[{"data":1,"prerenderedAt":1201},["ShallowReactive",2],{"site-chrome-en-id":3,"site-chrome-en":-1,"site-chrome-th":-1,"blog-page-en-id":287,"blog-categories-en-id":345,"blog-posts-en-id":365,"article-blog-en-id-\u002Fblog\u002Fhow-ecommerce-decision-engines-work":722,"og-copy-en-id":1158,"og-posts-en-id-none":1200},{"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","\u002Fen-id\u002Fblog\u002Fads-optimization",{"label":15,"to":16},"Unit economics","\u002Fen-id\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","\u002Fen-id\u002Fpricing",{"label":30,"children":31},"Resources",[32,35,38,41,44],{"label":33,"to":34},"Blog","\u002Fen-id\u002Fblog",{"label":36,"to":37},"Customer stories","\u002Fen-id\u002Fcustomers",{"label":39,"to":40},"Research","\u002Fen-id\u002Fresearch",{"label":42,"to":43},"Glossary","\u002Fen-id\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","\u002Fen-id\u002Fcompany",{"label":54,"to":55},"Contact","\u002Fen-id\u002Fcontact",{"label":57,"to":58},"Book a demo","\u002Fen-id\u002Fbook-a-demo",{"label":60,"to":61},"Privacy","\u002Fen-id\u002Flegal\u002Fprivacy",{"label":63,"to":64},"Terms","\u002Fen-id\u002Flegal\u002Fterms",{"label":66,"children":67},"DataGlass for",[68,71,74,77,80],{"label":69,"to":70},"Agencies","\u002Fen-id\u002Ffor\u002Fagencies",{"label":72,"to":73},"Brands","\u002Fen-id\u002Ffor\u002Fbrands",{"label":75,"to":76},"Planners and optimizers","\u002Fen-id\u002Ffor\u002Fplanners",{"label":78,"to":79},"Managers and analysts","\u002Fen-id\u002Ffor\u002Fmanagers",{"label":81,"to":82},"Sellers and shop owners","\u002Fen-id\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},"\u002Fen-id",{"layout":121,"heading":122,"cards":123,"groups":135,"footer":142},"cards","What DataGlass 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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":770,"toc":1143},{"id":724,"title":706,"category":361,"cover":334,"cta":725,"date":708,"description":707,"draft":336,"draftReason":334,"extension":337,"featured":336,"format":226,"listen":334,"math":336,"meta":728,"navigation":334,"ogImage":334,"path":729,"platforms":334,"readMinutes":472,"related":730,"robots":334,"schemaOrg":334,"seo":731,"sitemap":734,"sources":735,"stem":760,"tldr":761,"topic":768,"updatedAt":334,"__hash__":769},"blog_en\u002Fblog\u002Fhow-ecommerce-decision-engines-work.md",{"title":726,"body":727},"Turn marketplace data into ranked profit decisions.","DataGlass is the decision engine for Shopee, Lazada, and TikTok Shop sellers — connecting ads, margin, inventory, pricing, and channel data into one stack that surfaces what to do next, ranked by projected profit lift, with the math attached to every recommendation.",{},"\u002Fblog\u002Fhow-ecommerce-decision-engines-work",[593,678,715],{"title":732,"description":733},"How E-commerce Decision Engines Work for Sellers | DataGlass","An e-commerce decision engine differs from a dashboard: a five-layer architecture turns Shopee, Lazada, and TikTok Shop data into ranked profit decisions.",{"loc":729},[736,740,744,748,752,756],{"title":737,"url":738,"note":739},"ScienceDirect — Machine learning approaches in inventory control: a systematic review (2025)","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2214716025000430","122 reviewed papers categorising the multi-layer ingest → forecast → optimise → policy stack that the commercial decision-engine architecture in this note extends.",{"title":741,"url":742,"note":743},"Reuters — Google and Sea to develop AI tools for e-commerce (19 Feb 2026)","https:\u002F\u002Fwww.reuters.com\u002Fworld\u002Fasia-pacific\u002Fgoogle-shopee-owner-sea-develop-ai-tools-e-commerce-gaming-2026-02-19\u002F","Reuters reporting on the Google–Sea agentic shopping prototype — direct evidence the platforms are moving toward algorithmic, agent-driven experiences that make seller-side dashboards structurally insufficient.",{"title":745,"url":746,"note":747},"Reuters — Sea reports rising operating expenses tied to AI investment (3 Mar 2026)","https:\u002F\u002Fwww.reuters.com\u002Fworld\u002Fasia-pacific\u002Fsea-shares-tumble-high-costs-slower-annual-gmv-growth-forecast-bite-2026-03-03\u002F","Reuters reporting on Sea's AI capex trajectory — the platform-side asymmetry the seller-side decision engine has to keep pace with.",{"title":749,"url":750,"note":751},"Google, Temasek & Bain — e-Conomy SEA 2025","https:\u002F\u002Fwww.temasek.com.sg\u002Fen\u002Fnews-and-resources\u002Fnews-room\u002Fnews\u002F2025\u002Fe-conomy-sea-2025-report-aseans-digital-economy-poised-to-surpass-300-billion","Macro context on the speed and scale of SEA marketplace commerce — the velocity gap the architecture is designed to close.",{"title":753,"url":754,"note":755},"McKinsey — Operating model decisions in algorithmic retail","https:\u002F\u002Fwww.mckinsey.com\u002Findustries\u002Fretail\u002Four-insights","McKinsey commentary on the shift from descriptive analytics to prescriptive analytics in retail operating models — the framing this note's five-layer architecture operationalises.",{"title":757,"url":758,"note":759},"Sea Limited — Investor Relations","https:\u002F\u002Fwww.sea.com\u002Finvestor\u002Fhome","Sea Limited 4Q25 \u002F 1Q26 disclosures on Shopee's AI investment — the public record behind the platform-side optimisation cadence the seller-side stack has to match.","blog\u002Fhow-ecommerce-decision-engines-work",[762,763,764,765,766,767],"Thesis: dashboards are descriptive (what happened); decision engines are prescriptive (what to do next, ranked by projected profit lift, with the math attached). For SEA marketplace sellers in 2026, the descriptive-only model has stopped being sufficient — the platform-side optimisation cadence is faster than monthly P&L review can keep up with.","The architecture has five layers: ingestion, normalisation, metric calculation, pattern detection, recommendation. The first two are unglamorous data plumbing; the last three are where the operating value lives. Most spreadsheet-based seller setups fail at layer 2.","The recommendation layer is not 'another chart.' It is a ranked list of suggested actions attached to specific entities — pause this campaign, reorder this SKU, change this price — with the math behind each recommendation auditable in one click.","In our data across ~400 Thai SEA-6 accounts, operators running a decision-engine workflow recover 6–10 percentage points of annual contribution margin vs. operators running dashboard-only workflows. The gap is not from any single recommendation; it is from the cumulative effect of catching cross-domain conflicts (high ad spend on stockout-risk SKU, deep discount on margin-trap SKU) before they fire.","The seller still owns the decision. The engine eliminates the analysis tax of getting to the decision in the first place. Auditable math is the trust layer — without it adoption stalls; with it the system compounds across decision cycles.","Limitation: decision engines compound on data quality. Below ~THB 200K monthly revenue or with significant order-line data gaps, the operational overhead of running the architecture exceeds recovered margin; simpler heuristics outperform.","decision-science","8UBp5DeqlXeCNraafmuBNnF1shBaYJR_CWzKrupuPCY",{"frontmatter":771,"meta":783,"nodes":825},{"title":706,"description":707,"seo":772,"category":361,"format":226,"topic":768,"date":708,"readMinutes":472,"tldr":773,"related":774,"sources":775,"cta":782},{"title":732,"description":733},[762,763,764,765,766,767],[593,678,715],[776,777,778,779,780,781],{"title":737,"url":738,"note":739},{"title":741,"url":742,"note":743},{"title":745,"url":746,"note":747},{"title":749,"url":750,"note":751},{"title":753,"url":754,"note":755},{"title":757,"url":758,"note":759},{"title":726,"body":727},{"toc":784},{"title":331,"searchDepth":386,"depth":386,"links":785},[786,789,808,811,814,817,820,822],{"id":787,"depth":386,"text":788},"same-data-two-tools-two-different-answers","Same data, two tools, two different answers",{"id":790,"depth":386,"text":791,"children":792},"what-is-behind-that-prescriptive-column","What is behind that prescriptive column",[793,796,799,802,805],{"id":794,"depth":332,"text":795},"what-is-behind-that-prescriptive-column-layer-1-ingestion","Layer 1 — Ingestion",{"id":797,"depth":332,"text":798},"what-is-behind-that-prescriptive-column-layer-2-normalisation","Layer 2 — Normalisation",{"id":800,"depth":332,"text":801},"what-is-behind-that-prescriptive-column-layer-3-metric-calculation","Layer 3 — Metric calculation",{"id":803,"depth":332,"text":804},"what-is-behind-that-prescriptive-column-layer-4-pattern-detection","Layer 4 — Pattern detection",{"id":806,"depth":332,"text":807},"what-is-behind-that-prescriptive-column-layer-5-recommendation","Layer 5 — Recommendation",{"id":809,"depth":386,"text":810},"decision-making-as-a-cost-problem","Decision-making as a cost problem",{"id":812,"depth":386,"text":813},"why-decision-engines-matter-more-now","Why decision engines matter more now",{"id":815,"depth":386,"text":816},"human-judgment-still-matters","Human judgment still matters",{"id":818,"depth":386,"text":819},"limitations-and-where-this-argument-breaks","Limitations and where this argument breaks",{"id":577,"depth":386,"text":821},"Methodology",{"id":823,"depth":386,"text":824},"faq","FAQ",[826,829,833,839,972,977,980,982,985,988,991,993,996,998,1001,1003,1006,1008,1011,1021,1023,1026,1036,1039,1041,1044,1048,1050,1053,1055,1077,1081,1083,1105,1114,1116,1119,1123,1127,1131,1135,1138],[827,828,788],"h2",{"id":787},[830,831,832],"p",{},"A dashboard tells the seller what happened. A decision engine tells the seller what to do next, ranks the options by projected profit lift, and shows the math behind every recommendation. The clearest way to feel the difference is to put ten common operating decisions through both tools and read the columns side by side.",[830,834,835],{},[836,837,838],"em",{},"Ten common marketplace decisions — dashboard view vs decision-engine view",[840,841,842,858],"table",{},[843,844,845],"thead",{},[846,847,848,852,855],"tr",{},[849,850,851],"th",{},"Operating decision",[849,853,854],{},"Dashboard view (descriptive)",[849,856,857],{},"Decision engine view (prescriptive)",[859,860,861,873,884,895,906,917,928,939,950,961],"tbody",{},[846,862,863,867,870],{},[864,865,866],"td",{},"Should I scale Campaign A?",[864,868,869],{},"Campaign A produced THB 80,000 in attributed sales last week.",[864,871,872],{},"Campaign A's true ROAS is 1.2 vs SKU break-even 2.4 → REDUCE budget by ~40%; reallocate to Campaign D (true ROAS 3.8 vs break-even 2.1).",[846,874,875,878,881],{},[864,876,877],{},"Should I reorder SKU B?",[864,879,880],{},"SKU B sold 500 units last month; current stock 240 units.",[864,882,883],{},"Days-of-supply 14d; lead time 18d; 11.11 demand-spike window in 21d → REORDER NOW (450 units, ranked #2 on profit-loss-on-stockout list).",[846,885,886,889,892],{},[864,887,888],{},"Should I discount SKU C for Pay Day?",[864,890,891],{},"SKU C average ROAS during last Pay Day: 4.6×.",[864,893,894],{},"Contribution margin 18%; required volume lift to break even on 12% discount: +200%; historical Pay Day lift on this SKU: ~80% → DECLINE Pay Day participation; protect margin.",[846,896,897,900,903],{},[864,898,899],{},"Should I match competitor price drop?",[864,901,902],{},"Competitor SKU now THB 290 (vs my THB 320).",[864,904,905],{},"Competitor margin estimate ~12% (vs my 28%); likely inventory clearance; price-elasticity history suggests \u003C5% volume loss at THB 320 → HOLD price; competitor reverts in 7–10 days.",[846,907,908,911,914],{},[864,909,910],{},"Is TikTok Shop earning more budget?",[864,912,913],{},"TikTok Shop ROAS 3.4× this month, +18% MoM.",[864,915,916],{},"True ROAS net of affiliate commission + elevated return rate is 2.1× vs break-even 2.6× → HOLD TikTok budget; reallocate to Shopee where marginal-ROAS curve sits higher at current spend.",[846,918,919,922,925],{},[864,920,921],{},"Why is my hero SKU losing impressions?",[864,923,924],{},"Sponsored Search position dropped 3.2 → 5.8 over 14 days.",[864,926,927],{},"Keyword-cluster CPC +42% during pre-Pay-Day window; competitor raised bid 1 day after inventory clearance → INCREASE bid 18% for 11 days OR shift to Sponsored Discovery on broader query (same conversion at 31% lower CPC).",[846,929,930,933,936],{},[864,931,932],{},"Should I stay in Free Shipping Programme?",[864,934,935],{},"Free Shipping Programme orders +24% YoY.",[864,937,938],{},"FSP costs THB 8.40\u002Forder seller-funded; FSP-margin 2.1pp lower than non-FSP, but conversion +14% → KEEP for SKUs A\u002FB (margin absorbs); EXIT for SKU C (margin already 9%; FSP turns it loss-making).",[846,940,941,944,947],{},[864,942,943],{},"Should I shift SKU D from Lazada to Shopee?",[864,945,946],{},"SKU D Lazada GMV THB 89K; Shopee GMV THB 152K.",[864,948,949],{},"Lazada per-unit contribution THB 18 (after Voucher Wallet seller portion + LazMall fee); Shopee per-unit THB 27. Lazada needs 21% LazMall conversion-lift to break even, currently 11% → REDIRECT 60% of remaining Lazada inventory to Shopee over 30d.",[846,951,952,955,958],{},[864,953,954],{},"Should I bundle SKU E with SKU F?",[864,956,957],{},"SKU E and SKU F bought together by 18% of SKU E buyers.",[864,959,960],{},"SKU E margin 12% (margin-trap); SKU F margin 31%. Bundling lifts blended margin to 22% if F attaches 1:1; historical attach 18% with 1.4× lift at bundle-discount pricing → LAUNCH bundle at THB 480; model predicts 6.4% margin recovery over 90d.",[846,962,963,966,969],{},[864,964,965],{},"Should I exit Category Z?",[864,967,968],{},"Category Z produced THB 2.1M GMV last quarter.",[864,970,971],{},"Aggregate contribution margin −3.4% after ads, fees, returns, 27-day payout lag. Three of fourteen SKUs profitable; carry 84% of category gross margin on \u003C12% of volume → EXIT 11 loss-making SKUs over 60d; retain 3-SKU positive cohort with focused ad budget.",[830,973,974],{},[836,975,976],{},"The dashboard column answers \"what happened\" for each entity. The decision-engine column answers \"what to do, ranked by projected profit lift, with the math attached.\" The seller approves the decision; the engine eliminates the analysis tax of getting to it.",[830,978,979],{},"The pattern repeats across every cell of the table. The dashboard surfaces an observation; the decision engine resolves it into an instruction the seller can approve, override with reason, or escalate. The instruction comes with three things the dashboard cannot supply: a comparison to the right break-even, a ranking against the other live decisions, and the math behind the recommendation.",[827,981,791],{"id":790},[830,983,984],{},"Every recommendation in the right column is the output of a five-layer stack, each layer producing the inputs the next layer needs to be honest. Most failed seller-software deployments fail at Layer 2 — canonical SKU mapping across shops and platforms — because the data fragmentation is worse than vendors anticipate. Quality at that layer dominates everything above it.",[986,987,795],"h3",{"id":794},[830,989,990],{},"Pulls data from marketplaces, ad platforms, inventory systems, cost files, pricing history, and settlement records. Without it, every layer above is missing inputs.",[986,992,798],{"id":797},[830,994,995],{},"Maps SKUs, variations, campaigns, costs, and orders into one consistent structure across shops and platforms. The unglamorous step that makes everything else possible — and the step most spreadsheet-based setups never quite finish.",[986,997,801],{"id":800},[830,999,1000],{},"Computes contribution margin, true ROAS, break-even ROAS, ad waste, stockout risk, days-of-supply, promotion impact, price-elasticity signals, and channel margin. These are the numbers the recommendation column actually compares against.",[986,1002,804],{"id":803},[830,1004,1005],{},"Watches for the recognised failure modes — high revenue with low profit, ads on stockout-risk SKUs, discount-driven revenue, deteriorating margin, campaigns underperforming break-even, channel mismatches where the same SKU prints money on one marketplace and bleeds on another.",[986,1007,807],{"id":806},[830,1009,1010],{},"Outputs ranked actions attached to specific entities — the right column of the table. The seller still decides; the engine eliminates the analysis tax of getting to a defensible decision in the first place.",[1012,1013,1015,1016,1020],"blog-question",{"question":1014},"What's the difference between a marketplace dashboard and a decision engine?","A dashboard is descriptive: it surfaces what happened (campaign sales, SKU units, attributed revenue, ROAS). A decision engine is prescriptive: it surfaces what to do next, ranked by projected profit lift, with the math behind every recommendation. The five-layer architecture (ingestion → normalisation → metric calculation → pattern detection → recommendation) is what produces the prescriptive output. The seller still owns the decision; the engine eliminates the analysis tax of getting to it. For SEA marketplace sellers in 2026 the descriptive-only model has stopped being sufficient because the platform-side optimisation cadence is faster than monthly P&L review can keep up with — Sea Limited's documented AI investment cycle, the Google–Sea agentic shopping prototype, and the Bain-documented retail-media inflation all push the seller-side decision frequency upward ",[1017,1018],"blog-cite",{"n":1019},"2",":blog-cite:blog-cite.",[827,1022,810],{"id":809},[830,1024,1025],{},"Every seller decision has an opportunity cost. If budget goes to SKU A, it cannot go to SKU B. If cash is used to reorder slow inventory, it cannot be used for high-margin winners. If a seller discounts unnecessarily, margin is permanently gone. If a seller ignores stockout risk, future campaign performance pays the bill. The total cost of suboptimal decision-making is the sum of four kinds of leakage:",[1027,1028,1031],"pre",{"language":1029,"filename":1030},"text","Suboptimal decision cost",[1032,1033,1035],"code",{"class":1034},"language-text","wasted spend + lost margin + missed upside + operational drag",[830,1037,1038],{},"A decision engine reduces that cost by surfacing the conflict between two decisions early — before either one has fully fired.",[827,1040,813],{"id":812},[830,1042,1043],{},"Commerce is becoming more algorithmic. Marketplaces optimize search, ads, recommendations, logistics, and pricing signals at increasing resolution. Video commerce and retail media make discovery faster and more competitive. Major platforms are investing heavily in AI-driven shopping and advertising. For sellers, the practical effect is that the operating environment is moving faster than monthly P&L review cycles can keep up with.",[1045,1046],"blog-pullquote",{"text":1047},"Manual reporting cannot keep up with real-time decisions.",[827,1049,816],{"id":815},[830,1051,1052],{},"A decision engine should not replace the seller. It should improve the seller's judgment. The seller still understands brand, supplier relationships, product quality, customer expectations, and the longer-term strategy that does not show up in any dataset. The system contributes the things software is better at — connecting data, calculating metrics, detecting patterns, monitoring change, and surfacing anomalies the human eye would miss in a spreadsheet at 2 a.m. The best operating model combines human context with machine consistency.",[827,1054,819],{"id":818},[1056,1057,1058,1062,1065,1068,1071,1074],"ul",{},[1059,1060,1061],"li",{},"Account-size lower bound. Decision engines compound on data quality. Below ~THB 200K monthly revenue, the operational overhead of running the architecture exceeds recovered margin; simpler heuristics (a per-category margin lookup, manual weekly reconciliation) outperform.",[1059,1063,1064],{},"Layer 2 is the silent failure point. The architecture sketched here looks clean in a diagram and is brutal to implement in production. Most failed seller-software deployments fail at Layer 2 (canonical SKU mapping across shops and platforms) because the data fragmentation is worse than vendors anticipate. Quality at this layer dominates everything above it.",[1059,1066,1067],{},"Recommendation framing risk. A recommendation layer that overclaims confidence will burn trust faster than no recommendations at all. The output should be 'here is what we think you should do, here is the math, here is the confidence' — not 'do this' as a directive. Auditable math is the trust layer.",[1059,1069,1070],{},"Pattern-detection lag. Layer 4 detects recognised failure modes but cannot anticipate genuinely novel patterns (a new campaign mechanic, a sudden category disruption, a one-off platform policy change). Human judgment still owns the long tail.",[1059,1072,1073],{},"Cross-platform coordination. The architecture works cleanly within one platform's data. Cross-platform coordination (Shopee × Lazada × TikTok Shop) requires the canonical product catalog at Layer 2 to bind the same physical SKU across platforms — an additional step that scales the operational complexity meaningfully.",[1059,1075,1076],{},"Internal-data scope. The 6–10 percentage-point margin-recovery figure is aggregated across the SEA-6 Thai marketplace seller accounts we model directly. Not a population claim about all decision-engine deployments; explicitly excludes accounts below the size bound and the negotiated-rate enterprise tier.",[1012,1078,1080],{"question":1079},"When is a decision engine not worth using?","Below roughly THB 200K in monthly revenue, the operational overhead of running the architecture exceeds the margin recovered — simpler heuristics (a per-category margin lookup, a manual weekly reconciliation) outperform. The engine also struggles when order-line data is materially incomplete, when SKUs cannot be mapped reliably across shops (Layer 2 failure), or when the decision frame is genuinely novel (a new campaign mechanic, a one-off platform policy change) where pattern detection has nothing to anchor on. In those cases, human judgment still wins.",[827,1082,821],{"id":577},[830,1084,1085,1086,1089,1090,1093,1094,1096,1097,1100,1101,1104],{},"Public-data citations are taken from the 2025 ScienceDirect systematic review of ML approaches in inventory control (which formalises the multi-layer ingest → forecast → optimise → policy stack) ",[1017,1087],{"n":1088},"1",", Reuters reporting on Sea Limited's AI investment cadence (3 March 2026) ",[1017,1091],{"n":1092},"3"," and the Google–Sea agentic shopping partnership (19 February 2026) ",[1017,1095],{"n":1019},", Sea Limited's 4Q25 \u002F 1Q26 investor disclosures ",[1017,1098],{"n":1099},"6",", and the Bain e-Conomy SEA 2025 commentary on regional commerce velocity ",[1017,1102],{"n":1103},"4",":blog-cite.",[830,1106,1107,1108,1113],{},"Internal-data claims — the 6–10 percentage-point margin-recovery figure, the layer-by-layer value-compounding pattern in the chart, the typical failure-mode distribution — are aggregated across approximately 400 active marketplace seller accounts across the ",[1109,1110,1112],"a",{"href":1111},"\u002Fen-id\u002Fblog\u002Fmethodology","DataGlass research methodology"," sample frame (Jan 2024 – Apr 2026, 28-month observation window). Margin-gap measurements are computed on rolling 90-day windows.",[827,1115,824],{"id":823},[830,1117,1118],{},"Four short, query-shaped questions and answers that compress the report for skimmers and AI search. The \"vs dashboard\" and \"when is it not worth using?\" questions live next to the relevant evidence in the body above.",[1012,1120,1122],{"question":1121},"What is an e-commerce decision engine?","An e-commerce decision engine is a software stack that ingests marketplace data — orders, ads, fees, inventory, costs, returns — and outputs ranked, auditable next-actions for the seller, attached to specific entities (campaigns, SKUs, prices, reorders). It differs from a dashboard in that the dashboard answers \"what happened?\" while the decision engine answers \"what should I do next, and why?\" The seller still owns the decision; the engine eliminates the analysis tax of getting to one.",[1012,1124,1126],{"question":1125},"What are the five layers of a decision engine?","Layer 1 — Ingestion: pulls data from marketplaces, ad platforms, inventory, costs, pricing history. Layer 2 — Normalisation: maps SKUs, variations, campaigns, costs, and orders into one canonical structure across shops and platforms. Layer 3 — Metric calculation: contribution margin, true ROAS, break-even ROAS, ad waste, stockout risk, days-of-supply, price-elasticity signals. Layer 4 — Pattern detection: recognises failure modes (high revenue with low profit, ads on stockout-risk SKUs, discount-driven revenue, deteriorating margin). Layer 5 — Recommendation: outputs a ranked list of suggested actions attached to specific entities, with the math attached for audit. Most spreadsheet-based seller setups fail at Layer 2.",[1012,1128,1130],{"question":1129},"What data does a marketplace decision engine use?","Order-line data from Shopee, Lazada, and TikTok Shop (so contribution margin can be reconstructed per order, not estimated per category); ad-platform data from each marketplace's ad surface; inventory state and lead times; per-SKU COGS and supplier costs; pricing history; fee schedules including transaction fees, commission, voucher mechanics, and platform-funded promotions; and settlement\u002Fpayout schedules so working capital is in view alongside P&L. Without all of these joined at the canonical-SKU level, the recommendation layer can't be honest.",[1012,1132,1134],{"question":1133},"Can a decision engine replace human judgment?","No, and that is the point. The engine eliminates the analysis tax of getting to a defensible decision; the seller still approves the action and owns context the engine doesn't see (a supplier delay, a product launch, a reputational reason to keep a campaign live). Good decision engines surface confidence and the math behind every recommendation so the seller can override with a known reason. A decision engine that overclaims confidence will burn trust faster than no recommendations at all.",[1045,1136],{"text":1137},"Not another dashboard. A profit-aware decision engine — five layers, ranked recommendations, auditable math, human judgment on top.",[1139,1140,1142],"blog-callout",{"title":1141},"Descriptive is reporting; prescriptive is operating","The descriptive-vs-prescriptive distinction is not vocabulary; it is operating model. Dashboards make sellers slower as the platform-side optimisation cadence accelerates; decision engines make them faster — but only if the five-layer architecture is implemented honestly, with auditable math at the recommendation layer and the seller's judgment owning the long tail.",[1144,1145,1152,1153,1154,1155,1156,1157],{"id":787,"depth":386,"text":788},{"id":790,"depth":386,"text":791,"children":1146},[1147,1148,1149,1150,1151],{"id":794,"depth":332,"text":795},{"id":797,"depth":332,"text":798},{"id":800,"depth":332,"text":801},{"id":803,"depth":332,"text":804},{"id":806,"depth":332,"text":807},{"id":809,"depth":386,"text":810},{"id":812,"depth":386,"text":813},{"id":815,"depth":386,"text":816},{"id":818,"depth":386,"text":819},{"id":577,"depth":386,"text":821},{"id":823,"depth":386,"text":824},{"id":1159,"categories":1160,"domain":1167,"extension":5,"fallback":1168,"home":1171,"industry":1180,"meta":1185,"pricing":1186,"stem":1198,"__hash__":1199},"og_en\u002Fen\u002Fog.yml",[1161,1162,1163,1164,1165,1166],{"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":1169,"description":1170},"DataGlass","The decision engine for Shopee, Lazada and TikTok Shop sellers.",{"chart":1172,"metrics":1173},"Net profit, last 30 days",[1174,1177],{"label":1175,"value":1176},"Net profit","฿1.24M",{"label":1178,"value":1179},"Profit ROAS","3.4x",{"axis":1181,"point":1182},"Ad spend",{"label":1183,"value":1184},"Next baht still pays","+฿0.42",{},{"plans":1187},[1188,1192,1195],{"name":1189,"price":1190,"period":1191},"Free","฿0","\u002Fmonth",{"name":1193,"price":1194,"period":1191},"Standard","฿1,499",{"name":1196,"price":1197,"period":1191},"Pro","฿2,499","en\u002Fog","63L8y4Lv8I2q3NvR26fMXU1sE9Wd7TZaz2l_tV6oZuo",[],1792785048739]