Intern Project - Tech

Intern Project - Final run-through7/28/2016

Team Technology AKA ‘Notorious Data’

Desean

Front End Developer

Zach

FinanceGus

Product Manager Niket

Software EngineerShamanth

Business Analyst

Monetizing Data byIncreasing Conversion through Travel Recommendations

Customers Love Recommendations

40%40 million / 100 million

75%62 million / 83 million

?13 million unique users / month

Sources: Spotify,Statistica, Kissmetrics, Priceline

http://www.slideshare.net/upload

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

PricelineGroup priceline.com

Data: June 21 ,2016 - July 21, 2016Booking records 1.2 Million, Searches records 263 Million

Tony PadovanoChief of Staff for CTO

Dan O’ConnorPrincipal Insights Analyst

Zachary HorneSolutions Architect

Tools

Mentors

Agenda

1. Timeline2. Insights3. Ideas4. Execution/Recommendation

Timeline

2016

Today

Week 1

Milestone 1Planning

Milestone 2SQL Query

Milestone 3Data analysis Milestone

4Findings from dataMilestone

5SlackBotMilestone 6Recommendations

June 20 - June 24Task 1

June 27 - July 1Task 2

July 4 - July 8 Task 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

Insights

© 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) matched2. Travel Dates +/- 2 Days of check-in and check-out dates3. Area ID/City ID Matched

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

Perc

ent o

f Tot

al H

otel

Sea

rche

s

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.

Perc

ent o

f Tot

al H

otel

Sea

rche

sNumber of Search Days

Data Analysis Trends

35%+ of customers search multiple cities before booking.

These customers might be more flexible in their travel plans.

Perc

ent o

f Tot

al H

otel

Sea

rche

s

Number of Cities

Data Analysis Trends

© 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

Ideas

© 2016 priceline.com

Tableau Demo

https://nw-tabprq-201.corp.pcln.com/#/site/finance/workbooks/1576/views

Makes and Confirms Bookings

Traditional Travel Agency Online Travel Agency

OTA?

Assists in Searching

Shares Advice and Knowledge

Booking Engine

Search Engine

Recommendation Engine

Execution/Recommendation

© 2016 priceline.com

Slackbot Live Demo

© 2016 priceline.com

Potential Customer Facing Applications of Bots

Destination Recommendation

Hotel Recommendation

Technical Details

Recommendation Engine API

Hotel Listings Search Request

www.priceline.com/stay/#/search/hotels/

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 StepA/B TestingUsing Existing Engine

Q&A