Mashandisiro anoita vatengesi dhata hombe kuvandudza munhu muzvinhu zvitatu zvakakosha zvemutengi - assortment, kupa uye kuendesa, zvakataurwa muUmbrella IT.
Data huru ndiyo mafuta matsva
Mukupera kwema1990, vezvemabhizimisi vanobva kumativi ose ehupenyu vakasvika pakuziva kuti data chinhu chinokosha icho, kana chikashandiswa zvakanaka, chinogona kuva chishandiso chine simba chepesvedzero. Dambudziko raive rekuti huwandu hwe data hwakawedzera zvakanyanya, uye nzira dzekugadzirisa uye kuongorora ruzivo rwakanga rwuripo panguva iyoyo rwakanga rusina kushanda zvakakwana.
Muma2000s, tekinoroji yakatora quantum leap. Scalable mhinduro dzakaonekwa pamusika dzinogona kugadzirisa ruzivo rusina kurongeka, kurarama nebasa rakawanda, kuvaka zvine musoro zvinongedzo uye kushandura chaotic data mune inodudzirwa fomati inogona kunzwisiswa nemunhu.
Nhasi, data hombe inosanganisirwa mune imwe yenzvimbo pfumbamwe dzeDigital Economy yeRussian Federation chirongwa, ichitora mitsetse yepamusoro mumitengo uye mari yezvinhu zvemakambani. Kudyara kukuru muhukuru hwedata matekinoroji kunogadzirwa nemakambani kubva kumakambani ekutengesa, emari uye ekufonera.
Maererano nekufungidzira kwakasiyana-siyana, huwandu hwazvino hwemusika mukuru weRussia data kubva ku10 bhiriyoni kusvika ku30 bhiriyoni rubles. Zvinoenderana nekufanotaura kweSangano reBig Data Market Vatori vechikamu, panosvika 2024 ichasvika 300 bhiriyoni rubles.
Mumakore 10-20, data hombe ichava nzira huru ye capitalization uye ichaita basa munharaunda inofananidzwa mukukosha kune indasitiri yemagetsi, vanoongorora vanodaro.
Retail Success Formulas
Vatengi vemazuva ano havachisiri huwandu husina huso hwehuwandu, asi vanhu vakanyatsotsanangurwa vane hunhu hwakasiyana uye zvavanoda. Ivo vanosarudza uye vanochinjira kune mukwikwidzi mhando pasina kuzvidemba kana yavo yekupa ichiita senge inoyevedza. Ndicho chikonzero vatengesi vanoshandisa data huru, iyo inovabvumira kuti vabatane nevatengi nenzira yakanangwa uye yakarurama, vachitarisa pamusimboti we "mutengi akasiyana - basa rakasiyana."
1. Personalized assortment uye kushandisa zvakanaka nzvimbo
Muzviitiko zvakawanda, chisarudzo chekupedzisira "kutenga kana kusatenga" chinoitika kare muchitoro pedyo nesherufu nezvinhu. Maererano nenhamba dzeNielsen, mutengi anopedza masekonzi gumi nemashanu chete achitsvaga chigadzirwa chakakodzera pasherufu. Izvi zvinoreva kuti zvakakosha kuti bhizinesi ripe yakaringana assortment kune chimwe chitoro uye nekuchipa nemazvo. Kuti iyo assortment isangane nezvinodiwa, uye chiratidziro kusimudzira kutengesa, zvinodikanwa kuti udzidze akasiyana mapoka e data hombe:
- nhamba dzevanhu vemunharaunda,
- solvency,
- kutenga maonero,
- kuvimbika chirongwa kutenga uye zvimwe zvakawanda.
Semuenzaniso, kuongorora kuwanda kwekutengwa kwechimwe chikamu chezvigadzirwa uye kuyera "kuchinjika" kwemutengi kubva kune chimwe chigadzirwa kuenda kune chimwe kuchabatsira kunzwisisa nekukurumidza kuti ndechipi chinhu chinotengesa zvirinani, icho chisina basa, uye, nekudaro, zvakanyanya kugovanisa mari. zviwanikwa uye kuronga chitoro nzvimbo.
Imwe nzira yakaparadzana mukugadzirwa kwemhinduro kunobva pane yakakura data ndiko kushandiswa kwakanaka kwenzvimbo. Iyo data, uye kwete intuition, iyo vatengesi vava kuvimba nayo kana vachiisa kunze zvinhu.
MuX5 Retail Group hypermarkets, marongero echigadzirwa anogadzirwa otomatiki, achifunga nezve zvivakwa zvemidziyo yekutengesa, zvinofarirwa nevatengi, data pane nhoroondo yekutengesa kwemamwe mapoka ezvigadzirwa, uye zvimwe zvinhu.
Panguva imwecheteyo, iko kurongeka kweiyo dhizaini uye huwandu hwezvinhu pasherufu zvinotariswa munguva chaiyo: vhidhiyo analytics uye komputa kuona tekinoroji inoongorora vhidhiyo yerukova inouya kubva kumakamera uye kusimbisa zviitiko zvinoenderana neyakatsanangurwa paramita. Semuenzaniso, vashandi vemuzvitoro vanogamuchira chiratidzo chekuti zvirongo zvepizi zvemumagaba zviri panzvimbo isiri iyo kana kuti mukaka wakapfupikiswa wapera pamasherufu.
2. Personalized offer
Kusarudzika kwevatengi chinhu chakakosha: maererano nekutsvagisa kwakaitwa naEdelman neAccenture, 80% yevatengi vanogona kutenga chigadzirwa kana mutengesi akaita kupihwa kwemunhu kana kupa mutero; uyezve, 48% yevakapindura havazeze kuenda kune vakwikwidzi kana zvigadzirwa zvinorumbidza zvisiri izvo uye zvisingaite zvinodiwa.
Kuti usangane nezvinotarisirwa nevatengi, vatengesi vari kushingaira kuita zvigadziriso zveIT uye analytics maturusi anounganidza, kuronga uye kuongorora data revatengi kubatsira kunzwisisa mutengi uye kuunza kudyidzana kune wega nhanho. Imwe yeanozivikanwa mafomati pakati pevatengi - chikamu chekurudziro yechigadzirwa "iwe unogona kufarira" uye "tenga nechigadzirwa ichi" - inoumbwa zvakare zvichienderana nekuongororwa kwekutenga kwakapfuura uye zvaunofarira.
Amazon inogadzira aya kurudziro ichishandisa mubatanidzwa kusefa algorithms (nzira yekurudziro inoshandisa zvinozivikanwa zvido zveboka revashandisi kufanotaura zvisingazivikanwe zvido zvemumwe mushandisi). Maererano nevamiriri vekambani, 30% yezvose zvinotengeswa zvinokonzerwa neAmazon recommender system.
3. Kuendesa kwakasarudzika
Zvakakosha kuti mutengi wemazuva ano agamuchire chigadzirwa chaanoda nekukurumidza, zvisinei kuti iko kuendesa kwekuraira kubva kuchitoro chendaneti kana kusvika kwezvinhu zvinodiwa pamasherufu esupamaketi. Asi kukurumidza chete hakuna kukwana: nhasi zvinhu zvose zvinoendeswa nokukurumidza. Nzira yemunhu oga inokoshawo.
Vazhinji vatengesi vakakura uye vatakuri vane mota dzakashongedzerwa akawanda masensa uye RFID tag (inoshandiswa kuona uye kuronda zvinhu), kubva iyo huwandu hukuru hweruzivo hunogamuchirwa: dhata pamusoro penzvimbo iripo, saizi uye huremu hwemutoro, kuzara kwemotokari, mamiriro ekunze. , uye kunyange maitiro emutyairi.
Kuongororwa kweiyi data hakungobatsiri chete kugadzira iyo yakanyanya hupfumi uye inokurumidza track yenzira munguva chaiyo, asi zvakare inovimbisa kuve pachena kweiyo nzira yekuendesa kune vatengi, vane mukana wekutarisa mafambiro ehurongwa hwavo.
Zvakakosha kuti mutengi wemazuva ano agamuchire chigadzirwa chaanoda nokukurumidza, asi izvi hazvina kukwana, mutengi anodawo nzira yega.
Delivery personalization chinhu chakakosha kumutengi padanho re "maira yekupedzisira". Mutengesi anosanganisa mutengi uye data data padanho rekuita sarudzo anozokwanisa kupa mutengi kuti atore zvinhu kubva padanho rekuburitswa, uko kuchave kukurumidza uye kwakachipa kuunza. Chipo chekugamuchira zvinhu pazuva rimwe chete kana rinotevera, pamwe chete nekuderedzwa pakutumira, zvinokurudzira mutengi kuenda kunyange kune rumwe rutivi rweguta.
Amazon, semazuva ese, yakaenderera mberi nemakwikwi nepetenting predictive logistics tekinoroji inofambiswa nekufungidzira analytics. Chinokosha ndechekuti mutengesi anounganidza data:
- nezve zvakatengwa nemushandisi kare,
- nezve zvigadzirwa zvakawedzerwa kungoro,
- nezve zvigadzirwa zvakawedzerwa kune wishlist,
- nezvekufamba kwecursor.
Machine yekudzidza algorithms inoongorora ruzivo urwu uye kufanotaura kuti ndechipi chigadzirwa chingangotengwa nemutengi. Chinhu chacho chinozotumirwa kuburikidza neyakachipa yakajairwa kutumira kune yekutumira hub iri padyo nemushandisi.
Mutengi wemazuva ano akagadzirira kubhadhara nzira yega yega uye ruzivo rwakasiyana kaviri - nemari uye ruzivo. Kupa chiyero chakakodzera chebasa, tichifunga nezvezvido zvevatengi, zvinogoneka chete nerubatsiro rwe data hombe. Nepo vatungamiriri vemaindasitiri vari kugadzira zvikamu zvese zvezvimiro zvekushanda nemapurojekiti mumunda wedata hombe, mabhizinesi madiki nepakati ari kubheja pamhinduro dzebhokisi. Asi chinangwa chakajairwa ndechekuvaka chimiro chemutengi chakaringana, kunzwisisa marwadzo evatengi uye kuona zvinokonzeresa zvinokanganisa sarudzo yekutenga, simbisa rondedzero yekutenga uye kugadzira yakazara yakasarudzika sevhisi inozokurudzira kutenga zvakanyanya uye zvakanyanya.