wrangle 2016: data science in the age of the on-demand economy
TRANSCRIPT
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Data Science in the Age of the On-Demand Economy
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@jeremystan
Our Value Proposition
Groceries from stores youlove
deliveredto your doorstep
in as little as an hour
+ + + =
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@jeremystan
Customer Experience
Select aStore
Shop for Groceries
Checkout Select Delivery Time
Delivered to Doorstep
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@jeremystan
Shopper Experience
Accept Order Find the Groceries
Out for Delivery
Scan BarcodeDelivered to
Doorstep
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@jeremystan
Four Sided Marketplace
Customers Shoppers
Products(Advertisers)
Search
Advertising
Shopping
Delivery
Customer Service
Inventory
Picking
Loyalty
Stores(Retailers)
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@jeremystan
Unit EconomicsCustomers Love Us
Can we succeed?
Huge Market
$600,000,000,000
infor
or
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@jeremystan
Our Unit Economics
Product Partnerships+$Retail Partnerships+$
Delivery Fees+$Tips (go to shoppers)+$
Transaction & insurance costs-$Shopping Time-$
-$ Driving TimeKey to bottom-line
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@jeremystan
Profitable Unit Economics
Instacart has achieved profitable unit economicsDriven (in part) by huge decreases in fulfillment time:
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@jeremystan
TimeVariance
Data Science Challenges
Marketplace
n4>>
2n 𝞵>>�
� 23:59:00>>00:59:00
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@jeremystan
Optimizing MinutesBalance Supply & Demand Optimize Fulfillment
Forecast AdaptSchedule Predict DispatchPlanMeasure Evaluate
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@jeremystan
What Was Demand?
Visitor
Total Demand = ∑ pr (convert | 100%
availability)
2. Lost
1. Checkout
3. No Intent
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@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
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@jeremystan
Predicting Fulfillment Times
#1
#2 #3#1 #3 #2
Due#2 Ordered
Shopper 1
Shopper 2Driver 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
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@jeremystan
Optimally Routing Shoppers
1,000 orders4 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
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@jeremystan
Overall Results
-20%-0% +15%
+20%latelost
speed
busy
Customer Shopper
Utilization
Lost Deliverie
s
Hard
Easy
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@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
DesignerData Scientist
Engineer
MobileProductAnalyst
Rare
Matrixed
Empowered
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@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