzce. Vyhodnocovat kampan\u011b pomoc\u00ed obdobn\u00e9ho modelu, kter\u00fdm optimalizuj\u00ed svoje zobrazov\u00e1n\u00ed (co nejefektivn\u011bj\u0161\u00ed parazitov\u00e1n\u00ed na p\u0159edchoz\u00edch zdroj\u00edch n\u00e1v\u0161t\u011bvnosti), je trochu chucpe.<\/p>\n

Nav\u00e1\u017eu na Pavla se sportovn\u00ed metaforou. Pepi\u010dek je stra\u0161n\u011b \u0161patn\u00fd fotbalista, jako spr\u00e1vn\u00fd nerd v\u0161ak um\u00ed pracovat s jednoduchou statistikou her a pr\u016fb\u011b\u017en\u011b zji\u0161\u0165uje, kte\u0159\u00ed hr\u00e1\u010di \u010dasto vyhr\u00e1vaj\u00ed. P\u0159i ka\u017ed\u00e9m dal\u0161\u00edm roz\u0159azov\u00e1n\u00ed do t\u00fdmu se tak sna\u017e\u00ed p\u0159idat k t\u00fdmu s nejv\u011bt\u0161\u00ed \u0161anc\u00ed na v\u00fdhru. Na konci roku je d\u00edky sv\u00e9 bilanci v\u00fdher-proher vyhl\u00e1\u0161en nejlep\u0161\u00edm hr\u00e1\u010dem \u0161koly.<\/p>\n

Z\u00e1kladn\u00ed ponau\u010den\u00ed by m\u011blo b\u00fdt: Nev\u011b\u0159te slep\u011b tomu, co v\u00e1m (jak\u00fdkoliv) atribu\u010dn\u00ed model \u0159\u00edk\u00e1, a berte v potaz zp\u016fsob, jak\u00fdm dan\u00fd kan\u00e1l optimalizuje.<\/strong><\/p>\n

Ono to v\u016fbec nen\u00ed o atribuci<\/h1>\n

Modely jsou st\u00e1le lep\u0161\u00ed a p\u0159esn\u011bj\u0161\u00ed, k nim m\u00e9 v\u00fdtky v\u016fbec nesm\u011b\u0159uj\u00ed. \u010c\u00edm d\u00e1l \u010dast\u011bji se ale setk\u00e1v\u00e1m s nepochopen\u00edm toho, co m\u00e1 atribuce v\u016fbec p\u0159in\u00e1\u0161et. Co mi na debat\u011b o atribu\u010dn\u00edch modelech vad\u00ed?<\/p>\n

1. Sb\u011br dat<\/h3>\n

V datov\u00fdch kruz\u00edch existuje kr\u00e1sn\u00e9 r\u010den\u00ed \u201egarbage in, garbage out\u201c. Nehled\u011b na to, jak skv\u011ble sofistikovan\u00fd a p\u0159esn\u00fd model m\u00e1me, pokud do n\u011bj budeme cp\u00e1t \u0161patn\u00e1 data, v\u00fdsledek n\u00e1m k ni\u010demu nebude.<\/p>\n

V \u010dem jsou data \u0161patn\u00e1 v tomto p\u0159\u00edpad\u011b? Naprosto z\u00e1sadn\u00ed bod je cross-device. St\u00e1le v\u00edce se zv\u011bt\u0161uje pod\u00edl z\u00e1kazn\u00edk\u016f, kte\u0159\u00ed p\u0159istupuj\u00ed ke str\u00e1nk\u00e1m z v\u00edce za\u0159\u00edzen\u00ed (a\u0165 ji\u017e mobil\u016f, pracovn\u00edch po\u010d\u00edta\u010d\u016f, \u010di jin\u00fdch prohl\u00ed\u017ee\u010d\u016f). Pokud nejste schopni tyto n\u00e1v\u0161t\u011bvy sp\u00e1rovat, bude v\u00e1m atribu\u010dn\u00ed model po\u010d\u00edtat p\u0159esnou hodnotu, ale v\u017edy jen na \u010d\u00e1sti cesty. Vzhledem k tomu, \u017ee lid\u00e9 rad\u011bji dokon\u010duj\u00ed objedn\u00e1vku na po\u010d\u00edta\u010di ne\u017e na mobilu, kampan\u011b c\u00edlen\u00e9 na mobily budou m\u00edt st\u00e1le velmi n\u00edzkou hodnotu nehled\u011b na atribu\u010dn\u00ed model, kter\u00fd zvol\u00edte.<\/p>\n

Tv\u00e1\u0159it se, \u017ee pou\u017e\u00edvat last click je hloup\u00e9, proto\u017ee nikdo nenakoup\u00ed b\u011bhem prvn\u00ed n\u00e1v\u0161t\u011bvy a p\u0159itom dod\u00e1vat, \u017ee data-driven modely m\u016f\u017eeme bez obav pou\u017e\u00edvat, proto\u017ee nikdo nenakupuje skrz v\u00edce za\u0159\u00edzen\u00ed \/ cookies, je dost pokryteck\u00e9.<\/p>\n

2. Rozd\u00edln\u00e1 funkce<\/h3>\n

Atribu\u010dn\u00ed modelov\u00e1n\u00ed spad\u00e1 pod popisnou statistiku. To znamen\u00e1, \u017ee n\u00e1m \u0159\u00edk\u00e1 pouze to, jak\u00fd vliv m\u00e1 kter\u00e1 kampa\u0148 na v\u00fdsledn\u00e9 prodeje v tomto konkr\u00e9tn\u00edm rozlo\u017een\u00ed (pro definici viz t\u0159eba wikipedie<\/a>). Sama o sob\u011b n\u00e1m ale ne\u0159\u00edk\u00e1, jak\u00e9 budou v\u00fdsledky, pokud rozpo\u010dty p\u0159eskup\u00edme. Lid\u00e9 bohu\u017eel \u010dasto zam\u011b\u0148uj\u00ed popisn\u00e9 n\u00e1stroje za prediktivn\u00ed a myln\u011b usuzuj\u00ed, \u017ee podle atribuce mohou \u0159\u00eddit kampan\u011b.<\/p>\n

Znamen\u00e1 to, \u017ee je atribuce k ni\u010demu?<\/strong> V\u016fbec ne, jen od n\u00ed nesm\u00edme cht\u00edt v\u011bci, na kter\u00e9 nen\u00ed stav\u011bna. Spr\u00e1vn\u00fd postup je na z\u00e1klad\u011b st\u00e1vaj\u00edc\u00edch v\u00fdsledk\u016f kampan\u00ed p\u0159ij\u00edt s hypot\u00e9zou (\u00fapravou rozpo\u010dt\u016f). Tu m\u016f\u017eeme d\u00e1le prozkoumat predik\u010dn\u00edm modelem (s hezk\u00fdm predik\u010dn\u00edm modelem p\u0159i\u0161li t\u0159eba aLook analytics<\/a>) a v z\u00e1v\u011bru otestovat \u0159\u00edzen\u00fdm experimentem.<\/p>\n

Jak vyhodnocovat<\/h1>\n

Z\u00e1v\u011br v\u011bnuji tomu, jak pracujeme s vyhodnocov\u00e1n\u00edm v igloonetu. N\u00e1\u0161 prim\u00e1rn\u00ed model, j\u00edm\u017e za\u010d\u00edn\u00e1me a kter\u00fd n\u00e1s prov\u00e1z\u00ed v\u011bt\u0161inou vyhodnocov\u00e1n\u00ed, je last click. U\u017e v druh\u00e9 \u010d\u00e1sti \u010dl\u00e1nku ho rozb\u00edj\u00edm t\u00edm, \u017ee last click dob\u0159e nepopisuje realitu, tak pro\u010d ho pou\u017e\u00edv\u00e1me?<\/p>\n

Last click je naprosto \u0161\u00edlen\u00e9 zjednodu\u0161en\u00ed n\u00e1kupn\u00edho chov\u00e1n\u00ed. To je premisa, z n\u00ed\u017e vych\u00e1z\u00edme a kter\u00e1 n\u00e1m sama o sob\u011b d\u00e1v\u00e1 dost informac\u00ed, se kter\u00fdmi m\u016f\u017eeme pracovat. T\u00edm, \u017ee last click d\u00e1v\u00e1 ve\u0161kerou hodnotu na posledn\u00ed zdroj n\u00e1v\u0161t\u011bvy, bude drasticky nadhodnocovat zdroje na konci n\u00e1kupn\u00edho cyklu (produktov\u00e9 inzer\u00e1ty, zbo\u017eov\u00e9 srovn\u00e1va\u010de, remarketing) a na druhou stranu podhodnocovat ty na jeho za\u010d\u00e1tku (z\u00e1jmov\u011b c\u00edlenou reklamu, obecn\u00e9 vyhled\u00e1vac\u00ed dotazy).<\/p>\n

U ka\u017ed\u00e9 kampan\u011b mus\u00edme kriticky zhodnotit, v jak\u00e9 f\u00e1zi n\u00e1kupn\u00edho cyklu se nach\u00e1z\u00ed. \u010c\u00edm bl\u00ed\u017ee ke konci, t\u00edm p\u0159\u00edsn\u011bj\u0161\u00ed na ni m\u00e1me last clickov\u00e9 ROAS po\u017eadavky. Nap\u0159\u00edklad ve vyhled\u00e1v\u00e1n\u00ed budou kampan\u011b boty obecn\u00e9<\/em> m\u00edt c\u00edlov\u00e9 ROAS 200 % a vans authentic<\/em> 600 %.<\/p>\n

Jednou za \u010das (v z\u00e1vislosti na velikosti vzorku \u2013 rozpo\u010dtu) srovn\u00e1me last clickov\u00fd popis v\u00fdsledk\u016f s jin\u00fdmi atribu\u010dn\u00edmi modely. U kampan\u00ed, u kter\u00fdch bude rozd\u00edl v\u011bt\u0161\u00ed, ne\u017e jsme o\u010dek\u00e1vali, by m\u011blo b\u00fdt dal\u0161\u00edm krokem nastaven\u00ed experimentu a p\u0159\u00edpadn\u011b korekce o\u010dek\u00e1v\u00e1n\u00ed u dal\u0161\u00edch kampan\u00ed.<\/p>\n

Nejv\u011bt\u0161\u00ed p\u0159\u00ednos atribu\u010dn\u00edch model\u016f vid\u00edm pr\u00e1v\u011b v tom, \u017ee otv\u00edr\u00e1 o\u010di. Nehled\u00e1me svat\u00fd gr\u00e1l \u2013 n\u00e1stroj, kter\u00fd n\u00e1m \u0159ekne, kolik p\u0159esn\u011b kter\u00e1 kampa\u0148 vyd\u011blala, ale n\u011bco, co n\u00e1m uk\u00e1\u017ee skryt\u00fd potenci\u00e1l.<\/p>\n

Co si o na\u0161em p\u0159\u00edstupu mysl\u00edte vy a jak k atribu\u010dn\u00edmu modelov\u00e1n\u00ed, pota\u017emo vyhodnocov\u00e1n\u00ed p\u0159istupujete? Napi\u0161te n\u00e1m do diskuse.<\/p>\n","protected":false},"excerpt":{"rendered":"

Jak vyhodnocujeme \u00fasp\u011b\u0161nost a pro\u010d si mysl\u00edme, \u017ee se od atribu\u010dn\u00edch model\u016f o\u010dek\u00e1v\u00e1 nesplniteln\u00e9? P\u0159esta\u0148te hledat svat\u00fd gr\u00e1l a za\u010dn\u011bte experimentovat. Anti-clickbait alert! Prvn\u00ed dva odstavce jsou stru\u010dn\u00fd exkurz do historie atribu\u010dn\u00edho modelov\u00e1n\u00ed.<\/p>\n","protected":false},"author":7,"featured_media":6763,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[70],"tags":[74],"class_list":["post-4958","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-marketing","tag-analytika"],"yoast_head":"\nLesk a b\u00edda atribuc\u00ed | igloonet blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/igloonet.cz\/blog\/lesk-bida-atribuci\/\" \/>\n<meta property=\"og:locale\" content=\"cs_CZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lesk a b\u00edda atribuc\u00ed | igloonet blog\" \/>\n<meta property=\"og:description\" content=\"Jak vyhodnocujeme \u00fasp\u011b\u0161nost a pro\u010d si mysl\u00edme, \u017ee se od atribu\u010dn\u00edch model\u016f o\u010dek\u00e1v\u00e1 nesplniteln\u00e9? 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