Post on 20-Mar-2017
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 ��
>>
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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