[{"data":1,"prerenderedAt":565},["ShallowReactive",2],{"site-chrome-vn":3,"site-chrome-en":-1,"site-chrome-th":-1,"blog-categories-vn":287,"glossary-term-vn":307,"article-glossary-vn-\u002Fglossary\u002Fattribution-inflation":320,"og-copy-vn":501,"og-posts-vn-none":543,"glossary-related-vn-attribution-inflation":544},{"id":4,"extension":5,"footer":6,"header":116,"indexFacets":202,"meta":243,"notFound":244,"stem":250,"ui":251,"__hash__":286},"chrome_en\u002Fen\u002Fchrome.yml","yml",{"columns":7,"legal":83,"quote":84,"askAi":87,"socials":103},[8,29,47,65],{"label":9,"children":10},"Product",[11,14,17,20,23,26],{"label":12,"to":13},"Ads optimization","\u002Fvn\u002Fblog\u002Fads-optimization",{"label":15,"to":16},"Unit economics","\u002Fvn\u002Fblog\u002Fprofit-margin",{"label":18,"to":19},"Agentic KAM","https:\u002F\u002Flearn.dataglasslabs.com\u002Fdocs\u002Fai-and-api\u002Fassistants",{"label":21,"to":22},"One-click deploy","https:\u002F\u002Flearn.dataglasslabs.com\u002Fdocs\u002Fgrowth\u002Factions\u002Faction-flow",{"label":24,"to":25},"Integrations","https:\u002F\u002Flearn.dataglasslabs.com\u002Fdocs\u002Foverview\u002Fget-started\u002Fsupported-platforms",{"label":27,"to":28},"Pricing","\u002Fvn\u002Fpricing",{"label":30,"children":31},"Resources",[32,35,38,41,44],{"label":33,"to":34},"Blog","\u002Fvn\u002Fblog",{"label":36,"to":37},"Customer stories","\u002Fvn\u002Fcustomers",{"label":39,"to":40},"Research","\u002Fvn\u002Fresearch",{"label":42,"to":43},"Glossary","\u002Fvn\u002Fglossary",{"label":45,"to":46},"Docs","https:\u002F\u002Flearn.dataglasslabs.com\u002Fdocs",{"label":48,"children":49},"Company",[50,53,56,59,62],{"label":51,"to":52},"About","\u002Fvn\u002Fcompany",{"label":54,"to":55},"Contact","\u002Fvn\u002Fcontact",{"label":57,"to":58},"Book a demo","\u002Fvn\u002Fbook-a-demo",{"label":60,"to":61},"Privacy","\u002Fvn\u002Flegal\u002Fprivacy",{"label":63,"to":64},"Terms","\u002Fvn\u002Flegal\u002Fterms",{"label":66,"children":67},"DataGlass for",[68,71,74,77,80],{"label":69,"to":70},"Agencies","\u002Fvn\u002Ffor\u002Fagencies",{"label":72,"to":73},"Brands","\u002Fvn\u002Ffor\u002Fbrands",{"label":75,"to":76},"Planners and optimizers","\u002Fvn\u002Ffor\u002Fplanners",{"label":78,"to":79},"Managers and analysts","\u002Fvn\u002Ffor\u002Fmanagers",{"label":81,"to":82},"Sellers and shop owners","\u002Fvn\u002Ffor\u002Fsellers","© 2026 DataGlass Labs. 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Shop",{"key":219,"label":220,"options":221},"format","Format",[222,225,228,231,234,237,240],{"value":223,"label":224},"guide","Guide",{"value":226,"label":227},"article","Article",{"value":229,"label":230},"comparison","Comparison",{"value":232,"label":233},"calculator","Calculator",{"value":235,"label":236},"paper","Paper",{"value":238,"label":239},"survey","Survey",{"value":241,"label":242},"dataset","Dataset",{},{"title":245,"description":246,"cta":247,"errorTitle":249},"Page not found","The page may have moved, or it never existed.",{"label":248,"to":119},"Back to home","Something went wrong","en\u002Fchrome",{"previous":252,"next":253,"pause":254,"play":255,"goTo":256,"expand":257,"close":258,"readMore":259,"learnMore":260,"seeAll":261,"menu":262,"back":263,"share":264,"shareOn":265,"copyLink":266,"copied":267,"onThisPage":268,"minRead":269,"sources":270,"viewMore":271,"searchPosts":272,"allPosts":273,"all":274,"filters":275,"search":276,"searchClose":277,"searchPlaceholder":278,"searchSuggested":279,"searchResults":280,"searchNoResults":281,"searchLoading":282,"relatedTitle":283,"mainNav":284,"footerNav":285},"Previous","Next","Pause","Play","Go to item","Expand","Close","Read more","Learn more","See all","Menu","Back","Share","Share on","Copy link","Link copied","On this page","{n} min read","Sources","View more","Search posts","All posts","All","Filters","Search","Close search","Search DataGlass","Suggested","Results","No results for","Loading","More to discover","Main","Footer","HZQSEulcpFGY0nWnUTNYhG2n06nO97XnUBc_pEVOFxo",[288,291,293,296,299,302,305],{"slug":289,"label":290},"profit","Profit",{"slug":292,"label":27},"pricing",{"slug":294,"label":295},"ads","Ads",{"slug":297,"label":298},"operations","Operations",{"slug":300,"label":301},"market","Market",{"slug":303,"label":304},"data-science","Data Science",{"slug":306,"label":48},"company",{"id":308,"cta":309,"extension":5,"glossary":42,"meta":315,"related":316,"stem":317,"whatIs":318,"__hash__":319},"glossaryTerm_en\u002Fen\u002Fglossary-term.yml",{"headline":310,"description":311,"primaryCta":312,"secondaryCta":313},"Put this number to work on your own shops","See it computed from your orders, fees and ads, with a demo on your data.",{"label":57,"to":58},{"label":314,"to":28},"See pricing",{},"Related terms","en\u002Fglossary-term","What is {term}?","lxyPsyClqTqVxZhmfDihDsuAz7Al3t22gFHKeu2Lza8",{"entry":321,"document":359,"toc":492},{"id":322,"title":323,"aliases":324,"category":294,"cta":325,"date":328,"description":329,"draft":330,"draftReason":324,"extension":331,"meta":332,"navigation":324,"ogImage":324,"path":333,"readMinutes":334,"related":335,"robots":324,"schemaOrg":324,"seo":341,"short":327,"sitemap":343,"sources":344,"stem":357,"updatedAt":324,"__hash__":358},"glossary_en\u002Fglossary\u002Fattribution-inflation.md","Attribution inflation",null,{"title":326,"body":327},"See it on your own shop, free.","The ratio of GMV a platform credits to your ads to the GMV your ads actually caused. It is a multiplier on every ad target you set, and it cannot be read off any dashboard.","2026-10-23","Attribution inflation is credited GMV ÷ incremental GMV. It multiplies the Target ROAS you should set, and only an experiment can measure it.",false,"md",{},"\u002Fglossary\u002Fattribution-inflation",4,[336,337,338,339,340],"incrementality","true-roas","attribution-window","break-even-roas","marginal-roas",{"title":342,"description":329},"What is Attribution Inflation? | DataGlass",{"loc":333},[345,349,353],{"title":346,"url":347,"note":348},"Blake, T., Nosko, C. & Tadelis, S. (2015) — Consumer Heterogeneity and Paid Search Effectiveness: A Large-Scale Field Experiment, Econometrica 83(1)","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.3982\u002FECTA12423","The eBay experiment finding returns to branded paid search close to zero against a randomised control — the canonical demonstration that attributed return and incremental return can differ by an order of magnitude.",{"title":350,"url":351,"note":352},"Lewis, R. A. & Rao, J. M. (2015) — The Unfavorable Economics of Measuring the Returns to Advertising, Quarterly Journal of Economics 130(4)","https:\u002F\u002Facademic.oup.com\u002Fqje\u002Farticle\u002F130\u002F4\u002F1941\u002F1916546","On the statistical power required to detect true advertising returns — why incrementality is rarely measured rather than rarely present.",{"title":354,"url":355,"note":356},"DataGlass Labs — CTR & Target ROAS under CPM and CPC (technical note)","\u002Fassets\u002Fpapers\u002FDataGlass_CTR_and_Target_ROAS_under_CPM_and_CPC_2026-08-18.pdf","Lemma 4 states the attributed-versus-incremental decomposition and Lemma 1d shows attribution inflation entering as a haircut on the contribution rate, hence as a multiplier on the profit-maximizing target.","glossary\u002Fattribution-inflation","aM_nXJLlCqQ2T53kMPJ4x6QMIqrt7A1XFzhlAmEios0",{"frontmatter":360,"meta":368,"nodes":397},{"title":323,"description":329,"seo":361,"short":327,"category":294,"related":362,"date":328,"sources":363,"cta":367,"readMinutes":334},{"title":342,"description":329},[336,337,338,339,340],[364,365,366],{"title":346,"url":347,"note":348},{"title":350,"url":351,"note":352},{"title":354,"url":355,"note":356},{"title":326,"body":327},{"toc":369},{"title":370,"searchDepth":371,"depth":371,"links":372},"",2,[373,376,379,382,385,388,391,394],{"id":374,"depth":371,"text":375},"formula","Formula",{"id":377,"depth":371,"text":378},"example","Example",{"id":380,"depth":371,"text":381},"how-do-you-measure-attribution-inflation","How do you measure attribution inflation?",{"id":383,"depth":371,"text":384},"how-large-is-attribution-inflation-in-practice","How large is attribution inflation in practice?",{"id":386,"depth":371,"text":387},"why-does-attribution-inflation-raise-the-target-roas-i-should-set","Why does attribution inflation raise the Target ROAS I should set?",{"id":389,"depth":371,"text":390},"why-it-matters","Why it matters",{"id":392,"depth":371,"text":393},"in-dataglass","In DataGlass",{"id":395,"depth":371,"text":396},"where-this-shows-up","Where this shows up",[398,402,405,414,416,421,423,426,428,431,433,436,438,441,446,448,451,453],[399,400,401],"p",{},"Attribution inflation is the ratio of the GMV a marketplace credits to your advertising to the GMV your advertising actually caused. It is at least one by construction — a platform’s attribution rule credits sales within a window that the ad may or may not have produced, and it has no mechanism to subtract the ones that would have happened anyway. The gap matters because it does not merely make reported returns look better; it changes the correct decision. Working through the profit arithmetic, attribution inflation enters as a haircut on your contribution rate, which means it multiplies the Target ROAS at which advertising stops paying. An inflation factor of 1.4 raises the correct target by 40% before any other adjustment.",[403,404,375],"h2",{"id":374},[406,407,409],"pre",{"language":408},"text",[410,411,413],"code",{"class":412},"language-text","attribution inflation a = attributed GMV \u002F incremental GMV,  a >= 1\n\neffective contribution rate  =  contribution rate \u002F a\nbreak-even ROAS              =  a \u002F contribution rate\n\nExample: 20% contribution, a = 1.4\n  naive break-even  = 1 \u002F 0.20      = 5.00\n  true break-even   = 1.4 \u002F 0.20    = 7.00",[403,415,378],{"id":377},[406,417,418],{"language":408},[410,419,420],{"class":412},"A Shopee campaign reports ฿100,000 of attributed GMV on ฿16,000 of spend.\nA holdout test shows ฿71,400 would have arrived without the ads running.\n\nincremental GMV = 100,000 − 71,400 = ฿28,600\nattribution inflation = 100,000 \u002F 28,600 = 3.50\n\nReported ROAS = 6.25.  Incremental ROAS = 28,600 \u002F 16,000 = 1.79.\nAt a 20% contribution rate the campaign needed 5.00 incremental to\nbreak even, so it lost money while reporting a 25% overshoot.",[403,422,381],{"id":380},[399,424,425],{},"There are exactly two credible methods and neither uses platform reporting. A holdout randomises which users or which listings are eligible to see the ad and compares outcomes between arms. A geo test switches campaigns off in some regions and on in others, then compares the difference in sales against the difference in spend. Both are experiments, and both cost money in the arm that is switched off, which is why they are rare. What cannot substitute for either is a last-click report, an attribution-window comparison or a before-and-after read: none of them contains a counterfactual, so none of them can separate demand the ad created from demand it merely intercepted.",[403,427,384],{"id":383},[399,429,430],{},"Large enough to invert conclusions, and largest exactly where campaigns look best. The eBay field experiment run by Blake, Nosko and Tadelis found that returns to branded paid search were statistically indistinguishable from zero once measured against a randomised control — customers searching the brand name converted whether or not the ad ran, so essentially the whole of the credited revenue was inflation. Lewis and Rao showed separately that detecting true advertising returns requires far more statistical power than typical campaigns can supply, which is why attributed figures dominate the record. On a marketplace the same mechanism operates through broad attribution: credit flows to the ad for purchases of other products from the same shop, and for buyers who were already in a checkout flow.",[403,432,387],{"id":386},[399,434,435],{},"Because the target is enforced on attributed GMV, and the profit is earned on incremental GMV. If a baht of credited revenue is only worth 1 ÷ a baht of real revenue to you, then your effective contribution rate is your true contribution rate divided by a, and break-even ROAS — which is one over the contribution rate — rises by a factor of a. That is a multiplicative correction and it compounds with the other two adjustments a profit-maximizing target requires: the markup for the fact that buying more clicks reprices the clicks you already had, and the markup for the gap between your average click and your marginal one.",[403,437,390],{"id":389},[399,439,440],{},"Attribution inflation is the reason two well-run shops can follow identical ad advice and get opposite results. One sells a considered purchase to cold traffic and creates most of the demand it is credited with; the other sells a repeat-purchase staple to an audience already searching for it and is credited with demand it merely intercepted. Nothing on either dashboard distinguishes them, and the correct Target ROAS for the second can be several times the first.",[442,443,445],"blog-callout",{"title":444},"Common mistake","Assuming a shorter attribution window fixes it. Narrowing a 7-day window to 1 day removes some credited sales, but it removes them by recency rather than by causality — a genuinely ad-driven purchase made on day three is dropped while an intercepted purchase made ten minutes later is kept. Window length is a reporting choice; incrementality is an experimental question.",[403,447,393],{"id":392},[399,449,450],{},"DataGlass keeps attributed and incrementality-adjusted views distinct rather than blending them, so a recommendation states which basis it was computed on. Where a shop has run a holdout or a geo test, the measured factor is applied to the targets that depend on it; where it has not, the attributed basis is used and labelled as such rather than presented as profit.",[403,452,396],{"id":395},[454,455,456,462,468,474,480,486],"ul",{},[457,458,459],"li",{},[460,461,12],"a",{"href":13},[457,463,464],{},[460,465,467],{"href":466},"\u002Fvn\u002Fblog\u002Foptimize-ecommerce-profit","Optimize e-commerce profit",[457,469,470],{},[460,471,473],{"href":472},"\u002Fvn\u002Fblog\u002Fincrease-shopee-profit","Increase Shopee profit",[457,475,476],{},[460,477,479],{"href":478},"\u002Fvn\u002Fblog\u002Fprofit-maximizing-target-roas","Break-even ROAS is the zero line: the Target ROAS that actually maximizes Shopee profit",[457,481,482],{},[460,483,485],{"href":484},"\u002Fvn\u002Fblog\u002Fplatform-roas-vs-true-roas","Platform ROAS vs true ROAS: why your ads make sales but no profit",[457,487,488],{},[460,489,491],{"href":490},"\u002Fvn\u002Fblog\u002Fhow-to-calculate-true-shopee-roas","How to calculate true Shopee ROAS for profit",[493,494,495,496,497,498,499,500],{"id":374,"depth":371,"text":375},{"id":377,"depth":371,"text":378},{"id":380,"depth":371,"text":381},{"id":383,"depth":371,"text":384},{"id":386,"depth":371,"text":387},{"id":389,"depth":371,"text":390},{"id":392,"depth":371,"text":393},{"id":395,"depth":371,"text":396},{"id":502,"categories":503,"domain":510,"extension":5,"fallback":511,"home":514,"industry":523,"meta":528,"pricing":529,"stem":541,"__hash__":542},"og_en\u002Fen\u002Fog.yml",[504,505,506,507,508,509],{"slug":289,"label":290},{"slug":292,"label":27},{"slug":294,"label":295},{"slug":297,"label":298},{"slug":303,"label":304},{"slug":306,"label":48},"dataglasslabs.com",{"title":512,"description":513},"DataGlass","The decision engine for Shopee, Lazada and TikTok Shop sellers.",{"chart":515,"metrics":516},"Net profit, last 30 days",[517,520],{"label":518,"value":519},"Net profit","฿1.24M",{"label":521,"value":522},"Profit ROAS","3.4x",{"axis":524,"point":525},"Ad spend",{"label":526,"value":527},"Next baht still pays","+฿0.42",{},{"plans":530},[531,535,538],{"name":532,"price":533,"period":534},"Free","฿0","\u002Fmonth",{"name":536,"price":537,"period":534},"Standard","฿1,499",{"name":539,"price":540,"period":534},"Pro","฿2,499","en\u002Fog","63L8y4Lv8I2q3NvR26fMXU1sE9Wd7TZaz2l_tV6oZuo",[],[545,549,553,557,561],{"path":546,"title":547,"short":548,"draft":330},"\u002Fglossary\u002Fincrementality","Incrementality","The sales an ad actually caused, as opposed to the sales it was credited with. Usually a much smaller number.",{"path":550,"title":551,"short":552,"draft":330},"\u002Fglossary\u002Ftrue-roas","True ROAS","Advertising return after platform fees, COGS, vouchers, and fulfillment — the profit-aware number platform dashboards rarely show.",{"path":554,"title":555,"short":556,"draft":330},"\u002Fglossary\u002Fattribution-window","Attribution window","The period after a click or view in which a resulting order still counts as the ad’s. Change the window and the same campaign changes ROAS.",{"path":558,"title":559,"short":560,"draft":330},"\u002Fglossary\u002Fbreak-even-roas","Break-even ROAS","The platform ROAS at which a campaign returns exactly what it costs. One divided by your contribution rate — the single number every ad target should be built on.",{"path":562,"title":563,"short":564,"draft":330},"\u002Fglossary\u002Fmarginal-roas","Marginal ROAS","The return on the next baht of ad spend, not the average across all of it. The number that decides whether to scale a campaign.",1792784977804]