Data Science at InstacartMaking On-Demand Profitable

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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)

@jeremystan

Unit EconomicsCustomers Love Us

Can we succeed?

Huge Market

$600,000,000,000infor

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

Path to Profitability

“Since the beginning of last year, revenue has grown by 500%”

“90% of our customers are repeat customers”

“Instacart Express customers spent about $500 a month on Instacart on average”

“In the next 12 months Instacart is going to be a profitable company … cash flow positive”- Apoorva Mehta

techcrunch.com/2016/09/14/how-apoorva-mehta-hopes-to-build-an-instacart-empire-with-a-promoted-ad-business/

@jeremystan

TimeVariance

Data Science Challenges

Marketplace

n4 >>

2n ��

>>

��

23:59:00>>00:59:0

0

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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 Demand?

… in a region?

… at a retailer?

… on a day?

… at an hour?

… for delivery in 2 hours?

→ Millions of forecasts

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@jeremystan

Many Sources of Outliers

Date

Markets

Holidays

Storms

Regional Events

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@jeremystan

Backtesting

Testing Design

Algorithm Performance over

Time

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@jeremystan

Demand Shock Absorbers

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@jeremystan

Predicting Fulfillment Times

Early On-Time Late

Cust

omer

Happ

ines

s

Delivery Window

Google Maps Travel Time

Instacart Delivery Model

Actual Delivery

Time

● Delivering on-time (or early) is critical for customer happiness

● Our predictions are better than using the Google Maps API

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@jeremystan

Optimally Routing Shoppers

● Variance is as important as mean → quantile regression

● GBMs for complex time & space features

● Scale to millions of predictions per minute in planning(shoppers x orders x sequence)

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@jeremystan

Optimally Routing Shoppers

300 orders3 orders per trip

x 100 shoppers = 445 million

● Start with greedy heuristics● Wait to last minute to dispatch● Unify objectives● Solve subproblems optimally● Simulate for broader changes

➔ Maximize expected # of items found➔ Maximize probability of delivering on time➔ Minimize total time spent delivering

CVRPTW ProblemCapacitated Vehicle Route Planning with Time

Windows

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@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 Instacart Organizes

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