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Picnic Big Data Expo

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From Big Data to Right Data – How Picnic became the Supermarket in your Pocket
Daniel Gebler

Picnic Big Data Expo

  1. 1. @daniel_gebler @picnic How Picnic became the Supermarket in your Pocket
  2. 2. Our Value Proposition Groceries + Mobile Home On-Time Lowest Price + + + =
  3. 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. 4. 3 Challenges
  5. 5. 30 Articles in 3 Minutes The Mobile Shopping Challenge
  6. 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. 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. 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. 9. Formalization of the Challenge PR(next= |hist=(( ,t-4),( ,t-7),( ,t-9)))
  10. 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. 11. 50%
  12. 12. Shallow data
  13. 13. Small training set
  14. 14. Pre-Training
  15. 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. 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. 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. 18. 1000s of suggestions each week The Co-Creation Challenge
  19. 19. Step 1: Create Visibility, Encourage Accountability, Celebrate Success
  20. 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. 21. Result: Auto-classification 3 out of 4 suggestions Insufficient training data Max 91% accuracy
  22. 22. Data Science is the MVP for AI Products
  23. 23. The Distribution Challenge 99% on-time delivery 1% no show 5-star rating
  24. 24. Formalization of the Challenge Tdrop = Carea + C1 + Cambient|chilled·Nambient|chilled + Cfrozen·Nfrozen + Tdelta
  25. 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. 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
  27. 27. Creating a mobile super service @picnic @daniel_gebler
  • bartsmits

    Sep. 29, 2018

From Big Data to Right Data – How Picnic became the Supermarket in your Pocket Daniel Gebler

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