How Picnic became the Supermarket in your pocket
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Picnic makes grocery shopping simple, fun, and affordable for everyone. In this talk we discuss the following challenges and data-driven solutions: 1. Co-creation Challenge: How to build a product and service together with customers but stay lean and agile. 2. Distribution challenge: How to build reliable logistic planning in an uncertain world. The slides are from a presentation at the Big Data Expo in Utrecht (September 2017).
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How Picnic became the Supermarket in your pocket
- 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. 1000s of suggestions each week The Co-Creation Challenge
- 5. Step 1: Create Visibility, Encourage Accountability, Celebrate Success
- 6. 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)
- 7. Result: Auto-classification 3 out of 4 suggestions Insufficient training data Max 91% accuracy
- 8. Data Science is the MVP for AI Products
- 9. The Distribution Challenge 99% on-time delivery 1% no show 5-star rating
- 10. Formalization of the Challenge Tdrop = Carea + C1 + Cambient|chilled·Nambient|chilled + Cfrozen·Nfrozen + Tdelta
- 11. Fitness Measure RMSE = 𝑖=1 𝑛 ∆𝑇 𝑑𝑟𝑜𝑝 (𝑖)2 𝑛 Adjusted drop time deviation • Asymmetry (early vs. late) • Error filtering Normalization • Time of day • Driver • Vehicle
- 12. 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
- 14. Creating a mobile super service @picnic @daniel_gebler
Editor's Notes
- From Big Data to Right Data - Picnic makes grocery shopping simple, fun, and affordable for everyone. - We co-created our shopping app and assortment together with our customers. How do we ensure that first-time visitors become quickly loyal fans? - We deliver our groceries by connected vehicles that are smart, safe, and sustainable. How do we translate ubiquitous data into continuous innovations and operational excellence?
- Grocery shopping low penetration (while electronics etc. very high). Why? Expensive Waiting Cumbersome
- Order Feedback Assortment Feedback
- 20 min delivery window
- Tdrop Planned drop time. Carea Drop time per delivery area. This also includes the constant parking delivery time (80 secs). Baseline. C1st-cust Additional 60 secs for the first customer delivery. Cambient|chilled·Nambient|chilled Time needed to take an ambient/chilled tote multiplied by the number of these totes. Cfrozen·Nfrozen Time needed to take a frozen bag multiplied by the number of these bags. Tdelta Additional drop time (either positive or negative) to adjust the final drop time. Fitness measure MSE Tweaking to have less overestimation for underestimation
- Normal distribution confirmed by Shapiro-Wilk test, hence Root mean square error test is fine Overprediction penalty = 0.2 Filter measurement errors <-300s, >500s