DG·DG · Research Lab
Research for the seller floor.
Technical reports, market research, and methodology notes from the DataGlass Research Lab. Each report is grounded in primary sources, opens its own data, and ends with the operating implication for marketplace sellers.
- Lab · 2026
- 2026
- Reports
- 05
- Latest
- May 7
- Cadence
- On publish
Available reports.
In reverse-chronological order. Each report links to the full publication; cross-listed market research lives on the blog system but is research-grade.
- 02Working paper · May 2026
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.
Bhum Soonjun · DataGlass Labs Research- May 7, 2026
- Published
- 46 min
- Read time
- Advanced
- Difficulty
- 03Research report · May 2026
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.
DataGlass Research Lab- May 4, 2026
- Published
- 42 min
- Read time
- Advanced
- Difficulty
- 04Working paper · May 2026
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.
DataGlass Research- May 4, 2026
- Published
- 28 min
- Read time
- Advanced
- Difficulty
- 05Working paper · May 2026
Prediction and Risk Optimization Under Uncertainty: A Cross-Domain Meta-Review of Methods in Finance, Operations, Causal Inference, and E-Commerce Decision Intelligence
A structured meta-review (213 primary works, 254 references) arguing that mature decision systems across finance, operations, insurance, energy, healthcare, causal inference, and e-commerce share four primitives — calibrated probabilistic models, coherent risk-aware objectives, explicit operational constraint sets, and principled exploration. Eleven worked cases ground the framework, with the DataGlass marketplace ad-budget system as the connecting tissue.
DataGlass Labs Research- May 4, 2026
- Published
- 75 min
- Read time
- Advanced
- Difficulty