From 1-click buying to 0-click logistics
Picnic is the world’s fastest growing online supermarket that makes grocery shopping simple, fun, and affordable for everyone. We will show you how we transformed an already convenient shopping experience into a delightful ultra-fast shopping blitz-stop. In this talk we provide a view behind the scenes of our deep-learning based behavioral analytics and prediction engine. We will talk you through our ups-and-downs of product, category and promotional recommendations of FMCGs and do not shy away from demoing also the failures. Now we are able to predict with >95% likelihood the top 12 articles of the next order of each of our customers. We will show you how our connected devices, ubiquitous data, and a full-fledged machine-learning logistics backend fuels all our innovations and operations. Data Science Challenge 1: Shopping challenge. How to shop for 30 items in 3 minutes while it takes for 3 items usually 30 minutes with common apps? Data Science Challenge 2: Distribution challenge: How to delivery in 20 minute windows while the industry still struggles with 2 hour windows? Data Science Challenge 3: Innovation challenge: How to innovate with 2000 people as you did when you were in a garage with 20?
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- 1. From 1-Click Buying To 0-Click Logistics @daniel_gebler @picnic
- 2. 20 Days per year 912km Trips to Store 230kg Waste per person
- 3. Our Value Proposition Groceries + Mobile Home On-Time Lowest Price + + + =
- 5. LESS IS MORE
- 9. The Distribution Challenge 20 min window 99% on-time 1% no show
- 22. The Safety Challenge Nobody gets hurt Top-level objective All inclusive
- 23. The Safety Solution Gather Accelerometer Data Identify unsafe driving Communicate & Educate
- 24. Driver coaching based on ultra-high resolution data and actionable insights
- 26. 30 Articles in 3 Minutes The Mobile Shopping Challenge
- 27. 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
- 28. Input Hidden Layers Output Monday (wk -2) Friday (wk -2) Wednesday (wk -1) Tomorrow Solution: 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
- 29. 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)
- 30. The Customer Service Challenge Blitzscaling business Ultra-personal service Exceptional service quality
- 31. The Customer Service Solution
- 32. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … many more
- 33. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … many more
- 34. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … many more
- 35. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … many more
- 36. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … many more
- 37. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … many more
- 38. Automation with Natural Language Processing Design Ticket enrichment Issue identification Product identification Ticket routing Proposing solutions Customer Ticket … and many more
- 39. Creating happiness by sharing happiness with your team
- 42. 1. Dream Big, Act small 2. Mission first, Data as support 3. Data Science first, AI second 4. Launch first, Scale second 5. Great products come from small teams Learnings from a disruptive Scale-up
- 43. Milkman 2.0 serving millions of families http://picnic.app