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Wrangle 2016: Data Science in the Age of the On-Demand Economy

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By Jeremy Stanley, Instacart

Fifteen years ago, Webvan spectacularly failed to bring grocery delivery online. Speculation has been high that the current wave of on-demand grocery delivery startups - and other companies in the on-demand economy - will meet similar fates. Jeremy explains why this time the story will be different—data science is the key. Innovations in mobile applications have paved the way, but significant investments in algorithms to optimize efficiency will drive profitable growth.

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Wrangle 2016: Data Science in the Age of the On-Demand Economy

  1. 1. Data Science in the Age of the On-Demand Economy
  2. 2. v @jeremystan Our Value Proposition Groceries from stores you love delivered to your doorstep in as little as an hour + + + =
  3. 3. v @jeremystan Customer Experience Select a Store Shop for Groceries Checkout Select Delivery Time Delivered to Doorstep
  4. 4. v @jeremystan Shopper Experience Accept Order Find the Groceries Out for Delivery Scan Barcode Delivered to Doorstep
  5. 5. v @jeremystan Four Sided Marketplace Customers Shoppers Products (Advertisers) Customer Service Stores (Retailers)
  6. 6. @jeremystan Unit EconomicsCustomers Love Us Can we succeed? Huge Market $600,000,000,000 infor or
  7. 7. v @jeremystan Our Unit Economics Product Partnerships+$ Retail Partnerships+$ Delivery Fees+$ Tips (go to shoppers)+$ Transaction & insurance costs-$ Shopping Time-$ -$ Driving Time Key to bottom-line
  8. 8. v @jeremystan Profitable Unit Economics Instacart has achieved profitable unit economics Driven (in part) by huge decreases in fulfillment time:
  9. 9. @jeremystan TimeVariance Data Science Challenges Marketplace n 4 >> 2 n 𝞵>>𝞼 23:59:00>>00:59:00
  10. 10. v @jeremystan Optimizing Minutes Balance Supply & Demand Optimize Fulfillment Forecast AdaptSchedule Predict DispatchPlanMeasure Evaluate
  11. 11. v @jeremystan What Was Demand? Visitor Total Demand = ∑ pr (convert | 100% availability) 2. Lost 1. Checkout 3. No Intent
  12. 12. v @jeremystan Forecasting? Q. How many shoppers? … next Sunday? … at 7pm? … in San Francisco? … for Potrero Whole Foods? … for delivery in 2 hours? f ( prior week active ) → exponential decay → automated outlier removal → time series models → simulation model
  13. 13. v @jeremystan Predicting Fulfillment Times #1 #2 #3 #1 #3 #2 Due#2 Ordered Shopper 1 Shopper 2 Driver 1 Handoff Due#1 Ordered Due#3 Ordered ● Variance is as important as mean → quantile regression ● Gradient boosting machines for complex time & space features ● Update predictions frequently throughout fulfillment ● Scale to millions of predictions per minute (shoppers x orders x sequence) Delivery TimesPicking Times
  14. 14. v @jeremystan Optimally Routing Shoppers 1,000 orders 4 orders per trip x 100 shoppers = 400 million ● Remove constraints, unify objectives ● Recompute every minute, in every market ● Start with greedy heuristics ● Solve sub-problems optimally to benchmark ● Wait to last minute to dispatch ➔ Maximize expected # of items found ➔ Maximize probability of delivering on time ➔ Minimize total time spent delivering
  15. 15. v @jeremystan Overall Results -20% -0% +15% +20%late lost speed busy Customer Shopper Utilization Lost Deliveries
  16. 16. @jeremystan Mission Driven Working GroupsIntegrated ● Aligned with products ● Operate independently ● Cross eng team & org ● Single threaded leader ● All skills necessary ● Open code base How We Organize Engineering ConsumerLogistics Availability Fulfillment Growth Experience Orders 1 6 15 Designer Data Scientist Engineer Mobile ProductAnalyst Rare Matrixed Empowered
  17. 17. @jeremystan Urgency OwnershipTransparency ● Set clear goals ● Be uncomfortable ● Clear accountability ● Measure performance ● Share everything ● Seven different times Principles “If everything seems under control, you're not going fast enough.” ― Mario Andretti
  18. 18. WE’RE HIRING! @jeremystan

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