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FMCGData PlatformForecastingAnalytics
Revenue Growth Management Platform
End-to-end RGM backend for forecasting, pricing, promotion, and purchase-structure intelligence
WeBuildTech·November 10, 2025
At a Glance
IndustryFMCG / Consumer Goods
EngagementEnd-to-end RGM backend
ScopeForecasting, pricing, promotion, purchase structure, API orchestration
Data scale27,778 enriched records · 646 SKU-level similarity inputs · 12-step forecasts
Core stackPython, pandas, NumPy, FastAPI, Typer CLI, joblib, SSE-Starlette
The Business Challenge
- Retail and syndicated market data typically lives across CSVs, Excel workbooks, and ad hoc analyses, making it difficult to run one consistent commercial planning model.
- Commercial teams need answers to connected questions: What will volume and share look like? How sensitive is demand to price? Which promotions create incremental value? How do shoppers switch across products?
- The client needed a reusable backend that could be run by stage, monitored centrally, and exposed to downstream dashboards instead of remaining trapped in offline notebooks.
What WeBuildTech Built
1. Data Engineering Foundation
Mapped pack sizes, base prices, price tiers, discounts, promo flags, market share, category sales, event signals, similarity inputs, and macro enrichments into a clean modelling base.
2. Forecasting Engine
Built SARIMAX-based forecasting for both volume and market share using configurable exogenous drivers such as discounts, holidays, price indices, GDP growth, and FX variables where available.
3. Pricing Engine
Generated own- and cross-price elasticity views, price indices, and category-loss style outputs to support smarter price corridor and substitution analysis.
4. Promotion Engine
Created baseline models, response-curve workflows, lift calculations, incremental revenue/profit KPIs, and ROI outputs for evaluating trade-spend efficiency.
5. Purchase-Structure Logic
Used transition behaviour, similarity, correlation, and clustering logic to build a purchase-structure tree that surfaces the strongest split variables inside the category.
6. Platformisation Layer
Wrapped the system with a Typer CLI, FastAPI endpoints, persisted run states, and SSE-based log streaming so model execution could be operationalised, not just analysed.
Platform Architecture
The backend follows a clear operating model: ingest structured retail inputs, enrich them into a decision-grade analytical base, run specialised RGM engines, and expose outputs through reusable APIs and execution workflows.
Why This Matters for the Client
- One commercial data foundation instead of multiple disconnected workbooks and manual handoffs.
- Explainable decision support through coefficients, response curves, price matrices, and tree splits rather than opaque black-box outputs.
- Reusable execution through CLI and API layers, making the platform suitable for dashboarding, planning workflows, and future market/category rollout.
- A strong base for scenario planning across pricing, promotions, demand forecasting, and assortment or purchase-structure decisions.
Technical Snapshot
Core stack
Python, pandas, NumPy, FastAPI, Typer CLI, joblib, SSE-Starlette
Analytical methods
SARIMAX forecasting, elasticity matrices, transition logic, clustering, ROI and response-curve modelling
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