Building a First ML Component from 0
to Optimization with a Product-First
Approach
by Getaround VP of Product
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5
Raphaël Korach
VP Product - 8years @
Build a first
machine learning component
with a
product-first approach

Transportation Electricity Industry Agriculture Commercial
Cars
2970 kg
Trucks
1210 kg
Coal
2310 kg
Natural
gas
1880 kg
Chemicals
790 kg
Refining
750kg
Cattle
700 kg
Soil
management
960 kg
Others
Metal
Others
1
2
3
4
5
0
Yearly tCO2e per capita - source United States Environmental Protection Agency
Residential
Planes
495 kg
1200 kg 1020 kg
Personal car is the
#1 cause of GHG
emissions worldwide
USA cars: 18%
󰑔

7
4.2 million premature
deaths from air
pollution worldwide
each year
Source: World Health Organization - 2019

20% of public space
is wasted on idle cars
that are used
less than 1x / week
8
Sources: Voirie de Paris, Opendata Paris

Product beekly
Empower people to carshare
everywhere.
Our mission at Getaround

10
We provide our customers
with access to safe,
convenient, and
affordable cars
everywhere around them.
Our vision at Getaround

First connected
Carsharing
Marketplace
1000+ cities in 8 countries
1.7M users
11
Getaround

Product beekly
Build a machine learning component
from scratch
with a product-first approach.

Money and Time 💸
Hard to find the right price,
Time-consuming to update regularly
Smart Pricing 📈
Machine learning component to
recommend optimal prices to
owners
Context: Smart Pricing
13
Scope: EU 󰎾

“Launching a machine
learning component is
a technical challenge.”
14

15
Launching a machine
learning component is
a technical challenge.”
“

1
It’s a product marketing
challenge 📣
2
3
16
Ok, It’s also a technical challenge ⚙
(but…)
It’s a design challenge 󰳒
Launching a machine
learning component is
a technical challenge.”
“

Adoption rate by
owners
50%
10%
Incremental revenue
for adopters
17
Rollout objectives of
Smart Pricing

The Marketplace Dynamics squad
18
Its mission is to maximise long term revenues of
the marketplace
2 Data Scientists
2 Full Stack developers
2 Pricing Managers
1 Product Manager
1 Product Designer
Meet:
Product
Manager
Designer
Engineer Data

1It’s a design challenge.

Product Design at Getaround
20
UI/UX
Design
User
Research
UX
Writing

Product beekly
User tests.

Remote user-tests
Designs on Figma
8 private owners (existing user base)
Goal: Improve & adjust design to increase conversion
22
➔
➔
➔

Results

24
“I hope the algorithm does its job well enough so that I don’t lose
money”
“I would have tried Smart Pricing just to see prices that the algorithm
suggest.”
“There is a button to deactivate at anytime. Reversibility in one click,
that’s reassuring”
Learnings - It’s all about trust.

They don’t trust the
“machine”
Owners understood how smart
pricing works, but are suspicious on
the fact that the algorithm can do
better than their manual work.
👉 Reinforce trust with
transparency (Preview)
They are afraid to lose
money
Owners need proof they would earn
more with smart pricing.
More bookings ?
More earnings ?
👉 Give more info before
activating Smart Pricing (copy)
Learnings - It’s all about trust.
25
They want to keep some
control
Owners feared the switch would be
a one-way thing.
They wanted a preview of prices
before making their mind.
👉 Leave control on smart pricing
(min price, price edit)

Follow the language used by owners
26
“Area”
“Renters”
“Client”
“Modulate prices” “Booking stream”
“My car part”
“Edit prices”
“How much I get / In my
pocket”
“High tier”
“Old option”
“You set prices”
“Minimum price”

27

28
Propose &
engage
+
Present
benefits

29
Make the algo sound legitimate
Vulgarization

Stay in control
30

31
Preview based on minimum price
Preview to
reassure

32
Make the user feel something
important is happening
“Make it
count”

2
It’s a Product Marketing
challenge.

34
Product Marketing
1
1 - Announcement
Announce the new offer when
opening the app
2 - Banner
Promote the feature in-product
(here, pricing page)
3 - Emailing
To all eligible owners, with
repeated campaigns
4 - Blog post
On the owner community blog
2
1

3
It’s a technical challenge.
(But it’s also a people and process challenge)

36
��
½ human ½ machine

1 2 3
Organisation 😊 Oracle 🤖 Optimisation 🤖
The four O
37
4
Observation 😊

Organisation

Organisation 😊
Data
39

40
Organisation 😊
Routine
Mon.
Tue.
Wed.
Thur.
Price computations
Analysis
Adjustments
Import

Oracle

42
Oracle 🤖
Searches/Cars

43
Oracle 🤖
Searches
Searches (Paris)
All searches Scrappers filtered out

44
Oracle 🤖
Bookings
< 3 weeks
3+ weeks
Now

45
Oracle 🤖
Results
Result of prediction demand / offer
Prediction Ground truth

Observation

47
Observation 😊
Competition

48
Observation 😊
Trends

Optimisation

50
Booking probability
Price
Revenue = Booking probability x Price
Optimisation 🤖
Revenue

51
Demand/Supply market context Characteristics for a given car

52
Optimisation 🤖
Time
Lun.
Mar.
Mer.
Jeu.
Price computations
Analysis
Adjustments
Import
Before Now
󰛍
󰛍
󰛍
󰛍
🦾
󰛍
🦾
🦾

Recap.

54
In order to boost adoption, invest early in qualitative
research and product marketing

55
Double-down on UX writing to make your
algorithms tangibles

56
A good routine is as important as a
precise model

57
If you are starting with machine learning,
start narrow but vertically integrated.

58
Data org.
Prediction
Model precision
Adjustments
Production releases
Monitoring
Pedagogy
Benchmark
Narrow perimeter
Large perimeter ✔
🚫
Data org.
Prediction
Model precision
Adjustments
Production releases
Monitoring
- limited region
- limited car types
- limited owners typology
- …
Pedagogy
Benchmark
- all regions
- all cars
- all owners
- …
Start narrow, vertically integrated

Getaround
Raphaël Korach - linkedin.com/in/raphaelkorach
Thanks!
Inside Getaround - medium.com/getaround-eu

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