Nneɛma Titiriw a Wɔde Fa Kɔ
Ɔkwan biako biara nni hɔ a wɔfa so susuw nneɛma a ebetumi abua nsɛmmisa a nnɛyi aguadi akannifo hyia no nyinaa. Layered stack a ɛka nnwinnade pii bom ho hia.
Asɛnnennen a ɛwɔ aguadi ho attribution mu no yɛ nhyehyɛe: ɛde credit ma touchpoints nanso entumi nkyerɛ sɛ nea ɛde ba. Ɛyɛ adwuma yiye ma tactical optimization, ɛnyɛ strategic gyinaesi ahorow.
Marketing mix modeling kyerɛ marginal returns ne channel saturation, ɛboa ma wɔkyerɛ bere tenten sikasɛm nhyehyɛe kwan.
Nkɔanim sɔhwɛ ne ɔkwan a wotumi de ho to so sen biara a wɔfa so hu sɛ ebia aguadi dwumadi no de nea efii mu bae ankasa, sen sɛ ɛbɛkyere ahwehwɛde a na ɛwɔ hɔ dedaw no.
Nsusuwii akuw a wɔhyehyɛ ma wɔyɛ akwampaefo, atubrafo, ne nhyehyɛefo no hwɛ hu sɛ adwuma biara benya gyinapɛn a ɛfata ne ahoɔhare a wɔde besisi gyinae.
Adwumayɛ ho akannifo dodow no ara nim asɛnnennen a ɛwɔ aguadi ho attribution mu no yiye: wowɔ dashboard ahorow a data ahyɛ mu ma, nanso akontaabu no ntumi mmua sika a wɔde asie a ɛde nkɔso ba ankasa. Awosu mu nkate ne sɛ wobɛhwehwɛ adwinnade a eye, nhwɛsode a nyansa wom, anaasɛ nhyehyɛe a ɛyɛ pɛpɛɛpɛ a ɛkyerɛ sɛnea obi te. Nanso ahyehyɛde ahorow a wɔrenya susuw yiye no atwam wɔ saa awosu no ho.
Wɔagyae nokware fibea biako pɛ a wɔhwehwɛ. Asɛnnennen a ɛwɔ aguadi mu nneɛma a wɔde di dwuma ho no yɛ ɔhaw bi a ɛtrɛw no fã: nnɛyi aguadi tebea horow no yɛ nea emu yɛ den dodo sɛ ɔkwan biako betumi akata biribiara so. Nneɛma a wohu kɔ so wɔ asɛnka agua pii so, adetɔfo akwantu mu apaapae dodo, na nsakrae a aba wɔ kokoam nsɛm mu no asɛe sɛnkyerɛnne pii dodo sɛ adwinnade biako biara betumi ama mfonini a edi mũ.
Nea ɛyɛ adwuma mmom ne ɔkwan a wɔfa so yɛ no ntoatoaso. Akwan ahorow a wɔfa so susuw nneɛma bua nsɛmmisa ahorow, na ahyehyɛde ahorow a ɛrenya nkɔso kɛse no hyɛ da ka bom. Marketing mix modeling kyerɛ ɔkwan a wɔfa so kyekyɛ sikasɛm nhyehyɛe kwan. Nkɔanim sɔhwɛ no si so dua sɛ ebia dwumadi pɔtee bi na ɛde nea efii mu bae bae anaa. Platform data di da biara da ɔsatu optimization ho dwuma. Wɔn mu biara di dwuma bi a wɔakyerɛkyerɛ mu. Wɔn mu biara nyɛ adwuma sɛ ɔkwan a egyina hɔ ma ne ho.
Eyi ne ade a ɛto so abien wɔ afã abiɛsa a ɛtoatoa so a ɛfa nnɛyi aguadi susuw ho no mu. Ɔfã a edi kan no hwehwɛɛ nea enti a atetesɛm mu metrics te sɛ traffic, rankings, ne ROAS reyɛ nea wontumi mfa ho nto so kɛse. Saa afã yi ka sɛnea wɔkyekye susudua nhyehyɛe a ɛboa nkɔso ho gyinaesi ahorow ankasa ho asɛm.
Nea Enti a Ɔkwan Baako Biara a Wɔfa so Sua Nneɛma Biara Nni Adwuma Bio
Wɔkyekyeree dijitaal aguadi attribution nnwinnade a akuw dodow no ara de wɔn ho to so no maa tebea soronko. Wɔyɛɛ adwuma yiye bere a na wɔn a wɔde di dwuma no akwantu yɛ linear kakra, cookies dii akyi wɔ ahotoso mu wɔ nhyiam ahorow no nyinaa mu, na nea wohui dodow no ara nam akwan a ɛnyɛ den sɛ wɔbɛkyerɛw so na ɛbae. Saa tebea no nni hɔ bio.
Ɛnnɛ, ebia adetɔfo bi behyia ahyɛnsode bi denam mmuae a AI ayɛ so, ayɛ ho nhwehwɛmu wɔ YouTube, asusuw ho wɔ kokoam nkrasɛm nhama mu, na wasakra denam ahyɛnsode a wɔhwehwɛ so adapɛn abiɛsa akyi. Attribution nhyehyɛe no de touchpoint a etwa to no ho anuonyam ma. Akwan a ɛhyehyɛɛ gyinaesi no ankasa no nnya kakraa bi anaasɛ wonnya hwee.
Eyi ne nhyehyɛe mu ɔhaw titiriw no. Wɔayɛ marketing attribution models sɛ wɔde credit bɛma, na ɛnyɛ sɛ wɔde besi nea ɛde ba no so dua. Multi-touch attribution marketing akwan a ɛyɛ nwonwa mpo da so ara yɛ adwuma wɔ anohyeto titiriw koro no ara mu: wobetumi akyerɛ touchpoints a edii nsakrae anim, nanso wontumi nkyerɛ sɛ sɛ woyi emu biara fi hɔ a, anka ɛbɛsakra nea ebefi mu aba no.
Nea ahyehyɛde ahorow a ɛrenya nkɔso kɛse ahu ne sɛ nnwinnade ahorow a wɔde susuw nneɛma bua nsɛmmisa ahorow. Attribution modeling mmuaeɛ: touchpoints bɛn na na ɛwɔ hɔ ansa na wɔresakra? Marketing mix modeling mmuae: ɛhe na marginal returns mu yɛ den sen biara wɔ akwan horow so wɔ bere mu? Nkɔanim sɔhwɛ mmuae: so dwumadi pɔtee yi sesaa nea efii mu bae no ankasa?
Asɛmmisa biara ho hia. Ɛsono ɔkwan a emu biara hwehwɛ. Sɛnea NP Digital nhwehwɛmu kyerɛ no, aguadifo a wɔanya nkɔso kɛse no mu ɔha biara mu 90 de incrementality testing di kan, ɔha biara mu 61 de attribution modeling di dwuma, na ɔha biara mu 42 de marketing mix modeling di dwuma. Akuw a etu mpɔn sen biara no de abiɛsa no nyinaa di dwuma, na gyinaesi a ɛwɔ hɔ no kari wɔn.
Marketing Mix Modeling sɛ Akwankyerɛ a Wɔde Di Dwuma
Marketing mix modeling, anaa MMM, fa ɔkwan soronko so wɔ susudua mu sen attribution. Sɛ anka ɛbɛdi ankorankoro a wɔde di dwuma no akwantuo akyi no, ɛde abakɔsɛm mu nsɛm a wɔaboaboa ano di dwuma de yɛ abusuabɔ a ɛda aguadi ho sika a wɔsɛe no ne adwumayɛ mu nsunsuansoɔ ntam wɔ akwan ahodoɔ so wɔ berɛ mu no ho nhwɛsoɔ. Nea efi mu ba ne adwene a ɛfa marginal returns a attribution nhyehyɛe ahorow ntumi mfa mma ho.
MMM ho wɔ mfaso kɛse ma wohu baabi a dɔla biara a wɔde bɛka ho no sɛe wɔ achannel no ma wonya mfaso a ɛso tew. Ebia ɔkwan bi a ɛretu mmirika wɔ ROAS a wɔde afra a ɛyɛ den so no bɛyɛ te sɛ nea ɛyɛ adwuma yiye wɔ dashboard mu bere a ne sikasɛm nhyehyɛe mu ɔha biara mu nkyem 30 a etwa to no renya sika a ɛkɔ soro a ɛnyɛ hwee. MMM da saa inefficiency no so. Ɛboa nso ma wohu cross-channel effects, te sɛ sɛnea video anaa brand investment upstream nya nsakraeɛ dodoɔ so nkɛntɛnsoɔ wɔ paid search downstream mu.
Wɔ sikasɛm nhyehyɛe a wɔde kyekyɛ mu wɔ ɔkwan a ɛfata so no, eyi ma MMM yɛ adwinnade a wotumi de ho to so sen biara a ɛwɔ hɔ. Ɛnhwehwɛ sɛ wɔdi nea ɔde di dwuma no akyi, a ɛkyerɛ sɛ nsakrae a wɔyɛ wɔ kokoam nsɛm mu ne kuki a wɔpow no nsɛe ne pɛpɛɛpɛyɛ sɛnea wɔyɛ ma attribution no. MMM mmirikatu a wɔyɛ no afe nkyem abiɛsa biara no betumi ama sikasɛm nhyehyɛe ho gyinaesi ahorow a ɛtra hɔ kyɛ no atu mpɔn bere nyinaa mpo bere a da biara da attribution sɛnkyerɛnne ahorow yɛ dede no.
MMM wɔ anohyeto ankasa ampa. Ɛpere sɛ ɛbɛkyerɛ dodow a ɛwɔ soro-funnel brand dan no pɛpɛɛpɛ, efisɛ lag a ɛda brand adwene ne downstream nsakrae ntam no ware dodo na ɛnteɛ dodo sɛ abakɔsɛm mu nkitahodi ahorow bɛkyere no yiye. Ahyehyɛde ahorow a wɔde MMM di dwuma ma akwankyerɛ a ɛfa ɔkwan a wɔfa so yɛ adwuma ho bere a wɔde brand tracking ne perception adesua ka ho no nya mfonini a edi mũ sen biara.
Nkɔanim Sɔhwɛ sɛ Nneɛma a Ɛde Ba
Sɛ MMM de akwankyerɛ a ɛfa ɔkwan a wɔfa so yɛ adwuma ma a, incrementality testing de nea ɛde ba ho adanse ma. Asɛmmisa a ɛbua no yɛ pɔtee: so anka nea ebefi mu aba yi bɛba sɛ saa aguadi dwumadi yi ansi a? Ɛno yɛ asɛmmisa soronko koraa wɔ nea attribution models bisa no ho, na mmuae no ho wɔ mfaso kɛse wɔ baabi a wɔde sika bɛto ho gyinaesi mu.
Akwan a wɔtaa fa so kɔ soro no bi ne geo experiments, holdout tests, ne campaign pauses. Wɔ geo sɔhwɛ mu no, wɔkyerɛ asasesin mu gua ahorow a ɛne ne ho hyia na wɔkora sika a wɔsɛe no so wɔ kuw biako mu bere a wɔhwɛ so wɔ kuw foforo mu no. Nsonsonoe a ɛwɔ nea efi mu ba mu wɔ akuw abien no ntam no tew nea ɛde ba a wɔma so no fi aguadi dwumadi no ho. Holdout sɔhwɛ ahorow de ntease koro no ara di dwuma wɔ atiefo gyinabea. Bere a ɔsatu mu home a wɔde gyina hɔ ma no yɛ nea ɛyɛ katee de, nanso ebetumi ada no adi sɛ ebia nea efi mu ba no so tew bere a sika a wɔsɛe no gyae no.
Wɔ akuw a wɔde Amazon attribution anaa gua so susudua foforo di dwuma fam no, nkɔanim sɔhwɛ som bo titiriw efisɛ nsakrae a wɔbɔ ho amanneɛ wɔ platform so no taa da ahwehwɛde a na ɛwɔ hɔ dedaw adi sen sɛ wɔbɛhwehwɛ ɔsatu a wɔayɛ no.
NP Digital nhwehwɛmu akyi di incremental versus attributed conversions wɔ akwan horow so no huu nsonsonoe a ntease wom wɔ ɛkame ayɛ sɛ asɛm biara mu. Organic social kyerɛɛ ɔha biara mu nkyem 13 nkɔanim a ɛkɔ soro bere a ɔha biara mu 3 kyerɛ sɛ wɔama so no. Paid social kyerɛɛ ɔha biara mu 17 nkɔanim nkɔanim bere a wɔde totoo ɔha biara mu 24 a wɔkyerɛe no ho, a ɛkyerɛ sɛ na attribution rema saa kwan no aboro so. Saa nsonsonoe yi nya baabi a ɛsɛ sɛ sikasɛm nhyehyɛe kɔ no so nkɛntɛnso tẽẽ, na wontumi nhu a wɔmfa nkɔanim sɔhwɛ nka ho.
Nkɔanim sɔhwɛ hwehwɛ nhyehyɛe ne data a ɛho tew, nanso enhia sikasɛm nhyehyɛe kɛse. Geo holdout biako a wɔayɛ no yiye wɔ ɔkwan kɛse bi so mpo ma yenya nhumu a wotumi de ho to so kɛse wɔ nkɛntɛnso a ɛde ba ho sen asram pii a wɔde bɛbɔ amanneɛ a ɛfa attribution ho.
Platform Data Da so Ho Hia, Nanso Ɛyɛ Optimization Nkutoo
Platform dashboards a efi Google, Meta, ne ad platform afoforo so da so ara yɛ nea mfaso wɔ so, nanso wɔn dwumadi no sua sen sɛnea akuw dodow no ara di ho dwuma. Attribution blind spots a wɔasisi wɔ platform amanneɛbɔ mu no yɛ nhyehyɛe, ɛnyɛ akwanhyia. Wɔayɛ nhyiam ase sɛnea ɛbɛyɛ a ɔsatu no bɛyɛ yiye wɔ wɔn ankasa abɔde a nkwa wom nhyehyɛe mu. Wɔannwene wɔn sɛ wɔbɛkyerɛ wo sɛ ebia saa adwumayɛ no sesaa w’adwuma anaa.
Wɔ da biara da gyinaesi ahorow mu no, platform data ne adwinnade a ɛfata. Pacing spend against budget, adjusting bids gyina performance signals so, hu adebɔ mu ɔbrɛ, ne diagnosing delivery nsɛm nyinaa nyinaa gyina platform metrics so. Eyinom yɛ adwumayɛ ho gyinaesi ahorow, na platform data di ho dwuma yiye.
Baabi a platform data bɛyɛ nea wontumi mfa ho nto so ne gyinaesi ahorow a ɛfa ɔkwan a wɔfa so yɛ adwuma ho. Algorithms optimize toward users most likely to convert, a ɛkyerɛ sɛ wɔyɛ nhyehyɛe fa ahwehwɛde a wɔkyere sen ahwehwɛde adebɔ. ROAS akontaabu a ɛkorɔn wɔ platform dashboard mu betumi ada algorithm a etu mpɔn adi, na ɛnyɛ aguadi a etu mpɔn.
Sɛnea NP Digital nhwehwɛmu kyerɛ no, sɛ wɔkyekyem pɛpɛɛpɛ a, sɛ wɔkyekyem pɛpɛɛpɛ a, sɛ wɔmmɔ nnwuma nketewa ka ɔha biara mu nkyem 19.4 wɔ aguade ho dawurubɔ ho, nnwumakuw a ɛwɔ mfinimfini gua so no hwere ɔha biara mu nkyem 11.5, na nnwumakuw ahyɛnsode ahorow hwere ɔha biara mu nkyem 7.7. Saa sika a wɔsɛe no no yɛ nea wontumi nhu kɛse wɔ asɛnka agua so amanneɛbɔ mu efisɛ asɛnka agua ahorow no nni biribiara a ɛkanyan wɔn ma wɔda adi.
Akwankyerɛ a mfaso wɔ so ne sɛ wode platform metrics bedi dwuma ama nea ɛyɛ: tactical steering, ɛnyɛ strategic truth.
Ɔkwampaefo–Ɔtubrafo–Nhyehyɛefo Nsusuwii noNhwɛsode
Nsusuwii nhyehyɛe a ɛwɔ ntoatoaso a wɔbɛkyekye no nyɛ mfiridwuma mu asɛnnennen ara kwa. Ɛyɛ ahyehyɛde mu de. Dwumadi ahorow abiɛsa na ɛsono emu biara a susuw ahyehyɛde biara a etu mpɔn hia: akwampaefo, atubrafo, ne nhyehyɛefo.
Akwampaefo yɛ adwuma wɔ nea wotumi susuw mprempren no anoano. Wɔyɛ incrementality experiments, kyekye mfitiaseɛ marketing mix models, sɔ geo holdouts hwɛ, ne pressure-test assumptions a ebia ɛrenkura bio. Wɔn adwuma no yɛ nea wontumi nsi pi wɔ wɔn nhyehyɛe mu. Akwampaefo mfa ahotoso mma; wɔde akwankyerɛ ma. Sɛ wɔde wɔn kura akontaabu mu ahotoso gyinapɛn koro no ara mu te sɛ adwumayɛ ho amanneɛbɔ a, ɛbɛma adwuma yi agyae ansa na ɛde mfaso aba.
Atubrafo fa nea efi sɔhwɛ mu ba no na wɔdan no nneɛma a wotumi yɛ no mpɛn pii. Wɔyɛ nhwɛso ahorow mu nsakrae, wɔhyɛ nsusuwii ahorow mu den, na wɔde nhumu ahorow bata nhyehyɛe ho gyinaesi ahorow ho. Eyi ne baabi a MMM mmirikatu a edi kan no nyin kɔ agoru nhoma mu, na baabi a nkɔanim sɔhwɛ aba no bɛyɛ nhyehyɛe a akuw betumi de adi dwuma daa. Atubrafo nya ahotoso denam akwankyerɛ ho nhumu a wɔkyerɛ ase kɔ nhyehyɛe ahorow a wobetumi de adi dwuma ankasa mu no so.
Nhyehyɛefo ma da biara da dwumadi ahorow kɔ so yɛ adwuma. Wɔde wɔn ho to platform data, attribution signals, ne conversion mechanics so de di sika a wɔsɛe no ho dwuma wɔ bere ankasa mu. Saa ɔfa yi ho hia; sɛ enni hɔ a, kum a wokum obi no hwe ase. Nanso ɛnsɛ sɛ wɔka kyerɛ nhyehyɛefo sɛ wɔnkyerɛkyerɛ nkɔso a ɛba bere tenten mu anaasɛ wɔnhu nsakrae a ɛba wɔ nhyehyɛe mu wɔ adwumayɛ mu. Wɔn adwene ne sɛ wɔbɛma adwumayɛ ayɛ yiye wɔ akwan anohyeto ahorow mu.
Ɔkwan a ahyehyɛde dodow no ara hwe ase wɔ huammɔdi mu ne sɛ wɔde nhyehyɛefo gyinabea gyinapɛn ahorow a ɛkyerɛ sɛ ɛyɛ nokware bedi dwuma wɔ akwampaefo gyinabea adwuma mu. Akontaabu mu ahotoso ɔha biara mu nkyem 95 a wɔhwehwɛ fi sɔhwɛ ahorow a ehia bere na wɔde ayɛ mu no ma awerɛhyem sɛ biribi foforo biara rensi. Model a ɛwɔ akwankyerɛ mu ahotoso ɔha biara mu nkyem 60, a wɔde afrafra ntɛmntɛm iteration, yɛ adwuma sen mmuae a edi mũ a ɛba akyiri dodo nkyem anan mu biako bere nyinaa.
Sɛnea Nnwumakuw a Wɔnya Nkɔso Kɛse Kyɛ Nsusuwii Nneɛma
NP Digital nhwehwɛmu a ɛdi susudua nneyɛeɛ akyi wɔ Canada ahyɛnsodeɛ ahodoɔ nyinaa mu no hunuu mpaapaemu a ɛda adi pefee wɔ ahyehyɛdeɛ a wɔkyekyɛ mu ne wɔn a wɔnya nkɔsoɔ kɛseɛ ntam. Sɛ wɔkyekyem pɛpɛɛpɛ a, akuw ahorow de wɔn nkɛntɛnso a wɔde susuw nneɛma no bɛyɛ ɔha biara mu nkyem 65 ma platform dashboard ahorow na wɔde ɔha biara mu nkyem 25 ma nnwinnade a wɔde kyerɛ sɛnea obi te, na ɛmma akwan a wɔfa so susuw nneɛma pii ho kwan.
Nneɛma a wɔde yɛ nneɛma a ɛrenya nkɔso kɛse a wɔde bɛboro dɔla 750,000 a wɔde hyɛ nsɛm ho amanneɛbɔfo mu afe biara no te sɛ nea ɛsono nea ntease wom. Platform dashboard a wɔde wɔn ho to so no so tew kodu bɛyɛ ɔha biara mu nkyem 45. Attribution adwinnade a wɔde di dwuma no so tew kodu ɔha biara mu nkyem 15. MMM nyin fi ɔha biara mu nkyem 5 kosi ɔha biara mu nkyem 20. Nkɔanim sɔhwɛ du ɔha biara mu nkyem 10, na ntɛm generative search optimization adwuma no yɛ ɔha biara mu nkyem 10 foforo.
Saa ahyehyɛde ahorow yi nnyae attribution anaa platform data. Wɔresan akari wɔn. Ntease no yɛ tẽẽ: wɔ gua ahorow a ɛkɔ so sesa mu no, wokyekye tumi a wɔde susuw nneɛma wɔ baabi a nsakrae rekɔ so, na ɛnyɛ baabi a wote nka sɛ ahobammɔ wɔ nimdefo. Botae a ɛwɔ akwan yi nyinaa so ne ahotoso a ɛfa akwankyerɛ ho, a ɛkyerɛ sɛnkyerɛnne a ɛdɔɔso a ɛbɛma wɔasisi sikasɛm ho gyinae pa ntɛmntɛm, ɛnyɛ ahotoso a edi mũ a ɛba bere a hokwan no atoto mu akyi.
Anamɔn Ason a Wobɛfa so Asakra Wo Nsusuwii Nhyehyɛe
Sɛ wɔbɛsan akyekye susuw nhyehyɛe bi a, enhia sɛ wɔsesa biribiara prɛko pɛ. Ahyehyɛde ahorow a wɔyɛ eyi yiye no dannan nkakrankakra, na wɔde tumi ka ho wɔ nhyehyɛe a ɛfata mu sen sɛ wɔbɛbɔ mmɔden sɛ wɔbɛyɛ nsakrae koraa.
Map wo mprempren susudua inputs. Kyerɛw adwinnade ne data fibea biara a wo kuw no de di dwuma na kyerɛ baabi a emu biara te: adwumayɛ kwan so data, attribution modeling, MMM, anaa incrementality. Akuw dodow no ara hu sɛ wɔde wɔn adwene asi abien a edi kan no so kɛse.
Kyerɛ gyinaesi mu nsonsonoe ahorow. Ka nsɛmmisa a ɛfa ɔkwan a wɔfa so yɛ adwuma a wo mprempren stack no ntumi mmua ho pefee. Asɛnnennen a ɛwɔ aguadi ho attribution mu no da adi kɛse wɔ ha: ɛhe na woresisi sikasɛm ho gyinaesi ahorow a egyina ROAS a wɔadi afra so a wuntumi nhu nkɔ mfaso kakraa bi so? Ɛhe na wode akwan a ebia ɛregye ahwehwɛde a ɛwɔ hɔ dedaw no ara kwa no rema?
Fa mfitiase nhwɛsode di dwuma. MMM mmirikatu a ɛnyɛ den mpo wɔ asram abiɛsa biara mu no ma akwankyerɛ a ɛfa ɔkwan a wɔfa so yɛ adwuma ho sen attribution nkutoo. Fi ase denam wo akwan a wosɛe sika kɛse ne nea efi adwuma mu ba a ɛbata sika a wunya ho tẽẽ kɛse no so.
Tu mmirika wo incrementality sɔhwɛ a edi kan no. Paw ɔkwan titiriw biako na yɛ geo holdout anaa holdout atiefo sɔhwɛ ho nhyehyɛe. Botae no nyɛ pɛyɛ; ɛde saa susudua yi rekyekye ahyehyɛde no tumi ne awerɛkyekye.
Sesa nniso akwanhwɛ ahorow. Attribution amanneɛbɔ renyera mfi akannifo nhwehwɛmu mu anadwo biako pɛ. Mmirikatu aparallel track a ɛkyerɛ incrementality ne MMM findings a ɛka attribution data ho no ma ahotoso wɔ ɔkwan foforo no mu a enhia sɛ wɔyɛ nsakrae a edi mũ.
Si nhyehyɛe ahorow nkakrankakra. Atubrafo dan akwampaefo sɔhwɛ ahorow ma ɛbɛyɛ adwuma a wotumi yɛ no mpɛn pii. Ɛsɛ sɛ nkɔanim sɔhwɛ biara ma wonya ɔkwan a wɔakyerɛw so a ɛma nea edi hɔ no yɛ ntɛmntɛm na ne bo nyɛ den.
Ma gyinaesi cadence nkɔ soro. Mfaso biako a ɛwɔ akwankyerɛ mu ahotoso so sen ahotoso a edi mũ ne ahoɔhare. Nnawɔtwe biara sikasɛm mu nsakraeɛ a egyina nkɔanim nsɛnkyerɛnneɛ ne MMM nsunsuansoɔ so no yɛ adwuma sene bosome mmiɛnsa biara a wɔsan kyekyɛ a egyina attribution amanneɛbɔ so.
Nsɛm a wobisabisa nkurɔfo
Dɛn Ne Marketing Attribution?
Marketing attribution yɛ ɔkwan a wɔfa so de credit ma marketing touchpoints a ɛboaa ma wɔsakraa no. Nneɛma a wɔtaa de kyerɛ sɛnea wɔtɔn nneɛma no bi ne nea etwa to a wɔde klik, nea edi kan a wɔde kyerɛ, linear, ne data-driven attribution. Ɛsono sɛnea obiara de bosea ma wɔ adetɔfo akwantu no nyinaa mu. Attribution ho wɔ mfaso kɛse ma ɔsatu adwumayɛ a ɛyɛ papa wɔ akwan horow mu, nanso entumi nkyerɛ sɛ ebia aguadi na ɛde adwumayɛ mu aba bi bae anaa.
Ɔkwan Bɛn so na Wosusuw Marketing Attribution?
Wɔnam nsakraeɛ data a wɔde bata touchpoints a ɛdi n’anim no so, de tracking pixels, UTM parameters, ne CRM data di dwuma de susuw ɔkwan no ho. Marketing attribution software platforms yɛ saa adeyɛ yi automate na ɛde attribution model ahorow ma a wobɛpaw bi. Anohyeto titiriw a ɛsɛ sɛ yɛte ase ne sɛ attribution akwan nyinaa de credit ma a egyina abusuabɔ so, na ɛnyɛ nea ɛde ba.
Ebɛn Software a Ɛyɛ Paara a Wɔde Di Marketing Attribution akyi?
Software a eye sen biara a ɛkyerɛ sɛnea wɔtɔn nneɛma no gyina w’adwumayɛ kwan ne wo susuw botae ahorow so. Google Analytics 4 ne platform-native dashboards di mfitiase attribution ho dwuma yiye. Wɔayɛ nnwinnade te sɛ Northbeam, Triple Whale, ne Rockerbox ama mmuae tẽẽ ne e-commerce nsɛm a ɛfa ho. Wɔ gyinaesi ahorow a ɛfa ɔkwan a wɔfa so yɛ adwuma ho no, attribution software yɛ adwuma yiye bere a wɔde ka MMM ne incrementality testing ho sen sɛ wɔde bedi dwuma wɔ baabi a ɛyɛ soronko.
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Awiei
Asɛnnennen a ɛwɔ aguadi mu nneɛma a wɔde ma ho no nyɛ ɔhaw a softwea a eye kyɛn so nkutoo di ho dwuma. Ɛyɛ nhyehyɛe anohyeto a ɛfa nea attribution betumi ayɛ ho. Boa a wɔde ma ne nea ɛde ba ho adanse yɛ nneɛma ahorow, na sɛ wɔde frafra mu a, ɛde sikasɛm ho gyinaesi ahorow a ɛpɛ ahwehwɛde a wɔkyere sen ahwehwɛde a wɔbɔ no ba.
Ahyehyɛde ahorow a ɛrenya nkɔso kɛse adi eyi ho dwuma denam susuw nhyehyɛe ahorow a wɔayɛ a adwinnade biara di dwuma a wɔakyerɛkyerɛ mu so: platform data ma adwumayɛ kwankyerɛ, attribution ma tacticalnsɛnkyerɛnne, MMM ma ɔkwan a wɔfa so kyekyɛ, ne nkɔanim sɔhwɛ a wɔde hwehwɛ nea ɛde ba no mu den. Afã a edi hɔ wɔ saa nsɛm a ɛtoatoa so yi mu no hwehwɛ sɛnea aguadi akannifo de saa nsɛnkyerɛnne yi bom di dwuma de si baabi a ɛsɛ sɛ dɔla a edi hɔ a wɔde bɛto mu no kɔ no mu.
Sɛ wopɛ sɛ wokɔ akyiri wɔ baabi a attribution sɛe ansa na woakɔ saa afã no so a, saa mpaapaemu yi a ɛfa marketing attribution blind spots ho yi kata huammɔdi akwan pɔtee no so kɔ akyiri. Sɛ wopɛ adwene a ɛtrɛw wɔ sɛnea wɔde susudua bɛbata sika a wonya ho gyinaesi ho a, saa akwankyerɛ yi a ɛfa dijitaal aguadi attribution ho yi yɛ nhwɛso a mfaso wɔ so.
