[{"data":1,"prerenderedAt":1568},["ShallowReactive",2],{"site-chrome-ph":3,"site-chrome-en":-1,"site-chrome-th":-1,"blog-page-ph":287,"blog-categories-ph":345,"blog-posts-ph":365,"article-blog-ph-\u002Fblog\u002Fprofit-maximizing-target-roas":722,"og-copy-ph":1525,"og-posts-ph-none":1567},{"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 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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,"title":33,"blog":289,"body":327,"customers":334,"description":335,"draft":336,"draftReason":334,"extension":337,"glossary":334,"industry":334,"meta":338,"navigation":336,"ogImage":339,"path":340,"research":334,"robots":334,"schemaOrg":334,"seo":341,"showcase":336,"sitemap":342,"stem":343,"__hash__":344},"pages_en\u002Fblog.md",{"emptyLabel":290,"labels":291,"indexLabels":296,"listenLabels":307,"indexCta":320,"ctaButtons":323},"No posts match yet. Try another category or a shorter search.",{"tldr":292,"published":293,"updated":294,"related":295},"In short","Published","Updated","Keep reading",{"categories":297,"sort":298,"newest":299,"oldest":300,"grid":301,"list":302,"loadMore":303,"results":304,"result":305,"clearSearch":306},"Categories","Sort","Newest first","Oldest first","Grid view","List view","Load more","{n} results for “{q}”","1 result for “{q}”","Clear search",{"listen":308,"loading":309,"play":310,"pause":311,"back":312,"forward":313,"speed":314,"seek":315,"player":316,"close":317,"follow":318,"of":319},"Listen to article","Loading…","Play audio of this article","Pause audio","Back 15 seconds","Forward 15 seconds","Playback speed","Seek","Audio player","Pause audio and close player","Follow the text","{a} of {b}",{"headline":321,"subline":322},"Turn every post into a decision","See the numbers behind these notes for your own shops, with a demo on your data.",{"primaryCta":324,"secondaryCta":325},{"label":57,"to":58},{"label":326,"to":28},"See pricing",{"type":328,"value":329,"toc":330},"minimark",[],{"title":331,"searchDepth":332,"depth":332,"links":333},"",3,[],null,"Notes on profit, pricing and ads for sellers on Shopee, Lazada and TikTok Shop, with the numbers behind them.",false,"md",{},{"component":33},"\u002Fblog",{"title":33,"description":335},{"loc":340},"blog","VuN4_Knpp3IpkJqH-kRoXag4vgoPV2p71ZFiR-VpgzQ",[346,349,351,354,357,360,363],{"slug":347,"label":348},"profit","Profit",{"slug":350,"label":27},"pricing",{"slug":352,"label":353},"ads","Ads",{"slug":355,"label":356},"operations","Operations",{"slug":358,"label":359},"market","Market",{"slug":361,"label":362},"data-science","Data Science",{"slug":364,"label":48},"company",[366,373,379,383,388,393,397,402,407,412,418,423,428,433,438,444,450,456,462,469,475,480,486,491,496,501,506,511,517,523,528,534,540,546,551,557,563,569,574,578,583,588,594,601,607,613,619,625,631,637,643,649,655,661,667,673,679,685,690,695,700,705,710,716],{"title":367,"description":368,"category":352,"date":369,"readMinutes":370,"featured":336,"cover":334,"format":223,"platforms":371,"draft":336,"slug":372},"E-commerce ads optimization for Shopee, Lazada, and TikTok Shop, on profit","E-commerce ads optimization on profit: True ROAS, margin-based Target ROAS and nightly budget allocation for Shopee, Lazada and TikTok Shop ads, applied for you.","2026-10-23",4,[210,213,216],"ads-optimization",{"title":374,"description":375,"category":352,"date":369,"readMinutes":376,"featured":336,"cover":334,"format":232,"platforms":377,"draft":336,"slug":378},"What ROAS is break-even? Calculate break-even ROAS and your maximum ad spend per order","Break-even ROAS is 1 ÷ your contribution-margin ratio. It does not depend on how much you spend on ads. This calculator derives it from a dated Shopee Thailand fee rate — or one you type in — then tells you the number sellers actually need: the most baht one order can pay for ads before it stops making money.",9,[210],"break-even-roas-calculator",{"title":380,"description":381,"category":364,"date":369,"readMinutes":332,"featured":336,"cover":334,"format":229,"platforms":334,"draft":336,"slug":382},"DataGlass vs a marketing agency for marketplace profit","When an agency is the right hire, where the agency model has structural blind spots for marketplace profit, and how DataGlass complements an agency engagement.","dataglass-vs-agency",{"title":384,"description":385,"category":364,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":229,"platforms":334,"draft":336,"slug":387},"DataGlass vs an ERP for marketplace decision-making","When an ERP is the right tool, where it falls short for marketplace ad\u002Fpricing decisions, and how DataGlass complements (not replaces) the ERP for SEA sellers.",2,"dataglass-vs-erp",{"title":389,"description":390,"category":364,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":229,"platforms":391,"draft":336,"slug":392},"DataGlass vs Shopee Seller Center for profit-aware operations","Shopee Seller Center is essential for running a Shopee shop, but it does not show profit-aware metrics or cross-platform views. How DataGlass complements it.",[210],"dataglass-vs-shopee-seller-center",{"title":394,"description":395,"category":364,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":229,"platforms":334,"draft":336,"slug":396},"DataGlass vs spreadsheets for marketplace sellers","When spreadsheets are enough, where they break for marketplace sellers, and how DataGlass connects ads, COGS, fees, and inventory into one decision-ready view.","dataglass-vs-spreadsheet",{"title":398,"description":399,"category":355,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":223,"platforms":400,"draft":336,"slug":401},"Prevent stockouts and overstock across Shopee, Lazada, and TikTok Shop","Prevent Shopee, Lazada, and TikTok Shop stockouts with demand-aware forecasting, campaign-day planning, and cross-platform inventory unification.",[210,213,216],"inventory-stockout",{"title":403,"description":404,"category":355,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":226,"platforms":405,"draft":336,"slug":406},"Lazada integration for marketplace profit optimization","DataGlass integrates with Lazada to model true profit per SKU, detect ad waste, evaluate voucher impact, and forecast stockout risk.",[213],"lazada-integration",{"title":408,"description":409,"category":347,"date":369,"readMinutes":376,"featured":336,"cover":334,"format":232,"platforms":410,"draft":336,"slug":411},"Shopee raised its fees on 4 August 2026 — how much more is your shop paying?","The new rate table is everywhere. Your number is not. Enter last month’s GMV by category and this page computes the baht difference between the schedule effective 1 June 2026 and the one effective 4 August 2026 — including the extra hit for shops that were on the discounted-commission programme.",[210],"marketplace-fee-impact-calculator",{"title":413,"description":414,"category":347,"date":369,"readMinutes":415,"featured":336,"cover":334,"format":223,"platforms":416,"draft":336,"slug":417},"Optimize e-commerce profit across Shopee, Lazada, and TikTok Shop","How to optimize e-commerce profit across Shopee, Lazada and TikTok Shop: one objective, five decision variables, and the constraint that binds each.",7,[210,213,216],"optimize-ecommerce-profit",{"title":419,"description":420,"category":350,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":223,"platforms":421,"draft":336,"slug":422},"Price and promote without destroying margin","Optimize Shopee, Lazada, and TikTok Shop discounts, bundle deals, and campaign-day pricing on true margin. Project promotion impact before publishing.",[210,213,216],"pricing-promotions",{"title":424,"description":425,"category":347,"date":369,"readMinutes":370,"featured":336,"cover":334,"format":232,"platforms":426,"draft":336,"slug":427},"E-commerce profit calculator for Shopee sellers","Calculate true Shopee profit per SKU with a per-order breakdown covering commission fees, ads, COGS, and fulfillment.",[210],"profit-margin",{"title":429,"description":430,"category":355,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":226,"platforms":431,"draft":336,"slug":432},"Shopee integration for profit, ads, stock, and promotion decisions","DataGlass connects to Shopee to model true profit per SKU, detect wasted ad spend, and surface daily decisions. Live integration, no manual exports.",[210],"shopee-integration",{"title":434,"description":435,"category":355,"date":369,"readMinutes":386,"featured":336,"cover":334,"format":226,"platforms":436,"draft":336,"slug":437},"TikTok Shop integration for ads, inventory, and profit decisions","DataGlass integrates with TikTok Shop for True ROAS on video and live ads, margin-aware discount analysis, and stock prioritization before live events.",[216],"tiktok-shop-integration",{"title":439,"description":440,"category":352,"date":441,"readMinutes":415,"featured":336,"cover":334,"format":223,"platforms":442,"draft":336,"slug":443},"Shopee ROAS protection and TikTok ROI protection: when the credit actually protects profit","Shopee ROAS protection credits campaigns back to 80% of target, TikTok to 90%. Why that protects profit only above 1.25× break-even, with a worked example.","2026-10-08",[210,216],"roas-protection",{"title":445,"description":446,"category":352,"date":441,"readMinutes":447,"featured":336,"cover":334,"format":223,"platforms":448,"draft":336,"slug":449},"Shopee Ads optimization: set Target ROAS, budget and timing from profit","How to optimize Shopee Ads for profit: break-even ROAS per SKU, which lever binds, and CPC by hour from 9.11M Thai clicks. Worked THB example.",10,[210],"shopee-ads-optimization",{"title":451,"description":452,"category":347,"date":441,"readMinutes":453,"featured":336,"cover":334,"format":223,"platforms":454,"draft":336,"slug":455},"Shopee seller fees in the Philippines 2026: every fee, what it is charged on, and your break-even ROAS","Shopee Philippines seller fees 2026: commission, 2.24% transaction fee, 5.6% platform shipping, seller growth support and ₱5 order fee, worked on one order.",5,[210],"shopee-seller-fees",{"title":457,"description":458,"category":352,"date":441,"readMinutes":459,"featured":336,"cover":334,"format":223,"platforms":460,"draft":336,"slug":461},"TikTok Shop ads optimization: set GMV Max ROI from margin and affiliate mix","How to optimize TikTok Shop GMV Max for profit: break-even ROI after TikTok fees, the affiliate commission trap, and the 3% commission rule. Worked THB example.",8,[216],"tiktok-shop-ads-optimization",{"title":463,"description":464,"category":352,"date":465,"readMinutes":466,"featured":336,"cover":334,"format":226,"platforms":467,"draft":336,"slug":468},"How Shopee ads bidding works: what your Target ROAS and budget actually do to a bid","How Shopee ads bidding works, derived: GMV Max gives you two boxes — a Target ROAS and a daily budget — and every guide treats them as two levers. They are not. Both collapse into a single threshold on value-to-cost before any bid is placed, only one is binding at a time, and the other is doing nothing at all. Here is the derivation, the three regimes a campaign can be in, and the one-line test that tells you which of your two levers is switched off.","2026-09-01",13,[210],"how-shopee-ads-bidding-works",{"title":470,"description":471,"category":352,"date":465,"readMinutes":472,"featured":336,"cover":334,"format":223,"platforms":473,"draft":336,"slug":474},"Break-even ROAS is the zero line: the Target ROAS that actually maximizes Shopee profit","Setting your Target ROAS to your break-even ROAS does not protect profit — it targets exactly zero, for every possible volume. The target that maximizes profit is break-even marked up three times: by the demand your ads did not create, by the price your own expansion pays, and by the gap between your average click and your last one. On mid-market parameters those compound to 3.3×, moving the correct target on a 20%-margin product from 5.00 to 16.67.",12,[210],"profit-maximizing-target-roas",{"title":476,"description":477,"category":361,"date":465,"readMinutes":466,"featured":336,"cover":334,"format":241,"platforms":478,"draft":336,"slug":479},"Shopee ad benchmarks 2026: what a click and a thousand impressions actually cost","Across 333.9 million Shopee impressions and 9.11 million clicks on 80+ Thai shops, the price of a click varies 2.01× by hour of day while the rate at which people click varies 1.14×. CPC ranges THB 3.36 to THB 6.75, CPM ranges THB 89.16 to THB 200.15, and every behavioural input is nearly flat. The full table, the methodology, and the two ways a benchmark like this gets misused.",[210],"shopee-ads-benchmarks-cpc-cpm",{"title":481,"description":482,"category":364,"date":483,"readMinutes":484,"featured":336,"cover":334,"format":229,"platforms":334,"draft":336,"slug":485},"DataGlass is not an OMS — what the difference is, and when to run both","An OMS makes the order happen. DataGlass decides what to do next. The daily-question boundary, an explicit list of what DataGlass does not do, and the one seam: price.","2026-08-25",6,"dataglass-vs-oms",{"title":487,"description":488,"category":352,"date":483,"readMinutes":415,"featured":336,"cover":334,"format":223,"platforms":489,"draft":336,"slug":490},"How much should you spend on Shopee Ads? Derive it from margin, not from feel","Break-even ROAS is 1 ÷ contribution margin ratio. Worked THB example: a fashion SKU allows THB 71 per attributed order and roughly THB 994 a day. Budget is an output.",[210],"shopee-ads-budget",{"title":492,"description":493,"category":347,"date":483,"readMinutes":415,"featured":336,"cover":334,"format":223,"platforms":494,"draft":336,"slug":495},"Losing money on Shopee — the leaks a fee calculator cannot see","Shopee deducts 7.49–17.12% plus a 3.21% payment fee — the half a calculator sees. Six month-level leaks turn a 28.6% \"profit\" into 1.6%. Sourced THB waterfall.",[210],"shopee-losing-money",{"title":497,"description":498,"category":358,"date":483,"readMinutes":415,"featured":336,"cover":334,"format":229,"platforms":499,"draft":336,"slug":500},"Shopee vs TikTok Shop vs Lazada — what survives after fees and the ads it takes to sell","TikTok Shop's 11.77% fashion commission becomes a 23.2% take rate once the Commerce Growth and Transaction Fees stack. The crossover with Shopee sits near THB 3,720.",[210,213,216],"where-to-sell-shopee-tiktok-lazada",{"title":502,"description":503,"category":352,"date":504,"readMinutes":376,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":505},"CTR and Target ROAS under CPM vs CPC: what changes when marketplaces bill for impressions","Under cost-per-click billing, a better creative is worth nothing to your unit economics — it buys the same clicks from fewer impressions, and your cost per click does not move. Under cost-per-impression billing the same improvement cuts the price of every click by g\u002F(1+g). Five lemmas on the transition, and why click-through rate is the input most marketplace sellers are least prepared to have priced.","2026-08-18","ctr-target-roas-cpm-vs-cpc",{"title":507,"description":508,"category":352,"date":509,"readMinutes":447,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":510},"What is ROAS — and why a high ROAS doesn't always mean more sales or more profit","ROAS (Return on Ad Spend) is sales ÷ ad spend: spend THB 1, get THB 20 of sales back, and your ROAS is 20. But a high ROAS only means a high return per ad baht — not the highest total sales, and not real profit once you subtract COGS, platform fees, payment fees, vouchers, shipping, and returns. Here is what ROAS is, what it hides, and the number sellers should actually watch.","2026-06-01","what-is-roas",{"title":512,"description":513,"category":352,"date":514,"readMinutes":459,"featured":336,"cover":334,"format":223,"platforms":515,"draft":336,"slug":516},"What is Target ROAS? How to set it in Shopee GMV Max without burning profit","What is Target ROAS? It is the revenue-to-ad-spend ratio Shopee GMV Max optimises toward — and on the Shopee fee schedule effective 4 August 2026, a THB 1,000 product with THB 600 of COGS is left with 7.7% margin before ads, so a Target ROAS of 5 recovers THB 0.39 of contribution per THB 1.00 of ad spend while the campaign reports that it hit its target. What Target ROAS means, how to set it in GMV Max, and the two formulas — break-even ROAS and target-margin ROAS — that protect your margin.","2026-05-30",[210],"how-to-set-target-roas-gmv-max",{"title":518,"description":519,"category":352,"date":520,"readMinutes":484,"featured":336,"cover":334,"format":226,"platforms":521,"draft":336,"slug":522},"Platform ROAS vs true ROAS: why your ads make sales but no profit","A ROAS of 5 feels like a win — until you notice the platform computed it on gross attributed sales, before fees, vouchers, shipping, and COGS, and while still counting orders that were later returned or cancelled. The dashboard number is a revenue ratio wearing a profit costume. A primer on what platform ROAS hides and how true ROAS is calculated across Shopee, Lazada, and TikTok Shop.","2026-05-29",[210,213,216],"platform-roas-vs-true-roas",{"title":524,"description":525,"category":361,"date":520,"readMinutes":484,"featured":336,"cover":334,"format":226,"platforms":526,"draft":336,"slug":527},"What is e-commerce optimization?","Optimization is not a marketing word — it is an operations-research discipline: use data and math to compute the best decision against an explicit objective. Point it at a Shopee, Lazada, or TikTok Shop store and the objective is true profit, not sales. A primer on why those two numbers stopped moving together, and what optimization actually answers.",[210,213,216],"what-is-ecommerce-optimization",{"title":529,"description":530,"category":347,"date":531,"readMinutes":376,"featured":336,"cover":334,"format":223,"platforms":532,"draft":336,"slug":533},"How to cut Shopee seller costs: the supply-side margin audit","The median gap between COGS refreshes on a top-50 Shopee catalogue is 87 days, and over that window supplier and packaging drift alone compresses contribution margin by 1.5–3.0 percentage points. The eight supply-side cost lines that compound against Shopee margin between audits, ranked by recoverable magnitude, with the review cadence that holds each one.","2026-05-08",[210],"how-to-increase-shopee-profit-without-more-sales",{"title":535,"description":536,"category":352,"date":531,"readMinutes":537,"featured":336,"cover":334,"format":223,"platforms":538,"draft":336,"slug":539},"How to sell more on Shopee without burning margin","The standard Shopee growth advice — discount harder, scale ad budget, opt into every campaign — works against the operator at scale. A research note on the lever order that actually compounds Shopee revenue, and the metric that tells you whether the lift was earned or bought.",11,[210],"how-to-sell-more-on-shopee",{"title":541,"description":542,"category":347,"date":543,"readMinutes":484,"featured":336,"cover":334,"format":223,"platforms":544,"draft":336,"slug":545},"Increase profit on Shopee without selling more","Shopee ROAS hides commission, fees, vouchers and shipping. Optimize Shopee profit with the per-SKU break-even framework that replaces it — 280 Thai accounts.","2026-05-07",[210],"increase-shopee-profit",{"title":547,"description":548,"category":358,"date":543,"readMinutes":484,"featured":336,"cover":334,"format":223,"platforms":549,"draft":336,"slug":550},"Increase sales on Shopee in 2026","Shopee CPC is climbing — buying demand got pricier while repeat demand stayed flat. The three levers that compound revenue, plus a 90-day operator cadence.",[210],"increase-shopee-sales",{"title":552,"description":553,"category":361,"date":543,"readMinutes":554,"featured":336,"cover":334,"format":235,"platforms":555,"draft":336,"slug":556},"Mechanism-Aware Hierarchical Causal Elasticity Modeling for Platform E-Commerce","A methodological working paper proposing MA-HCEM: a hierarchical Bayesian, double-machine-learning, mechanism-aware estimator for SKU-level price elasticity on Shopee, Lazada, and TikTok Shop, with Bundle Deal and voucher mechanisms treated as structured price discrimination.",27,[210,213,216],"mechanism-aware-hierarchical-causal-elasticity-modeling-platform-ecommerce",{"title":558,"description":559,"category":350,"date":543,"readMinutes":560,"featured":336,"cover":334,"format":226,"platforms":561,"draft":336,"slug":562},"Price elasticity modeling for marketplace sellers — why Shopee, Lazada, and TikTok Shop pricing decisions need it","A practical guide from DataGlass Labs Research on the most under-used lever in Southeast Asian e-commerce: knowing how customers actually respond to price changes.",14,[210,213,216],"price-elasticity-modeling-shopee-lazada-tiktok-shop",{"title":564,"description":565,"category":361,"date":566,"readMinutes":567,"featured":336,"cover":334,"format":235,"platforms":334,"draft":336,"slug":568},"From Gut Feel to Posterior Inference: A Research Article on the DataGlass Decision-Intelligence System for E-Commerce Ad Budget Allocation","A rigorous public communication of the DataGlass decision-intelligence system for daily ad-budget allocation on platform-controlled marketplaces — the pre-algorithmic baseline, the analytical reasons it fails (the reported-vs-true ROAS gap and the rolling-mean bias), the Bayesian + bandits-with-knapsacks methodology, and the empirical 18–24% portfolio-profit lift with reduced reallocation frequency.","2026-05-04",20,"bayesian-budget-allocation-for-marketplace-ads",{"title":570,"description":571,"category":361,"date":566,"readMinutes":572,"featured":336,"cover":334,"format":238,"platforms":334,"draft":336,"slug":573},"Decision Intelligence for E-commerce: How Retailers Optimise Pricing, Demand Forecasting, Inventory, Promotions & Personalization","Pricing, forecasting, inventory, promotions, and personalization — a deep technical survey of the techniques large retailers use, the variants that matter, and how to deploy them",42,"decision-intelligence-playbook-for-e-commerce",{"title":575,"description":576,"category":361,"date":566,"readMinutes":332,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":577},"DataGlass Research Methodology","A single canonical reference for the sample frame, time window, attribution rules, and exclusions that calibrate every \"in our data\" claim across the blog and research surfaces.","methodology",{"title":579,"description":580,"category":361,"date":566,"readMinutes":581,"featured":336,"cover":334,"format":238,"platforms":334,"draft":336,"slug":582},"Prediction and Risk Optimization Under Uncertainty: A Cross-Domain Meta-Review","Mature decision systems in finance, operations, insurance, energy, healthcare, causal inference, and e-commerce share the same four primitives — a calibrated probabilistic model, a coherent risk-aware objective, an operational constraint set, and a principled exploration mechanism. Eleven worked cases ground the framework, with the DataGlass marketplace ad-budget system as the connecting tissue.",49,"prediction-and-risk-optimization-meta-review",{"title":584,"description":585,"category":355,"date":566,"readMinutes":560,"featured":336,"cover":334,"format":226,"platforms":586,"draft":336,"slug":587},"Shopee optimization in 2026 is a feedback loop, not a checklist","The popular \"10 ways to optimize your Shopee shop\" advice optimises the wrong objective. The lever that actually moves margin is the tightness of the diagnose → decide → operate loop — and most checklists skip the diagnose stage entirely.",[210],"shopee-optimization-in-2026",{"title":589,"description":590,"category":358,"date":566,"readMinutes":591,"featured":134,"cover":334,"format":241,"platforms":592,"draft":336,"slug":593},"Shopee Sellers in 2026: Southeast Asia E-commerce Market Research, GMV & Seller Economics","Market growth has resumed; seller economics have become more exacting. Southeast Asia's platform e-commerce reached US$157.6B in 2025 (up 22.8% YoY), top-three platforms now control about 98.8% of platform GMV, and content commerce accounts for ~32% of platform GMV. The 2026 question for sellers is no longer \"how big is the market?\" — it is \"who controls the decision loop?\"",22,[210],"southeast-asia-seller-market-research-2026",{"title":595,"description":596,"category":355,"date":597,"readMinutes":598,"featured":336,"cover":334,"format":223,"platforms":599,"draft":336,"slug":600},"Shopee fee structure and commission calculation in 2026: the six layers, the four-term formula","Most 'Shopee fees' explainers stop at commission and transaction fee. The four layers underneath — voucher co-funding, Free Shipping Program subsidy, Mall premium, payment-channel pass-through — are where the gap between dashboard revenue and bank deposit actually lives. With a sourced layer-by-layer breakdown, the four-term commission-per-order formula, two worked THB examples, a sensitivity table, and the reconciliation procedure.","2026-05-03",18,[210],"shopee-fee-structure-2026",{"title":602,"description":603,"category":352,"date":604,"readMinutes":537,"featured":336,"cover":334,"format":223,"platforms":605,"draft":336,"slug":606},"Cross-platform ad budget allocation for SEA marketplace sellers","Most multi-platform sellers split ad budget across Shopee, Lazada, and TikTok Shop by historical revenue share. The math says that's wrong. Optimal allocation equalises marginal ROAS, not historical share — and the gap between the two on a typical account is 4–7 percentage points of net contribution margin per quarter.","2026-04-29",[210,213,216],"cross-platform-ad-budget-allocation",{"title":608,"description":609,"category":352,"date":610,"readMinutes":415,"featured":336,"cover":334,"format":223,"platforms":611,"draft":336,"slug":612},"Why scaling Shopee ad budget grows revenue and shrinks profit","A Shopee account moving its monthly ad budget from THB 40,000 to THB 100,000 commonly grows revenue 60% and loses a third of its net profit — and not one decision in that sequence looks wrong on the dashboard. The arithmetic of the scaling trap, why platform-reported ROAS is blind to it by construction, and the per-SKU break-even bar that stops it.","2026-04-22",[210],"how-to-increase-profit-on-shopee",{"title":614,"description":615,"category":347,"date":616,"readMinutes":466,"featured":336,"cover":334,"format":223,"platforms":617,"draft":336,"slug":618},"How to increase profit on Lazada in 2026","The LazMall badge lifts conversion. It also raises commission, mandates free-shipping subsidies, and pulls the price ceiling down through the platform's own competitive-parity rules. Whether the badge pays is a per-SKU question. A research note on the LazMall economics, why Sponsored Discovery leaks more margin than Sponsored Search, and the audit that recovers 4–6 percentage points of margin in 30 days.","2026-04-15",[213],"how-to-increase-profit-on-lazada",{"title":620,"description":621,"category":352,"date":622,"readMinutes":466,"featured":336,"cover":334,"format":223,"platforms":623,"draft":336,"slug":624},"How to reduce Shopee ad waste without killing sales","On a typical Shopee account, 20–30% of ad spend runs at a structural loss the platform dashboard ranks as winning campaigns. Pausing \"underperformers\" misses the leak. A research note on the two structural defaults that cause hidden ad waste — and the audit that surfaces it without losing revenue.","2026-04-08",[210],"how-to-reduce-shopee-ad-waste",{"title":626,"description":627,"category":347,"date":628,"readMinutes":537,"featured":336,"cover":334,"format":223,"platforms":629,"draft":336,"slug":630},"How to calculate Shopee seller margin","The math, the inputs, and the program-specific traps. Why Shopee's in-dashboard income view almost always overstates margin — and how to reconstruct true contribution margin per SKU using the order-line data Shopee already exposes via Open Platform.","2026-04-05",[210],"how-to-calculate-shopee-seller-margin",{"title":632,"description":633,"category":347,"date":634,"readMinutes":537,"featured":336,"cover":334,"format":223,"platforms":635,"draft":336,"slug":636},"How to calculate Lazada seller margin","The math, the inputs, and the program-specific traps. Why Lazada's in-platform P&L summary almost always overstates margin — and how to reconstruct true contribution margin per SKU using the order-line data Lazada exposes via the Open Platform.","2026-04-01",[213],"how-to-calculate-lazada-seller-margin",{"title":638,"description":639,"category":352,"date":640,"readMinutes":560,"featured":336,"cover":334,"format":223,"platforms":641,"draft":336,"slug":642},"How to calculate true Shopee ROAS for profit","A methodology note. Shopee's in-platform ROAS is gross-revenue based and structurally biased toward overspend at scale. True ROAS is the same formula with one input substituted — and that substitution flips winners into losses on roughly half the typical Shopee catalog. With charts, three SKU profiles, sensitivity analysis, and the operating procedure that applies the substitution at production cadence.","2026-03-25",[210],"how-to-calculate-true-shopee-roas",{"title":644,"description":645,"category":352,"date":646,"readMinutes":537,"featured":336,"cover":334,"format":223,"platforms":647,"draft":336,"slug":648},"How to reduce Lazada ad waste","Lazada runs two structurally different ad products — Sponsored Search and Sponsored Discovery — with break-even economics that diverge by ~1.4–1.8× in true-ROAS terms. A single account-wide ROAS target misjudges both. A research note on the audit that surfaces the structural waste, the pruning order that recovers margin without losing revenue, and the per-placement framework that survives Pay Day and 11.11.","2026-03-18",[213],"how-to-reduce-lazada-ad-waste",{"title":650,"description":651,"category":347,"date":652,"readMinutes":466,"featured":336,"cover":334,"format":223,"platforms":653,"draft":336,"slug":654},"How to find low-margin SKUs on Shopee","On a typical Shopee account, the top-10 SKUs by revenue and the top-10 by contribution profit overlap by roughly 50%. Half of every shop's bestsellers are not the most profitable products. A research note on the audit that surfaces the gap, the patterns hiding inside it, and the per-SKU operating decisions that recover margin.","2026-03-12",[210],"how-to-find-low-margin-skus-on-shopee",{"title":656,"description":657,"category":350,"date":658,"readMinutes":560,"featured":336,"cover":334,"format":223,"platforms":659,"draft":336,"slug":660},"How to plan Lazada campaigns around profit","Pay Day, Mega Sale, 11.11, and the long tail of category windows make participation feel mandatory. The platform documents the eligibility tiers; the seller absorbs them. A research note on what each campaign actually costs, when participation pays, and the margin-first rubric that survives all three.","2026-03-04",[213],"how-to-plan-lazada-campaigns-around-profit",{"title":662,"description":663,"category":347,"date":664,"readMinutes":472,"featured":336,"cover":334,"format":223,"platforms":665,"draft":336,"slug":666},"How to increase profit on TikTok Shop in 2026","TikTok Shop is the only SEA marketplace with a stacked second commission — affiliate commission (10–25% via the Open Affiliate Plan) layered on top of platform commission. A 6.0 platform ROAS routinely becomes ~1.4 true ROAS once the full four-line cost stack is subtracted. A research note on the affiliate-stack arithmetic, live-stream pricing discipline, and the per-SKU framework that recovers margin without retreating from the platform.","2026-02-26",[216],"how-to-increase-profit-on-tiktok-shop",{"title":668,"description":669,"category":358,"date":670,"readMinutes":671,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":672},"The SEA marketplace in 2026: a fragmented arena with one survival rule","Eighteen distinct platform-market cells, six countries, three platforms, and roughly a million sellers competing on a saturated product surface. The aggregate is growing; the per-cell margin distribution is compressing. A research note on the structural shifts and the operating rule that survives them.","2026-02-22",16,"sea-marketplace-2026",{"title":674,"description":675,"category":361,"date":676,"readMinutes":537,"featured":336,"cover":334,"format":226,"platforms":677,"draft":336,"slug":678},"Data ingestion for Shopee sellers: why zero-setup analytics matters","Most Shopee sellers don't have a strategy problem first. They have a data plumbing problem — orders, ads, COGS, fees, vouchers, inventory, pricing, and returns live in seven different surfaces, and by the time the seller has stitched them together the campaign is over. A research note on the data-source matrix, the canonical-entity model, and the zero-setup architecture that recovers ~10 hours per week.","2026-02-18",[210],"data-ingestion-for-shopee-sellers",{"title":680,"description":681,"category":358,"date":682,"readMinutes":415,"featured":336,"cover":334,"format":226,"platforms":683,"draft":336,"slug":684},"The race to zero margin — and how Shopee, Lazada, and TikTok Shop got there","Read the platform documentation end-to-end and the conclusion is uncomfortable: the discounts buyers see, the free shipping that drives conversion, and the affiliate spend that drives reach are all paid by the seller. The race-to-zero is the equilibrium output of that design.","2026-02-08",[210,213,216],"race-to-zero-margin",{"title":686,"description":687,"category":361,"date":688,"readMinutes":560,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":689},"ML demand forecasting for e-commerce sellers","Machine learning in e-commerce gets discussed in vague terms; for marketplace sellers the operating question is concrete — how many units of this SKU will sell in the next N days, with what confidence, and what decision flows from the answer? A research note on the practical model architecture, the stockout-distortion problem, sensitivity analysis, and the operating decisions forecasts feed.","2026-02-04","ml-demand-forecasting-for-ecommerce",{"title":691,"description":692,"category":358,"date":693,"readMinutes":484,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":694},"Complexity is the new tax on small sellers","A multi-shop seller in 2026 logs into seven platforms, reconciles four fee schedules, exports six CSVs, and re-enters COGS by hand. Big brands absorb the cost with a data team. Small sellers absorb it with their evenings — and it's the single biggest reason multi-shop operators stall before they reach scale.","2026-01-25","complexity-is-the-tax",{"title":696,"description":697,"category":355,"date":698,"readMinutes":472,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":699},"Stockout math for e-commerce sellers","A stockout is not one cost; it is five compounding costs. Lost contribution profit on the missed unit, plus wasted ad spend during the stockout window, plus algorithmic ranking demotion, plus repeat-buyer trust erosion, plus distorted forecasting that increases the likelihood of the next stockout. A research note on the multi-line stockout cost function, the per-SKU reorder-point math that accounts for it, and the campaign-aware adjustment that survives Pay Day and 11.11.","2026-01-18","inventory-stockout-math-ecommerce",{"title":701,"description":702,"category":350,"date":703,"readMinutes":537,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":704},"Dynamic pricing for marketplace sellers","Discounting is easy. Profitable pricing is hard. A 30% volume lift on a 10% price cut routinely lowers total contribution profit — the math says volume must lift by ~33% just to break even, and most SKUs underperform that bar. A research note on the price-elasticity arithmetic, the inventory × demand four-quadrant framework, and the per-SKU pricing decision that survives campaign-window pressure.","2026-01-04","dynamic-pricing-ecommerce-marketplaces",{"title":706,"description":707,"category":361,"date":708,"readMinutes":472,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":709},"E-commerce Decision Engine: How Marketplace Sellers Turn Data Into Profit Recommendations","A dashboard tells you what happened. A decision engine tells you what to do next, ranks the options by projected profit lift, and surfaces the math behind every recommendation. A research note on the five-layer architecture that separates the two, why marketplace commerce now requires the latter, and where the operating model breaks.","2025-12-12","how-ecommerce-decision-engines-work",{"title":711,"description":712,"category":355,"date":713,"readMinutes":459,"featured":336,"cover":334,"format":226,"platforms":714,"draft":336,"slug":715},"Multi-shop analytics for Shopee, Lazada, and TikTok Shop sellers","Modern marketplace sellers rarely operate on one channel. They have multiple dashboards but not one operating view. They know sales by channel — but not profit by channel.","2025-11-26",[210,213,216],"multi-shop-analytics-lazada-shopee-tiktok",{"title":717,"description":718,"category":358,"date":719,"readMinutes":720,"featured":336,"cover":334,"format":226,"platforms":334,"draft":336,"slug":721},"The new competitive world of commerce","A research note on what changed in marketplace commerce between 2022 and 2026. Platforms became active algorithmic counterparties; sellers who treat them as passive listing markets are competing against optimization systems they cannot see. The fix is to run your own optimization layer on top.","2025-11-12",15,"new-competitive-world-of-commerce",{"entry":723,"document":769,"toc":1514},{"id":724,"title":470,"category":352,"cover":334,"cta":725,"date":465,"description":471,"draft":336,"draftReason":334,"extension":337,"featured":336,"format":223,"listen":334,"math":336,"meta":728,"navigation":334,"ogImage":334,"path":729,"platforms":730,"readMinutes":472,"related":731,"robots":334,"schemaOrg":334,"seo":732,"sitemap":735,"sources":736,"stem":760,"tldr":761,"topic":334,"updatedAt":334,"__hash__":768},"blog_en\u002Fblog\u002Fprofit-maximizing-target-roas.md",{"title":726,"body":727},"Set the target on the number that decides, not the one that reports.","DataGlass reconstructs contribution per product from your Shopee, Lazada and TikTok Shop order lines — cost of goods, commission, payment fees, vouchers, the free-shipping cost share and a returns reserve — then derives the Target ROAS each product can carry and reports the marginal return alongside the platform’s average.",{},"\u002Fblog\u002Fprofit-maximizing-target-roas",[210],[468,516,642],{"title":733,"description":734},"The Target ROAS That Maximizes Profit | DataGlass","Break-even ROAS targets exactly zero profit. The profit-maximizing Target ROAS is break-even × attribution × (1 + 1\u002Felasticity) ÷ (1 − decay). Worked in THB.",{"loc":729},[737,741,745,749,753,756],{"title":738,"url":739,"note":740},"DataGlass Labs — CTR & Target ROAS under CPM and CPC (technical note, 18 August 2026)","\u002Fassets\u002Fpapers\u002FDataGlass_CTR_and_Target_ROAS_under_CPM_and_CPC_2026-08-18.pdf","Lemma 1c derives the profit optimum under constant click value and the asymmetry about break-even; Lemma 1d adds attribution inflation and value decay and gives the reported-to-marginal ratio. The worked-example tables are reproduced from Cases 3 and 4 of the same note.",{"title":742,"url":743,"note":744},"Shopee — Seller commission and fee schedule (Help Center)","https:\u002F\u002Fhelp.shopee.co.th\u002Fportal\u002Farticle\u002F77790","Commission by category, transaction and payment fees, voucher mechanics and Free Shipping Program cost-share — the cost stack that determines the contribution margin m, and therefore break-even ROAS, per product.",{"title":746,"url":747,"note":748},"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 field experiment finding returns to branded paid search close to zero once measured against a randomised control — the empirical basis for treating attribution inflation as materially above one rather than as a rounding error.",{"title":750,"url":751,"note":752},"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 attributed figures dominate the record and why attribution inflation is rarely measured rather than rarely present.",{"title":754,"note":755},"DataGlass — Marketplace benchmarks (open dataset, CC BY 4.0)","Aggregated first-party Shopee measurements of CPC, CPM, CTR, conversion rate and margin by hour of day across 9.11 million clicks and 333.9 million impressions — the measured counterpart to the assumed parameters used in this post.",{"title":757,"url":758,"note":759},"Shopee Ads — GMV Max and Target ROAS bidding (Seller Education Hub)","https:\u002F\u002Fseller.shopee.co.th\u002Fedu\u002F","Shopee seller documentation on GMV Max as an auto-bid campaign type in which the seller enters a budget and a Target ROAS — the two instructions whose profit consequences this post prices.","blog\u002Fprofit-maximizing-target-roas",[762,763,764,765,766,767],"Thesis: the profit-maximizing Target ROAS is break-even multiplied by three markups — attribution inflation, the click-supply markup (1 + 1\u002Fe), and value decay 1\u002F(1−d). None appears in marketplace ad reporting.","A target set at break-even permits a bid equal to the whole contribution a click carries, so profit is exactly zero for every possible volume. Break-even is the zero line, not a floor.","The curve is asymmetric: above break-even you forgo a bounded profit, below it the permitted bid is reciprocal in the target and the loss is unbounded. Two points below break-even loses 7.7x the most the campaign could earn.","On mid-market parameters the markups compound to 3.3x, moving the correct target on a 20%-margin product from 5.00 to 16.67 — and the campaign reporting itself on target at 5.00 loses THB 827 a day.","At the optimum, reported ROAS exceeds marginal ROAS by (1 + 1\u002Fe)\u002F(1 − d) — 2.38x on the worked parameters. The average is held up by early cheap clicks; the decision lives at the margin.","Limitation: two of the three markups cannot be read off platform reporting. Attribution inflation needs a holdout or geo test; value decay needs bid-level variation. Both are carried here as parameters, not measurements.","P01sNkRe5O19W--Jcyg77H-lONLBhe2HZ33InmBE83s",{"frontmatter":770,"meta":783,"nodes":815},{"title":470,"category":352,"cta":771,"date":465,"description":471,"draft":336,"featured":336,"format":223,"math":336,"platforms":772,"readMinutes":472,"related":773,"seo":774,"sources":775,"tldr":782},{"title":726,"body":727},[210],[468,516,642],{"title":733,"description":734},[776,777,778,779,780,781],{"title":738,"url":739,"note":740},{"title":742,"url":743,"note":744},{"title":746,"url":747,"note":748},{"title":750,"url":751,"note":752},{"title":754,"note":755},{"title":757,"url":758,"note":759},[762,763,764,765,766,767],{"toc":784},{"title":331,"searchDepth":386,"depth":386,"links":785},[786,789,792,795,798,801,804,807,810,812],{"id":787,"depth":386,"text":788},"why-break-even-earns-exactly-zero","Why break-even earns exactly zero",{"id":790,"depth":386,"text":791},"the-curve-is-asymmetric-and-the-wrong-side-is-much-steeper","The curve is asymmetric, and the wrong side is much steeper",{"id":793,"depth":386,"text":794},"markup-one-buying-more-volume-reprices-the-volume-you-already-had","Markup one: buying more volume reprices the volume you already had",{"id":796,"depth":386,"text":797},"markup-two-some-of-the-credited-gmv-was-going-to-happen-anyway","Markup two: some of the credited GMV was going to happen anyway",{"id":799,"depth":386,"text":800},"markup-three-the-average-click-is-worth-more-than-the-marginal-one","Markup three: the average click is worth more than the marginal one",{"id":802,"depth":386,"text":803},"the-same-campaign-measured-properly","The same campaign, measured properly",{"id":805,"depth":386,"text":806},"what-this-changes-about-optimizing-shopee-profit","What this changes about optimizing Shopee profit",{"id":808,"depth":386,"text":809},"where-this-argument-breaks","Where this argument breaks",{"id":577,"depth":386,"text":811},"Methodology",{"id":813,"depth":386,"text":814},"frequently-asked-questions","Frequently asked questions",[816,832,846,849,859,862,865,875,878,881,894,900,903,905,908,914,1045,1053,1056,1077,1081,1083,1086,1092,1095,1100,1187,1194,1196,1205,1208,1210,1213,1219,1222,1227,1299,1306,1308,1314,1319,1422,1429,1432,1435,1437,1443,1449,1451,1473,1475,1491,1493,1498,1502,1506,1510],[817,818],"blog-stat-strip",{"items":819},[820,823,826,829],{"value":821,"label":822},"0","Profit at break-even ROAS",{"value":824,"label":825},"3","Markups on break-even",{"value":827,"label":828},"16.67","Correct target, 20% margin",{"value":830,"label":831},"2.38×","Reported ÷ marginal, at the optimum",[833,834,835,839],"blockquote",{},[836,837,838],"p",{},"The curve is asymmetric about break-even: above it a mis-set target forgoes profit, below it destroys capital, and the reciprocal makes the second far the steeper of the two.",[836,840,841],{},[842,843,845],"span",{"class":844},"cite","DataGlass Labs, CTR & Target ROAS under CPM and CPC, Corollary 1c",[836,847,848],{},"That is Corollary 1c of the note this post unpacks, and it contradicts the advice everyone gives — ours included, until now. The standard guidance is to compute break-even ROAS as one divided by your contribution margin and set your Target ROAS above it. The first half is right. The second half is far too weak, because break-even is not a floor you stand on. It is the line where the whole of your margin goes to the auction while the campaign reports itself perfectly on target.",[850,851,854],"pre",{"language":852,"filename":853},"text","The Target ROAS that maximizes profit",[855,856,858],"code",{"class":857},"language-text","R*_profit  =  ( a \u002F m ) x ( 1 + 1\u002Fe ) \u002F ( 1 - d )\n\n  m = contribution retained per baht of attributed GMV\n  a = attributed GMV \u002F incremental GMV       (>= 1)\n  e = elasticity of click supply to price\n  d = rate at which the marginal click decays in value\n\n  1\u002Fm alone is BREAK-EVEN. The other three factors are\n  the reason break-even is not a floor.",[836,860,861],{},"The reason is visible in one line of algebra. Setting the target exactly at break-even permits a bid equal to the entire contribution a click carries. Profit is then exactly zero at every possible volume, so no amount of scale rescues it. The three factors beside 1\u002Fm in the formula above are what separate the line where profit vanishes from the target that maximises it.",[836,863,864],{},"The thesis: the Target ROAS that maximizes profit is break-even multiplied by three separate markups, none of which appears anywhere in marketplace ad reporting. On plausible mid-market parameters those markups compound to more than 3x, which moves the correct target on a 20%-margin product from 5.00 to about 16.7. A campaign that entered 5.00 and reports 5.00 is not on target. It is spending roughly eight times what it should. What would falsify this: a click-supply curve flat enough that buying more volume did not raise the price of the volume you were already buying.",[866,867,869,870,874],"blog-callout",{"title":868},"The full derivation","This post is the readable version of Lemmas 1c and 1d of our technical note. The first-order conditions, the shadow-price result and the sensitivity tables are in the PDF: ",[871,872,873],"a",{"href":739},"CTR & Target ROAS under CPM and CPC (PDF)",".",[876,877],"hr",{},[879,880,788],"h2",{"id":787},[836,882,883,884,888,889,893],{},"Write m for the contribution you retain per baht of attributed GMV — what survives cost of goods, category commission, transaction and payment fees, seller-funded vouchers, the free-shipping cost share and a returns reserve, every line of which is set out in Shopee Thailand’s published fee schedule ",[885,886],"blog-cite",{"n":887},"2",". Break-even ROAS is 1 ÷ m: the return at which advertising revenue covers advertising cost and nothing else. Now put that target into the bid ceiling derived in ",[871,890,892],{"href":891},"\u002Fph\u002Fblog\u002Fhow-shopee-ads-bidding-works","how Shopee ads bidding works",". The permitted price per click becomes exactly the contribution a click carries.",[850,895,897],{"language":852,"filename":896},"Profit at the ceiling, as a function of the entered target",[855,898,899],{"class":857},"profit(bid at ceiling) = ( m x v  -  v \u002F R* ) x Q\n\n  at R* = 1\u002Fm :  m x v - v\u002FR* = m x v - m x v = 0\n\n  -> zero profit, for EVERY Q, k and elasticity.\n     The volume is real, the GMV is the largest on the page,\n     and all of the contribution goes to the auction.",[836,901,902],{},"The zero is independent of volume, and that is the part that catches people out. A seller at break-even who raises spend buys more clicks, more attributed GMV and more reported revenue — and still earns nothing, at any volume the campaign can reach. Clicks, orders, revenue and the platform’s own ROAS reading all improve. Profit does not, because there is no quantity of clicks bought at exactly their own contribution that adds up to more than zero.",[879,904,791],{"id":790},[836,906,907],{},"Below break-even the loss is not symmetric with the profit forgone above it. The permitted price is the value of a click divided by the target, so it is reciprocal in the target: cutting the target raises the bid without limit. Above break-even the most you can lose is the finite amount you were going to make. Below it, there is no bound at all. Here is the same campaign from the previous post — a click worth THB 10.00, a 20% contribution margin, break-even at 5.00, iso-elastic click supply at elasticity 1.5 — swept across entered targets with a slack budget.",[836,909,910],{},[911,912,913],"em",{},"One campaign, budget slack, five entered targets. Every visible metric improves all the way down. Only the last column reverses.",[915,916,917,942],"table",{},[918,919,920],"thead",{},[921,922,923,927,930,933,936,939],"tr",{},[924,925,926],"th",{},"Target entered",[924,928,929],{},"Bid (THB)",[924,931,932],{},"Clicks\u002Fday",[924,934,935],{},"Spend (THB)",[924,937,938],{},"Attributed GMV (THB)",[924,940,941],{},"Profit after ads (THB)",[943,944,945,966,986,1005,1025],"tbody",{},[921,946,947,951,954,957,960,963],{},[948,949,950],"td",{},"8.33 — the optimum here",[948,952,953],{},"1.20",[948,955,956],{},"1,000",[948,958,959],{},"1,200",[948,961,962],{},"10,000",[948,964,965],{},"+800",[921,967,968,971,974,977,980,983],{},[948,969,970],{},"6.32",[948,972,973],{},"1.58",[948,975,976],{},"1,514",[948,978,979],{},"2,396",[948,981,982],{},"15,141",[948,984,985],{},"+632",[921,987,988,991,994,997,1000,1003],{},[948,989,990],{},"5.00 — break-even",[948,992,993],{},"2.00",[948,995,996],{},"2,152",[948,998,999],{},"4,303",[948,1001,1002],{},"21,517",[948,1004,821],{},[921,1006,1007,1010,1013,1016,1019,1022],{},[948,1008,1009],{},"4.00",[948,1011,1012],{},"2.50",[948,1014,1015],{},"3,007",[948,1017,1018],{},"7,518",[948,1020,1021],{},"30,070",[948,1023,1024],{},"−1,504",[921,1026,1027,1030,1033,1036,1039,1042],{},[948,1028,1029],{},"3.00",[948,1031,1032],{},"3.33",[948,1034,1035],{},"4,630",[948,1037,1038],{},"15,432",[948,1040,1041],{},"46,296",[948,1043,1044],{},"−6,173",[836,1046,1047,1050,1051],{},[911,1048,1049],{},"Computed on an assumed iso-elastic click-supply curve with elasticity 1.5, fitted to no account. Source: DataGlass Labs technical note, worked example, Case 3"," :blog-cite",[911,1052,874],{},[836,1054,1055],{},"Setting the target two points below break-even loses THB 6,173 a day — nearly eight times the most this campaign could ever have earned — while posting the largest GMV on the page, four and a half times the optimum’s. Clicks, orders, revenue, and the platform’s own ROAS reading against a lowered target all look better as the campaign walks off the cliff.",[1057,1058],"blog-chart",{"labels":1059,"values":1064,"caption":1072,"kind":1073,"title":1074,"xLabel":1075,"yLabel":1076},[1029,1009,1060,970,1061,1062,1063],"5.00","8.33","10.00","12.00",[1065,1066,1067,1068,1069,1070,1071],-6173,-1504,0,632,800,761,675,"The peak sits at 8.33 on these parameters and the curve falls away gently to its right — overshooting the target forgoes a bounded amount. To the left of break-even at 5.00 it falls without bound, because the permitted bid is reciprocal in the target. Computed on the assumed supply curve.","line","Daily profit against the entered Target ROAS — the asymmetry in figures","Target ROAS entered","Profit after ads, THB\u002Fday",[1078,1079],"blog-pullquote",{"text":1080},"Break-even ROAS is the zero line, not a floor. Above it you forgo a bounded profit; below it there is nothing bounding the loss.",[879,1082,794],{"id":793},[836,1084,1085],{},"The first markup has nothing to do with attribution or with your product. It is a property of buying anything in an auction. Winning more clicks requires bidding higher, and the higher bid is paid on every click you were already winning, not only on the marginal one. So the cost of the next click is not the price you see. It is the price you see, multiplied by one plus the reciprocal of the elasticity of click supply.",[850,1087,1089],{"language":852,"filename":1088},"The marginal cost of a click",[855,1090,1091],{"class":857},"observed price per click:   p\nmarginal cost of a click:   c = p x ( 1 + 1\u002Fe )\n\nprofit is maximized where  m x v = c,  not where  m x v = p\n\nso the profit-maximizing target on this markup alone is\n\n  R*  =  (1\u002Fm) x (1 + 1\u002Fe)      i.e. break-even, marked up",[836,1093,1094],{},"The elasticity e is finite for any real campaign — audiences are not unlimited — so the markup is strictly greater than one, always. Only as click supply becomes perfectly elastic does bidding to break-even become correct, and perfectly elastic supply means an infinite audience at a fixed price, which is not a description of any marketplace. The table below shows what the markup is worth on a 20%-margin product, where break-even is 5.00 throughout.",[836,1096,1097],{},[911,1098,1099],{},"The click-supply markup, at a 20% contribution margin (break-even ROAS 5.00).",[915,1101,1102,1118],{},[918,1103,1104],{},[921,1105,1106,1109,1112,1115],{},[924,1107,1108],{},"Click-supply elasticity",[924,1110,1111],{},"Markup (1 + 1\u002Fe)",[924,1113,1114],{},"Profit-maximizing target",[924,1116,1117],{},"Bid, where full contribution = THB 2.00",[943,1119,1120,1134,1147,1160,1174],{},[921,1121,1122,1125,1128,1131],{},[948,1123,1124],{},"0.5 — near-saturated audience",[948,1126,1127],{},"3.00x",[948,1129,1130],{},"15.00",[948,1132,1133],{},"THB 0.67",[921,1135,1136,1139,1142,1144],{},[948,1137,1138],{},"1.0",[948,1140,1141],{},"2.00x",[948,1143,1062],{},[948,1145,1146],{},"THB 1.00",[921,1148,1149,1152,1155,1157],{},[948,1150,1151],{},"1.5",[948,1153,1154],{},"1.67x",[948,1156,1061],{},[948,1158,1159],{},"THB 1.20",[921,1161,1162,1165,1168,1171],{},[948,1163,1164],{},"3.0",[948,1166,1167],{},"1.33x",[948,1169,1170],{},"6.67",[948,1172,1173],{},"THB 1.50",[921,1175,1176,1179,1182,1184],{},[948,1177,1178],{},"Unlimited supply (theoretical)",[948,1180,1181],{},"1.00x",[948,1183,1060],{},[948,1185,1186],{},"THB 2.00",[836,1188,1189,1050,1192],{},[911,1190,1191],{},"Computed, not measured. The lower the elasticity — the more saturated your audience — the further above break-even the correct target sits. Source: DataGlass Labs technical note, Lemma 1c",[911,1193,874],{},[879,1195,797],{"id":796},[836,1197,1198,1199,1201,1202,874],{},"The second markup is attribution inflation: the ratio of GMV the platform credits to your ads to GMV your ads actually caused. Write it as a. The evidence that a is materially above one is not speculative. In eBay’s large-scale field experiment, Blake, Nosko and Tadelis found that returns to branded paid search were close to zero once measured against a proper control — the traffic converted whether or not the ad ran ",[885,1200],{"n":824},". Lewis and Rao showed separately that the statistical power required to detect true advertising returns is far beyond what most campaigns can supply, which is why attributed figures dominate the record ",[885,1203],{"n":1204},"4",[836,1206,1207],{},"Attribution inflation enters as a haircut on the margin: your true contribution per credited baht is m ÷ a rather than m. Every result above survives that substitution unchanged, which is a convenient property — it means a is a multiplier on the correct target rather than a separate correction. Note that a is the one parameter here that platform reporting cannot supply even in principle. It requires a holdout or a geo test.",[879,1209,800],{"id":799},[836,1211,1212],{},"The third markup is the one that makes the reported number structurally misleading rather than merely optimistic. A campaign buys its cheapest, best-converting traffic first. As it expands, the value of the marginal click declines. Write d for the rate of that decay. Then the average click across the campaign is worth 1 ÷ (1 − d) times the marginal one — and the Target ROAS is enforced on the average while the profit decision lives at the margin.",[850,1214,1216],{"language":852,"filename":1215},"Reported ROAS against marginal ROAS at the profit optimum",[855,1217,1218],{"class":857},"at the profit optimum:\n\n  reported ROAS  =  (a\u002Fm) x (1 + 1\u002Fe) \u002F (1 - d)\n  marginal ROAS  =   a\u002Fm\n\n  reported \u002F marginal  =  (1 + 1\u002Fe) \u002F (1 - d)\n\nExample at e = 1.5, d = 0.30:\n  (1 + 0.667) \u002F 0.70  =  2.38x\n\nThe same campaign, the same instant, measured two ways.",[836,1220,1221],{},"This overturns a comfortable reading — that a slack budget is inert. It is not. A target enforced on the campaign average keeps buying while the marginal click is already unprofitable, because the early cheap clicks hold the average up. The budget is then the only thing that stops the expansion, and raising it funds precisely the volume that lifts the price on every click and lowers the value of the last one.",[836,1223,1224],{},[911,1225,1226],{},"How value decay compounds with the other two markups. Break-even 5.00; attribution inflation 1.4; click-supply elasticity 1.5.",[915,1228,1229,1244],{},[918,1230,1231],{},[921,1232,1233,1236,1239,1242],{},[924,1234,1235],{},"Value decay d",[924,1237,1238],{},"Markup 1\u002F(1−d)",[924,1240,1241],{},"Reported ÷ marginal ROAS",[924,1243,1114],{},[943,1245,1246,1258,1272,1285],{},[921,1247,1248,1251,1253,1255],{},[948,1249,1250],{},"0.00 — no decay",[948,1252,1181],{},[948,1254,1154],{},[948,1256,1257],{},"11.67",[921,1259,1260,1263,1266,1269],{},[948,1261,1262],{},"0.15",[948,1264,1265],{},"1.18x",[948,1267,1268],{},"1.96x",[948,1270,1271],{},"13.73",[921,1273,1274,1277,1280,1283],{},[948,1275,1276],{},"0.30",[948,1278,1279],{},"1.43x",[948,1281,1282],{},"2.38x",[948,1284,827],{},[921,1286,1287,1290,1293,1296],{},[948,1288,1289],{},"0.45",[948,1291,1292],{},"1.82x",[948,1294,1295],{},"3.03x",[948,1297,1298],{},"21.21",[836,1300,1301,1050,1304],{},[911,1302,1303],{},"Computed on stated parameters. Attribution inflation needs a holdout or geo test to estimate and cannot be read off platform reporting; value decay needs bid-level variation. Source: DataGlass Labs technical note, Lemma 1d",[911,1305,874],{},[879,1307,803],{"id":802},[836,1309,1310,1311,1313],{},"Take the campaign from the top of this post and remove the two flattering assumptions. Let the marginal click decay at 0.30, normalised so the average click is still worth THB 10.00 at 1,000 clicks. Let attribution inflate by 1.4 — meaning 29% of credited GMV is demand the campaign captured rather than created, well inside the range the eBay experiment implies for branded traffic ",[885,1312],{"n":824},". The seller enters 5.00, the break-even figure, as the guides advise.",[836,1315,1316],{},[911,1317,1318],{},"A campaign reporting itself exactly on target, and the same campaign at the target that maximizes profit.",[915,1320,1321,1334],{},[918,1322,1323],{},[921,1324,1325,1328,1331],{},[924,1326,1327],{},"Per day",[924,1329,1330],{},"Entered target 5.00",[924,1332,1333],{},"Optimum, target 16.67",[943,1335,1336,1347,1358,1369,1380,1391,1400,1411],{},[921,1337,1338,1341,1344],{},[948,1339,1340],{},"Bid \u002F observed effective CPC",[948,1342,1343],{},"THB 1.71",[948,1345,1346],{},"THB 0.74",[921,1348,1349,1352,1355],{},[948,1350,1351],{},"Marginal cost of a click",[948,1353,1354],{},"THB 2.85",[948,1356,1357],{},"THB 1.24",[921,1359,1360,1363,1366],{},[948,1361,1362],{},"Clicks",[948,1364,1365],{},"1,696",[948,1367,1368],{},"488",[921,1370,1371,1374,1377],{},[948,1372,1373],{},"Spend",[948,1375,1376],{},"THB 2,895",[948,1378,1379],{},"THB 363",[921,1381,1382,1385,1388],{},[948,1383,1384],{},"Attributed GMV",[948,1386,1387],{},"THB 14,476",[948,1389,1390],{},"THB 6,054",[921,1392,1393,1396,1398],{},[948,1394,1395],{},"Reported ROAS",[948,1397,1060],{},[948,1399,827],{},[921,1401,1402,1405,1408],{},[948,1403,1404],{},"Marginal ROAS",[948,1406,1407],{},"2.10",[948,1409,1410],{},"7.00",[921,1412,1413,1416,1419],{},[948,1414,1415],{},"Profit on a true, incremental basis",[948,1417,1418],{},"−THB 827",[948,1420,1421],{},"+THB 502",[836,1423,1424,1050,1427],{},[911,1425,1426],{},"Attribution inflation 1.4, value decay 0.30, click-supply elasticity 1.5, contribution margin 20%. Computed on assumed parameters; no account was measured. Source: DataGlass Labs technical note, worked example, Case 4",[911,1428,874],{},[836,1430,1431],{},"The campaign reporting itself on target is spending eight times what it should and losing THB 827 a day. Its reported 5.00 is an average over 1,696 clicks; the 1,696th click returns 2.10 against the 7.00 it must clear to pay for itself. The reported figure cannot disclose this, because the average is held up by the first few hundred clicks, which are cheap and convert well. The corrected target of 16.67 is break-even of 5.00 multiplied by 1.40 for attribution, by 1.67 because extra volume reprices every click, and by 1.43 because the average click beats the marginal one.",[1078,1433],{"text":1434},"A campaign hitting its Target ROAS exactly is not evidence that the target was right. It is evidence that the platform did what you asked.",[879,1436,806],{"id":805},[850,1438,1440],{"language":852,"filename":1439},"The same three markups on a different product",[855,1441,1442],{"class":857},"markup block  =  a x (1 + 1\u002Fe) \u002F (1 - d)\n              =  1.4 x 1.6667 \u002F 0.70  =  3.333x\n\nA 35% contribution accessory\n  break-even ROAS = 1 \u002F 0.35           =  2.857\n  profit-maximizing target = 2.857 x 3.333 =  9.52\n\nA 20% contribution appliance\n  break-even ROAS = 1 \u002F 0.20           =  5.00\n  profit-maximizing target = 5.00 x 3.333 = 16.67\n\nSame shop, same ad account, same auction.\nOne shop-wide Target ROAS cannot be right for both.",[836,1444,1445,1446,1448],{},"Three practical consequences, in the order they are worth acting on. First, stop treating break-even as a safety line — it is the zero line, and a target set at or below it bids above the profit optimum by construction. If you have not computed it per product, our free break-even ROAS calculator does the arithmetic. Second, the correct target is product-specific and margin-specific, because break-even is 1 ÷ m and m varies across a catalogue far more than most sellers assume once vouchers and the free-shipping cost share are attributed per SKU. Third, a budget is not a crude instrument. It is the only lever denominated in money spent rather than value claimed, and in the loss region it is the only thing that can bound the damage: at a target of 4.00 in the budget sweep in ",[871,1447,892],{"href":891},", a THB 2,400 budget turns a loss of THB 1,504 into a profit of THB 631, a swing of THB 2,135 a day.",[879,1450,809],{"id":808},[1452,1453,1454,1458,1464,1467,1470],"ul",{},[1455,1456,1457],"li",{},"The click-supply curve is assumed iso-elastic and is not fitted to any account. Elasticity almost certainly varies by product, by daypart and by how saturated the campaign already is, and the markup is sensitive to it — the difference between elasticity 0.5 and 3.0 moves the correct target from 15.00 to 6.67 on the same product.",[1455,1459,1460,1461,1463],{},"Attribution inflation cannot be read off platform reporting. Estimating it requires a holdout or a geo test, and the eBay result ",[885,1462],{"n":824}," is a branded-search finding at one large retailer, not a Shopee constant. Treat 1.4 as an illustrative parameter, not a benchmark.",[1455,1465,1466],{},"Value decay requires bid-level variation to identify. A campaign that has never varied its bid contains no information about how quickly its marginal click loses value.",[1455,1468,1469],{},"The optimum is derived for one campaign in isolation. With several campaigns and a shared budget, the correct rule is to equalise marginal return across them, and a per-campaign optimum is only the right answer when the budget is genuinely unconstrained.",[1455,1471,1472],{},"All of this prices a static auction. It says nothing about how competitors respond if many sellers raise their targets at once, which would lower clearing prices and raise every optimum in the market.",[879,1474,811],{"id":577},[836,1476,1477,1478,1481,1482,1484,1485,1487,1488,874],{},"No claim in this post rests on DataGlass customer data. The three markups and the profit-optimum formula are derived from the definitions stated in our technical note ",[885,1479],{"n":1480},"1",", which uses no dataset. Every table is arithmetic on stated parameters — a 2% click conversion rate, a THB 500 attributed order value, a 20% contribution margin, iso-elastic click supply, and where noted an attribution inflation of 1.4 and a value decay of 0.30 — chosen to be representative of a mid-market Thai marketplace listing rather than fitted to one. The two empirical results cited for attribution inflation are Blake, Nosko and Tadelis in Econometrica ",[885,1483],{"n":824}," and Lewis and Rao in the Quarterly Journal of Economics ",[885,1486],{"n":1204},", neither of which is a Shopee study. Our own measured Shopee figures, including CPC and CPM by hour of day across 333.9 million impressions, are published separately as an open CC BY 4.0 dataset ",[885,1489],{"n":1490},"5",[879,1492,814],{"id":813},[1494,1495,1497],"blog-question",{"question":1496},"What Target ROAS should I set on Shopee to maximize profit?","Not your break-even ROAS. Break-even — one divided by your contribution margin — is the target at which the entire margin on a click goes to the auction, so profit is exactly zero regardless of how much volume you buy. The profit-maximizing target is break-even multiplied by three markups: attribution inflation, because some credited GMV would have happened anyway; one plus the reciprocal of your click-supply elasticity, because winning more clicks raises the price of the clicks you were already winning; and one over one minus the value-decay rate, because your average click is worth more than your marginal one. On mid-market parameters those compound to roughly 3.3x, which moves the correct target on a 20%-margin product from 5.00 to about 16.7. The practical starting point is to set the target well above break-even, then lower it only while the marginal return — not the reported average — stays above your true contribution.",[1494,1499,1501],{"question":1500},"Why is my Shopee campaign hitting its Target ROAS but losing money?","Because the Target ROAS is enforced on the campaign average while profit is decided at the margin, and because attributed GMV is not incremental GMV. A campaign reporting 5.00 against an entered 5.00 has done exactly what you asked. If the target was set at break-even, what you asked for was a campaign that returns its own cost and nothing more — before accounting for the fact that some of the credited revenue would have arrived without the ad. In the worked example in this post, a campaign reporting 5.00 is earning 2.10 on its marginal click and losing THB 827 a day on a true incremental basis, while every metric in Seller Centre looks correct.",[1494,1503,1505],{"question":1504},"Is break-even ROAS a safe floor for a Shopee ad campaign?","No — it is the zero line, and the curve around it is asymmetric. Above break-even a mis-set target forgoes a bounded amount of profit: the most you can lose is what you were going to make. Below break-even the permitted bid is reciprocal in the target, so lowering it raises the bid without limit and the loss with it. In the worked example, the best this campaign can do is THB 800 a day; setting the target two points below break-even loses THB 6,173 a day, nearly eight times that, while posting the largest GMV of any row in the table. Treating break-even as a floor puts you on the steep side of the curve with every visible metric telling you it is working.",[866,1507,1509],{"title":1508},"How this looks operationally","Two of the three markups can be estimated from data a seller already generates — contribution per product from order lines, and the click-supply response from bid variation the account has already run. DataGlass reconstructs the first per SKU and reports the marginal return alongside the reported one, so the decision is made on the number that decides it.",[866,1511,1513],{"title":1512},"Where the standard guidance goes wrong","Break-even ROAS tells you where profit becomes zero, and the target that maximizes profit is that number marked up three times — by the demand you did not create, by the price your own expansion pays, and by the distance between your average click and your last one.",[1515,1516,1517,1518,1519,1520,1521,1522,1523,1524],{"id":787,"depth":386,"text":788},{"id":790,"depth":386,"text":791},{"id":793,"depth":386,"text":794},{"id":796,"depth":386,"text":797},{"id":799,"depth":386,"text":800},{"id":802,"depth":386,"text":803},{"id":805,"depth":386,"text":806},{"id":808,"depth":386,"text":809},{"id":577,"depth":386,"text":811},{"id":813,"depth":386,"text":814},{"id":1526,"categories":1527,"domain":1534,"extension":5,"fallback":1535,"home":1538,"industry":1547,"meta":1552,"pricing":1553,"stem":1565,"__hash__":1566},"og_en\u002Fen\u002Fog.yml",[1528,1529,1530,1531,1532,1533],{"slug":347,"label":348},{"slug":350,"label":27},{"slug":352,"label":353},{"slug":355,"label":356},{"slug":361,"label":362},{"slug":364,"label":48},"dataglasslabs.com",{"title":1536,"description":1537},"DataGlass","The decision engine for Shopee, Lazada and TikTok Shop sellers.",{"chart":1539,"metrics":1540},"Net profit, last 30 days",[1541,1544],{"label":1542,"value":1543},"Net profit","฿1.24M",{"label":1545,"value":1546},"Profit ROAS","3.4x",{"axis":1548,"point":1549},"Ad spend",{"label":1550,"value":1551},"Next baht still pays","+฿0.42",{},{"plans":1554},[1555,1559,1562],{"name":1556,"price":1557,"period":1558},"Free","฿0","\u002Fmonth",{"name":1560,"price":1561,"period":1558},"Standard","฿1,499",{"name":1563,"price":1564,"period":1558},"Pro","฿2,499","en\u002Fog","63L8y4Lv8I2q3NvR26fMXU1sE9Wd7TZaz2l_tV6oZuo",[],1792785022226]