This document summarizes a case study about how Airbnb scales data science. It discusses Airbnb's business model as a two-sided marketplace matching people seeking accommodation with people renting space. It describes how Airbnb uses data science techniques like predictive modeling, A/B testing and natural language processing to understand customer experiences. It notes that Airbnb's data science team of 70 employees, including 30+ engineers, is focused on scaling data science across the company to propel growth.

1. Analytics in Action
Community
Management
February 2018
http://DSign4.education

2. Introduction
©2016 L. SCHLENKER
Agenda
Introduction
Communities
Community Management
Case Study - AirBnB
Lessons Learned

3. • What proof do we have today that many
organizations have lost their sense of community?
• What does the author infer by communityship?
• How can we facilitate the transformation or
organizations towards communityship?
• In what ways can Data Science contiribute to
businesses becoming places of engagement?
Rebuilding Companies as Communities ?
Introduction
Mintzberg, H., (2009) Rebuilding Companies as
Communities

4. Introduction
• Place - changes in geography, time, physical
resources and budget
• Platform – enriching how information is produced
and consumed
• People – modifying the frame of reference
• Practice - impacting the reality of management
Schlenker (2015)

5. ©2016 LHST sarl
Introduction

6. ©2006 LHST sarl
How does work
get done? Networks

7. Characteristic Value
Degree Centrality Number of links
Betweeness
Centrality
Role of brokerage
Closeness
Centrality
Vector of visibility
Network
Centralization
Centralized vs
Decentralized
Network Reach Importance of first 3
levels
Boundary
Spanners
Linked to Innovation
Peripheral Players Potential Gateways
Networks

8. The Conversation Prism v2.0
• You are at the center of the prism
• The first layer of circles displays the
activity of learning and organizing
engagement strategies…
• The second ring maps specific
authorities within an organization to
provide a competent and helpful
response.
• The third ring represents the continual
rotation of listening, responding, and
learning online and in the real world.
The Conversation Prism
Technology

9. Integrated Community
Management
Conversations that make sense
PEOPLE
Dynamic profiles of all your people,
with info captured from anywhere.
Includes followups & targeting.
WEBSITE
Multiple page types & user profiles.
Build custom responsive designs
using NationBuilder Theme Sync.
SALES
Customizable finance pages with
goal tracking, social sharing and
personal fundraising
COMMUNICATIONS
Email & text blasting, free phone
number with voicemail, and deeply
integrated social media.
©2016 LHST sarl

10. A digital strategy rather than a website
The Customer Experience
WEBSITE
MEMBER
DATABASE
COMMUNICATIONS
FINANCES
©2016 LHST sarl

11. A 360 degree view of each participant
The Customer Journey
FINANCES
©2016 LHST sarl
PROCESS
PROFILE
RESULTS

12. Secure, hosted
website
Subscriptions
Emailblasting
Member
database
Social media
integration
Process
management
Mobile engagement
Web pages built for
action
Dynamic profiles
Key components

13. CONFEREN
CES
VOICEMAI
L
EMAI
L
TWITTE
R
PHON
E
TEX
T
FACEBOO
K
Multichannel Communication

14. • What is the organization’s business
model?
• Why does the organization focus on
data?
• Which data science techniques does
the organization favor ?
• What is the link between data science
and decision making?
• How is the Data Science team
organized?
• How does the organization use Data
Science to propel growth
Case Study Questions
Technology

15. Data Infrastructure at Airbnb
• 2500 Employees with rapid growth –
the company has opened a dozen
international offices simultaneously
while explanding their product,
marketing, and customer support
teams
• The site draws 100M users browsing
over 2M listings
• 10 million nights booked for more than
25 million people across 192 countries
and 34,000 cities
• A valuation of $25.5 billion as of June
2015
AirBNB
Technology

16. 2-sided markets
• AirBnB matches people who are
looking for accommodation with
people who are willing to rent out
their place
• A two-sided marketplace
• Network effects, strong seasonality,
infrequent transactions, and long time
horizons
• A valuation of $25.5 billion as of June
2015
Business Model
Technology

17. How we scaled Data Science
• AirBnB uses data science to understand
individual experiences and aggregate
those experiences to identify trends
• Data reflects a decision made by a
person.
• Recreate the sequence of events
leading up to decisions to identify what
customers like and don’t like
• They translate the customer’s “voice”
into a language more suitable for
decision-making
Why Data Science ?
Technology

18. How we scaled Data Science
• Predictive Analytics - use insights to
influence decisions
• The Team begins by scanning the context
of the problem, putting together a full
synopsis of past research.
• That synopsis translates to a plan, which
encompasses prioritizing the levers we
intend to utilize
• As the plan gets underway, the team
designs a controlled experiment through
which to roll the plan out.
• Finally, they measure the results of the
experiment, identifying the causal impact
of our efforts.
• The Data Science team partners directly
with engineers, designers, product
managers, marketers, and others.
Impact on Decision Making
Technology

19. • The unstructured information about the
rooms, room owners, locations of the room is
sorted and analysed using Hadoop
• AirBnB uses host guest interaction, current
events and local market history to provide real
time recommendations
• AirBnB serves approximately 10 million
requests a day and processes one million
search queries
• This represents 20 TB of data created daily
and 1.4 petabytes of archived data
Data Sources
Technology
AirbnB uses R to scale data science

20. • Airbnb has invested in building an internal R
package called Rbnb
• A set of collaborative solutions to common
problems, standardizes visual presentations
• The package includes more than 60 functions
• It is used to move aggregated or filtered data
into R, impute missing values, compute year-
over-year trends, and perform data
aggregations.
• The objectives are to solve problems
like automating the detection of host
preferences and using guest ratings to predict
rebooking rates
Data Tools
Technology
AirbnB uses R to scale data science

21. • 70 Employees, 30+ Engineers, USD 5
million
• 80% are proficient in R - 64% use R as
their primary data analysis language),
• Airbnb organizes monthly week-long
data bootcamps for new hires and
current team members
Data Science Team
Technology
Scaling Data Science at AirBnB

22. • A/B Testing
• Image Recognition and Analysis
• Natural Language Processing
• Predictive Modeling
• Regression Analysis
• Collaborative Filtering
Data Science TechniquesIntroduction

23. • What is the organization’s business
model?
• 70 employees, 30+ Engineers, USD 5
million
• Which data science techniques does
the organization favor ?
• What is the link between data science
and decision making?
• How is the Data Science team
organized?
• How does the organization use Data
Science to propel growth
Data Science Team
Technology
AIRBNB: LESSONS ON DIGITAL,
START-UPS, BIG DATA

24. • Scaling data science is enabling guests and
hosts to learn from each other
• They think about the culture of data in the
company as a whole rather than individual
teams.
• Individual interactions become more
efficient as data scientists are empowered to
move more quickly
• Empowering teams is about removing the
burden of reporting and basic data
exploration to focus on impactful work.
Lessons learned
Technology
AIRBNB: LESSONS ON DIGITAL,
START-UPS, BIG DATA

25. • Libert, K., Your Network's Structure Matters More than
It's Size
• Manville, B., You Need a Community, Not a Network
• Mintzberg, H., (2009), Rebuilding Companies as
Communities, HBR
• Neuman, R. (2016), How we scaled data science to all
sides of Airbnb , Venture Beat
• Pugh, K., (2013), Designing Effective Knowledge
Networks, Sloan Management Review
Bibliography
Next Steps