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Picnic Big Data Expo
- 1. @daniel_gebler @picnic How Picnic became the Supermarket in your Pocket
- 2. Our Value Proposition Groceries + Mobile Home On-Time Lowest Price + + + =
- 3. Our Mobile Shopping Ecosystem 10s of cities 100s of suppliers 1,000s of local products 10,000s of global products 100,000s of customers
- 4. 3 Challenges
- 5. 30 Articles in 3 Minutes The Mobile Shopping Challenge
- 6. The Mobile Shopping Solution - Bulk recommendations • Set of 4, 8 or 12 articles • Buy all with a single tap • 1-click shopping for half of your basket • Purchase confidence >90% • Covering repetitive & boring items
- 7. Challenges of bulk recommendations Precision challenge • 90% single item precision 28% precision for 12 items • 90% precision for 12 items 99% single item precision • Factor 10 better recommendations required! Item challenge • Seasonal availability • Seasonal variations • Event-based buying patterns
- 8. Our Shopping Time Journey 0 50 100 150 200 250 300 350 400 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Sessiontime(inseconds) 2 1 3
- 9. Formalization of the Challenge PR(next= |hist=(( ,t-4),( ,t-7),( ,t-9)))
- 10. Input Hidden Layers Output Monday (wk -2) Friday (wk -2) Wednesday (wk -1) Tomorrow Solution 1: Deep Recurrent Neural Network (LSTM) • Item likelihood to buy • Cat likelihood to buy • Next 7 days • Item/cat buying interval • Order history (articles, dates) • Normalized quantities • Days between orders x 1 x 2 x 0 x 3 x 0 x 1 x 2 x 2 x 0 x2 x 1 x 1 Item - Item relations Item - Day relations Itemset - Day relations y2 x1 x2 x3 y3 y1 z2 z3 z1
- 11. 50%
- 12. Shallow data
- 14. Pre-Training
- 15. Solution 2: RFM-based order prediction Get last 10 orders Select top items (80% orders) Rank by [freq, qty] Display best items (min 4, max 12) … | | | Filter by seasonality … … … … … …
- 16. Result: Big and Deep data for optimal RFM prediction parameters 65% 70% 75% 80% 85% 90% 0 20 40 60 80 100 120 140 Precision Number of orders Big data (lack of depth) Big & Deep data Deep data (lack of breadth)
- 17. BIG DATA Insights from scale of collected data points (large sample) Insights from depth of stories (small sample) DEEP DATA DEPTH OF INSIGHTS N Summary: Deep Learning requires both Big Data and Deep Data
- 18. 1000s of suggestions each week The Co-Creation Challenge
- 19. Step 1: Create Visibility, Encourage Accountability, Celebrate Success
- 20. Step 2: ML-based classification of product suggestions Customer input (free text) Picnic Retail Platform (storage) Force.com (processing & analytics) Zendesk (Customer feedback) Picnic Retail Platform (status update) Azure ML (NeuralNet classification)
- 21. Result: Auto-classification 3 out of 4 suggestions Insufficient training data Max 91% accuracy
- 22. Data Science is the MVP for AI Products
- 23. The Distribution Challenge 99% on-time delivery 1% no show 5-star rating
- 24. Formalization of the Challenge Tdrop = Carea + C1 + Cambient|chilled·Nambient|chilled + Cfrozen·Nfrozen + Tdelta
- 25. Fitness Measure RMSE = σ 𝑖=1 𝑛 ∆𝑇 𝑑𝑟𝑜𝑝 (𝑖)2 𝑛 Adjusted drop time deviation • Asymmetry (early vs. late) • Error filtering Normalization • Time of day • Driver • Vehicle
- 26. Calibration process ▪ Daily update (params) ▪ Weekly review (params) ▪ Monthly review (model) ▪ Granularity (PC6 vs. address) Parametrization ▪ Init by defaults (avg. optima) ▪ Household size ▪ Proximity House vs. Street ▪ City Maturity ▪ Hub Maturity ▪ Runner Maturity Result: 50% improvement after 10 drops, oscillating convergence -10 -5 0 5 10 20 40 60 80 DropTimedeviation(inseconds) Drop time deviation Moving average
- 28. Creating a mobile super service @picnic @daniel_gebler