DG·Engineering
Engineering the decision layer.
Write-ups on how DataGlass is built: pulling Shopee, Lazada, and TikTok Shop data into one canonical catalog, reconstructing contribution margin from order lines, estimating true ROAS when attribution windows disagree, and running demand forecasts in production.
Written for data and ML engineers, technical founders, and anyone who would rather inspect the modelling than the marketing. Every note carries the arithmetic in currency units, the failure modes, and the point where the argument breaks.
- Engineering · 2026
- 2026
- Notes
- 10
- Latest
- Aug 18
- Topics
- 05
- Cadence
- On publish
Every engineering note.
In reverse-chronological order. Each note lives in the blog archive at its own URL and is tagged Technical there; this page is the technical cut of that archive.
- 02Pricing
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.
DataGlass Labs ResearchTechnical- May 7, 2026
- Published
- 14 min
- Read time
- Pricing
- Topic
- 03Landscape
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?"
DataGlass ResearchTechnical- May 4, 2026
- Published
- 22 min
- Read time
- Landscape
- Topic
- 04Ads
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.
DataGlass ResearchTechnical- April 29, 2026
- Published
- 11 min
- Read time
- Ads
- Topic
- 05Ads
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.
DataGlass ResearchTechnical- March 25, 2026
- Published
- 14 min
- Read time
- Ads
- Topic
- 06Data Science
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.
DataGlass ResearchTechnical- February 18, 2026
- Published
- 11 min
- Read time
- Data Science
- Topic
- 07Data Science
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.
DataGlass ResearchTechnical- February 4, 2026
- Published
- 14 min
- Read time
- Data Science
- Topic
- 08Operations
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.
DataGlass ResearchTechnical- January 18, 2026
- Published
- 12 min
- Read time
- Operations
- Topic
- 09Pricing
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.
DataGlass ResearchTechnical- January 4, 2026
- Published
- 11 min
- Read time
- Pricing
- Topic
- 10Data Science
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.
DataGlass ResearchTechnical- December 12, 2025
- Published
- 12 min
- Read time
- Data Science
- Topic