DataGlass

Company/Team

Bhum Soonjun

Co-founder · Research, Data & Domain

Bhum Soonjun

Leads research, data, and domain — owns the probabilistic models, decision logic, and marketplace economics that drive the platform.

Profile

Background.

Data scientist and systems engineer working at the intersection of statistical modeling, optimization, and real-world system deployment. First-class honors in Computer Science from Mahidol University; awarded a PhD fellowship in Operations Research at North Carolina State University, and a PhD fellowship at VISTEC's School of Information Science and Technology (IST). Research published in IEEE Access. The thread across all of it is mathematics and probabilistic modeling — the discipline of stating an economic question precisely enough that it has an answer.

Backend and data at scale.

Career began in backend and data engineering at Agoda — building financial data pipelines, reconciliation systems, and distributed processing workflows on Kafka, Hadoop, and Spark. Years of operating financial infrastructure in production: high-reliability systems, complex business logic, and the discipline that comes from running money-handling pipelines that cannot drop a row. Backend engineering and scalable applications remain the foundation under everything that follows.

From financial models to decision systems.

Transitioned into a data-science role at Siam Commercial Bank, focused on predictive modeling for financial products including mortgage refinancing strategies — applying statistical models to drive business decisions at scale. The pattern that carried through to DataGlass was already there: probabilistic models, financial outcomes, and real production systems.

DataGlass — research-grade modeling, deployed.

Founder of DataGlass, an autonomous decision-making platform for e-commerce sellers. Unlike traditional analytics dashboards, DataGlass directly optimizes profit by deploying model-driven decisions across advertising, pricing, promotions, and inventory. The work centers on Bayesian modeling, causal inference, demand modeling under sparsity, and optimization under uncertainty — closed-loop systems where models do not only predict but execute and adapt decisions in real time.

Long-term focus.

The direction is autonomous decision systems that operate reliably under uncertainty and produce measurable economic outcomes. The technical interest is Bayesian modeling, stochastic optimization, and robust systems, particularly in financial and marketplace environments. Beyond DataGlass, has led teams as a software engineer and engineering manager and founded multiple startups, including award-winning projects — but the long-term direction is a single one: mathematics and engineering, in production.

Selected work

Representative research and shipped systems that ground the DataGlass operating layer.

  • 01Bayesian modeling for SKU-level marketplace economics under fragmented fee schedules.
  • 02Probabilistic demand forecasting under sparse, campaign-driven signal.
  • 03Causal inference for ROAS attribution under platform-funded promotions.
  • 04Stochastic optimization for budget allocation across advertising, pricing, and inventory.
  • 05Distributed data pipelines and backend systems for production-grade decision deployment.

The full publication record is on Google Scholar .

Writing

  1. Profit · 12 min

    How to increase Shopee profit without more sales

    Most "increase profit on Shopee" advice assumes you can grow GMV. The cost-side path — refusing structural waste in the supply stack rather than buying more units — usually clears more margin per quarter at lower working-capital risk. A research note on the supply-side cost stack and where the recoverable margin actually sits.

  2. Ads · 11 min

    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.

  3. Research report · May 2026 · 42 min

    Decision Intelligence for E-commerce: How Retailers Optimise Pricing, 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.

  4. Working paper · May 2026 · 28 min

    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 system for daily ad-budget allocation on platform-controlled marketplaces — the analytical reasons rolling-mean heuristics fail, the Bayesian + bandits-with-knapsacks methodology, and the empirical 18–24% portfolio-profit lift.

  5. Operations · 14 min

    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.

  6. Landscape · 22 min

    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?"

  7. Operations · 18 min

    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.

  8. Ads · 11 min

    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.

  9. Profit · 6 min

    How to increase profit on Shopee without just selling more

    The standard advice — chase ROAS, scale what works — is structurally biased toward overspend. Why platform ROAS misleads at scale, and the per-SKU break-even bar that replaces it.

  10. Profit · 13 min

    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.

  11. Ads · 13 min

    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.

  12. Profit · 11 min

    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.

  13. Profit · 11 min

    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.

  14. Ads · 14 min

    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.

  15. Ads · 11 min

    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.

  16. Profit · 13 min

    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.

  17. Pricing · 14 min

    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.

  18. Profit · 12 min

    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.

  19. Landscape · 16 min

    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.

  20. Data Science · 11 min

    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.

  21. Competition · 7 min

    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.

  22. Data Science · 14 min

    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.

  23. Complexity · 6 min

    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.

  24. Operations · 12 min

    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.

  25. Pricing · 11 min

    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.

  26. Data Science · 12 min

    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.

  27. Operations · 8 min

    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.

  28. Landscape · 15 min

    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.

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