This project was made by us for an E-commerce based on an article "How much is your spare room worth?"

1. How much is your Spare Room
Worth?
E - Commerce
Group 9: Faruk Hasic | Ananya Jain

3. http://nextjuggernaut.com/blog/airbnb-business-model-canvas-how-airbnb-works-revenue-insights/
: Business Model

4. Every one of the million-plus listings on our site is unique, having its own address, size, and decor.
Our hosts also vary in their willingness to play concierge, cook, or tour guide
: Initial pricing issue

5. …..unlike

6. 1. Price-setting algorithms
2. Price tips
: Pricing mechanism faced trouble

7. 1. Interact with users
2. Adjust to demand
: Solution seeked

8. : Situations

9. : Aerosolve

10. : The tool
Bases a price
tip from the
median
Factors in
Demand and
seasonality
Finds the
ones which
have been
successfully
booked
Looks at
listings
with
similar
attributes
in similar
areas
System
attracts
key
attributes
of that
listing
Host
adds a
space

11. Figuring out the key attributes themselves
: The tricky part…

12. : SOLUTION
SIMILARITY
RECENCY
LOCATION
Looked up
highly
quantifiable
common
attributes for
which the client
will pay
Travel market
is dynamic and
seasonal.
Listings in
almost the entire
city, so detailed
micro-
neighborhoods

13. : Another FLAW - corrected
FLAW:
Expanding circle around a listing, considering similar properties at
varying radii from the listing location
CORRECTED:
Map the boundaries of neighborhoods: extremely accurate and relevant
geospatial definitions to cluster listings around major geographical and
structural listings.

14. : Remaining weaknesses
1. Static pricing tips
2. Tool itself is static

15. : Recent improvements
1. Each host gets new pricing tip every day for each available data in
the future
2. A lot of historical data, requires a lot of computing resources
3. Allow easy interpretation and in some cases influences the thought
process

16. : Machine learning model

17. How much is your Spare Room
Worth?
E - Commerce
Group 9: Faruk Hasic | Ananya Jain