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.

1. Data Science in the Age of the On-Demand Economy

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@jeremystan
Our Value Proposition
Groceries from stores
you
love
delivered
to your
doorstep
in as little
as an hour
+ + + =

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@jeremystan
Customer Experience
Select a
Store
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 Barcode
Delivered to
Doorstep

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@jeremystan
Four Sided Marketplace
Customers Shoppers
Products
(Advertisers)
Customer Service
Stores
(Retailers)

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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 Time
Key to bottom-line

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@jeremystan
Profitable Unit Economics
Instacart has achieved profitable unit economics
Driven (in part) by huge decreases in fulfillment time:

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TimeVariance
Data Science Challenges
Marketplace
n
4
>>
2
n 𝞵>>𝞼 23:59:00>>00:59:00

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@jeremystan
Optimizing Minutes
Balance 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 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

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@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

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@jeremystan
Overall Results
-20%
-0% +15%
+20%late
lost
speed
busy
Customer Shopper
Utilization
Lost
Deliveries

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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

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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. WE’RE HIRING!
@jeremystan