Data Science in the Age of the On-Demand Economy

v

@jeremystan

Our Value Proposition

Groceries from stores youlove

deliveredto your doorstep

in as little as an hour

+ + + =

v

@jeremystan

Customer Experience

Select aStore

Shop for Groceries

Checkout Select Delivery Time

Delivered to Doorstep

v

@jeremystan

Shopper Experience

Accept Order Find the Groceries

Out for Delivery

Scan BarcodeDelivered to

Doorstep

v

@jeremystan

Four Sided Marketplace

Customers Shoppers

Products(Advertisers)

Search

Advertising

Shopping

Delivery

Customer Service

Inventory

Picking

Loyalty

Stores(Retailers)

@jeremystan

Unit EconomicsCustomers Love Us

Can we succeed?

Huge Market

$600,000,000,000

infor

or

v

@jeremystan

Our Unit Economics

Product Partnerships+$Retail Partnerships+$

Delivery Fees+$Tips (go to shoppers)+$

Transaction & insurance costs-$Shopping Time-$

-$ Driving TimeKey to bottom-line

v

@jeremystan

Profitable Unit Economics

Instacart has achieved profitable unit economicsDriven (in part) by huge decreases in fulfillment time:

@jeremystan

TimeVariance

Data Science Challenges

Marketplace

n4>>

2n 𝞵>>�

� 23:59:00>>00:59:00

v

@jeremystan

Optimizing MinutesBalance Supply & Demand Optimize Fulfillment

Forecast AdaptSchedule Predict DispatchPlanMeasure Evaluate

v

@jeremystan

What Was Demand?

Visitor

Total Demand = ∑ pr (convert | 100%

availability)

2. Lost

1. Checkout

3. No Intent

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

v

@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

v

@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

v

@jeremystan

Overall Results

-20%-0% +15%

+20%latelost

speed

busy

Customer Shopper

Utilization

Lost Deliverie

s

Hard

Easy

@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

@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

WE’RE HIRING!

@jeremystan