The document outlines an intern project by Team Technology at Priceline, focusing on analyzing user search and booking data to generate monetizable travel recommendations. It details methodologies for understanding customer behavior, revealing trends such as multiple searches and city flexibility among travelers. The project aims to enhance customer engagement and conversion rates through a recommendation engine, leveraging insights from 1.2 million booking records and 263 million search records between June and July 2016.

1. Intern Project - Team Technology
7/28/2016

2. Team Technology
AKA ‘Notorious Data’
Desean
Front End Developer
Zach
Finance
Gus
Product Manager
Niket
Software Engineer
Shamanth
Business Analyst

3. Monetizing Data by
Increasing Conversion through
Travel Recommendations

4. Customers Love
Recommendations
40%
40 million / 100 million
75%
62 million / 83 million
?
13 million unique users / month
Sources: Spotify,Statistica, Kissmetrics, Priceline

5. Overview
ResourcesProject Goal Specification
Analyze search and booking
data to identify user patterns
and make monetizable
recommendations
Air Hotel
Rental
Car
Package Cruise
Retail SOPQ OPQ
Priceline
Group
priceline.com
Data: June 21 ,2016 - July 21, 2016
Booking records 1.2 Million, Searches records 263 Million
Tony Padovano
Chief of Staff for CTO
Dan O’Connor
Principal Insights Analyst
Zachary Horne
Solutions Architect
Tools
Mentors

6. Agenda

7. 1. Timeline
2. Insights
3. Ideas
4. Execution/Recommendation

8. Timeline

9. 2016
Today
Week 1
Milestone 1
Planning
Milestone 2
SQL Query
Milestone 3
Data analysis Milestone 4
Findings from data
Milestone 5
SlackBot
Milestone 6
Recommendations
June 20 - June 24Task 1
June 27 - July 1Task 2
July 4 - July 8Task 3
July 11 - July 15Task 4
July 18 - July 22Task 5
July 25 - July 29Task 6
Week 2 Week 3 Week 4 Week 5 Week 6
1. Understand data 2. Analyze data
3. Visualize
data

10. Insights

11. © 2016 priceline.com
Data Analysis Methodology
To better understand customer search behavior, we mapped searches to bookings...
How did we map a search to a booking?
1. Site Server ID (Cookie) matched
2. Travel Dates +/- 2 Days of check-in and check-out dates
3. Area ID/City ID Matched

12. Data Analysis Trends
80% of customers search for hotels
multiple times before booking.
Customers search more than once,
perhaps looking for a better price or
considering travel alternatives.
Number of Hotel Searches
PercentofTotalHotelSearches

13. 25% of customers return to search
for hotels on multiple days.
Customers could be considering
alternate plans, or looking for better
deals during this time.
PercentofTotalHotelSearches Number of Search Days
Data Analysis Trends

14. 35%+ of customers search
multiple cities before booking.
These customers might be more
flexible in their travel plans.
PercentofTotalHotelSearches
Number of Cities
Data Analysis Trends

15. © 2016 priceline.com
Insights Summary
• Many customers search for hotels:
– Multiple times
– In multiple cities
– From multiple properties
• Data allows us to…
– See which destinations have the most flexible travelers
– See what other destinations customers have considered
– And the same for specific hotels

16. Ideas

17. © 2016 priceline.com
Tableau Demo
https://nw-tabprq-
201.corp.pcln.com/#/site/finance/workbooks/1576/v
iews

18. Makes and
Confirms
Bookings
Traditional Travel Agency Online Travel Agency
OTA?
Assists in
Searching
Shares
Advice and
Knowledge
Booking
Engine
Search
Engine
Recommendation
Engine

19. Execution/Recommendation

20. © 2016 priceline.com
Slackbot Live Demo

21. © 2016 priceline.com

22. © 2016 priceline.com

23. © 2016 priceline.com
Potential Customer Facing Applications of Bots

24. Destination
Recommendation

25. Hotel
Recommendation

26. Technical Details

27. Recommendation Engine API
Hotel Listings Search Request
www.priceline.com/stay/#/search/hotels/

28. Impact
★ Multisource Value Creation
★ Valuable Destination and Property Insights for MDMs
★ Unique Site Feature with Potential to Drive Direct Traffic
★ Value Add can Increase Repeat Propensity
5.5% ?
Average New Customer 12 Month Repeat Propensity
★ Increase Conversion by Offering Relevant and Compelling Recommendations
★ Increase Customer Engagement via Interactive features
1.4% ?
Average Hotel Retail Conversion
Next Step
A/B Testing
Using Existing Engine

29. Q&A