Success case of modern algorithms applied in FMCG industry
summary
Delivers a concrete FMCG deployment: builds 18 business-driven segments by training the sales force to label 5,000 points of sale, then scores ~150,000 venues with probabilistic “spider” outputs so marketing runs micro‑targeted campaigns; models price elasticity with extreme gradient boosting using Nielsen competitor prices, transaction logs, promotions and costs to generate deal‑level recommended prices stored in CRM. Adds web scrapers for social data and is rolling out Google Vision API + TensorFlow image classification to detect new openings in real time. Results: targeted actions and measurable pricing guidance.
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chapters (7)
- 0:00Introduction
- 4:25Business case
- 9:17Approach
- 13:09Machine Learning
- 16:26The Spider
- 18:32Pricing elasticity modeling
- 27:10Conclusion
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