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Data Science at Instacart
Making On-Demand Profitable
v
@jeremystan
Our Value Proposition
Groceries from
stores you
love
delivered
to your
doorstep
in as little
as an hour
+ + ...
v
@jeremystan
Customer Experience
Select a
Store
Shop for
Groceries
Checkout Select Delivery
Time
Delivered to
Doorstep
v
@jeremystan
Shopper Experience
Accept Order Find the
Groceries
Out for
Delivery
Scan Barcode
Delivered to
Doorstep
v
@jeremystan
Four Sided Marketplace
Customers Shoppers
Products
(Advertisers)
Customer Service
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)+$
Tran...
v
@jeremystan
Profitable Unit Economics
Instacart has achieved profitable unit economics
Driven (in part) by huge decrease...
v
@jeremystan
Path to Profitability
“Since the beginning of last year, revenue has
grown by 500%”
“90% of our customers ar...
@jeremystan
TimeVariance
Data Science Challenges
Marketplace
n
4
>>
2
n 𝞵>>𝞼 23:59:00>>00:59:00
v
@jeremystan
Optimizing Minutes
Balance Supply & Demand Optimize Fulfillment
Forecast AdaptSchedule Predict DispatchPlanM...
v
@jeremystan
What Was Demand?
Visitor
Total Demand =
∑ pr (convert | 100%
availability)
2. Lost
1. Checkout
3. No Intent
v
@jeremystan
Forecasting Demand?
… in a region?
… at a retailer?
… on a day?
… at an hour?
… for delivery in 2 hours?
→ M...
v
@jeremystan
Many Sources of Outliers
Date
Markets
Holidays
Storms
Regional
Events
v
@jeremystan
Backtesting
Testing Design
Algorithm Performance
over Time
v
@jeremystan
Demand Shock Absorbers
v
@jeremystan
Predicting Fulfillment Times
Early On-Time Late
Customer
Happiness
Delivery
Window
Google Maps Travel
Time
I...
v
@jeremystan
Optimally Routing Shoppers
● Variance is as important as mean → quantile regression
● GBMs for complex time ...
v
@jeremystan
Optimally Routing Shoppers
300 orders
3 orders per trip
x 100 shoppers = 445 million
● Start with greedy heu...
v
@jeremystan
Overall Results
-20%
-0% +15%
+20%late
lost
speed
busy
Customer Shopper
Utilization
Lost
Deliveries
@jeremystan
Mission Driven Working GroupsIntegrated
● Aligned with products
● Operate independently
● Cross eng team & org...
@jeremystan
Urgency OwnershipTransparency
● Set clear goals
● Be uncomfortable
● Clear accountability
● Measure performanc...
WE’RE HIRING!
@jeremystan
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Data Science at Instacart: Making On-Demand Profitable

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Published on

by Jeremy Stanley
VP of Data Science at Instacart

The on-demand economy is a notoriously difficult business, proven by the number of companies that have tried to deliver products and services within hours and failed. Take two established players who threw in the towel last year – eBay gave up on their same-day delivery service initiative, and Google shuttered two Bay Area facilities set up for a competing service of its own.

In this session, Jeremy Stanley, VP of Data Science at Instacart, explains how Instacart is making on-demand profitable with data science, putting them at the top of the food chain. He will cover the following:
How Instacart has used data science to optimize last-mile delivery and balance supply and demand to drive efficiency gains that are transforming unit economics
How improvements in predicting outcomes, batching algorithms, and forecasting have contributed to delivery efficiency
How data science is organized at Instacart and how it collaborates with product, engineering, and field operators to make rapid innovation possible

Published in: Technology
  • Slide 17: The paired graphs on the right are either making a quite remarkable claim, or a rather misleading one. The labeling is somewhat unclear, but the two graphs appear to be comparing the correlation of ACTUAL TOTAL DELIVERY Time to, in one case, Google Maps estimated TRAVEL TIME, and in the other, Instacart's estimated TOTAL DELIVERY TIME. Unless the instacart model only estimates travel time, such a comparison is problematic, especially since the Google Maps Directions API incorporates live traffic information as well as historical traffic levels for time of day when estimating travel times using 'best_guess' as the traffic_model.
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Data Science at Instacart: Making On-Demand Profitable

  1. 1. Data Science at Instacart Making On-Demand Profitable
  2. 2. v @jeremystan Our Value Proposition Groceries from stores you love delivered to your doorstep in as little as an hour + + + =
  3. 3. v @jeremystan Customer Experience Select a Store Shop for Groceries Checkout Select Delivery Time Delivered to Doorstep
  4. 4. v @jeremystan Shopper Experience Accept Order Find the Groceries Out for Delivery Scan Barcode Delivered to Doorstep
  5. 5. v @jeremystan Four Sided Marketplace Customers Shoppers Products (Advertisers) Customer Service Stores (Retailers)
  6. 6. @jeremystan Unit EconomicsCustomers Love Us Can we succeed? Huge Market $600,000,000,000 infor or
  7. 7. v @jeremystan Our Unit Economics Product Partnerships+$ Retail Partnerships+$ Delivery Fees+$ Tips (go to shoppers)+$ Transaction & insurance costs-$ Shopping Time-$ -$ Driving Time Key to bottom-line
  8. 8. v @jeremystan Profitable Unit Economics Instacart has achieved profitable unit economics Driven (in part) by huge decreases in fulfillment time:
  9. 9. v @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/
  10. 10. @jeremystan TimeVariance Data Science Challenges Marketplace n 4 >> 2 n 𝞵>>𝞼 23:59:00>>00:59:00
  11. 11. v @jeremystan Optimizing Minutes Balance Supply & Demand Optimize Fulfillment Forecast AdaptSchedule Predict DispatchPlanMeasure Evaluate
  12. 12. v @jeremystan What Was Demand? Visitor Total Demand = ∑ pr (convert | 100% availability) 2. Lost 1. Checkout 3. No Intent
  13. 13. v @jeremystan Forecasting Demand? … in a region? … at a retailer? … on a day? … at an hour? … for delivery in 2 hours? → Millions of forecasts
  14. 14. v @jeremystan Many Sources of Outliers Date Markets Holidays Storms Regional Events
  15. 15. v @jeremystan Backtesting Testing Design Algorithm Performance over Time
  16. 16. v @jeremystan Demand Shock Absorbers
  17. 17. v @jeremystan Predicting Fulfillment Times Early On-Time Late Customer Happiness 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
  18. 18. v @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)
  19. 19. v @jeremystan Optimally Routing Shoppers 300 orders 3 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 Problem Capacitated Vehicle Route Planning with Time Windows
  20. 20. v @jeremystan Overall Results -20% -0% +15% +20%late lost speed busy Customer Shopper Utilization Lost Deliveries
  21. 21. @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 Designer Data Scientist Engineer Mobile ProductAnalyst Rare Matrixed Empowered
  22. 22. @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
  23. 23. WE’RE HIRING! @jeremystan

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