[{"data":1,"prerenderedAt":559},["ShallowReactive",2],{"site-chrome-ph":3,"site-chrome-en":-1,"site-chrome-th":-1,"glossary-term-ph":287,"blog-categories-ph":300,"article-glossary-ph-\u002Fglossary\u002Fincrementality":320,"og-copy-ph":491,"og-posts-ph-none":533,"glossary-related-ph-incrementality":534},{"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","\u002Fph\u002Fblog\u002Fads-optimization",{"label":15,"to":16},"Unit economics","\u002Fph\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","\u002Fph\u002Fpricing",{"label":30,"children":31},"Resources",[32,35,38,41,44],{"label":33,"to":34},"Blog","\u002Fph\u002Fblog",{"label":36,"to":37},"Customer stories","\u002Fph\u002Fcustomers",{"label":39,"to":40},"Research","\u002Fph\u002Fresearch",{"label":42,"to":43},"Glossary","\u002Fph\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","\u002Fph\u002Fcompany",{"label":54,"to":55},"Contact","\u002Fph\u002Fcontact",{"label":57,"to":58},"Book a demo","\u002Fph\u002Fbook-a-demo",{"label":60,"to":61},"Privacy","\u002Fph\u002Flegal\u002Fprivacy",{"label":63,"to":64},"Terms","\u002Fph\u002Flegal\u002Fterms",{"label":66,"children":67},"DataGlass for",[68,71,74,77,80],{"label":69,"to":70},"Agencies","\u002Fph\u002Ffor\u002Fagencies",{"label":72,"to":73},"Brands","\u002Fph\u002Ffor\u002Fbrands",{"label":75,"to":76},"Planners and optimizers","\u002Fph\u002Ffor\u002Fplanners",{"label":78,"to":79},"Managers and analysts","\u002Fph\u002Ffor\u002Fmanagers",{"label":81,"to":82},"Sellers and shop owners","\u002Fph\u002Ffor\u002Fsellers","© 2026 DataGlass Labs. All rights reserved.",{"text":85,"caption":86},"Measure what you keep, and then grow what you measure.","The DataGlass team",{"label":88,"prompt":89,"assistants":90},"Ask AI about DataGlass","I want to understand DataGlass, a decision engine for Shopee, Lazada and TikTok Shop sellers that optimizes ads, pricing and promotions for true profit. Use web search to read their website at https:\u002F\u002Fwww.dataglasslabs.com (with an agent-readable summary at https:\u002F\u002Fwww.dataglasslabs.com\u002Fllms.txt), then explain what DataGlass does, who it is for and how it differs from an analytics dashboard.",[91,95,99],{"label":92,"icon":93,"url":94},"ChatGPT","chatgpt","https:\u002F\u002Fchatgpt.com\u002F?prompt={prompt}",{"label":96,"icon":97,"url":98},"Claude","claude","https:\u002F\u002Fclaude.ai\u002Fnew?q={prompt}",{"label":100,"icon":101,"url":102},"Perplexity","perplexity","https:\u002F\u002Fwww.perplexity.ai\u002Fsearch?q={prompt}",[104,108,112],{"label":105,"icon":106,"to":107},"LinkedIn","i-lucide-linkedin","https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fdataglass-labs",{"label":109,"icon":110,"to":111},"Facebook","i-lucide-facebook","https:\u002F\u002Fwww.facebook.com\u002Fdataglasslabsth",{"label":113,"icon":114,"to":115},"Instagram","i-lucide-instagram","https:\u002F\u002Fwww.instagram.com\u002Fdataglasslabs\u002F",{"links":117,"cta":196,"secondaryCta":199},[118,152,191,193,194,195],{"label":9,"to":119,"menu":120},"\u002Fph",{"layout":121,"heading":122,"cards":123,"groups":135,"footer":142},"cards","What DataGlass does",[124,128,132],{"label":125,"description":126,"to":13,"ui":127},"True profit ads optimization","Budgets, pacing and Target ROAS tuned for profit.","budget-tomorrow",{"label":129,"description":130,"to":16,"ui":131},"Unit economics breakdown","What you actually earn from every sale.","unit-economics",{"label":18,"description":133,"to":19,"motif":134},"An AI agent that knows your shops.",true,[136],{"label":137,"size":138,"links":139},"More","secondary",[140,141],{"label":21,"to":22},{"label":24,"to":25},[143,148],{"lead":144,"label":145,"icon":146,"to":147},"DataGlass v.2.6.","See what's new","i-lucide-box","https:\u002F\u002Flearn.dataglasslabs.com\u002Freleases",{"lead":149,"label":150,"icon":151,"to":37},"Read our","customer stories","i-lucide-book-open",{"label":30,"to":34,"menu":153},{"layout":154,"groups":155},"directory",[156,164,170,182],{"label":66,"size":157,"links":158},"primary",[159,160,161,162,163],{"label":69,"to":70},{"label":72,"to":73},{"label":75,"to":76},{"label":78,"to":79},{"label":81,"to":82},{"label":165,"size":157,"links":166},"Insights",[167,168,169],{"label":33,"to":34},{"label":36,"to":37},{"label":39,"to":40},{"label":171,"size":157,"links":172},"Learn",[173,174,177,180],{"label":45,"to":46},{"label":175,"to":176},"Use cases","https:\u002F\u002Flearn.dataglasslabs.com\u002Fuse-cases",{"label":178,"to":179},"Training","https:\u002F\u002Flearn.dataglasslabs.com\u002Ftraining",{"label":181,"to":147},"Changelog",{"label":183,"size":138,"links":184},"Support",[185,186,189],{"label":42,"to":43},{"label":187,"to":188},"Brand kit","\u002Fph\u002Fbrand",{"label":190,"to":55},"Contact us",{"label":192,"to":37},"Customers",{"label":27,"to":28},{"label":48,"to":52},{"label":54,"to":55},{"label":197,"to":198},"Start for free","https:\u002F\u002Fapp.dataglasslabs.com\u002Fsign-up",{"label":200,"to":201},"Sign in","https:\u002F\u002Fapp.dataglasslabs.com\u002Fsign-in",{"clear":203,"groups":204},"Clear filters",[205,218],{"key":206,"label":207,"options":208},"platforms","Platform",[209,212,215],{"value":210,"label":211},"shopee","Shopee",{"value":213,"label":214},"lazada","Lazada",{"value":216,"label":217},"tiktok","TikTok 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",{"id":288,"cta":289,"extension":5,"glossary":42,"meta":295,"related":296,"stem":297,"whatIs":298,"__hash__":299},"glossaryTerm_en\u002Fen\u002Fglossary-term.yml",{"headline":290,"description":291,"primaryCta":292,"secondaryCta":293},"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":294,"to":28},"See pricing",{},"Related terms","en\u002Fglossary-term","What is {term}?","lxyPsyClqTqVxZhmfDihDsuAz7Al3t22gFHKeu2Lza8",[301,304,306,309,312,315,318],{"slug":302,"label":303},"profit","Profit",{"slug":305,"label":27},"pricing",{"slug":307,"label":308},"ads","Ads",{"slug":310,"label":311},"operations","Operations",{"slug":313,"label":314},"market","Market",{"slug":316,"label":317},"data-science","Data Science",{"slug":319,"label":48},"company",{"entry":321,"document":356,"toc":482},{"id":322,"title":323,"aliases":324,"category":307,"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":342,"short":327,"sitemap":344,"sources":345,"stem":354,"updatedAt":324,"__hash__":355},"glossary_en\u002Fglossary\u002Fincrementality.md","Incrementality",null,{"title":326,"body":327},"See it on your own shop, free.","The sales an ad actually caused, as opposed to the sales it was credited with. Usually a much smaller number.","2026-10-23","Incrementality is the share of attributed sales that would not have happened without the ad. How holdout tests measure it, and why brand keywords rarely pass.",false,"md",{},"\u002Fglossary\u002Fincrementality",3,[336,337,338,339,340,341],"attribution-window","true-roas","ad-waste","marginal-roas","recommendation-confidence","attribution-inflation",{"title":343,"description":329},"What is Incrementality in Advertising? | DataGlass",{"loc":333},[346,350],{"title":347,"url":348,"note":349},"Blake, Nosko & Tadelis — Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment (NBER Working Paper 20171)","https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw20171","The eBay experiment that found returns to branded paid search close to zero once the ads were switched off in randomised markets.",{"title":351,"url":352,"note":353},"Lewis & Rao — Measuring the Effects of Advertising: The Digital Frontier (NBER Working Paper 19520)","https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw19520","Documents how large sales volatility is relative to typical advertising effects, and therefore how much data an honest incrementality test requires.","glossary\u002Fincrementality","UOa7wy34Vw_O1oItOaws7GwUy2KxRT9yc2XnTbyV7C8",{"frontmatter":357,"meta":364,"nodes":393},{"title":323,"description":329,"seo":358,"short":327,"category":307,"related":359,"date":328,"sources":360,"cta":363,"readMinutes":334},{"title":343,"description":329},[336,337,338,339,340,341],[361,362],{"title":347,"url":348,"note":349},{"title":351,"url":352,"note":353},{"title":326,"body":327},{"toc":365},{"title":366,"searchDepth":367,"depth":367,"links":368},"",2,[369,372,375,378,381,384,387,390],{"id":370,"depth":367,"text":371},"formula","Formula",{"id":373,"depth":367,"text":374},"example","Example",{"id":376,"depth":367,"text":377},"how-do-you-measure-incrementality","How do you measure incrementality?",{"id":379,"depth":367,"text":380},"which-campaigns-are-least-incremental","Which campaigns are least incremental?",{"id":382,"depth":367,"text":383},"what-can-you-do-without-a-formal-test","What can you do without a formal test?",{"id":385,"depth":367,"text":386},"why-it-matters","Why it matters",{"id":388,"depth":367,"text":389},"in-dataglass","In DataGlass",{"id":391,"depth":367,"text":392},"where-this-shows-up","Where this shows up",[394,398,401,410,412,417,419,422,424,427,429,432,434,437,442,444,447,449],[395,396,397],"p",{},"Incrementality is the portion of attributed sales that would not have occurred without the advertising — the causal effect, as opposed to the credit. An ad that appears in front of a shopper who was already going to buy generates attributed revenue and zero incremental revenue. Because attribution assigns credit by proximity in time rather than by causation, reported ROAS is an upper bound on true causal return, and the gap between them is often large. Measuring incrementality requires withholding the ad from a comparable group and comparing outcomes; nothing in a standard ad report can substitute for that comparison.",[399,400,371],"h2",{"id":370},[402,403,405],"pre",{"language":404},"text",[406,407,409],"code",{"class":408},"language-text","Incremental sales = Sales(exposed) − Sales(withheld)\nIncremental ROAS = Incremental sales \u002F Ad spend\nIncrementality rate = Incremental sales \u002F Attributed sales",[399,411,374],{"id":373},[402,413,414],{"language":404},[406,415,416],{"class":408},"A campaign reports ฿300,000 attributed revenue on ฿50,000 spend — ROAS 6.0.\nA geographic holdout shows withheld regions ran at 82% of exposed regions.\n\nIncremental revenue = 300,000 × 0.18 = ฿54,000\nIncremental ROAS = 54,000 \u002F 50,000 = 1.08\nAt a 20% contribution rate, break-even is 5.0. The campaign is deeply\nunprofitable on a causal basis while reporting a ROAS of 6.",[399,418,377],{"id":376},[395,420,421],{},"By withholding. The credible designs all rest on the same idea: create a group that does not see the ad, keep it otherwise comparable, and measure the difference. Geographic holdouts split regions; time-based tests alternate on and off periods on a fixed schedule; audience holdouts, where a platform supports them, randomise at the user level. The hard part is statistical power rather than mechanics. Order counts on a single marketplace campaign are small and volatile, so a test needs either a large effect or a long run to distinguish a real lift from ordinary week-to-week variance — which is precisely why so few sellers ever run one, and why the intuition-based alternative persists.",[399,423,380],{"id":379},[395,425,426],{},"Brand and exact-model-number search, consistently. In a large field experiment on eBay, Blake, Nosko and Tadelis found that returns to branded paid search were close to zero — shoppers who searched the brand overwhelmingly arrived anyway through organic results when the ads were switched off. The marketplace analogue is a shopper searching your exact product name and clicking the sponsored placement that sits above the organic one you would have won regardless. Retargeting an existing cart is the second usual suspect. Broad discovery terms and genuinely new-audience placements sit at the other end: lower reported ROAS, far higher incrementality.",[399,428,383],{"id":382},[395,430,431],{},"Several cheap approximations catch the worst cases. Compare attributed revenue growth against total shop revenue growth: if attributed revenue rises while the shop total is flat, the campaign is reallocating credit rather than creating demand. Watch what happens to organic orders on a SKU when its campaign scales — a fall that mirrors the ad-driven rise is cannibalisation in plain sight. And pause a campaign deliberately for a defined period and read the shop-level effect rather than the campaign-level one. None of these is a controlled experiment, and all of them are better than assuming the attributed number is causal.",[399,433,386],{"id":385},[395,435,436],{},"Every budget decision made from attributed ROAS implicitly assumes incrementality of one hundred percent. Measured incrementality on mature marketplace accounts is frequently a fraction of that, which means a campaign clearing break-even on reported numbers can be losing money on real ones. This is the largest single gap between what ad reporting says and what a shop’s bank balance does, and it is invisible without deliberately withholding something.",[438,439,441],"blog-callout",{"title":440},"Common mistake","Treating a strong before-and-after result as an incrementality test. Turning a campaign on during a promotional period and observing more sales confounds the ad with the promotion, the season, and the platform’s own traffic. Without a comparable withheld group measured over the same period, a before-and-after comparison measures the calendar.",[399,443,389],{"id":388},[395,445,446],{},"DataGlass compares advertised products against a control set of the seller’s own comparable, unadvertised products over the same window, and reports the difference as an observed gap rather than as a proven causal lift. Where the comparison is too thin to be informative, the surface says so instead of producing a number.",[399,448,392],{"id":391},[450,451,452,458,464,470,476],"ul",{},[453,454,455],"li",{},[456,457,12],"a",{"href":13},[453,459,460],{},[456,461,463],{"href":462},"\u002Fph\u002Fblog\u002Fincrease-shopee-profit","Increase Shopee profit",[453,465,466],{},[456,467,469],{"href":468},"\u002Fph\u002Fblog\u002Fplatform-roas-vs-true-roas","Platform ROAS vs true ROAS: why your ads make sales but no profit",[453,471,472],{},[456,473,475],{"href":474},"\u002Fph\u002Fblog\u002Fhow-ecommerce-decision-engines-work","E-commerce Decision Engine: How Marketplace Sellers Turn Data Into Profit Recommendations",[453,477,478],{},[456,479,481],{"href":480},"\u002Fph\u002Fblog\u002Fprofit-maximizing-target-roas","Break-even ROAS is the zero line: the Target ROAS that actually maximizes Shopee profit",[483,484,485,486,487,488,489,490],{"id":370,"depth":367,"text":371},{"id":373,"depth":367,"text":374},{"id":376,"depth":367,"text":377},{"id":379,"depth":367,"text":380},{"id":382,"depth":367,"text":383},{"id":385,"depth":367,"text":386},{"id":388,"depth":367,"text":389},{"id":391,"depth":367,"text":392},{"id":492,"categories":493,"domain":500,"extension":5,"fallback":501,"home":504,"industry":513,"meta":518,"pricing":519,"stem":531,"__hash__":532},"og_en\u002Fen\u002Fog.yml",[494,495,496,497,498,499],{"slug":302,"label":303},{"slug":305,"label":27},{"slug":307,"label":308},{"slug":310,"label":311},{"slug":316,"label":317},{"slug":319,"label":48},"dataglasslabs.com",{"title":502,"description":503},"DataGlass","The decision engine for Shopee, Lazada and TikTok Shop sellers.",{"chart":505,"metrics":506},"Net profit, last 30 days",[507,510],{"label":508,"value":509},"Net profit","฿1.24M",{"label":511,"value":512},"Profit ROAS","3.4x",{"axis":514,"point":515},"Ad spend",{"label":516,"value":517},"Next baht still pays","+฿0.42",{},{"plans":520},[521,525,528],{"name":522,"price":523,"period":524},"Free","฿0","\u002Fmonth",{"name":526,"price":527,"period":524},"Standard","฿1,499",{"name":529,"price":530,"period":524},"Pro","฿2,499","en\u002Fog","63L8y4Lv8I2q3NvR26fMXU1sE9Wd7TZaz2l_tV6oZuo",[],[535,539,543,547,551,555],{"path":536,"title":537,"short":538,"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":540,"title":541,"short":542,"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":544,"title":545,"short":546,"draft":330},"\u002Fglossary\u002Fad-waste","Ad waste","Ad spend that did not pay back after fees, COGS, and fulfillment — the unprofitable share of a marketplace ad budget.",{"path":548,"title":549,"short":550,"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.",{"path":552,"title":553,"short":554,"draft":330},"\u002Fglossary\u002Frecommendation-confidence","Recommendation confidence","How often a proposed change beats doing nothing. A comparison against the status quo, not a score for the campaign.",{"path":556,"title":557,"short":558,"draft":330},"\u002Fglossary\u002Fattribution-inflation","Attribution inflation","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.",1792785034325]